The Curious Case of FMCG Sales Beat Optimization

Indian Retail is a Buyer’s game today. In E-Commerce as well as offline modern trade, consumers are truly the king, with every tick of the clock bringing new choices. Hence it has become both, vital and challenging for enterprises to be close to the end consumer. An efficient fmcg distribution system, which reduces order to delivery & replenishment time becomes the key arrow in the arsenal of an FMCG company. The key element to this is “Sales Beat Planning”.

What is a Beat Plan?

Sales Beat Plan” also called “Permanent Journey Plan” is a day level sales route plan made for field sales & marketing executives to visit several stores at a pre-defined frequency. These visits are necessary not only to handle order collection but also for visual merchandising and most importantly competitor analysis.

Stock outages are periodically reported from retail stores as well as distributors to continuously replenish the orders. Stock displays at all stores are regularly audited and changed to gain a competitive advantage.

The goal of optimizing the sales beats was to have the right service levels at each retail outlet. The intent was to ensure that every outlet is serviced by the right person on the right day at the right time.

While doing so, it was also intended to come up with the most ideal beat size (the most optimal number of outlets within a beat) keeping the best possible mix of outlet types.

Additionally, what also needs to be suggested, was the sequence in which, the outlets in every beat would be visited. This indicated the need of a cluster with minimal back and forth, travel time and distance.

How to make Sales Beat Plan in FMCG?

The problem is indeed as complex as it is beautiful. Just to add to the complexity, any implementable solution envisioned for an NP-hard problem like this, requires beats to be intelligently designed. The design algorithm should be open to accommodate dynamic and fuzzy constraints that may occur on ground.

With gigabytes of sales data, location trails and timestamps at our disposal and the best data scientists to derive insights out of it, we started to mimic the existing modus operandi to find potential areas to change. And thus, the first thing that we started with was to cleanse few hundred thousand bad addresses. The addresses were so malformed that they couldn’t be sensibly tagged to a set of latitude and longitude. Now, this is a problem that exists in most developing economies. Lack of infrastructure around capturing addresses results in poor data collection.

Courtesy  proprietary geocoding algorithms, we were good to go after a few iterations through efficient Tokenising, Machine Learning and Natural Language Processing of these addresses.

Inferences:

We moved on to replicate the client’s existing beat plans and ended up drawing several ground breaking conclusions.

  1. Under-utilised salesmen/marketing executives  – They could be much more productive within the same working hours
  2. Long and cumbersome Beats  – A Salesman walks for a significant time in his beat hence its unfair to expect high productivity at all times
  3. Overlap in salesmen routes  –  With overlapped routes, salesmen were walking more than they had to. The time spent in transacting at every outlet was taking a hit resulting in potential revenue loss.

In parallel, we also analysed behavioural factors that impact B2B sales.