Retail Analytics: Enabling contextually-relevant decisions
Caroline Conway

The value of data is to empower enterprise decision making: to enable fast, effective decisions with a measurable impact on operational and strategic performance.

Fast and effective decisions are rooted in context. In the retail industry, the current context includes technological disruption, changing consumer expectations on access and convenience, and exceptional cost pressures. Layered on top is the subjective context of unique challenges and strategic objectives experienced by each retail firm.

Big data and analytics programs have to be designed to meet retailers’ contextual needs if they are to successfully enable decisions. However, the current approach to big data and analytics in retail is still largely focused on getting data and technology as structured as possible. The potential for analytics to help retailers solve some of their most pressing business problems and drive critical business decisions remains untapped.

To overcome this, the sector has to adopt a more holistic problem solving approach, one that is tool, technique and technology agnostic, in order to extract sustainable value from its big data investments. The emphasis has to be on relevant problem solving built on a deep understanding of the strategic objectives and business context of each retailer.

With this subtle shift, retailers can more quickly shift to tailor made solutions that solve relevant problems through a blend of strategy, design, analytics, process engineering and technology rather than data and analytics in isolation. There are countless opportunities across operations, merchandising, supply chain, and home/back office using this approach.

Here, we take a look at three examples:


  • Streamlining customer returns
  • Optimizing future assortments
  • Structured cost reductions


Streamlining customer returns

A streamlined returns process is as much about ensuring a frictionless experience for customers as it is about maximizing value recovery for retailers. This experience is becoming more essential in a clicks-to-bricks omnichannel world, and as customers shift online, the volume of returns is becoming more critical for retailers to manage effectively. The conventional approach where teams deploy discrete solutions in silos to streamline their part in the returns process will no longer work. Instead, retailers need to bring multiple organizational functions like technology, operations, merchandising, and supply chain together and heavily leverage a blend of strategy, data, analytics, and process design to define a complete solution.

A holistic approach begins with analyzing returns data both from the perspective of customer experience and recovery value. Defining the baseline by mapping current data and process flows allows retailers to set strategic goals and align all functions to a unified purpose. Once clear goals are set, process redesign, pricing and costing analytics, and network modeling techniques can be combined to build a comprehensive roadmap to reach new levels of customer experience and financial value. This then carries through to implementation where linking multiple datasets into a single source of truth and providing all stakeholders with the right analytics tools based on that source will enable each group to evaluate returns performance within their area while staying connected to the overall goal of better customer experience and cost recovery.

Optimizing future assortments

Retailers are also being challenged to reevaluate their assortments both short and long term as customer demands evolve rapidly. In both cases, retailers are faced with the twin challenges of making sense of data that may not be comprehensive enough and analytics solutions that are unable to cope with the dynamic interplay of variables impacting assortment. In the fast-paced world of merchandising, the technology-led approach of amassing all relevant data and building the most sophisticated models cannot match the dynamism and speed of customer changes and merchandising decision making.

The alternative is to start blending customer data and assortment analytics with the distinct practices of strategy and design thinking. Instead of starting with data, a retailer must start with the core decisions that need to be made and then design a framework to assess the impact of a variety of merchandising decisions on performance. This allows merchandisers to identify the levers that actually drive changes, which is especially relevant when data is not complete or linked together. Once this framework is in place, it is possible to focus on the more granular interactions of assortment, price, promotion, placement, and factors like cannibalization using the data that is already available and applying advanced optimization to provide relevant guidance. This can finally be integrated into the buying process by using design thinking techniques to develop tools that enhance merchandising decision making and enable merchants to incorporate the quantitative data with their qualitative knowledge to make final assortment decisions.

Structured cost reductions

An across-the-board cost cutting exercise may seem like a quick and easy way to deal with the increasing cost pressures on retail businesses. But in the long term, this approach is often counterproductive to performance while still leaving most of the waste on the table. Advanced analytics incorporated with budgeting and operational decision making allows retailers to assess cost drivers and predict future costs across sites based on site-specific drivers like sales, customer traffic, store size, and other variables. By modeling predicted behavior, retailers can then set accurate budgets and target and prioritize outliers for cost reduction programs that address the issues specific to each site.

This integrated strategy and analytics approach is quick to deploy as a surgical strike across the enterprise. It can be used to target specific cost areas, like labor, maintenance, goods not for resale, utilities, and other cost lines across the organizational footprint including repeatable head or back office activities. By using data and analytics to get a more strategic view of costs down to the site level, retailers can make substantial cost reductions while maintaining performance.

These are just a few examples of how the holistic problem solving approach – blending multiple practices and techniques, including analytics – can help retailers address some of their most pressing and immediate business problems. There are numerous instances, across the diverse and complex retail value chain, where a problem solving approach rather than a technology-first approach will yield faster, more effective, and more sustainable results.

Big data and advanced analytics will play a central role in translating data into actionable insights and business outcomes for all retailers. But retailers that adopt a more holistic approach accounting for their unique business context will be able to differentiate themselves through the short term decisions and long term strategic decisions they can make.