#Sweet spot drivers#
Also, it often turns out some segments perform better than others, which in turn allows you to discover distinct drivers (metrics distinguishing a segment) of performance. Hence within a segment, a best performing location can be set as a benchmark for (potential) other locations. Clustered locations (segments) have very similar market dynamics and therefore allow comparison. Clustering is data driven (based on the indicators identified by business hypotheses for distinguishing location success) and performed on external variables, such as those outlined previously describing the consumer profile and market. Next to that, it provides a means to compare performance of current stores and optimize accordingly. To be able to quantify market potential and predict performance in areas without current presence, geographical clustering (segregating cities or neighborhoods into groups based on similar traits) is key. Successful clustering and benchmarking leads to performance insight and precise predictions
#Sweet spot driver#
With a business driver framework and information identified to make a proper trade-off (as well as hypotheses to test), data gathering can begin. sales are higher in districts with universities and public facilities. Hypothesizing one level deeper on what actually drives success is then key to pinpoint data indicators required: e.g. If prioritizing markets for expansion, initial business questions could be: Where should I open my new store to be accessible to as many potential customers as possible? Or Where do I find the most potential customers who are attracted to my brand?.
Be explicit about the level of insight required to make the choice actionable – if you want to analyze new countries or cities for expansion, you need data at this level, but if you want to optimize your store network within a city, you likely need data at the neighborhood or postcode level. Without clear business questions to guide the analytics, the greatest risk is ending up with lots of data but no actionable insights.
Most important is to first be clear about the location decision to be made and the information required to optimize for that decision. Advanced analytics is the core enabler in a precise location approach - using a smart combination of integrated data, clustering and predictive analytics techniques as the means to effectively translate information into action.ĭetermining clear-cut business questions is the essential starting point Now more than ever, there is more pressure to get it right: where a store should be and what it should offer are increasingly complex decisions. Online retailers, such as Coolblue also use stores as the place to forge customer confidence in their purchase choice, while others such as and Lush create an experience that adds value to the product itself. Many consumers still want to directly see and touch the product, or be advised.
On average we see that when a new door opens, there is a 50% result in retailers’ sales growth in the surrounding area. Yet, brick and mortar channels remain a first principle and compelling competitive advantage for many retailers. Dutch shopping streets have lost multiple prominent stores since the bankruptcy of V&D in 2015, with Intertoys, CoolCat, and Sissy Boy being the latest examples meanwhile Amazon is preparing to enter the market. 2019 was a record-breaking year for the US with 6000 store closures.
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