Are You Embracing Best Practices for Merchandising Displays?

Written by Ed Anderson | Jan 9, 2017 5:00:00 PM

McKinsey recently published a report from the McKinsey Global Institute titled, "Making data analytics work for you, instead of the other way around. " Best practices are often not obvious, at least until someone proves a new approach is superior. In the 1896 Olympic Games, there was no best practice on how to start the 100-meter dash. McKinsey showed this picture in the report:



Virtually all sprinters embraced Thomas Burke's starting technique.

Fast forward to today. Whether you are a retailer or a Consumer Packaged Goods Company (CPG), you are looking to get better. It's well established that the best way to drive incremental sales is with displays. However, very little data exists around the tens of millions of displays sent to retail stores annually:

  • Which brand campaigns respond best to being promoted...not all brands respond equally
  • Which stores produce the greatest lift for an individual campaign
  • What was the sales lift for that campaign while it was up
  • Did the display get set up in the front of the store, and how long was it up
  • Which display type works best (power wings, floor stands, end caps etc)
  • What changes should you make next year to out perform this year's results

The absence of this data about display performance and store performance causes stores and CPG's to have costs that aren't productive and campaigns that are not optimized.  Although the costs wasted are small for each display and the lost opportunity for sales when a winning individual display is not executed may also be small, McKinsey points out about Data Analytics

"The impact of “big data” analytics is often manifested by thousands—or more—of incrementally small improvements. If an organization can atomize a single process into its smallest parts and implement advances where possible, the payoffs can be profound."

Why don't we measure every display sent to every store?

The easy answer is that:

  1. Cost effective technology to capture all the data above did not exist until recently  
  2. Costs of capturing the data by sending someone into the stores is prohibitive at scale.

McKinsey's opinion on the improvement in the technology around Data Analytics in the last 5 years from the article above:

"Data and analytics capabilities have made a leap forward in recent years. The volume of available data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The convergence of these trends is fueling rapid technology advances and business disruptions."

Seize the day.

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