Drawing on data from the online and physical worlds to drive intelligent decision-making

By Bryan Palma, Senior Manager at Kinaxis

Retailers have been moving at a steady pace online and into the eCommerce world over recent years. The current pandemic has served to further accelerate this migration.

Bringing two worlds together
In the past, traditional retailers who had built their business in the bricks and mortar world would have spent minimal time looking at eCommerce data. If it was only a small percentage of the business, they would not assign an analyst or a category manager to look at that data but would typically instead deprioritise. As the move online gathers pace in the wake of Covid, however, that is no longer a viable option for many businesses. For retailers, it is now increasingly key to take data from the online environment and interpret it quickly to make informed decisions.

Many are now adding more resources to it as part of a renewed focus. This alone is not enough. Pulling data into Excel spreadsheets and analysing it manually can never be the way forward, especially in the unpredictable environment which we have today, where historical forecasting approaches used in the past have less and less value.

In this complex new world, retailers need to understand the growing importance of artificial intelligence (AI) and machine learning (ML). Moreover, they need to be aware of its potential across both brick and mortar retail and eCommerce to power intelligent automation and augment human decision-making to better deliver on customer promises, remove waste and increase resiliency for effective risk management.

Indeed, today and going forward, the best approach is to aggregate data from the physical and online worlds and implementing a unified repository or data model to run AI and ML on. That will help to automate decisions, facilitate end-to-end planning across demand planning, forecasting, promotions, replenishment and inventory, and drive operational agility.

That ability to see online and physical as part of a single retail entity will be key for retailers moving forward not least when it comes to thinking about how they use data to drive competitive advantage.

Reaping the rewards
One of the key areas of insight retailers and CPG companies will be looking to tap into as we establish a new normal following the pandemic will be around the behavioural changes in customers. The balance will have shifted and probably permanently, away from in-store purchases towards ‘click and collect’ and online deliveries.

Retailers need to get a handle on what the data, across their operations both online and physical, is telling them. They need to be able to run analytics on it and use AI and ML to get optimum value from the results Moreover, as the retail landscape evolves, it will be key that they adapt quickly and make use of the more granular level of detail about their organisation and its markets, increasingly required to drive short-term and long-term planning alike. Doing that may well require a change in tools, technologies and planning processes moving forward to achieve the agility required.

Another key area retailers and CPG companies need to think about is around warehouse capacity. Generally most of these businesses will use service centres and warehouses to service more eCommerce purchases as opposed to those that will send product to physical retail stores. To meet demand more efficiently though, retailers need to use available data from both physical and online channels to understand where best to stock up first and where inventory could most effectively be stored.

During lockdown and around events like Black Friday, or Cyber Monday, there may be even more focus than before on getting the online warehouses stocked up, but it is crucial that they have the right inventory in place to be able to do this.

From the perspective of what is likely to happen around holidays and other big events from Halloween to Easter and from Black Friday to Christmas, it is possible to look at historical forecasting, of course: the volume of orders, the promotions that were run and at which times, for example. Unfortunately though, post-Covid, these have less resonance. Getting a real-time perspective on what is actually happening on the ground enables retailers to react much more quickly. These new techniques rely much less on pure time series-based historical forecasting and much more on real-time, automated machine learning models, and incorporate short-term demand signals, for example.

As they do so, it is becoming increasingly important that retailers not only use AI and ML to get real-time insights but that they don’t work in silos and instead draw on data from both the online and physical brick and mortar channels to achieve their business objectives.

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