Although store owners have refined their understanding of how to optimize inventory and improve sales, they have also suspected that there are relationships between events, products, marketing, customers and competition that have obstinately remained hidden from them. With today's sophisticated data and analytical technology, blind spots are a thing of the past. Even large retail chains that have multiple formats and are spread over large geographies can go beyond historical transaction data to find answers to complex questions like "Why is a store doing -- or not doing -- well?" and "What should be done to make performance better?" In other words, stores can go from a description of what happened to a prescription of what should happen at the store quickly and easily - with major improvements in performance.
The problem is the data. The question isn't "Where is the data?" but rather it is, "What data to use?"
Understanding and improving store performance begins with segmenting stores and defining the variables that drive performance. These can be largely broken up basis the KPIs of Merchandising, Sales, Marketing and Finance. Each must be distinctively measurable and each must be given equal weightage and importance. Emphasis must be placed on making the KPIs comparable between clusters of stores that are similar in terms of format, size, customer profile, inventory, price range, business hours, customer support, marketing budgets, type of competition, etc.
The next step is to link the KPIs (between 10 and 15 KPIs for each category) with the factors that influence the KPIs. This extracts true performance insights, making it possible for store managers to confidently base their decisions on real triggers for improvement. Action becomes finely tuned to cause.
When segmentation is done carefully and the KPIs to be measured are chosen with greater precision, a better of view of what impedes or improves performance is unlocked. Some insights are so deeply hidden that they may sometimes never surface - not for lack of effort, but for lack of time. And in today's environment, no store manager can afford to lose time on improving performance. Instead, they must rely on technology that uses data-based store optimization techniques.