Next Generation of Retail


It is now common knowledge that customer analytics will help retailers better understand and anticipate shopping behavior. Based on McKinsey’s Me Commerce and the Future of Retail findings, retailers can provide tailored product recommendations increase shopping volume by 5-10%, improve supplier negotiations, reducing costs by 1-2%, and schedule promotions for multiple locations increasing sales volumes by 1-2%.

Based on that same report, the EBITDA growth rate for grocery retailers focusing on customer analytics is 11% vs 3% for those who don’t, and 10% vs 2% for big box retailers who do it. There’s a 10% increase in volume when ad campaigns are personalized, and 75% of NetFlix content is based on personal recommendations.

But if we could understand what happens *after* the product is brought home, we can better serve the customer, better anticipate her/his needs, and better connect them with the new types of products which may be of use for them.

What it would take is sensors to track and report on what’s happening, volumes of data and the capacity to integrate and manage it, applications that filter relevant information, actionable reports based on preferences, programs to ensure that various stakeholders receive relevant, meaningful information, as they define it, and the applications and processes to ensure that goods are cost-effectively transferred to the customer?

If we could bring it all together, we would better understand what’s in the ‘black box’, what’s behind the curtain, what happens after products are brought home. And if we know that, the world of retail would be forever transformed.

Description Point of Purchase Point of Use
  1. Understand customer behaviors and preferences.
Understand the volumes (quantity, location) and types (sizes, colors) of merchandise purchased. Know not just what was purchased but also whether and often it was used and when you are running out.
  1. Use aggregated data to see and even predict buying trends.
Aggregated point of sale purchases show purchasing trends and help retailers make educated predictions on what will sell and how best to engage with the customer. Supplement point of purchase data with usage patterns at home to more clearly identify trends and make educated predictions on future purchases.
  1. Proactively manage supply.
Supply each store location on specific styles, colors, sizes and brands based on past purchase patterns. Use both purchase and usage patterns and information to manage store supply.
  1. Target new product introductions.
Experiment with new styles, colors, and products based on past preferences. Understand which experiments work, based on who’s buying and how many are buying. Extend the experiment beyond the point of purchase to the point of use. Who’s buying and using something how often and with which other products?
  1. Recommend other products based on current and past purchases.
Coupons for similar products from the same or different manufacturers presented at the point of purchase. Coupons at the point of use would be more compelling as they are sent when a product is running low, not just provided at the point of sale when you have a fresh supply, and it’s not a given whether it would be used at home, or how long it would take to use it.
  1. Tailor product recommendations and coupons.
Provide tailored product recommendations based on purchase patterns and know which categories of people and which specific individuals take advantage of these recommendations and coupons. Provide tailored product recommendations and coupons based on usage patterns and also time them based on how much of the product is remaining.
  1. Encourage feedback.
Invite customers at point of purchase to go online and report on shopping experience and reward them with coupons for doing so. Invite customers who don’t use products purchased to give feedback on why not. Offer coupons for feedback and even replacement product if it makes sense.
  1. Manage inventories.
Use analytics at point of sale to manage inventory proactively. Couple POS data with POU data to augment your data set and better manage inventory.
  1. Report purchasing trends to retailers and manufacturers.
Provide detailed analytics to retailers so that they can stock their shelves and manufacturers so that they can understand and predict the needs of the consumer. Add point of use information to see which purchased products are actually used, which ones are used in conjunction with which products, etc.
    10. Negotiate with suppliers on volume orders. Better negotiate with suppliers on volume orders as you better understand purchase patterns. Add point of use data to increase amount of data you have to anticipate product orders.


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