Brick-and-mortar stores are under increasing pressure to perform in an environment, where most growth figures appear in the world of online shopping. According to analysis from FGRT 1), major US retailers closed nearly 7,000 stores in 2017, the highest recorded rate of closures, while a PWC report identifies that online sales experiences 7 percentage points higher growth than store sales 2).
Yet, stores can and should continue to serve a purpose – to serve the customers – not merely as a depot for collecting online orders or a showroom for browsing, but as a point of differentiation in a multi-channel strategy.
Recent years have seen a strong emphasis on ‘customer centricity’ in the area of marketing. Consumers and businesses alike benefit from investments in capabilities to deliver personalized content especially across digital channels. In essence; stop treating every consumer as if they have the same preferences as the average of their demographic segment. Can customer centricity and the shift from making decisions based on averages be extended into stores? Intuitively, we know that regional performance varies, as does performance by store within each region. And we know that no two weeks experience exactly the same demand. Yet, many businesses still make assortment plans and set sales targets based on averages. This results in out-of-stocks, heavy end of life discounting, disruption and costs in the supply chain as well as shopper dissatisfaction. Such issues are hitting both top and bottom lines.
As has also been the case in area of marketing; data and analytics come to the rescue. Organizations possess a vast amount of data that (often) is not fully milked, not turned into granular insights informing decisions at the local level. If you add to the pool of internal data some time and location stamped 3rd party datasets such as weather, points of interest, events and demographics, you have a very powerful mix of indicators and drivers of performance. With advanced analytics you can compute such data at scale and speed and convert it into succinct and actionable insights into historical performance and from that derive predictions of future performance.
With models trained on the data to identify the relationships, interdependencies and relative strength of attributes in relation to outcomes, you can understand the demographic and economic characteristics of the neighborhood, block by block, and anticipate how demand for a specific SKU or category can suddenly change if a major event is taking place nearby, or how weather will affect demand. Importantly, you can bring all these data points together to give you an optimized demand forecast and an assortment plan that reflect the dynamic nature of synergistic and competing forces, neighborhood by neighborhood. You can identify suitable locations for new stores, by understanding the potential for sales in a given each locality.
Read more about how IBM MetroPulse can help retailers bring customer centricity into their planning, block by block and make stores great again by combining enterprise and hyper-local, curated third party data and advanced analytics.
