11 May 2017
Despite having more data at their disposal than ever before, many players in the grocery retail industry, namely traditional bricks & mortar supermarkets, still struggle to effectively identify the root cause of their sales performance problems. From Category Managers to Buyers, from Marketing Directors to Commercial Heads, these folk within a retailer’s HQ will spend hours each week trying to understand what’s working and what isn’t, often struggling to interpret multiple data sets across channel, customer segment, region, store format and shopping mission. All retailers are facing this problem, everyday – and desperately trying to stay one step ahead of their competition.
I don’t think anyone would disagree that true competitive advantage in this scenario would be the ability to identify and course correct problems faster and more efficiently. Imagine the impact of saving hours each week trying to understand what’s really happening with your retail performance? How having meaningful analysis which delivers a clear action plan could avoid wasted resource? Imagine the outcome for your business if you could make decisions each day with more confidence and more accuracy than your competitors? It would be a game-changer.
Growth is still the number one aim for most retailers, but harsh economic conditions, increased competition and changing consumer behaviour is making this vision much more difficult to achieve. Maximising the value from investments in customers and business insights is not aspirational, but critical to survival. Slow and steady no longer wins the race.
The problem is that everyone is drowning in data – and retail is one of the most data-rich verticals. Just think briefly about the sheer number of data sources which could help pinpoint performance problems: promotions data, sales data, margin data, customer service call logs, clickstream and social media data – plus competitor data. You’ve got a complex array of information, all of which could be useful in helping identify performance issues, but only if combined and understood in context.
While many understand the performance of each data source in isolation, analysing the interplay across them is where things get complicated. In addition retailers often struggle to learn the impact of softer performance measures they monitor (e.g. price perception, quality, in-store experience) on financial measures. This makes it hard to answer questions like “is the decline in my hypermarkets driven by a decline in the format or the decline in the mid-market customer segment?” or “is it quality or price which is the main driver of our sales performance?” This is where AI (artificial intelligence) can play an important role, drawing meaningful insight from seemingly disparate datasets.
The focus is firmly on monitoring sales, margin, market share, wastage and availability, but it’s surprising that little to no behavioural data is being used, particularly when this provides a richer picture of how customer behaviour is impacting the change in sales performance. What is driving sales is not particularly well understood, with much of this being inferred rather than known. As a consequence, it’s hard for buyers / category managers to know what levers they need to adjust – should it be price, promotions or assortment, for example? The inherent danger here is that a heavy reliance falls onto supplier data, to help understand what’s going on. Suppliers’ primary concern is about their own brands, not the overall strategy of the retailer, leading to a default position of “promote more” as the way to improve last year’s figures. Having your strategy directed by supplier tactics is not a sustainable way to run your business.
Taking control of decision making, through a greater understanding of what impact customer behaviours are having, puts the retailer firmly in the driving seat for collaboration with suppliers, giving the advantage that you can approach the relevant CPG to identify when you have a problem in this category and find a solution together before much damage is done.
Imagine how different weekly trade meetings would be for retailers if armed, not only with clear direction about what they should focus on, but also with outlined actions of what they should do about it. The type of business intelligence that doesn’t cloud you with insight and bombard you with data. With a complete picture of customers and their position in the market. And the ability to predict future trends. With competitive advantage becoming more and more difficult to achieve in retail, science-based, AI-powered decision making is the answer.