How Big Data Analysis helped increase Walmarts Sales turnover?

Walmart Big Data Case Study-Understand how Walmart Big Data is used to leverage analytics to increase sales by improving Customer Emotional Intelligence Quotient.

How Big Data Analysis helped increase Walmarts Sales turnover?

With more than 245 million customers visiting 10,900 stores and with 10 active websites across the globe, Walmart is definitely a name to reckon with in the retail sector. Whether it is in-store purchases or social mentions or any other online activity, Walmart has always been one of the best retailers in the world. The Global Customer Insights analysis estimates that Walmart sees close to 300,000 social mentions every week. With 2 million associates and approximately half a million associates hired every year, Walmart’s employee numbers are more than some of the retailer’s customer numbers. It takes in approximately $36  million dollars from across 4300 US stores everyday.This article details into Walmart Big Data Analytical culture to understand how big data analytics is leveraged to improve Customer Emotional Intelligence Quotient and Employee Intelligence Quotient.

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Walmart Sales Forecasting Data Science Project

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Table of Contents

How Walmart uses Big Data?

How walmart is tracking its customers, how walmart is making a real difference to increase sales.

  • Social Media Big Data Solutions
  • Mobile Big Data Analytics Solutions

Walmart’ Carts – Engaging Consumers in the Produce Department

World's biggest private cloud at walmart- data cafe, how walmart is fighting the battle against big data skills crisis, 2014 kaggle competition walmart recruiting – predicting store sales using historical data, description of walmart dataset for predicting store sales, use market basket analysis to classify shopping trips.

Walmart Data Analyst Interview Questions

Walmart Hadoop Interview Questions

  • Walmart Data Scientist Interview Question

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Walmart Big Data

American multinational retail giant Walmart collects 2.5 petabytes of unstructured data from 1 million customers every hour. One petabyte is equivalent to 20 million filing cabinets; worth of text or one quadrillion bytes. The data generated by Walmart every hour is equivalent to 167 times the books in America’s Library of Congress. With tons of unstructured data being generated every hour, Walmart is improving its operational efficiency by leveraging big data analytics. Walmart has created value with big data and it is no secret how Walmart became successful.

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“The most important thing about Wal-Mart is the scale of Wal-Mart. Its scale in terms of customers, its scale in terms of products and its scale in terms of technology.”-said Anand Rajaram, head of WalmartLabs

“We want to know what every product in the world is. We want to know who every person in the world is. And we want to have the ability to connect them together in a transaction.” –said Walmart’s CEO of global e-commerce in 2013.

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Walmart was the world’s largest retailer in 2014 in terms of revenue. Walmart makes $36 million dollars from across 4300 retail stores in US, daily and employs close to 2 million people. Walmart started making use of big data analytics much before the term  Big Data became popular in the industry. In 2012, Walmart made a move from the experiential 10 node  Hadoop cluster to a 250 node Hadoop cluster. The main objective of migrating the Hadoop clusters was to combine 10 different websites into a single website so that all the unstructured data generated is collected into a new  Hadoop  cluster. Since then, Walmart has been speeding along big data analysis to provide best-in-class e-commerce technologies with a motive to deliver pre-eminent customer experience. The main objective of leveraging big data at Walmart is to optimize the shopping experience of customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion. Big data solutions at Walmart are developed with the intent of redesigning global websites and building innovative applications to customize shopping experience for customers whilst increasing logistics efficiency.Hadoop and NOSQL technologies are used to provide internal customers with access to real-time data collected from different sources and centralized for effective use.

Walmart acquired a small startup Inkiru based in Palo Alto, California to boost its big data capabilites. Inkiru Inc. helps in targeted marketing , merchandising and fraud prevention. Inkiru's predictive technology platform pulls data from diverse sources and helps Walmart improve personalization through data analytics. The predictive analytics platform of Inkiru incorporates machine learning technologies to automatically enhance the accuracy of algorithms and can integrate with diverse external and internal data sources.

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Walmart has a broad big data ecosystem. The big data ecosystem at Walmart processes multiple Terabytes of new data and petabytes of historical data every day. The analysis covers millions of products and 100’s of millions customers from different sources. The analytics systems at Walmart analyse close to 100 million keywords on daily basis to optimize the bidding of each keyword.The main objective of leveraging big data at Walmart is to optimize the shopping experience for customers when they are in a Walmart store, or browsing the Walmart website or browsing through mobile devices when they are in motion. Big data solutions at Walmart are developed with the intent of redesigning global websites.

Work with the world's largest retail dataset- Walmart Store Sales Forecasting Data Science Project

Walmart Big Data Analytics Ecosystem

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Walmart has transformed decision making in the business world resulting in repeated sales. Walmart observed a significant 10% to 15% increase in online sales for $1 billion in incremental revenue. Big data analysts were able to identify the value of the changes Walmart made by analysing the sales before and after big data analytics were leveraged to change the retail giant’s e-commerce strategy.

First Applications to Ride the Hadoop Data at Walmart

  • Savings Catcher –An application that alerts the customers whenever its neighbouring competitor reduces the cost of an item the customer already bought. This application then sends a gift voucher to the customer to compensate the price difference.
  • eReceipts application provides customers with the electronic copies of their purchases.
  • A mapping application at Walmart uses Hadoop to maintain the most recent maps of 1000’s of Walmart stores across the globe. These maps specify the exact location where a small bar of soap resides in the widespread Walmart store.

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Mupd8- Map Update Application

To fulfil the need for a general purpose real time stream processing platform which can tackle issues like performance and scalability, Walmart developed Mupd8 for Fast Data. With Mupd8, stream processing applications could emphasize on the quality of generated data. Mupd8 does for fast data, what hadoop mapreduce computational model does for big data.

Mupd8 allows developers to write applications easily and process them using the Map Update framework (a workflow of Map and Update operators), an easy way to express streaming computation. Writing an application as a combination of customized map and update operators, big data developers can focus on the business logic of the application and let Mupd8 handle load and data distribution across various CPU cores.

For example, an application can be written to subscribe to the Twitter firehose of every tweet written; such an application can analyse the tweets to determine Twitter's most influential users, or identify suddenly prominent events as they occur. Alternatively, an application can be written to subscribe to a log of all user activity on a Web site; such an application can detect service problems users’ face as they occur, or compute suggestions for users' next steps based on up-to-the-moment activity.

“Our ability to pull data together is unmatched”- said Walmart CEO Bill Simon.

Walmart uses data mining to discover patterns in point of sales data. Data mining helps Walmart find patterns that can be used to provide product recommendations to users based on which products were bought together or which products were bought before the purchase of a particular product. Effective data mining at Walmart has increased its conversion rate of customers. A familiar example of effective data mining through association rule learning technique at Walmart is – finding that Strawberry pop-tarts sales increased by 7 times before a Hurricane. After Walmart identified this association between Hurricane and Strawberry pop-tarts through data mining, it places all the Strawberry pop-tarts at the checkouts before a hurricane. Another noted example is during Halloween, sales analysts at Walmart could look at the data in real-time and found that thought a specific cookie was popular across all walmart stores, there were 2 stores where it was not selling at all. The situation was immediately investigated and it was found that simple stocking oversight caused the cookies not being put on the shelves for sales. This issue was rectified immeadiately which prevented further loss of sales.

Walmart tracks and targets every consumer individually. Walmart has exhaustive customer data of close to 145 million Americans of which 60% of the data is of U.S adults. Walmart gathers information on what customer’s buy, where they live and what are the products they like through in-store Wi-Fi.The big data team at Walmart Labs analyses every clickable action on Walmart.com-what consumers buy in-store and online, what is trending on Twitter, local events such as San Francisco giants winning the World Series, how local weather deviations affect the buying patterns, etc. All the events are captured and analysed intelligently by big data algorithms to discern meaningful big data insights for the millions of customers to enjoy a personalized shopping experience.

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Big Data Analytics at Walmart

Launching New Products

 Walmart is leveraging social media data to find about the trending products so that they can be introduced to the Walmart stores across the world. For instance, Walmart analysed social media data to find out the users were frantic about “Cake Pops” .Walmart responded to this data analysis quickly and Cake Pops hit the Walmart stores.

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Better Predictive Analytics

​ Walmart has recently modified its shipping policy for products based on big data analysis. Walmart leveraged predictive analytics and increased the minimum amount for an online order to be eligible for free shipping. According to the new shipping policy at Walmart, the minimum amount for free shipping is increased from $45 to $50 with addition of several new products to enhance the customer shopping experience.

Customized Recommendations

​ Just the manner in which Google tracks tailor made advertisements, Walmart's big data algorithms analyse credit card purchases to provide specialized recommendation to its customers based on their purchase history.

Big Data Analytics Solutions at Walmart

1)  social media big data solutions.

Social Media Data is unstructured, informal and generally ungrammatical. Analysing and mining petabytes of social media data to find out what is important and then map it to meaning products at Walmart is an arduous task.

Social Media Data driven decisions and technologies are more of a norm than an exception at Walmart. A big part of Walmart’s data driven decision are based on social media data- Facebook comments, Pinterest pins, Twitter Tweets, LinkedIn shares and so on. WalmartLabs is leveraging social medial analytics to generate retail related  big data insights.

Walmart launched a social media crowdsourcing contest that helped entrepreneurs get their products on the shelf. The contest attracted more than 5000 entries and more than 1 million votes across US. Anybody could pitch in their products and get exposure to millions of audience. The best products were declared as winners and sold at Walmart stores to be made available to millions of customers.

“Social Media Analytics is all about mining retail-related insights from social channels, a perilous and personally exciting task to us. When our team spent the 22nd of November feverishly following the social retail pulse on Black Friday, we knew the world wasn’t preparing for an apocalypse.”- said Arun Prasath, a Principal Engineer at WalmartLabs

Social genome.

Social Genome is a big data analytics solution developed by WalmartLabs that analyses millions and billions of Facebook messages, tweets, YouTube videos, blog postings and more. Through the Social Genome analytics solution, Walmart is reaching customer or friends customers who tweet or mention something about the products of Walmart to inform them about the product and provide them special discount.

The Social Genome product combines public data from the web, social media data and proprietary data like contact information, email address and customer purchasing data. This data helps Walmart better analyse the context of their users.

For example, if the Social Genome identifies that a lady frequently tweets about movies, then when she tweets something like “I love Salt”, the social genome solution of Walmart is able to understand that the lady is referring to the popular Hollywood movie Salt and not the condiment salt.

“ It is only after conquering all of these multifold challenges that meaningful recommendation can be made….Our social media analytics project operates on top of a searchable index of 60 billion social documents and helps merchants at Walmart monitor sentiments and popular interests real-time, or inquire into trends in the past. One can also see geographical variations of social sentiments and buzz levels. There are also tools that marry search trends on walmart.com, sales trends in our brick-and-mortar stores and social buzz all in one place, to help make correlations. Together, these tools provide powerful social insights.”- said Arun Prasath, Principal Engineer at WalmartLabs.

Shopycat-Gift Recommendation Engine at Walmart

If you are confused on finding the perfect gift for your friends then Walmart’s Shopycat app will help you buy the ideal gift for your friend during the holiday buying rush. Walmart’s Shopycat recommends gifts for friends based on the social data extracted from their Facebook profiles. The app also provides links to the Walmart products so that users can easily purchase the product without any hassle and strive towards creating a broader marketplace . Shopycat is a part of Walmart’s Facebook page that has close to 10 million fans.

The app also suggests friends for whom users must by gifts depending on the level of interaction with them. When people click on a suggested gift, Shopycat also tells why a particular gift was suggested. For instance, the suggestions can show that a friend has liked the product on Facebook or has commented on a wall post or has a status update related to the product.

Shopycat allows the users to message their friends mutually through Facebook and ask them if they would like to buy a gift voucher or a product.

Inventory Management at Walmart using Predictive Analytics

Predictive analytics is at the heart of supply chain process that helps Walmart reduce overstock and stay properly stocked on the most in-demand products. Suppliers to Walmart are required to use the real-time vendor inventory management system that helps them minimize the inventory for a particular product if there are no significant sales for it. This helps retailers to save funds to buy products that have greater demand and have increased probability for greater profits.

  • Improving the Store Checkout Process for Customers

Big data analytics is beign leveraged to determine the best form of checkout for a particular customer - facilitated checkout or self checkout. It is using predicitive analytics to predict the demand at specific hours and determine how many asociate would be needed at specific counters.

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2)  Mobile Big Data Analytics Solutions

According to Deloitte, the mobile influenced offline sales are anticipated to reach $700 billion by end of 2016. Walmart is harnessing the power of big data to drive tools and services in order to get its mobile strategy in order.

More than half of the Walmart’s customers use Smartphones and among these 35% of the shoppers are adults which is close to 3/4 th  of its overall customer base. Mobile phone customers are extremely important to Walmart as smartphone shoppers make 4 more trips and spend 77% more in-store. Thus, mobile users account for 1/3 rd of the Walmart traffic every year and approximately 40% during holidays.

“E-commerce is closely related to mobile purchase. The world’s largest retailer will use big data to enhance the consumers shopping experience in the store.” He also added: “Our mobile strategy is both simple and audacious. We want to make mobile tools become indispensable for our customers while they are shopping in our stores and online. The  retail will improve each customers personalized experience for competition in the future, and this all will happen on the small screen in their hands,” said Gib Thomas, Senior Vice President of Mobile and Digital at Walmart

Walmart is leveraging  big data analysis to develop predictive capabilities on their mobile app. The mobile app generates a shopping list by analysing the data of what the customers and other purchase every week. Walmart’s mobile application consists of a shopping list that can tell customers the position of their wants and helps them by providing discounts to similar products on Walmart.com.

Another way in which Walmart is harnessing the power of big data analysis is by leveraging analytics in real-time- when a customer actually enters the Walmart store. The geofencing feature of Walmart’s mobile app senses whenever a user enters the Walmart store in US. The app asks the user to enter into the “Store Mode”. The store mode of the mobile app helps users to scan QE codes for special discounts and offers on products they would like to buy.

With the intent of reduce waste and increasing consumer engagement, Walmart is introducing quality carts in produce departments across its stores. Walmart has employed quality carts in across 500 stores now and are expected to be present in all 5000 US stores by end of third quarter. Walmart knows that keeping its customers in the fresh produce department is the key to customer engagement and the implementation of quality carts has attractive offerings for them.Walmart is using big data and IoT sensors to find out how long people loiter in the fresh produce department. Big data analysis has helped them find that if the fresh produce looks fresh enough then people loiter for longer and this is the secret to make customers buy more things from the Walmart stores.

Walmart repurposed 200 of its existing outlets to provide grocery pickup in 30 cities. After knowing that consumers were increasingly concerned about the freshness of food, Walmart trained personnel to evaluate the quality of produce and showed food items to the customers before packing them.  If the wrap of frozen chicken is ripped or if the mango is not ripe, an exchange can be made immediately. All that the customers need to do is tap in their order through the app. Big data analytics helped Walmart win a bright spot in terms of grocery pickup.

Walmart is in the process of creating the world’s biggest private cloud for processing 2.5 PB of data every hour. Walmart has created its own analytics hub known as Data Café in Bentonville, Arkansas headquarters. At the data café, more than 200 streams of external and internal data along with 40 PB of transactional data can be manipulated, modelled and visualized.The data cafe pulls information from 200 varied sources that include Telecom data, social media data, economic data, meteorological data , Nielsen data , gas prices and local events databases  that accounts for 200 billion rows of transactional data for just few weeks. The solution to any particular problem can be found through these varied datasets and Walmart's analytic algorithms are designed to scan through the data in microsseconds to come up with a real-time solution for a particular problem.

Walmart Big Data is increasing exponentially at a rapid pace every day and the dearth of big data talent is a major roadblock for Walmart in performing analytics. With limited number of personnel possessing required big data skills –Walmart is taking every necessary step to overcome this challenge is that it does not have to fall behind its competitors. Whenever a new team member jobs the analytics team at Walmart Labs, he/she has to take part in the analytics rotation program. During this program the candidates are required to spend some time with the different departments in the company to understand how big data analytics is being leveraged across the company.

Walmart is having a tough time finding professionals with experience in cutting edge analytics applications and working knowledge of data science  programming languages like Python and R to build machine learning models. Walmart used the hashtag #lovedata for its recruitment campaign to raise its profile amongst the growing data science community in Bentonville and Arkansas.

Mandar Thakur, senior recruiter for Walmart’s Technology division said – “The staffing supply and demand gap is always there, especially when it comes to emerging technology”. With more than 40 petabytes of data available for analysis daily at Walmart, he says that there is going to be an unprecedented demand always for people who can do data science and analytics.

The secret to successful retailing of Walmart lies in delivering the right product at the right place and at the right time. Walmart continues to climb the retailing success ladder with remarkable results by leveraging big data analysis.

Walmart is fighting the big data skills gap by crowdsourcing analytics talent. Walmart hosted a Kaggle competition in 2014 where professionals where provided with historical sales dataset from sample of stores together with related sales events, price rollbacks and clearance sales. Candidates has to develop models that showed the impact of these events on the sales across various departments. The result of the competition helped Walmart find highly skilled and competent analytics talent.

In 2015, Walmart crowd sourced analytic talent with another Kaggle competition where candidates were required to predict the impact of weather on sales of different products in the store. Walmart has been able to hire skilled talent through these competition which they would not consider even interviewing based on the resume alone.

Mandar Thakur, senior recruiter for Walmart’s Technology division said- “One for example had a very strong background in physics but no formal analytics background. He has a different skillset – and if we hadn’t gone down the Kaggle route, we wouldn’t have acquired him.”

The biggest challenge for retailers like Walmart is to make predictions with limited historical data. If Thanksgiving or New Year comes once a year, retailers like Walmart have to make strategic decisions about how the sales will impact the bottom-line during the festive season. Walmart hosted a recruiting competition where job seekers were provided with historical sales data of 45 Walmart stores from different regions. Each store has multiple departments and the candidates participating in the crowdsourcing competition were required to predict the sales for each department in the store.Walmart also has promotional markdown events for prominent holidays like Christmas, Super Bowl, Labor Day, New Year, ThanksGiving, etc. Holiday markdown events were also included in the dataset provided by Walmart to add up to the challenge as the sales for holiday seasons were evaluated 5 times higher than the sales for non- holiday weeks.

The most challenging part of the competition was to predict which departments were largely affected by the holiday markdown events and what was the level of impact they had on the sales.

  • stores.csv – This file contains data about all the 45 stores indicating the type and size of each Walmart store.
  • train.csv- This file has historical training dataset from 2010 to 2012  containing the below information-

i) The Store Number

ii) The Department Number

iii) The Week

iv) Weekly Sales of a particular department in a particular store.

v) IsHoliday to indicate if it is a holiday week or not.

  • Features.csv- This file contains additional information about each store, the department, and regional activity for the mentioned dates with details like the store number , the average temperature in the region , the cost of fuel in that region, the unemployment rate, the consumer pricing index, whether the give date/week is a special holiday week or not, data related to promotional markdowns that Walmart is running.
  • Test.csv- It is just similar to train.csv except that the weekly sales are withheld in this file and the sales predictions have to be made for every triplet of the store, department and the date.

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To serve its customers better, Walmart enhances customer experiences by segmenting their store visits based on different trip types. Regardless of whether a customer is- on a last-minute run looking  for new puppy supplies or is just taking a leisurely troll down the store shopping for weekly grocery.

Classifying different trip types helps Walmart enhance customer shopping experience. Initially, Walmart’s trip types are created by combing art i.e. existing customer insights and science i.e. purchase history data. A new challenge that can be solved using the Walmart dataset is to classify customer trips to the Walmart store using only transactional dataset of the products purchased so that the segmentation process can be refined.

If you are preparing for a data analyst or data scientist interview at Walmart then here are few interview questions that will help you prepare for your data analyst or data scientist job interview at Walmart -

1) How will you deal with an experienced professional who consulted you but does not believe in  your analytical insights and sticks to his older analytical methods ?

2) Given the acess to Walmarts HR data, what would you be interested to search for ?

1) Explain about Hadoop architecture .

Walmart Data Scientist Interview Questions

1) How many sub-spaces can four hyperplanes divide in 3D?

2) How many sub-spaces can four lines divide in 2D ?

3) Write the code to reverse a linked list data structure.

If you want to work with one of the world's largest retail dataset, then drop us an email to [email protected]  to get the download link to Walmart Big dataset.

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Does Walmart use AWS or Azure?

Walmart has signed a five-year deal with Microsoft and turned to Azure cloud services.

Does Walmart use Teradata?

Walmart has the world's most giant data warehouse , capturing data on point-of-sale transactions every second from roughly 5,000 locations in six countries. It's a Teradata database with a capacity of 30 petabytes.

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Grocers can fuel growth with advanced analytics

Advanced analytics, including artificial intelligence, offers a big opportunity for the retail industry. McKinsey Global Institute estimated in a study the potential annual value of artificial intelligence for the retail industry at $400 billion to $800 billion globally.

About the authors

For grocery retail specifically, we see the potential for an incremental increase in earnings before interest and taxes (EBIT) of up to two percentage points if all use cases are implemented and the value is fully captured. Most of this value is driven by commercial use cases around assortment, pricing, promotions, and personalization (Exhibit 1). These are also some of the most mature use cases for which analytical approaches have begun to converge across the industry and standard analytics solutions are available on the market.

During the past five years, grocery retailers have moved beyond experimenting with advanced analytics and started to adopt these use cases in a systematic way. The majority of European grocery retailers are now embracing advanced analytics and are investing in capturing its value. For example, in 2020, Ahold Delhaize announced the implementation of tools for assortment, pricing, and promotions across its European brands. Players such as ICA, Migros, and REWE have well-established analytics organizations, and several retailers have hired additional data scientists, including discounters Aldi and Lidl.

About the research

This article is part of the report Disruption and Uncertainty—The State of Grocery Retail 2021: Europe , a partnership between McKinsey & Company and EuroCommerce designed to provide executives with a comprehensive view on an industry forced to adapt at unprecedented speed. We surveyed more than 10,000 consumers and around 50 grocery executives across Europe. We interviewed six industry thought leaders and pioneers. We combined EuroCommerce’s policy and sector knowledge with McKinsey’s global expertise and analytical rigor.

While we observe these investments, we so far do not see that the potential value is captured in the profit and loss (P&L). To determine what distinguishes analytics leaders from the pack, we analyzed grocery retailers along two dimensions: analytical and organizational maturity (Exhibit 2).

We found that capturing the value of advanced analytics depends even more on a retailer’s organizational maturity than its analytical maturity. In fact, retailers can achieve results only if organizational maturity is in place—which is still the exception in the industry rather than the rule.

Many grocers have made great progress on analytical maturity. Leaders in analytics have tackled the majority of fundamental use cases, such as pricing, mass promotion, and assortment optimization. Now, they have increasingly turned their focus to pursuing new use cases along the value chain and improving the existing use cases—for example, using more granular, real-time data. These efforts are often driven by a strong analytics unit, but adoption of these use cases in the business varies. The best analytics solution does not help if it is not used and understood by the respective decision makers (such as category managers).

Organizational maturity in many cases is the main barrier to going beyond partial adoption and realizing analytics’ full potential. Organizational maturity encompasses both processes to technically embed and continually improve use cases, as well as constant change management with the users of the analytical insights—fostering understanding of analytics, ensuring it is embedded in daily processes, and measuring against new key performance indicators (KPIs).

Our analysis of winners—both digital natives and traditional grocers—highlighted five strategies that have helped them excel, particularly in organizational maturity.

1. Focus on strategic use cases instead of on data

An analytics use case, defined.

An analytics use case describes an application of analytics and data to achieve an improvement of business performance and decisions. It defines the scope of change, a set of objectives with key performance indicators (KPIs), users affected, and data and analytics methods to be used.

Value is driven by business decisions based on insights provided by data (see sidebar, “An analytics use case, defined”); vast quantities of data do not generate any value by themselves. Transparency on the value of a use case, and a clear road map for how and when to realize it, is therefore key.

Grocers should create a prioritized portfolio of use cases derived from strategic priorities with clear business objectives, and reallocate resources to those with the highest risk–reward potential. They should also group the defined use cases into larger units or domains (such as store operations or merchandising). This accelerates the change in a given business domain, because almost all of their decisions become more data-driven and, ideally, interconnected.

2. Create agile, interdisciplinary product teams

One of the most crucial factors in extracting value from analytics insights is the translation process between business and technology. Many business teams don’t fully understand how technology and data science teams can support them, and vice versa. As a result, businesses don’t ask the right questions, while technology and data science teams try to answer questions that do not exist. This part of the analytics value chain can be regarded as the “secret sauce,” and traditional grocers have particular difficulty in achieving greater visibility and understanding between tech and business.

Winners create agile, interdisciplinary product teams that are led by the business and consist of people from business, analytics, and IT. Such an interdisciplinary team collaborates closely to achieve a defined business goal (for example, improve the delisting decision in assortment). In this approach, business is closely involved in the identification of use cases and also in designing the solution for the business case—either by providing a full-time resource in the role of product owner as part of the team or as part-time business owner. The business is key in closely defining the business objectives and use-case specifications, as well as in ensuring the necessary change in the business organization: process changes, understanding of analytics, and relentless measuring of P&L impact. The result is a product that ensures P&L impact and, above all, scalability.

3. Invest in large-scale change management to ensure use-case adoption

Many use cases require that someone approach decisions differently or work in a different way. Therefore, deploying a use case often requires adjustments to processes, roles and responsibilities, and incentives as well as the acquisition of new capabilities. Merely giving employees access to a new tool and explaining it in a training session often is not enough.

For example, if pricing is automated based on analytics, this will have profound implications for the role of a pricing or category manager. While in the past they might have been focused on doing tactical adjustments to prices, they now might be responsible for setting strategic guidelines and providing input to the analytics team on how to further improve the pricing logic.

Even with smaller changes, we find that embedding the analytics insights deeply into the existing processes and workflows and investing heavily in building the required capabilities and understanding of the users is an elementary prerequisite to harvest the expected impact­­—and which is often underestimated.

4. Develop a fit-for-purpose analytics platform to maintain and scale multiple use cases

Providing access to data and insights to many business users is a key way to drive adoption and promote organizational maturity. To achieve this goal, retailers must build a dedicated analytics platform.

Providing data and insights to many business users is a key way to drive adoption and promote organizational maturity.

Moving from legacy IT systems to a fully modernized big-data IT stack requires significant time and cost. But a complete transition may not be necessary—legacy systems can coexist with cloud-based data infrastructure. The most crucial components are data collection and an analytics platform consisting of a data lab, allowing for quick experimentation and a factory environment that can monitor, run, and scale use cases continuously. Grocers should then integrate analytics into back-end and front-end systems incrementally, one use case at a time. Eventually having an advanced analytics platform, the data layer itself, and a visualization and results layer accessible to the entire organization boosts grocery retailers’ organizational maturity.

5. Decide whether to buy an existing solution or develop one in-house

Grocers do not need to reinvent the wheel to achieve business impact. But the choice between buying a proven off-the-shelf solution and investing the resources to develop one can be surprisingly difficult. Many organizations lack expertise in certain use cases, and they must also navigate complex requirements from technical and business perspectives.

In our experience, a successful vendor strategy takes a staged and differentiated approach. For use cases that have become a commodity and are at least in a widely available base version in the market (for example, forecasting and assortment), using external vendors and tools might be the fastest, cheapest, and least risky route. For use cases that can generate a competitive advantage or are still in early stages (for example, personalized promotions), a bespoke solution might hold more impact. Another option is to buy tools or code from vendors to start or accelerate use cases before bringing the solution in-house to create distinctive, tailored solutions.

All grocers must master advanced analytics to remain relevant. By now, for many important use cases, such as assortment, price, and mass promotions, standardized software is available in the market. This also allows for smaller retailers or retailers with lower analytics maturity to achieve first results quickly. For more analytically mature retailers, more experimental use cases, including localization of assortment or personalization of promotions, are the next frontier. To succeed, grocers must invest not only in technical solutions but also organizational capabilities, which will require significant investment in change management, driven from the very top.

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Supermarket Sales Analysis with Data Science

Thecleverprogrammer

  • May 26, 2020
  • Machine Learning

data warehouse case study on supermarket

A supermarket is self-service shop offering a wide variety of food, beverages and household products, organized into sections. It is larger and has a wider selection than earlier grocery stores, but is smaller and more limited in the range of merchandise than a hypermarket or big-box market.

In this Data Science project I have used different techniques to analyse the sales data set of supermarket.

What will you discover from this analysis.

1.Relation of customers with SuperMarket 2.Payment methods used in supermarket. 3.Products relation with quantities. 4.Types of product and their sales. 5.Products and their ratings.

Let’s start by importing Libraries

You can download the data set you need for this project from here:

#Output (1000, 17)

data warehouse case study on supermarket

Data Cleaning

There are no missing value and the data set is clean so we will continue with data visualization.

Checking information of data set.

data warehouse case study on supermarket

Checking number of rows and columns

Visualization.

Now we use different visualization tools to check different aspects of Supermarket sales.

Let’s start with gender count

data warehouse case study on supermarket

Here we can see that the number of males and females entering the store is almost equal. But the visualization looks suspicious. Let’s check numeric data.

The visualization looks good. Let’s carry on.

Customer type

data warehouse case study on supermarket

The visualization looks suspicious let’s check numeric data.

Above we can see the type of customer in all branch combined now let’s check for different branch.

data warehouse case study on supermarket

Checking the different payment methods used.

data warehouse case study on supermarket

Payment method distribution in all branches

data warehouse case study on supermarket

Now let’s see the rating distribution in 3 branches

data warehouse case study on supermarket

We can see that the average rating of branch A and C is more than seven and branch B is less than 7.

Max sales time

data warehouse case study on supermarket

We can see that the supermarket makes most of it’s sells in 14:00 hrs local time.

Rating vs sales

data warehouse case study on supermarket

Using boxen plot

data warehouse case study on supermarket

Here we can see that the average sales of different lines of products. Health and beauty making the highest sales whereas Fashon accessories making the lowest sales.

Let’s see the sales count of these products.

data warehouse case study on supermarket

We can see the top sold products form the above figure.

Total sales of product using boxenplot

data warehouse case study on supermarket

Now let’s see average ratings of products.

data warehouse case study on supermarket

Product sales on the basis of gender

data warehouse case study on supermarket

Product and gross income

data warehouse case study on supermarket

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The retailer possesses one of the richest retail data assets in its market, every day significant volumes of data are generated as shoppers swipe their store loyalty card, which offers loyalty discounts on certain products and personal loyalty rewards.

The function of the retailer’s insights team is to enable better business decision-making using data, information and insight. Several years ago a growing sense of frustration began developing that data was not being used anywhere near its maximum potential. The key challenge was how to transform the retailer’s raw data, into accessible comprehensive insights – at speed.

The retailer’s commercial chief, says the true value in analytics and insights is the ability to influence future business decisions.

  “As one of the largest retailers in the country, it is important that we understand our customers preferences and behaviours through our data. By effectively analysing and transforming our dataset into actionable insights, we can effectively enable our business to make better and more informed decisions that meet our customers’ needs.”

data warehouse case study on supermarket

With 11Ants deployed, the lead time required by the team to answer customer-centric questions has dropped to minutes rather than days. It has also been able to take new and innovative ideas to the retailer’s merchandising team by utilizing 11Ants' pre-built modules.  These modules are based on customer-centric retail best practices and are fully operational from day one.

One of the most widely used modules is Key Measures. This module contains 50+ retail KPIs that can be applied to any corner of the business, from all stores all SKUs, down to a single SKU, in a single store, on a single day, bought by a single customer segment – or anything in between.

This flexibility is made possible by 11Ants proprietary AntScantechnology, which allows rapid scanning of the data at the transaction level, so the user is effectively building their cube as they build their query.

The experience of improved flexibility of queries without IT intervention was transformational. It opened entirely new possibilities as to the type of questions that could be answered ad hoc. As 11Ants’ CEO Tom Fuyala observes:

“The difference between the ability to answer a question in five minutes versus two days is generally the difference between bothering to ask the question or not.”  

Another popular module is  Basket Contents . This module empowers business users to understand which products are sold with other products, either at a basket level or a customer level. This also can be very broad, or extremely specific – for example ‘what is most likely to be found in the basket with 2L Coca-Cola on Monday mornings, by a female shopper, in store x, when sold on promotion y?’ This module allows the retailer to make decisions around co-location, cross-promotion and product bundling as well as measure the impact of these initiatives on shopper behaviour.

Busy Times  is another well-utilized module.  This module provides a very clear understanding of transaction time distribution across categories, stores, products, or customer types – by hour of day and day of week.  Understanding these patterns enables better merchandising decisions.

These modules are just a fraction of the modules contained in 11Ants, which offers a diverse cross-section of retail analytics modules designed to serve merchandizing, marketing and operational requirements.

data warehouse case study on supermarket

Using 11Ants allows this national retailer to unlock valuable data the supermarket chain was finding difficult to translate for business benefit.

Answers to analytically complex questions delivered often in minutes rather than several days.

As new modules are added to 11Ants, the retailer obtains the benefit of using them.

11Ants has made analytics accessible to many in the retailer, not only those with computer science degrees.

data warehouse case study on supermarket

Tangible Business Benefits

Key business benefits realized by the retailer include:

Efficiency improvements and the ability to answer questions fast;

Understanding of shopper buying behaviour and translating this understanding to appropriate business decisions on price, range, promotion opportunities and availability;

Understanding of impact on customers behaviour post operational challenges and making appropriate remedial action – e.g. availability and product recall;

Superior understanding of promotions and their impact on specific customer segments, SKUs, category and whole-store spend.

Grocery Store Analytics Case Study

Leveraging loyalty.

While rich in shopper data, this retailer was poor in insights. The key challenge was how to transform the retailer’s raw data, into actionable insights – at speed.

data warehouse case study on supermarket

See what 11Ants can do

Book cover

Advances in Mechanical Engineering pp 727–734 Cite as

Market Basket Analysis: Case Study of a Supermarket

  • Anup R. Pillai   ORCID: orcid.org/0000-0003-4211-6530 8 &
  • Dhananjay A. Jolhe   ORCID: orcid.org/0000-0001-5094-9669 8  
  • Conference paper
  • First Online: 30 June 2020

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7 Citations

Part of the Lecture Notes in Mechanical Engineering book series (LNME)

The relationships among various items in a group can be deciphered using a data mining technique such as market basket analysis (MBA). It plays a significant role in the analytical systems in supermarkets to determine the arrangement of goods, design of sales promotion and discounts for different customer segments to improve customer satisfaction and thereby increase the sales. This case study involves the use of data gathered from a supermarket as a database. Measures such as support, confidence and lift are used to measure the association between each product. Based on these values, association rules are generated. This information can give supermarket managers an edge over their consumer counterpart to strategically promote products and improve sales. These results also provide valuable insights for cross-selling, up-selling and new product integration tasks.

  • Market basket analysis
  • Association rule mining
  • Cross-selling

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Blattberg RC, Kim BD, Neslin SA (2008) Market basket analysis. In: Database marketing. international series in quantitative marketing, vol 18, Springer, New York

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Musungwini S, Zhou TG, Gumbo R, Mzikamwi T (2014) The relationship between (4 ps) & market basket analysis. A Case Study of Grocery Retail Shops in Gweru Zimbabwe. Int J Sci Technol Res 3(10):258–264

Kawale NM, Dahima S (2018) Market basket analysis using apriori algorithm in R language. Int J Trend Sci Res Develop 2(4):2628–2633

Silvers F (2012) Data warehouse designs- achieving ROI with market basket analysis and time variance. CRC Press, Taylor & Francis Group, Boca Raton

Boztug Y, Reutterer T (2008) A combined approach for segment-specific market basket analysis. Euro J Oper Res 187(1):294–312

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Griva A, Bardaki C, Pramatari K, Papakiriakopoulos D (2018) Retail business analytics: customer visit segmentation using market basket data. Expert Syst Appl 100:1–16

Kaur M, Kang S (2016) Market basket analysis: identify the changing trends of market data using association rule mining. Procedia Comput Sci 85:78–85

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Anup R. Pillai & Dhananjay A. Jolhe

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Vilas R. Kalamkar

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Pillai, A.R., Jolhe, D.A. (2021). Market Basket Analysis: Case Study of a Supermarket. In: Kalamkar, V., Monkova, K. (eds) Advances in Mechanical Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-3639-7_87

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What Is A Real-Time Data Warehouse?

Real-time data warehouse vs traditional data warehouse, 3 major types of data warehouses, 11 applications of real-time data warehouses across different sectors .

  • Manufacturing & Supply Chain
  • Banking & Finance 

Financial Auditing

Emergency services, telecommunications, online gaming, energy management, cybersecurity, real-time data warehouse: 3 real-life examples for enhanced business analytics, case study 1: beyerdynamic , case study 2: continental airlines , case study 3: d steel , enhancing real-time data warehousing: the role of estuary flow, popular articles.

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Gone are the days when organizations had to rely on stale, outdated data for their strategic planning and operational processes. Now,  real-time data warehouses process and analyze data as it is generated, helping overcome the limitations of their traditional counterparts. The impact of real-time data warehousing is far-reaching. From eCommerce businesses to healthcare providers, real-time data warehouse examples and applications span various sectors.

The significance of real-time data warehousing becomes even more evident when we consider the sheer volume of data being generated today. The global data sphere is projected to reach a staggering  180 zettabytes by 2025 . 

With these numbers, it’s no wonder every company is looking for solutions like real-time data warehousing for managing their data efficiently. However, getting the concept of a real-time data warehouse, particularly when compared with a traditional data warehouse, can be quite intimidating, even for the best of us. 

In this guide, with the help of a range of examples and real-life applications, we will explore how real-time data warehousing can help organizations across different sectors overcome the data overload challenge.

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A  Real-Time Data Warehouse (RTDW) is a  modern tool for data processing that provides immediate access to the most recent data. RTDWs use real-time  data pipelines to transport and collate data from multiple data sources to one central hub, eliminating the need for batch processing or outdated information.

Despite similarities with traditional data warehouses, RTDWs are capable of  faster data ingestion and processing speeds . They can detect and rectify errors instantly before storing the data, providing consistent data for an effective decision-making process.

Traditional data warehouses act as storage centers for  accumulating an organization’s historical data from diverse sources. They combine this varied data into a unified view and provide comprehensive insights into the past activities of the organization. However, these  insights are often outdated by the time they are put to use , as the data could be days, weeks, or even months old.

On the other hand, real-time data warehousing brings a significant enhancement to this model by  continuously updating the data they house. This dynamic process provides a current snapshot of the organization’s activities at any given time, enabling immediate analysis and action. 

Let’s look at some of the major differences between the two.

Complexity & Cost

RTDWs are  more complex and costly to implement and maintain than traditional data warehouses. This is because they require more advanced technology and infrastructure to handle real-time data processing.

Decision-Making Relevance

Traditional data warehouses predominantly assist in long-term strategic planning. However, the real-time data updates in RTDWs make them  suitable for both immediate, tactical decisions and long-term strategic planning.

Correlation To Business Results

Because of fresher data availability, RTDWs make it easier to  connect data-driven insights with real business results and provide immediate feedback.

Operational Requirements

RTDWs demand constant data updates, a process that can be carried out without causing downtime in the data warehouse operations . Typically, traditional warehouses don't need this feature but it becomes crucial when dealing with data updates happening every week.

Data Update Frequency

While the lines between traditional data warehouses and real-time data warehouses are now blurred due to some data warehouses adopting streaming methods to load data, traditionally, the former updated their data in batches on a daily, weekly, or monthly schedule. As a result, the data some of these data warehouses hold may not reflect the most recent state of the business. In contrast, real-time data warehouses  update their data almost immediately as new data arrives.

Let's take a closer look at different types of data warehouses and explore how they integrate real-time capabilities.

Enterprise Data Warehouse (EDW)

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An Enterprise Data Warehouse (EDW) is a  centralized repository that stores and manages large volumes of structured and sometimes unstructured data  from various sources within an organization. It serves as a comprehensive and unified data source for business intelligence, analytics, and reporting purposes. The EDW consolidates data from multiple operational systems and transforms it into a consistent and standardized format.

The EDW is designed to  handle and scale with large volumes of data . As the organization's data grows over time, the EDW can accommodate the increasing storage requirements and processing capabilities. It also acts as a  hub for integrating data from diverse sources across the organization . It gathers information from operational systems, data warehouses, external sources, cloud-based platforms, and more.

Operational Data Store (ODS)

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An Operational Data Store (ODS) is designed to  support operational processes and provide real-time or near-real-time access to current and frequently changing data. The primary purpose of an ODS is to facilitate operational reporting, data integration, and data consistency across different systems. 

ODS collects data from various sources, like transactional databases and external feeds, and  consolidates it in a more user-friendly and business-oriented format.  It typically stores detailed and granular data that reflects the most current state of the operational environment. 

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A  Data Mart is a specialized version of a data warehouse that is  designed to meet the specific analytical and reporting needs of a particular business unit , like sales, marketing, finance, or human resources.

Data Marts provide a more targeted and simplified view of data. It contains a  subset of data that is relevant to the specific business area , organized in a way that facilitates easy access and analysis.

Data Marts are  created by extracting, transforming, and loading (ETL) data from the data warehouse or other data sources and structuring it to support analytical needs. They can include pre-calculated metrics, aggregated data, and specific dimensions or attributes that are relevant to the subject area.

The use of RTDWs is now common across many sectors. The rapid access to information they provide significantly improves the operations of many businesses, from online retail to healthcare.

Let’s take a look at some major sectors that benefit from these warehouses for getting up-to-the-minute data.

In the dynamic eCommerce industry, RTDWs facilitate immediate data processing that is used to get insights into customer behavior, purchase patterns, and website interactions. This enables marketers to  deliver personalized content, targeted product recommendations, and swift customer service . Additionally, real-time inventory updates help maintain optimal stock levels, minimizing overstock or stock-out scenarios.

RTDWs empower AI/ML algorithms with new, up-to-date data. This ensures models make predictions and decisions based on the most current state of affairs. For instance, in automated trading systems, real-time data is critical for  making split-second buying and selling decisions.

Manufacturing & Supply Chain

RTDWs support advanced manufacturing processes such as  real-time inventory management, quality control, and predictive maintenance . It provides crucial support for business intelligence operations. You can make swift adjustments in production schedules based on instantaneous demand and supply data to  optimize resource allocation and reduce downtime.

RTDWs in healthcare help improve care coordination. It provides  instant access to patient records, laboratory results, and treatment plans, improving care coordination . They also support real-time monitoring of patient vitals and provide immediate responses to critical changes in patient conditions.

Banking & Finance 

In banking and finance, RTDWs give you the  latest updates on customer transactions, market fluctuations, and risk factors . This real-time financial data analysis helps with immediate fraud detection, instantaneous credit decisions, and real-time risk management.

RTDWs enable continuous auditing and monitoring to give auditors  real-time visibility into financial transactions . It helps identify discrepancies and anomalies immediately to enhance the accuracy of audits and financial reports.

RTDWs can keep track of critical data like the  location of incidents, available resources, and emergency personnel status . This ensures an efficient deployment of resources and faster response times, potentially saving lives in critical situations.

RTDWs play a vital role in enabling efficient network management and enhancing overall customer satisfaction. They provide  immediate analysis of network performance, customer usage patterns, and potential system issues . This improves service quality, optimizes resource utilization, and proactive problem resolution.

RTDWs provide  analytics on player behaviors, game performance, and in-game purchases  to support online gaming platforms. This enables game developers to promptly adjust game dynamics, improve player engagement, and optimize revenue generation.

In the energy sector, RTDWs provide  instantaneous data on energy consumption, grid performance, and outage situations. This enables efficient energy distribution, quick response to power outages, and optimized load balancing.

RTDWs are crucial for cybersecurity as they provide  real-time monitoring of network activities and immediate detection of security threats. This supports swift countermeasures, minimizes damage, and enhances the overall security posture.

To truly highlight the importance of real-time data warehouses, let’s discuss some real-life case studies.

Beyerdynamic , an audio product manufacturer from Germany, was facing difficulties with its previous method of analyzing sales data . In this process, they extracted data from their legacy systems into a spreadsheet and then compiled reports, all manually. It was time-consuming and often caused inaccurate reports.  

To overcome these challenges, Beyerdynamic developed a  data warehouse that automatically extracted transactions from its existing ERP and financial accounting systems. This data warehouse was carefully designed to store standard information for each transaction, like product codes, country codes, customers, and regions. 

They also implemented a web-based reporting solution that helped managers create their standard and ad-hoc reports based on the data held in the warehouse.

Supported by an optimized data model, the new system allowed the company to perform detailed sales data analyses and identify trends in different products or markets.

  • Production plans could be adjusted quickly based on changing demand , ensuring the company neither produced excessive inventory nor missed out on opportunities to capitalize on increased demand.
  • With the new system, the company could use  real-time data for performance measurement and appraisal . Managers compared actual sales with targets by region, assessed the success of promotions, and quickly responded to any adverse variances.
  • Sales and distribution strategies could be quickly adapted according to changing demands in the market. For instance, when gaming headphone sales started increasing in Japan, the company promptly responded with tailored promotions and advertising campaigns.

Continental Airlines is a major player in the aviation world. It  faced significant issues because of old, manual systems. Their outdated approach slowed down decision-making and blocked easy access to useful data from departments like customer service, flight operations, and financials. Also, the lack of real-time data meant that decisions were often based on outdated information.

They devised a robust plan that hinged on 2 key changes: the  ‘Go Forward’ strategy and a  ‘real-time data warehouse’

  • Go Forward Strategy:  This initiative focused on tailoring the airline’s services according to the customer’s preferences. The concept was simple but powerful –  understand what the customer wants and adapt services to fit that mold . In an industry where customer loyalty can swing on a single flight experience, this strategy aims to ensure satisfaction and foster brand loyalty.
  • Real-Time Data Warehouse:  In tandem with the new strategy, Continental also implemented an RTDW. This technological upgrade gave the airline quick access to current and historical data. The ability to extract insights from this data served as a vital reference point for strategic decision-making, optimizing operations, and enhancing customer experiences.

The new strategy and technology led to critical improvements:

  • The airline could offer a personalized touch by understanding and acting on customer preferences. This  raised customer satisfaction and made the airline a preferred choice for many.
  • The introduction of the RTDW brought simplicity and efficiency to the company’s operations. It facilitated quicker access to valuable data which was instrumental in  reducing the time spent on managing various systems. This, in turn, resulted in significant cost savings and increased profitability.

D Steel, a prominent steel production company, was facing a unique set of challenges when they aimed to  set up a real-time data warehouse to analyze their operations. While they tried to use their existing streams package for synchronization operations, several obstacles emerged.

The system was near real-time but it  couldn't achieve complete real-time functionality.  The load on the source server was significantly high and synchronization tasks required manual intervention.

More so, it lacked automation for  Data Definition Language (DDL) , compatibility with newer technologies, and had  difficulties with data consistency verification, recovery, and maintenance . These challenges pushed the steel company to seek a new solution.

The Solution

D Steel decided to implement real-time data warehouse solutions that enabled instant data access and analysis. 

The new RTDWs system proved to be extremely successful as it resolved all previous problems. It provided:

  • Real-time synchronization
  • Implementing DDL automation
  • Automated synchronization tasks
  • Reduced the load on the source server

The system also introduced a unique function that  compared current year data with that of the previous year  and helped the company in annual comparison analysis.

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Estuary’s Flow is our  data operations platform   that binds various systems by a central  data pipeline . With Flow, you get diverse systems for storage and analysis, like databases and data warehouses. Flow is pivotal in  maintaining synchronization amongst these systems, ensuring that new data feeds into them continuously.

Flow utilizes  real-time data lakes as an integral part of its data pipeline. This serves dual roles. 

First, it works as a transit route for data and facilitates an easy flow and swift redirection to distinct storage endpoints. This feature also helps in backfilling data from these storage points.

The  secondary role of the data lake in Flow is to serve as a reliable storage backbone . You can lean on this backbone without the fear of turning into a chaotic ‘data swamp.’ 

Flow assures automatic organization and management of the  data lake . As data collections move through the pipeline, Flow applies different schemas to them as per the need.

Remember that the data lake in Flow doesn’t replace your ultimate storage solution. Instead, it aims to  synchronize and enhance other storage systems crucial for powering key workflows , whether they're analytical or transactional.

As we have seen with real-time data warehouse examples, this solution transcends industry boundaries. Only those organizations that embrace real-time data warehousing to its fullest can unlock the true potential of their data assets. 

While it can be a little tough to implement, the benefits of real-time data warehousing far outweigh the initial complexities, and the long-term advantages it offers are indispensable in today's data-driven world.

If you’re considering setting up a real-time data warehouse, investing in a top-notch real-time data ingestion pipeline like  Estuary Flow should be your first step. Designed specifically for building real-time data management, Flow provides a no-code solution to synchronize your many data sources and integrate fresh data seamlessly.  Signup for Estuary Flow for free and seize the opportunity today.

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How Supermarkets Are Using Big Data & Predictive Analytics To Win (+ Infographic)

data warehouse case study on supermarket

Article Snapshot

​This knowledge is brought to you by big data and grocery expert  Tim Bowen , just one of the thousands of top management consultants on Expert360. Get the full document of this article as a printable PDF here.

  Table of Contents

The Current State Of The Australian Supermarket Industry

New entrants, what chance do the local supermarkets have against international competition.

  • How Retailers Can Use Big Data To Better Understand Consumer Behaviour

How Suppliers Can Use Big Data To Improve Trade Relationships and Consumer Loyalty

  • How Big Data Can Benefit Consumers

The Australian grocery industry is very concentrated. This level of concentration has occurred over many years as the 2 major supermarket chains , Coles and Woolworths, have successfully grown their sales and market share.  

Recent industry figures from IBISWorld’s 'Supermarket and Grocery Stores in Australia’, January 2017, estimates the big 4 players have about 80% market share of the $105 billion markets.

A major change that occurred in the Australian grocery market was when an international retailer, Aldi, entered the market in 2001. Since opening their first store in NSW in 2001, Aldi has opened 470 stores throughout Australia including entering the SA and WA markets in 2016. Aldi offered Australian consumers another type of supermarket, a hard discounter. Another international retailer Costco also entered the market in 2009 and now has 8 warehouses in Australia.

Costco again offers Australian consumers another type of supermarket - a big box or hypermarket. In addition to Aldi and Costco other foreign retailers are planning on entering the market.

In May 2017, UBS presented a supplier survey to the Australian Food and Grocery Council that suggested ‘Amazon Fresh/Grocery will enter Australia in the next three years’ and ‘Kaufland will have a physical presence by 2020’. Other evidence to support these foreign retailers entering the Australian grocery market shortly is that Amazon stated (April 2017) ‘The next step is to bring a retail offering to Australia, and we are making those plans now’. Kaufland is advertising for retail sites on their Australian website and the website states ‘Kaufland is coming to Australia’.

Amazon Fresh is a subsidiary of Amazon.com and in addition to offering a dedicated grocery delivery service also operates retail stores in some states of the USA, London, Tokyo and Berlin. Kaufland is owned by Schwarz Group and operates hypermarkets in Europe.

Today the Australian grocery industry is evolving into an international market and the major Australian supermarkets must compete with international retailers to survive. What is interesting is the 2 major supermarkets, Coles and Woolworths, with the most sales/share in the Australian market are smaller operations vs the new entrants.

​Due to increased competition from new entrants, Coles and Woolworths have invested in aggressive price promotions to try to defend their sales/share.  

Coles - The Current Landscape

Coles supermarkets were acquired by Wesfarmers in November 2007 and a new management team was brought in to revive the retailer’s performance. As part of the turnaround program, Coles has invested heavily in lower prices and its ‘down, down’ pricing initiative.

In the recent 2017 Third Quarter Retail Sales Results, Coles Managing Director John Durkan reported that ‘The business has now recorded 24 consecutive quarters of price deflation.’ Coles Managing Director John Durkan also said that “It is necessary that we continue to proactively invest in the customer offer throughout this period of lower growth and increased competition to ensure we maintain our market leading customer offer”    

The issue for Coles is that the lower prices to try to maintain sales/share will not necessarily increase profit. In the latest Wesfarmers Half Year Report to 31 December 2016, it was noted that: “Coles’ sales performance during the half-built on the strong growth achieved in the prior corresponding period.

Significant investment in value, particularly in the second quarter, led to lower earnings despite a reduction in costs” “Earnings before interest and tax (earnings or EBIT) at Coles decreased 2.6 per cent to $920 million for the half, with revenue in line with the previous corresponding period. Excluding the gains on the sale of Coles’ interest in a number of joint venture properties to ISPT, earnings declined 6.8 per cent.”  

Woolworths - The Current Landscape

The Woolworths Group, the owner of Woolworths supermarkets, has also invested heavily in price promotions particularly after closing the Masters hardware chain in December 2016.

In the recent Half-Year Profit and Dividend Announcement For the 27 Weeks Ended 1 January 2017 Brad Banducci, Woolworths CEO, noted: “During the second quarter, average prices declined by 2.6% relative to the same quarter in the prior year. Customer price perception is beginning to improve but still presents a major opportunity and reflects our efforts to improve customers’ trust in our prices by lowering shelf prices.

All major categories other than meat and tobacco were in deflation in HY17. “Sales momentum improved over the half for Australian Food with comparable sales in December the strongest for the year driven by strong comparable transaction growth and an improvement in items per basket. EBIT declined 13.9% on last year primarily impacted by the reinstatement of team incentive payments, team training and higher depreciation from our renewal and IT investments."  

Metcash - The Current Landscape

 Metcash, a wholesaler that supplies IGA branded independent stores, has also faced increased competition. An excerpt from Metcash Limited – 2017 Fully Year Results and Financial Report: ‘‘Wholesale sales (excluding tobacco) declined 4.3% over the comparable 52 week period. Sales growth from strategic initiatives and new store openings were more than offset by the impact of store sales and closures, deflation, difficult economic conditions in Western Australia, and increased competition including the expansion of competitor footprint in South Australia and Western Australia.’’

"Wholesale sales(excluding tobacco) declined 4.3% over the comparable 52 week period"

 IBIS World’s ‘Supermarket and Grocery Stores in Australia’, January 2017, describe the current retail environment as ‘‘The rise of ALDI has forced the two established industry giants, Woolworths and Coles, to cut prices and expand their private-label product ranges. Smaller players, such as Foodworks, have struggled to compete in an increasingly price-intense industry.

In addition, industry-wide profit margins have fallen over the past five years as players reduced prices and accepted lower margins to stay competitive.’’  

How can Australian retailers compete with the larger international retailers that are now active and planning to enter the Australian grocery market? Consumer loyalty and consumer behaviour. The major issue facing Australian retailers is that their consumers are prepared to try and switch to new entrants in the market.

This highlights a lack of consumer loyalty to the brand. To entice the switchers back retailers can lower prices in the short term but that will not necessarily grow sales and profit in the long term. Some international retailers, such as Aldi, have a lower cost of doing business so full-service supermarkets such as Coles and Woolworths will struggle to match Aldi pricing whilst maintaining acceptable margins/profit for their shareholders.  

The major issue facing Australian retailers is that their consumers are prepared to try and switch to new entrants in the market:"

An obvious example of Aldi's cheaper cost of doing business that is easily seen by consumers is the number of staff working in a store. It is usual for a full service supermarkets to have 30 plus team members working in the store during peak trading hours whereas Aldi may only have 6.

According to the Australian Government Productivity Commission Report (September 2014) Relative Costs of Doing Business in Australia: ‘‘Labour costs are the single largest area of expense for most retail businesses, whether based in Australia or elsewhere. In 2012-13 labour costs represented 47 per cent of the cost of doing business in the retail sector.’’ So how can Australian retailers improve their level of consumer loyalty and their understanding of consumer behaviour?  

 Big data can be defined as large or complex data sets. In the grocery industry, there are 2 distinct types of big data that are currently widely utilised – scan data and panel data. Scan data or EPOS is the data that is collected in-store when items are sold or ‘scanned’ at the checkout. This data provides a great deal of numeric data such as units sold, price sold at, time of day sold etc.

Panel data adds more depth to the data as consumers join the panel and normally will take their shopping home and then scan their purchases. Panel data will then include other data such as the age of the consumer, number of people in the household, income levels etc. In Australia, this data is normally provided by agencies such as IRI and Nielsen.

The third type of big data many supermarkets use is card data. This data is collected via consumer activity: opting to use loyalty cards and/or credit cards when they purchase products.

This data is not offered by all retailers but is normally offered by the larger supermarket chains that offer a full-service supermarket in a developed market. In Australia, Coles uses the Fly Buy program and Woolworths has its Rewards Program. The grocery industry is also starting to collect big data from social media platforms.

For example, if Australian consumers are researching topics such as ‘paleo diet’ or ‘fidget spinners’ are trending then retailers can review their ranges to ensure they are meeting the ever-changing demands of consumers. Big data in its different forms offer Australian retailers the opportunity to better understand their consumers and to tailor their offer to their needs/wants.

The ultimate objective of mining the different data sources is to increase consumer loyalty by personalising the offer.

With improvements in IT it is now possible to start to use the different data sets (scan, panel, card, social media) in conjunction to get a greater understanding of consumers. A simple example is why would a retailer pay to print and deliver a catalogue to a consumer that is health conscious that highlights unhealthy products such as soft drink, chocolate, chips and biscuits are on special this week?

That catalogue would probably have a negative impact on the relationship with the consumer so a smarter option would be a personalised email highlighting what health products are on special this week.  

How Retailers Can Use Big Data To Better Understand Consumer Behaviour

As noted in the Australian Food and Grocery Council Supply Chain Survey Report 2016 there "has been the huge focus on shopper loyalty, with consumers increasingly shopping at multiple outlets and retailers having to work hard to attract retail customers".

Coles and Woolworths are aware Australian consumers shop at different retailers and the opportunity big data offers them to better understand their consumers and improve consumer loyalty.

Over many years both have invested in big data to ensure they are able to make fact based decisions. For example, in 2013 Woolworths invested in a data analytics company Quantium. Woolworths CEO Grant O’Brien First Half Profit Report and Dividend Announcement For The 27 Weeks Ended 5 January 2014. “Data driven insights to continue to assist with the transformation of our business. Through our investment in Quantium, we can better understand the needs of our customers and deliver a better shopping experience.”

The major change that has now occurred is retailers don’t just want lots of data but rather wish to use this data to better connect with their consumers and offer personalised experiences. Amazon is widely regarded as a leader in this field as their websites utilise recommendation engines that analyse consumers personal data such as a user’s purchase history, shopping cart items, items they have liked and what other customers have viewed and purchased.

The website is then able to make a personalised recommendation of item/s they may wish to purchase. McKinsey, in their 2013 article ‘How retailers can keep up with consumers’, suggested: “Forward-thinking retailers are leveraging the vast amounts of data they possess and building analytical muscle to enable targeted marketing, tailored assortments, and effective pricing and promotions. Gathering and analysing data to understand the needs, preferences, and attitudes of growing consumer segments ... will be especially important, as will understanding individual consumers and customising offers on a one-on-one basis.”

With on-going IT developments in algorithms and artificial intelligence (AI) many more actionable insights will be able to be drawn from big data to improve consumer loyalty by creating a personalised offer. In February 2017 at the Mumbrella Marketing Retail Summit, Woolworths Director Loyalty, Data and Digital Media Ingrid Maes highlighted that "our core objective is to generate a genuine one-to-one relationship with every single member in a program".

This change in thinking means Australian retailers that historically focused on short term sales and share being driven by promotions are now starting to focus on building long term relationships with their consumers that is based on a greater understanding of their needs due to more big data being available and analytical tools being developed to create actionable insights from the data.

Retailers that are unable to improve their customer loyalty face the real risk of decreasing sales and profit as consumers switch to a better personalised offer from another retailer.  

Traditionally the larger blue chip, tier 1, suppliers have invested in big data and predictive analytics . Scan data, panel data and card data has been purchased and this data for internal business reviews and for category reviews with the retailers to support their business case. Due to the current trading environment, especially retail price deflation, suppliers are going to have to use this data more to justify cost and retail pricing. For example, in June 2017 Coles managing director John Durkan argued "Australian consumers are getting ripped off’ due to high cost prices from branded suppliers.

Suppliers are going to need to continue to use this data to present fact based arguments to justify their cost price increases to retailers". Over many years major supermarkets have been sharing more data with suppliers to try to increase collaboration between suppliers and retailers to better meet or exceed consumer needs. Coles and Woolworths both have supplier portals to share information and in 2017 Woolworths launched their new Supplier Connect portal that allows suppliers to access more data.

The objective of sharing this information is for the supplier and retailer to work together towards common goals, normally outlined in a scorecard. For suppliers to meet the demands of their retail clients it is expected that they also invest in data and support the retailers’ objectives in a collaborative partnership.

The Australian Food and Grocery Council Supply Chain Survey Report 2016 noted “some suppliers are already responding to the trading environment by striking a balance between price and promotional activity, developing shopper insights and driving supply chain efficiencies. Others have yet to make this shift and there is a clear mismatch between their trading priorities and those of their retail customers”. The risk for the suppliers that do not adopt this collaborative approach is that the retailer may range less of their products and in some cases, may cease doing business with that supplier.

Suppliers may feel they are being ‘forced’ to invest in big data and predictive analytics to maintain or grow their business with their retail clients however the current trading environment with new market entrants and price deflation is challenging and without investing in big data the relationship between the supplier and retailer could suffer.

This data can also be used to change the relationship between the supplier and retailer so that the retailer trusts the supplier to the point where the suppliers’ recommendations are actioned by the retailer with a minimum of fuss. On a positive note, suppliers that do invest in big data can learn more about consumers in the category in which they operate.

These suppliers can have a greater understanding of their current and the competitors’ consumers in both branded and private label ranges. This information can then be used to develop new products or ranges to increase sales. Breakthrough innovation is still limited in the grocery industry vs other industries such as telecommunications and IT hardware.

For example, the Nielsen Australia Breakthrough Innovation Report 2016 highlighted only 48 (0.4%) items were qualified as breakthrough innovation out of the 10,770 items launched in 2014. The report also noted: ‘’Successful innovation can often be the key to delivering real incremental dollar growth to a mature category or manufacturer who is trying to compete in a low growth environment – it’s critical to get right and, it’s astounding how many get it wrong.’’

So for suppliers to be successful in the current trading environment they should invest in big data to develop mutually beneficial trading relationships with the major supermarkets and to drive their range / NPD / sales by a greater understanding of the consumer.    

How Big Data Benefits Consumers

 The increased level of competition in the Australian grocery industry has many benefits for Australian consumers. Firstly, retail prices have already decreased as the 2 major supermarkets, Coles and Woolworths, fight to maintain their current sales and share. Aldi has forced Australian retailers to adjust their retail pricing to remain competitive in the market.

The new entrants are offering Australian consumers different shopping formats. Aldi as a hard discounter has a limited range with low prices and Costco with its big box concept offering a buy in bulk and save shopping to Australian consumers.

With Amazon planning to enter the Australian market other shopping formats, including an improved online experience, could be available in Australia in the next few years. For example, Amazon opened a trial store in December 2016 in the USA, Amazon Go, which is automated so there are no checkouts.

The next benefit for consumers shall be a more personalised offer. By mining big data (likely using a team of  data scientists ), retailers and suppliers will better understand what you want, when and how and they will develop their range, pricing and distribution models to meet those needs. The personalised offer will make your shopping experience more enjoyable by making the whole process faster, easier and simpler.  

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data warehouse case study on supermarket

  • Tomorrow's "Smart" Supermarket

Joining them were panelists representing highly innovative solutions that are shaping the future shopping experience: Curt Avallone (Chief Business Officer of Takeoff Technologies), Julia Wenner (Global Trade Marketing Director of HL Display), and Galen Karlan-Mason (CEO and Founder of GreenChoice). This article represents a  distillation of the webinar.

In 2030, how will consumers shop? Consumers are demanding greater transparency and personalization from their grocers so they can make informed choices that support their dietary needs, health goals, and values. These rapidly shifting consumer demands, paired with emerging technologies, will lead to a “smart” supermarket with a massively improved omni-channel experience  and more efficient operating model. Smart merchandising solutions will make the shelf more attractive for customers while reducing labor hours and waste.

Automation is a crucial element of the smart supermarket of the future, reducing costs and freeing up staff to focus on work that enhances the shopping experience. But at the heart of the brick-and-mortar business is an enhanced shopping experience focused on high-quality fresh produce, delicious food in an attractive gastro environment, and  fulfilling social interaction.

Consumer Shopping Habits Are Shifting 

Shifting consumer preferences, macro trends, and emerging innovations are reshaping the retail sector. The future supermarket needs to respond to these trends if it is to attract the  consumer of tomorrow:

  • Smaller households. There was a 34 percent increase in single-person households in the US from 2000 to 2018. Because cooking for one can be challenging, those in single-person households are less inclined to stock up and cook meals from scratch.
  • Aging population. By 2030, 25 percent of the US population will be 65 years or older. This aging population will be marked by specific shopping habits and dietary needs, along with a reduced capability to drive to stores.
  • Digital revolution. Double the number of US shoppers use online-only retailers to shop for groceries today as did in 2015. Online is becoming a powerful grocery channel that can also be leveraged as an avenue for marketing and sales.
  • Speed/convenience. Americans spend an average of 37 minutes a day on food preparation, serving, and cleanup: indicative of how little time Americans allocate in preparing meals.
  • Omnichannel mindset. US grocery shoppers use an average of 3.1 channels frequently. Americans are willing to use all channels available to get the best quality, price, and convenience.
  • Freedom to customize. Shoppers are willing to share their data to enable personalization of their products and services: Some 46 percent of US shoppers would provide their store with personal information for a better shopping experience.
  • Hassle free. US shoppers are looking for solutions that maximize convenience: 56 percent say that a long wait time at checkout is unacceptable.
  • Sustainable, naturally functional. Food inherently resembles medicine, with consumers seeking out pure, organic, and healthy foods. It is estimated that revenue generated by the functional food market worldwide will grow at a compounded annual rate of 8 percent from 2017 to 2022.
  • Food consciousness. Seventy percent of US shoppers say that product information displayed at the shelf or with the product is very important. They want to know more about their product beyond what’s on the label, seeking transparency on ingredients and ingredients’ origins, additives, and production chains.
  • Global discovery. Some 77 percent of millennials have a high interest in experimenting with new foods, indicative of a growing appetite for perceived to be new, innovative, and exotic.

In Response To Consumer Trends, The Retail Landscape Is Shifting 

Consequently, the retail industry must react  to the way customers shop, and we are already seeing significant changes. We see the following five primary dimensions of retail transformation:

  • Channel change from offline to online and  omnichannel
  • Direct-to-consumer channel and the rise of micro brands
  • New convenience and urban formats
  • Upgraded offers from (hard) discounters
  • Partnerships and acquisitions

As the shopping experience grows increasingly  omnichannel, physical outlets will continue to play an important but different function. (See Exhibit 1.) Successful stores will need to play more specialized roles than they do today and will need to give customers a reason to visit (versus shopping purely online).

Exhibit 1: Services customers will be asking for from brick-and-mortar stores

data warehouse case study on supermarket

New Technologies Will Reinvent The Shopping Experience And Store Operating Model

In addition to shifting demand, disruptive technologies and innovations will reinvent the shopping experience and the operating model. Robotics, Internet of Things (IoT), artificial intelligence (AI), virtual reality, blockchain technology, drones, and 3D printing are just a few examples of disruptive technologies that are changing the way we shop and how  supermarkets will operate in years to come.

  • Robotics are used in supermarkets already to serve meals, assist customers, or scan  aisles for misplaced products and out-of-  stock items.
  • Machine learning and AI have proven themselves capable of supporting demand forecasting and are being used increasingly for recommending products and customizing pricing and promotional decisions.
  • Drones and 3D printing are more distant solutions, but we have already seen tests around 3D printing in the nonfood area, drones used for deliveries, or drones used for out-of-stock scanning.
  • We think these technologies represent a huge opportunity but may also require a significant investment and only make sense if they really improve operations and/or have an impact on the customer experience. 

How Will This Come Together In Tomorrow's Smart Supermarket?

HOW WILL THIS COME TOGETHER IN TOMORROW’S SMART SUPERMARKET?

In response to shifting consumer preferences and technical innovations, the future supermarket will present a more efficient operating model and new shopping  experiences.

We do not claim to know exactly what the future store will look like, but we do believe  that we have a good idea about certain  emerging components of the future store from  extensive global research. (See Exhibit 2.) This perspective is based on dozens of interviews, work with retailers around the world, and in-depth research on emerging technologies and consumer macro trends.

What Does This Mean For Merchandising?

Innovations in the future supermarket come  with new opportunities for merchandising and impact on the 4Ps (price, product, place, and promotion). Here are some case studies of companies that are bringing solutions of the future into stores today.

Case Study 1

Takeoff Technologies provides an end-to-end eGrocery solution, using hyper-local automation to lower the cost-to-serve in online fulfillment. Its in-store micro-fulfillment centers are designed to create a  profitable eGrocery solution. With consumers demanding an omnichannel grocery offering and the volume becoming too significant to “hide the losses” of inefficiently servicing eGrocery, a transition from a defensive posture on eGrocery toward a profitable  solution is essential.

Micro-fulfillment centers offer a solution for the two primary eGrocery cost challenges: picking time and last-mile delivery. Traditionally, picking for an eGrocery delivery takes about 60 minutes, an effort that makes eGrocery an unprofitable burden for many grocers. Takeoff Technologies’ micro-fulfillment centers cut picking time down to about six minutes. Takeoff Technologies’ micro-fulfillment centers can be installed in the existing backroom of supermarkets, providing a hyper-local option that significantly reduces the cost of last-mile  delivery.

Case Study 2

HL Display is a shopper-experience company with expertise in improving the shopping event while increasing cost efficiency and maintaining environmental sustainability. It specializes in merchandising solutions designed to support efficient store operations and improve the customer’s shopping  experience. Those solutions include:

  • Shelf automation frees up employees to focus on creating a better shopping experience by engaging with shoppers (such as promoting fruits and vegetables, operating in-store delis, preparing fresh foods, and offering in-store services)
  • Packaging-free merchandising is a key trend across Europe, driven by desire to reduce packaging waste. Several trials are going on currently. These merchandising solutions enable differentiation, reduced waste, and higher margins.
  • “Sustainable choice” in-store solutions are currently being rolled out and developed by HL Display. These merchandising solutions are made of recycled or renewable resources (such as sugar cane and other bioplastics). HL expects one-third of its assortment to have a sustainable choice option by the end of the year.

CASE STUDY 3

GreenChoice is a data-driven tech startup offering a “digital food assistant” to consumers and plug-ins to retailers that provide shoppers with personalized dietary guidance supporting their health needs, goals, and values. 

The startup solution is empowering consumers with customized dietary guidance  by responding to the following trends:

  • Personalization. Consumers crave personalization to meet their wants and diet goals and are willing to share personal information to get there.
  • Transparency. Impending regulations are likely to ban certain additives and require greater transparency into others. Consumers are wising up to harmful additives/ingredients, and it’s best to start addressing these demands to get ahead of the curve.
  • Sustainability. The UN Intergovernmental Panel on Climate Change (IPCC) has stated that the world has just over a decade to get climate change under control before it leads to a crisis. Whether or not businesses believe this claim, the point is that young people do, and businesses must address their concerns.
  • Building trust. Consumers are demanding greater transparency from their grocers so that they can make informed choices that support their personal dietary needs, health goals, and values.

How Can You Leverage Innovations For Your Business?

Rapidly shifting consumer demands, in tandem with emerging technologies, are leading to the advent of the “smart” supermarket. But there are many opportunities that need to be considered as supermarkets develop their omnichannel offering. Here are some key questions a retailer needs to ask – and answer – to know where it stands in the journey to tomorrow’s  revolutionary digitalized supermarket:

  • Innovations and technology: Have you identified the use cases for technology-enabled improvements to enhance efficiency and customer experience?
  • Business case: Do you have transparency on the necessary investment and expected return on investment for innovations?
  • Workforce of the future: How will your workforce shift in the supermarket of the future and how can you prepare?
  • Pilots and tests: Are you already testing and piloting new technologies in-store or across your network?
  • Omnichannel: Have you thought through what an omnichannel offering should look like for your business?
  • Merchandising: Have you adapted merchandising processes to leverage store innovations?

data warehouse case study on supermarket

Future of Retail

  • Payments In Retail

Emerging New Consumerism

  • Why Brand Is An Essential Tool For Profitable Retail Growth
  • Myths In The Age of Personalization

Emerging Technology

  • Why Voice Is The Future Of Grocery
  • The Customer's New "Voice"

Workforce Challenges

  • Turning The Workforce Challenge Into An Opportunity
  • Redefining Retail Jobs
  • The Twin Trends of Aging And Automation

New Marketplace

  • Four Ways To Navigate Tariffs and Trade Policies
  • How Stores Can Fix The Free-Rider Quandary

Food Production

  • Pockets of Growth Within The Store
  • The Intersection Of Food Safety and Sustainability

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Mark Baum Senior Vice President & Chief Collaboration Officer, FMI

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D'Mart: Most Successful Indian Chain of Hypermarkets[DMart Case Study]

Varad Kitey

Varad Kitey

D'Mart is an Indian chain of hypermarkets established by DMart owner Radhakishan Damani on May 15, 2002. DMart has 214 stores in 72 cities across 11 states in India including Maharashtra, Andhra Pradesh, Telangana, Gujarat, Madhya Pradesh, Chhattisgarh, Rajasthan, National Capital Region, Tamil Nadu, Karnataka, Uttar Pradesh, Daman, and Punjab. So, let's get started with the D'mart case study .

Mumbai headquartered DMart is owned and operated by Avenue Supermarts Ltd. (ASL). After the IPO posting (as Avenue Supermarts Ltd.), it made a record opening on the National Stock Exchange(NSE). DMart’s valuation rose to ₹39,988 crore after the close of the stock on 22 March 2017. This made DMart the 65th most significant Indian firm, followed by Britannia Industries , Marico, and Bank of Baroda. As of 21 November 2019, the market capitalization of DMart was around ₹114,000 crore, taking it on 33rd position of all recorded organizations on the Bombay Stock Exchange.

This article will shed insights on the supply chain model of DMart, its business model, marketing strategies, How DMart was started, key financial highlights of DMart, growth and future of DMart in India & more.

Dmart Logo | Dmart Stores in India - D'Mart Case Study

In this Dmart Case Study, we have discussed the -

DMart - Company Highlights Foundation of DMart & Why DMart is Successful? Strategic & Organization Structure of DMart DMart - Business Model & Supply chain Model Marketing Strategy of DMart Factors Affecting the Profit of DMart DMart - Important Financial Metrics Growth of DMart in India Future of DMart

DMart - Company Highlights

Foundation of dmart & why dmart is successful.

Unlike Flipkart was established by two 25-year old youngsters toward the beginning of their professions, DMart's establishing story couldn't have been more extraordinary as DMart was established in 2002 by a then-45-year-old Radhakishan Damani at a moment that he'd effectively made his millions. When he established DMart, Damani was an incredible name in Indian securities exchanges. He had already got a few worth stocks that surpassed Gillette and HDFC Bank’s valuations.

Damani, who dropped out of a trade degree after the primary year, had first joined his dad's metal rollers business, yet had begun putting resources into stocks when he was 32. He wound up getting to be one of the greatest stock financial specialists of the 90s, and current securities exchange bull Rakesh Jhunjhunwala believes him to be a tutor. In any case, after an effective financial exchange profession putting resources into shopper confronting organizations, Damani chose to begin his own.

On May 15, 2002, Damani established grocery store chain DMart and embraced techniques that were one of a kind to Indian retail. Up to that point, most retail chains rented their stores, yet DMart picked carefully do its exploration and possessed its very own stores by and large. That technique appears to have worked as DMart has never needed to close down a store since it's opened in every one of the long periods of its activity.

While other retail players forayed into different classifications, including hardware and design, DMart stayed focussed on its center sustenance and basic food item business. What's more, when other store chains are on the whole propelling their very own private brands in an offer to improve edges, DMart still stocks just outsider items.

It's this moderate methodology that has worked for DMart. Other retail chains were picking development, yet for the initial 15 years, Dmart just worked its stores in 4 states. Indeed, even today, the company has 214 stores in 72 cities across 11 states. DMart had a benefit to-deals proportion of 3.7%.

In correlation, other significant Indian retailers don't passage very also Future Group has a benefit to deals proportion of 0.21%, Spencer's Retail had a negative benefit to deals proportion of - 8.9%, and Reliance Retail which works high-edge classifications including hardware and adornments and has more than double the incomes of DMart just dealt with a benefit to deals proportion of 1.6%.

DMart's traditionalist yet beneficial approach is by all accounts demonstrated after its author. Damani is famously media-bashful and gives no meetings. He's said to be modest, all things considered, also he doesn't appear to talk much, yet is evidently a decent audience, engrossing a lot of data rapidly, and afterward following up on it.

Radha Kishan Damani - D'mart Case Study

And keeping in mind that Damani's success has made him hugely rich because of the flood in DMart's stock value, he's currently worth $15.5 Billion (over Rs 116,200 Crores) regardless he wears a white shirt and white jeans to work, the dress he's been wearing since the 80s. Despite everything, he goes for night strolls on Girgaum Chowpatty in Mumbai and unconditionally converses with the outsiders who approach him after his Dmart's open achievement.

data warehouse case study on supermarket

Strategic & Organization Structure of DMart

The ultimate start with DMart needs to make a picture among the majority of a rebate store that offers the vast majority of the items from over every single real brand. Fundamentally, a store that offers an incentive for cash! Presently, since individuals for the most part come to DMart on the grounds that they all what they need under one rooftop; consequently, DMart stores are operational in high rush hour gridlock territories and crosswise over three organizations including Hypermarkets that are spread crosswise over 30,000-35,000 sqft, Express group, that is spread more than 7,000-10,000 sqft and in conclusion, the SuperCenters, that are set up at more than 1 lakh sqft.

What's more, Dmart's intended interest group being the center pay gathering, it uses Discount offers as a special instrument for baiting the clients and expanding deals too. Generally speaking – Dmart's prosperity is centered around three things: Customers, Vendors, and Employees ! Take Customers. Since Dmart is focusing on center salary family units, every one of their stores is in, or near, neighborhoods and not in shopping centers.

Their thought isn't to meet each customer's need like different contenders, yet rather, Dmart tries to meet most normal shopper needs, while offering some benefit for their cash. Furthermore, since, 90% of these stores are possessed legitimately by Dmart, they don't need to stress over month-to-month rentals and their ascent, or migration chance. Moreover, this is helping them manufacture resources on their books.

This likewise keeps Dmart all around promoted and obligation light, while its tasks produce extra money. All the cash that is spared utilizing this procedure is at the end offered back to the clients as limits! Sellers! Seller connections are the second mainstay of their model. Since he originates from a dealer foundation, his seller connections have been his greatest quality.

Dmart Case Study

The FMCG business has an installment standard of 12-21 days, however, Dmart pays its sellers on the eleventh day itself. This causes him to remain in the great books of the merchants and dodges stockouts. Furthermore, since Dmart purchases in mass and pays its sellers well in time, they additionally get the chance to win higher edges. Essentially, their procedure is to "Get it low, Stack it high and sell it shabby"!Workers! This is the third mainstay of their model. DMart offers great cash, adaptability, strengthening, and loose and effective work culture.

They even proceed to employ tenth standard dropouts with the correct frame of mind and duty. They incline toward procuring crude ability and afterward put intensely in preparing, to shape them according to their prerequisite. Representatives are simply educated once concerning the worth framework and arrangements at D-Mart and after that are enabled by giving them the opportunity to work without someone continually investigating their shoulders. There is outright lucidity on what should be accomplished, yet you don't have to dread targets.

DMart - Business Model & Supply chain Model

The business model lies at the core of a successful company. A good, foolproof business model not only acts as a pillar for a business to grow but also helps it prosper in a comparatively less amount of time.

DMart, often termed as the Walmart of India, has been quite successful in its business so far, and a major credit goes to the robust business model it has developed over the years.

The chain of DMart operates on a B2C (Business to Consumer) model in which the company sells its goods from the manufacturer’s house to that of the end-user. DMart sells a wide range of products ranging from home care and personal care to grocery and staples, daily essentials, home appliances, footwear, luggage, fruits and vegetables, men’s and women’s apparel, and more. These goods, as we all know, fulfill our everyday needs, and hence, have a significant demand throughout the year. Therefore, they wipe out the possibilities of fluctuations due to high demand and helps the brand get the stability that many others dream about.

DMart is recognized for its thrifty cost structure that has made the company keep its losses under control. Here are some prominent characteristics of DMart’s business model:

Low operational costs and fewer expenses

DMart believes in the effective utilization of the spaces instead of adorning its interiors and shelves fancifully. The company works in launching more and more products in fewer spaces for the customers to choose from, which can also be summed up as a low-interior-cost concept to reduce the operational costs. Besides, when you walk into a DMart store you would also find lesser billing counters, which further works in reducing employee costs.

Ownership model

Damani, the company’s founder, had decided quite early in the game to adopt a store-ownership model. This played a major part in making DMart a low or no debt company, thereby strengthening it financially. Furthermore, the company doesn’t accrue any rental costs, which helps DMart open more stores and gain high positive cash flows. The company owns around 80% of all the stores that it is credited for.

Affordable rates of products

It is usually observed that in the FMCG sector, the retailers pay off the credit to their vendors within a period of 3 weeks whereas DMart pays off their credit within a week. This helps the company benefit in many ways including the huge discounts that they get from the vendors , which in turn is entirely rewarding for the end-users too.

Affordable rate of products with tons of discounts on various products leads to increasing the overall footfall and spike up the sales volume. This increasing sale also helps the manufacturers to rely on the brand and bring in more stocks for the rising demand, which extends another volume discount from the manufacturers' end.

Also Read: The Complete Psychology behind Free Samples & How it Works

Slotting fee

DMart levies a ‘Slotting Fee’. As the term might indicate, it is a fee that DMart charges from the manufacturers to store their products on the shelves of DMart stores , which is also sometimes referred to as an entry fee. DMart, on the other hand, with its appealing marketing strategies and attractive discounts ensures that the products are sold out as quickly as possible.

Sales channel

As discussed earlier, DMart opts for a B2C (Business to Consumer) business model, where the company sells the products directly from manufacturers to the end-consumer. The company purchases its goods in bulk and this eliminates the middleman (distributors and wholesalers) from the chain, which helps in passing their commissions as discounts to the consumers.

Target customers

DMart’s target customers are the middle-class groups and lower-middle-class groups, those who often want to buy low-cost goods that come with hefty discounts but are of good quality. This makes DMart attract an extensive customer base than many other retailers.

Regional Goods

A land of diversity, India nurtures an array of region-specific goods. This gave DMart an amazing opportunity to capture the niche markets with products specific to different regions. DMart researches the popular local brands of a particular region and makes them available, thereby avoiding people’s need to go to the local Kirana stores. This has helped DMart to gain more market share.

Operating strategy

Contrary to their peers and rivals, DMart has always stuck to their own stores and deliberately avoided the malls, which might have otherwise risked the overall sales of the company and increased the expenditure.

Besides, the company is also not very comfortable expanding geographically. The company had its stores only in 4 Indian states until 2014, which only expanded in recent years to 11 states. One another thing is that DMart attracts low marketing costs because the main marketing strategy of DMart is that the company is recognized among its end-users via “ word of mouth ”.

data warehouse case study on supermarket

Marketing Strategy of DMart

DMart is a company that doesn’t believe in marketing aggressively unlike many of its competitors. The company maintains a marketing mix where its Unique Selling Position (USP) lies in offering the products at less than Maximum Retail Price (MRP). This is the most important factor that contributes to keeping the company ahead of its peers.

What DMart indulges in is aggressive CSR activities and other low-cost promotional activities . One of the most promising campaigns is:

Better School, Brighter Futures!

DMart is a company that takes pride in the laudable CSR initiatives that it takes. Over the years, the company has grown to be a huge support for its employees and other communities alike with the help of its socially responsible business practices. This undoubtedly spreads positive vibes all around.

In its “Better School, Bright Futures!” campaign, DMart has launched an amazing program in various schools that are there in and around Mumbai. The sole aim of which helps students understand things better and create an ecosystem that allows them to benefit from better education, mentoring research facilities, and new networking opportunities.

Embracing Low-Cost Advertising Mediums for Promotion

DMart looks up to visual and print mediums to promote its brand name and products. The print medium of advertising revolves around newspaper ads with information about their products, discounts, sales, and coupons.

On the other hand, the visual component of advertisement comprises the banners, flexes, and hoardings that are put to display in locations near the stores to mention the product-specific offers, seasonal discounts, and other freebies that the company offers from time to time.

Digital Presence of DMart

DMart was founded back in 2002 and boasts of an enviable offline presence but when it comes to digital presence it bothered little about it to be true. However, the company has taken a few steps to place it ahead on the digital front. These steps include the installation of a chatbot on Facebook Messenger and the launch DMart Ready.

As of now, DMart uses Facebook as a medium for information, which the brand uses to inform and clear customers’ doubts. The company is yet to explore Instagram and Twitter fully, the proper utilization in the upcoming times will surely help the company set itself more stable in the future.

Factors Affecting the Profit of DMart

Damani is a calm man who stays under the radar, yet his triumphant characteristics are too obvious to possibly be missed. The following are his ways to deal with a business that drove him to thundering achievement:

Design of Logo

Like Warren Buffett, Damani too has been a worth speculator who might take a shrewd perspective on the long haul. When he turned into a business person, he held a similar methodology and manufactured DMart without depending on any speedy alternate ways. For example, he never rents the property for his stores however gets it. In the long haul, it spares him from a major rental outgo. This was a key factor behind the productivity of DMart.

What Is Trifle That's Important

Damani began little and did not rush to grow. Low scale gave him superior control of the store network and enabled him to concentrate on benefits directly from the earliest starting point. In the 18 years of its reality, D Mart has turned a benefit every year.

Evaluation Of People

Damani started with purchasing an establishment of Apna Bazar. That was the point at which he started fabricating individual relations with merchants and providers. He esteems both and they never let him down. The stores never leave stock.

Selling As Cheap

Damani realized what he was doing: offering individuals buyer results of everyday use at substantial limits. That turned into his sole objective. One of his strategies was to pay his providers and sellers inside days rather than weeks which was the business standard. They gave the merchandise at a less expensive rate to him in lieu of early installment. He passed on the money-saving advantages to his clients, which guaranteed steady success.

Go Steady And Slow

In spite of the fact that D-Mart began 18 years prior, despite everything it has 119 stores in a couple of states, a modest number contrasted with those claimed by Ambani and Biyani. Rather than fast development, Damani received a moderate pace which gave him his emphasis on productivity. That is the reason D-Mart has not closed a solitary store since it began and creates higher per-store incomes than the stores of Ambani or Biyani.

Neglect the Herd

Damani had learned and drilled with the progress the craft of not following the crowd while he was a financial specialist. As a business person, he has a similar methodology. There have been such a large number of brand new thoughts in retail, for example, different online business patterns, which he didn't give any significance. Designs or patterns can't impact the man who realizes what he needs and how he can get it.

Available Locally

Despite the fact that DMart is the best basic food item retail chain in the nation, Damani has restricted it towards the western states. One reason is his dependence on neighborhood supplies rather than expand supply chains.

A Job Has Conversation

Damani stays under the radar which bears him all-out devotion to his work. His moderate and quiet ascent in a discouraged division is a sign of his resolute spotlight on work. He has once in a while given a meeting to a TV channel or a paper.

data warehouse case study on supermarket

DMart - Important Financial Metrics

The below table highlights the important financial metrics of DMart as per its audited, consolidated financial statements -

(Rs. in crores, unless otherwise stated)

Standalone Results -

For the quarter ended March 31, 2021 (Q4FY21):

  • Total Revenue stood at Rs. 7,303 Crore, YoY growth of 17.9%  
  • EBITDA of Rs. 617 Crore; YoY growth of 47.6%
  • PAT stood at Rs. 435 Crore; YoY growth of 51.6%  
  • Basic EPS for Q4FY21 stood at  Rs.6.71, as compared to Rs. 4.49  for Q4FY20
  • 13 stores were added in Q4FY21

For the year ended  March 31, 2021  (FY21):

  • Total Revenue stood at Rs. 23,787 Crore, lower by 3.6%
  • EBITDA of Rs. 1,742 Crore; YoY decline of 17.9%
  • PAT stood at Rs. 1,165 Crore; YoY decline  of 13.7%
  • Basic EPS for FY21 stood at Rs.17.99,  as compared to Rs. 21.49  for  FY20
  • 22 stores were added in FY21 and 2 stores were converted into fulfillment centers for Avenue ECommerce Limited.

data warehouse case study on supermarket

Growth of DMart in India

Avenue Supermarts running the DMart chain of stores in the nation revealed a 21.4 % year-on-year net benefit development and a 32.1 % year-on-year income development for the quarter finished March 31, 2019, (Q4) at Rs 203 crore and Rs 5,033 crore, separately.

For the three months finished December 31, 2018, DMart had announced its slowest net benefit development in eight quarters at 2.1 % as it pondered developing challenges in basic food item retail.

Second from last quarter income development came in at 33 % (year-on-year), which is likewise a merry quarter, said experts, suggesting the organization had figured out how to keep up its pace of development as far as the top line in Q4 in the midst of focused power. The numbers were comprehensively in accordance with Street gauges. A survey by investigators of Bloomberg had pegged net benefit at Rs 211 crore and income at Rs 5,122 crore for the quarter under audit.

Income before intrigue, duty, deterioration, and amortization (Ebitda) for Q4 was at Rs 377 crore, up 27.9 % throughout the year-prior period and again extensively in accordance with Street assessments of Rs 395 crore. Yet, Ebitda edges contracted for the third straight quarter, however, the drop was negligible at 20 premise focuses to 7.5 % from a year sooner.

This is additionally the most reduced as far as Ebitda edges for DMart in 75%. While the organization did not indicate same-store deals development for Q4, examiners said it was somewhere in the range of 15 and 18 % for the period under audit.

Same-store deals development is the development of a similar deal of stores for one year or more. For the entire year finished March 31, 2019, (FY19), Neville Noronha, overseeing executive (MD) and (CEO), Avenue Supermarts, said same-store deals development was 17.8 % even as income grew 32 % year-on-year to Rs 19,916 crore and net benefit went up 19 % from a year sooner to Rs 936 crore.

The FY19 same-store deals development was higher than the 14.2 % revealed for FY18, division examiners stated, as the firm drove higher deals throughput at its stores. Income from deals per square feet at DMart stores remained at Rs 35,647 for FY19 against Rs 32,719 in FY18, an ascent of about 9 %. The organization additionally included 21 stores in FY19, of which 12 were included in Q4 alone, taking the aggregate to 176 for the monetary year.

data warehouse case study on supermarket

Future of DMart

Avenue Supermarts runs the DMart grocery store chain of stores. If in any case, the nation experiences a crisis, financial specialists question whether the organization shows enough strength during these intense occasions. But examiners in a note from Systematix Shares and Stocks (India) Ltd. said, " The continuous crisis in utilization and higher aggressive force in staple retail should confine development in determining deals per square feet to 7% in the financial year 2020 from 13% in FY19."

While speculators will intently follow how that works out in the coming quarters, Avenue Supermarts' income development of almost 27% in the June quarter is nothing to get surprised at. Obviously, it should likewise be referenced at the same time that high development rates are a basic for the DMart share, which is one of the most costly stocks in the nation.

It currently exchanges at amazing multiple times evaluated income for FY20. FY20 has begun an idealistic note for the organization. The development in EBITDA (income before premium, assessment, deterioration, and amortization) edge in the June quarter will mitigate financial specialists' uneasiness about weights on productivity somewhat.

What is Dmart?

Founded in 2002, Dmart is an Indian retail corporation that is designed to stand as a one-stop supermarket chain that brings a wide range of products ranging from basic home products, personal products and more.

Where is the Dmart headquarters?

DMart headquarters is in Mumbai, Maharashtra.

Who founded Dmart?

Radhakishan Damani and his family founded Dmart in 2002.

Where was the first branch of D mart?

The first branch of D mart is in Powai's Hiranandani Gardens.

What is the vision and mission of Dmart?

The mission and vision of DMart is " to provide the best possible value for consumers so that every penny spends on shopping gives them more value for money than they would get anywhere else," as per the vision and mission statement of Dmart.

How many D mart stores in India are there in total?

Currently, the total number of D mart stores in India were reported to be more than 234 in number, spread across more than 11 states in India, as per February 2022's reports.

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