Point of Sale Analytics - we already know the math !

For the last few months, my posts on Point of Sales Analytics have focused on the Next Generation of Demand Signal Repository.   New technologies have combined to allow for simpler, cheaper systems that can handle vastly more data effectively.

A key feature of the Next Gen DSR is to provide a fast, stable, productive environment for prototyping and release of analytic tools that drive real insights into the data.    The analytic solutions below are based on math that is already known and data that is readily available to blend into your DSR if it's not already there.   Our lack of progress in this area has little to do with the magic of math or modeling and much more to do with the inability of older platforms to bring data and analytic tools together effectively.

So what analytic applications could/should we build out ?

There is a lot of interest in so called "sales driver" models.  Understanding what drives your sales helps you understand the fluctuations in your past, better predict the future and target sales activities (pricing, promotions, regions) more effectively.  

Historically, we have seen most interest around pricing (price-elasticities and cross-price elasticities) and promotional evaluation, but there are many other possibilities for sales drivers.  What drives your sales?

  • On-shelf availability
  • Weather: rain-fall, temperatures, wind speed, hurricane warnings
  • Seasonal events,
  • Day of the week, Day of the month. 
  • Government assistance  programs like SNAP or WIC
  • Pay cycles
  • Placement in store, placement on shelf, product adjacency.
  • Store-area demographics

Use Cluster Analysis to find groups of similar stores or products.  For many actvities (assortment selection, price modeling) we want to operate at a low level gain more accuracy but cannot monitor or execute against thousands of unique plans so we try to a manageable number of "similar"  groups.   Cluster analysis does that for your very effectively.

Use market basket analytics to find products often bought together and products associated with specifc shopping "missions".

Automatically detect retailer price changes and pricing zones whie ignoring the noise in the signal driven by one-off price matching and discounting activities.

Find the Missing Stars in your assortment.  Find the geo/demographic characteristics that flag unusually high rates of sale for your product then make sure they are listed in all the stores that share these characteristics.

Build Optimal Assortments by understanding what sells well where , quantifying and accounting for product substitution (cannibalization) rates.

Optimize shelf space to generate incremental revenue, margin or velocity.

Design planograms to minimize store labor, increase on shelf-availability and drive sales.

Generate consistent rules-driven planograms from your assortment selection in less time than it hours rather than weeks.

Continuously monitor and validate retailer replenishment settings (safety stocks, re-order points, forecasts) to ensure your product flows to the shelf effectively.

Optimize order quanities to provide the best balance of labor, warehousing and inventory costs.

Build cost-effective, clean orders to support promotions, events or recovery activity.

Generate accurate allocations of promotional inventories across stores based on anticipated demand and current stocks.

Accuractely detect and address off-shelf events, characterized by an unusually long period of low (or no sales) , to ensure your product is available for sale when a shopper wants it.

Dramatically enhance the accuracy of short-term shipment forecasts using point of sale and inventory data.

Forecast the needs for customer shipments even before a customer has ordered to get dibs on freight capacity in a tight market.

I have no pretensions to this being a complete list: it doesn't address all the good questions that have already been asked, and I'm sure many of the best questions haven't been asked yet, but it's a fair start and all of these can be built using math that is known (if a little obscure) today.

What point of sale questions do you need answered?   What anaytic solutions would you like to see ?