Data has grown exponentially since computers were invented (Moore’s Law) and the information we now store in a myriad of ways has shaped the world we live in. There was a time when hardware in computers just could not cope with storing the granularity of data required to drive fast, localized decisions, however, presented in the right fashion, this granularity of data allows us to make more informed decisions.
Reporting is built over the top of the business processes associated to ERPs, TPMs, CRMs and RE solutions to help present information to the end user – bringing multiple ‘big data’ sets to help with the reporting. The process of reporting has evolved into what we now call “Analytics”.
We are now in the next evolutionary stage of reporting – it’s not just about better analytics, but also about using the data to help suggest specific courses of action to the end user – ‘Smart Analytics’. Artificial Intelligence (AI) has crept into this area to complement big data / reporting to interrogate the data for the user and then present the recommendations through reporting views.
For a newcomer to Retail Execution (RE), it may be surprising to know that RE looks at not just the way in which stores perform, but also at ways to optimize the transportation logistics the sales representatives navigate, as they travel to different territories and stores.
Historically it could take up to 12 weeks to receive data to determine whether a promotion was performing as planned. With a much higher level of integration and data being available to ‘mine’ for insights – we are seeing a transformation in the capacities to receive, interrogate and make decisions much faster than we ever have before.
Field Movement data associated to the way in which a field sales team operative works, presents the opportunity to understand metrics associated to territory coverage, time in each store, and frequency of store visits. Merchandising data points at the shelf such as stock levels, live promotions, and shopper marketing products can all be tagged to help build a picture related to the level of compliance that store has achieved.
Point of Sale (POS) data is probably the most important data set, providing a fantastic way to understand how stores sales are performing, in terms of individual store performance, as well as understanding selling power of specific products and brands in different stores. Having the data at store level also allows for compliance monitoring for a given promotion.
Trying to interpret all these data points and variables would be almost impossible without RE software. The capability to push out insights through smart analytics allows the field sales team to make decisions that will hopefully influence future investment strategies.
With the support of technology overlayed with comprehensive smart analytics, the time to evaluate store performance and make decisions has been reduced significantly. Companies using RE solutions in their most advanced forms are achieving over 25% better sales across their brands.
Clearly there are gains to be made here – the capabilities in data, infrastructure, reporting, and AI are there, they just need be enabled.
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