Data analytics use cases in the retail industry

Data & Analytics is currently an omnipresent topic for nearly every industry. Data poses particularly interesting opportunities in the retail industry - from understanding the customer, to predicting the best assortment and optimizing the footprint of brick-and-mortar branches. In this article, we want to give a crisp overview on "classical" use cases for the retail industry, which should first and foremost spark interest in the topic and does not aim at giving a general recommendation.

Authors: Marion Sch?ffauer,?Eileen Slotta, Matthias Schlemmer

It is becoming increasingly challenging to understand the quickly changing shopper landscape. We have all heard countless times how critical it is to put customers at the heart of all decisions and processes for retail & consumer goods players. While this may sound complex at first glance, it can be managed with a sound data-driven approach. With this article we want to start a discussion on typical use cases around data & analytics as well as their fit-for-purpose within the retail & consumer goods industry.

Nowadays, customer expectations are increasing in regards to seamless omni-channel shopping experience, personalized offers, sustainability considerations and much more. Data & analytics provides companies with the crucial capability to gain a more detailed understanding of customers, products and operations. Business leaders need to think strategically on how to build up their data & analytics capabilities, how to prioritize potential use cases and how to ultimately turn their data into value.?

While there is an abundance of data & analytics opportunities, we have listed a number of “classical”, yet still very relevant use cases for companies in the industry. Needless to say that this is just the tip of the iceberg and only a top-level view on what can be done with data.

• Customer segmentation: Get a detailed perspective of your customer segments by analyzing a multitude of criteria, such as demographics or social media, that enable you to predict customer behavior. This may seem trivial, but asking oneself “Do I really know who my customer is, what she/he thinks and how she/he behaves?” is a worthwhile thing to do. Data-driven customer segmentation allows for establishing more personal relationships, e.g. by using the right communication channels and creating marketing activities and advertising campaigns tailored to the right audience. This way, customer segmentation is no longer a simple A-B-C classification, but takes external data into account, such as social media sentiment analysis.
• Assortment optimization: An effective, data-driven category management can significantly improve financial performance. Instead of basing assortment decisions on historical data only, make use of data & analytics to provide customers with personalized offers. Maintaining an optimal assortment will increasingly be connected to customer analyses, ultimately leading from “sell what you buy” to “buy what you sell” (more on that in our earlier LinkedIn series). This may well include taking location-specific dynamics into account as well as increasing complexities along the supply chain.
• Promotion strategy: Promotions nowadays beg the question: Who needs promotions when you are always offering the right customer the right product at the right time? That may or may not be right, but at least one thing is obvious - promotions shall no longer follow a “watering can” approach, but need to be thoroughly based on facts in order to yield the desired economic impact. It is thus important to maximize the value of available external and internal data when planning promotions, rather than solely looking at historical information. By establishing a data-driven promotion pool, including suggestions for discounts, periods and potential correlations among different offers, you can start simulating scenarios and forecast the effectiveness of your promotion activities.?
• Branch network planning: Maintaining an optimized branch network is crucial to reach the right customer segments in case you have brick-and-mortar assets. Create a branch network based on data, and take internal information into account such as current branch performance, incl. financial KPIs or geo-location insights. Further, include external data, such as socio-demographic data, competitor locations or market research. This will help in optimizing both the revenue and cost side of branches.?
• Data monetization: Direct monetization of data is a tempting opportunity for players within the retail & consumer goods industry. By nature, this industry has a lot of data at its hand, from supply chain insights to product performance data all the way to actual customer information. Selling or licensing data, e.g. by providing suppliers with data from their own loyalty programs, or product sell-offs is one of the most common ways to leverage the assets with partners located both upstream or downstream of your value chain. Needless to say, this is a use case that especially needs thorough alignment with your overall business strategy. Further, the legal framework regarding customer data is not easy to navigate and must be taken seriously.

While there are numerous more opportunities to apply data & analytics as a response to changing customer behavior, the question of how to collect, evaluate and prioritize them will continue to be different for each individual player. From our perspective, use cases should first be collected and prioritized systematically across the entire value chain as well as the product & service portfolio. This allows to establish an exhaustive long list of internal and commercial optimization potentials. Gathering use case requirements, including capabilities to actually put them into place, and evaluating their expected value and ease of implementation allows to create a shortlist in regards to potential benefit and feasibility. In order to prioritize shortlisted use cases, composing a quantitative business case based on estimated implementation cost and benefits helps to identify the most promising opportunities.?

Let us know what you think in the comments section or feel free to contact us if you’re interested to hear more on this topic!