The most recent annual Merchandise Planning Survey from retail business and IT consulting firm BRP contains especially interesting data points regarding retail data.
Among the more than 500 top North American retailers surveyed, the #1 planning priority is “Improve analytics” (36%), which is trailed closely by “Better integration of actionable customer data into planning activities” (31%). These two priorities point to the realization that retailers are now sitting on massive amounts of data on their customers – and in order to drive retail transformation, they urgently need to put that data to use.
Top Planning Priorities for North American Retailers
Source: BRP Consulting 2017 Merchandise Planning Survey
Regarding “Improve analytics,” BRP’s report says, “Analytics has received much recognition in the industry, but there still appears to be a marked delay in the integration and unification of business information enterprise-wide…. Insight into customer demand, price sensitivity, reaction to promotions, demographics and more are key to drive merchandise plans and actions that maximize profitability.”
In examining the closely related “Better integration of actionable customer data,” the report says, “Knowing the customer better than the competition empowers retailers to create better assortments, personalized promotions and marketing campaigns to drive sales and enhance customer loyalty. The ability for retailers to understand their customers, predict what they want to purchase, and even shape their buying behavior is now driving the need for better analytics.”
Artificial intelligence is coming to retail in a big way
Leveraging customer data in merchandise planning is a job for artificial intelligence (AI). Retailers already have the data. What they lack is the manpower it would take to mine it all for patterns and predictive intelligence – even as the mountains of data continue to grow.
This isn’t a job for people. Taking this on is a job for machine intelligence. When retailers say they need to “Improve analytics” and “Better integrate customer data into planning,” they are signaling the intention to incorporate AI engines into their planning infrastructure.
And that takes us to where WE come in.
Running at AI speed requires a new networking approach
Because major retail operations cover expansive geographic footprints, applying AI to retail customer data requires networks that can gather and analyze data from all locations, and deliver analytics outcomes back to those same widely disparate locations; and all with lightning speed. That can only be done with network infrastructure that supports collection at point of sale and processing in the cloud, in a way that supports cost efficiency while delivering high availability and bandwidth, and exceptional operational and information agility. All of which is beyond what traditional enterprise networks were designed to provide.
It’s the kind of new networking need that has caused so many enterprises – in excess of 40%, according to IDC – to have already started to move to SD-WAN (software defined wide area networks), with more than 30% planning to follow suit within a year. The advantages of SD-WAN compared to traditional distributed network technology are tailor-made to address the challenges facing retail, including:
For retailers that aren’t already well down that path, the time is now to prepare your enterprise network for AI – starting with a very close examination of SD-WAN. Working with a network provider with deep experience in both SD-WAN and accelerating retail transformation, and enabling cloud based analytics and artificial intelligence, can make the move smooth and easy.
Happy to discuss as always if interested. I’d also suggest checking out the study mentioned above from BRP – their 2017 Retail Merchandise Planning Survey Report – which delivers a lot of additional insight, as well.
Enter your business location zip code below for business solutions in your area.
Find business zip code