The pandemic has put to light some limitations of current data-driven decision making in retail. Past is no longer enough to predict the future. New alternative external data streams (such as the voice of the customer in social media) must be coupled with internal sources.
The increasing uncertainty in demand and supply is forcing companies to look beyond single-scenario plans and punctual projections of what may happen. Planning activities, that were typically performed on a top-down approach to estimate the outcomes, are now more granular and localized (e.g. SKU, store or customer level) and executed continuously.
It’s clear that improving performance in one area may undermine the metrics in other process or functional areas.
Therefore, silos must be blurred within the organizations to support enterprise-wide performance and cross-functional interaction between teams.
Vital retailer processes were affected by these limitations, which now demands accelerated analytics that take into account the following requirements:
There’s no doubt that the massive amount of data generated by retailers and their customers holds the potential to drive real bottom-line differentiation. By applying data-driven state-of-the-art techniques to augment or automate decision-making we can uncover and address these current retail business challenges.