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Interesting Case Studies of Data Analytics in Retail Industry

As one of the major global industries, retail sector represents 31% of the world’s GDP. The annual average growth of the industry is estimated to be 3.8% since 2008 and the revenue from the industry is expected to be $28 trillion by 2019. However, with this huge anticipated growth in sales and customer base, retail companies are likely to face some considerable challenges in their operations. First we will discuss what these challenges are and how they can be solved with savvy use of data analytics:

Providing 360 Customer Experience

Customer experience are at the heart of any business- but for retailers- it cannot get any truer than this. In this digital era, information has become readily accessible for customers. With a simple click, customers can learn about a host of information about retail stores- ranging from product availability, discount offers to customer feedback, payment details and so on. Today’s shoppers are socially connected individuals, they feel empowered to voice their opinion about anything in social media. Retail industry players should learn how to convert this wealth of social data to achieve business objectives. The public perception of certain aspects of their business can be tracked. Based on the findings, retailers should make modifications to delight the customers. For example, if a retail chain makes any change in store design or loyalty offers, they can track and analyze social data to understand if this change is working favorably for its business. Enhancing customer experience should be a continuous process- and it has to be driven by data insights.

Forecasting and Planning Inventory

Reducing the number of unused and outdated inventory item is a huge headache for retail business owners. Another equally important challenge is to ensure an optimum supply of products in the store. Retailers must track the sales and inventory data according to demographic, geographic and behavioral properties. Top data analytics solutions usually offer predictive modelling- using this feature, retailers can forecast the level of demand for a specific product in various stores. It will enable them to ensure optimal supply in the outlets.

Increased level of traffic is one of the downsides that come with rapid urbanization and economic mobility. It has a powerful impact on retail business as they need to get the stock delivered in the least possible time with least possible expense. Data analytics can help them out to determine an optimized route plan for the supplier and provide a detailed insight on supplier’s performance.

retail industry

Marketing and Promotional Campaigns

Targeted marketing is not a new thing now- and this is being increasingly explored by digital marketers across different industries. A survey revealed that 75% of the consumers fail to remember an advertisement and connect it with a brand accurately after 24 hours. The reason is obvious - they do not feel any connection or relevance with the advertisement. Therefore, marketers and advertisers now have a new challenge to overcome. They need to produce relatable promotional campaigns for different target segments of a brand. From identifying and segmenting the prospective customers to pushing contents to their user journey- massive use of data analytics is required in every nook and corner of this process. Most of the retail companies sell similar kind of products in their store. Targeted promotion can effectively be a point of differentiation for them. Kohl, a retail giant, use an indoor positioning system to provide real time contents to their in-store customers. This CRM strategy is helping them to know about customers' preference and purchasing habit extensively.

Detecting Fraud

Retail business owners often find themselves exposed to various kind of fraudulent activities; counterfeit balance and transactions, shoplifting, refund fraud, salary irregularities are to name a few. While it is true that there is no one right solution to detecting fraud, but a comprehensive data analytics solution can surely help them to find a way out of this problem. A good analytics platform will allow the owners to monitor every single data from different sources in real time. Whenever there is any unusual deviation, the user will get notified about it. Not just that, retail companies can also build a model for expected sales (in units and amount) for each shift and store. If the number is exceeded, an alarm will be sent to the person concerned.

Case Studies

Now we will discuss about few interesting use case of data analytics in retail industries. All these case studies on globally reputed retail companies gives us a solid idea about how to leverage data to power up the business:

Case Study-1

Argos is United Kingdom’s one of the leading retailer for toys, home furnishing and personal care offering more than 43,000 products. In 2013, the company wanted to embrace a “digital-first” approach, so they went for a colossal project of opening 53 new digital stores across UK.

But a huge challenge also came up with this project. Argos wanted to know their customers reaction to this heavy change in store’s look and feel. They decided to track and monitor what customers were saying about them in social media channels. Brandwatch Analytics became their provider of social listening platform. The platform offered sentiment analysis features- it allowed Argos to understand people’s general sentiment about the change. Other significant insights that Argos received with this platform are:

argos

  • Demographic and behavioral features of the individuals who commented about Argos
  • Difference of sentiments according to gender, region and user journey

The social monitoring data provide Argos some valuable inputs. For example, the male population welcomed the digital change more cordially than their female counterpart. Londoners were more favorable to the new concept of stores. Overall, these data continuously helping Argos to mold their operations to increase customer satisfaction level and boosting profitability.

Case Study-2

Kroger is Cincinnati based America’s largest retailing company. Their operation research team, which was initiated back in 2007, adopted data analytics solutions to evaluate and improve retail operations. As a part of their continuous effort to achieve operational excellence, the team implemented a project in 2010 with a view to decrease out-of-stock items, enhance customer experience and increase revenue. The outcome of the project has been particularly impressive:

  • Reduction in Inventory by $120 million
  • Reduction in number of out-of-stock prescription by 1.7 million
  • $80 million increase in revenue

Kroger

Customer experience is a major differentiating factor for retail business. Customers always want to avoid hassle while purchasing their desired product and prefer to spend least amount of time standing in queue. Addressing this important factor, Kroger decided to use QueVision Solution. It is a data analytics package that uses infrared sensors at major points of the store and tracks historical shopping data. Combining these two variables, it develops a predictive model and informs the store manager when to create new queue for customers. So far, this solution is being deployed at more than 2400 Kroger outlet. The company has successfully reduced their average wait time from 4 minutes to less than 30 seconds.

Case Study 3

This is an interesting case study of USA’s second largest discount store retailer Target Corporation. The company keeps the customer information in Guest ID that tracks extensive range of data like purchase history, card usage, survey responses, support issues, email responses, web site clicks and so on. This activity data is then further supplemented by purchasing demographic data like age, religion, education, marital status, children’s number, estimated income, job history and significant life events such as when you last moved or if you have been divorced or ever declared bankruptcy.

From the huge amount of customer data Target tracked, it saw a purchasing pattern of the woman who were in different phases of pregnancy. For instance, during the first 20 weeks pregnant women began purchasing calcium, magnesium and zinc supplements. In the second trimester, pregnant women began buying larger jeans and larger quantities of sanitizers. From the pattern, Target was able to identify pregnant customers even though they have not notified their pregnancy status to Target. Based on these customer information, Target started targeted product promotions to specific buyer segment. The financial result was astonishing for Target. It grew its revenue from $44 billion to $67 billion after they started utilizing this data analytics.
target

Case Study 4

IKEA is one of the reputed Home Retailers across the globe, attracting millions of footfalls in their physical and digital sphere. Their research team had this assumption that there is a strong correlation between people browsing their website and then visiting their store. A digital agency named Match Media was assigned to create a research model that links online behavior with sales performance. Match Media started off with pulling data from different sources to get the following metrics:

  • Products Added to “Shipping List”
  • Stock Availability Checks
  • Visits to local store page
  • Website Searches
  • Products Viewed

IKEA Infographic

Using these metrics, they created an online conversion model which was used to execute a promotional campaign titled “My Kitchen”. The following outcome was observed:

a) 11% of family members who were exposed to the online ad went to IKEA to purchase kitchen items
b) Average basket size of those who were exposed to the promotion was 45% higher than non-exposed
c) The campaign resulted in an impressive 464% ROI.

Conclusion

Previously retail business used to be viewed as a game of product sourcing strategy and execution. Now-a-days similar emphasis is given to customer experience and marketing campaigns to differentiate the value propositions from competitors. But there is no single right way of designing or planning your next move. So how do you start? Let the data guide you about what customer expects from your business. Track your operations as much as possible with a data analytics solution. It will enable you to take timely decision related to inventory & stock planning, supplier performance, product availability & demand and a lot other important aspects of retail business. The case studies which are discussed in this blog can give some solid guideline about how you can use data oriented software solutions to devise your business strategy and operational planning.

Rafi Nizamee

Rafi Nizamee

Graduated from IBA, Dhaka University. Aspires to be an expert in business process development and analysis. Loves meaningful and informative conversation on any topic. Reach him at rafi2169@gmail.com

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