Many organizations think they are correctly applying store or customer segmentation models. What they may not be aware of is that they may be applying traditional models that tend to be quite limited, static and inflexible. Changes in the behavior of the Shopper, like the ones we are currently experiencing, go unnoticed due to the lack of updating and rigidity of the system.
Is your company at the forefront or in the rear?
Today, organizations take advantage of existing data like never before, through Machine Learning (ML) technologies and practices, which allow them to generate dynamic store segmentation models, which are not only updated day by day, but also strengthened. and they improve over time.
For the Consumer and Retail industry, a successful and effective strategy aimed at the Shopper begins with an adequate Store Segmentation. Whether for Trade, Retail or Shopper Marketing initiatives, we must seek to establish, among other variables, the profile and the predominant purchase mission of the Shopper in each store, to optimize our efforts and maximize results and returns on our commercial investments.
Companies like Coca Cola, Diageo or Walmart have already known the great advantages that are obtained by taking advantage of Machine Learning in their Store Segmentation models.
In previous articles we have already talked about the importance of having a robust segmentation model and also how to segment stores based on the Shopper's profile or mission.
What is Machine Learning and why is it key in Store Segmentation?
Machine Learning, also known as "Automatic Learning", is a subfield of Artificial Intelligence that seeks to solve "how to build computer programs that improve automatically by gaining experience". It does this by adapting certain algorithms to its programming, in order to reduce the need for human intervention. This can be a great advantage when it comes to processing and controlling a huge amount of information in a much more effective way.
From a commercial point of view, algorithms allow, for example, to analyze hundreds of variables of any dimension of the buyer persona, profile, or purchasing behavior, and include them as valid attributes to find natural groups of customers or stores.
Store Segmentation with Machine Learning
Being able to associate the purchase of certain brands and references (SKUs) to shopper profiles and/or purchase missions allows us to create differentiated store segments where these two variables intersect.
These models work in the same way for stores of any channel: modern, traditional, proximity or digital. In the end, in our system we will have the stores of the traditional channel, for example, distributed in a matrix.
Advantages of Dynamic Store Segmentation
In addition to better satisfying the target Shopper, optimizing the assortment, store space, trade marketing investment in promotion and merchandising, focusing and simplifying the operation of sales and field teams, expired products are also reduced, among other advantages. .
Among the results achieved are: acceleration in the sales of strategic brands, increase in average billing per store, increase in market share, profitability and increased trust with the customer.
Improves Over Time
Unlike traditional store segmentation models that were prone to rapid obsolescence with limited possibilities to determine changes in purchasing behavior, the use of ML allows the algorithm to "learn" over time, making it more and more accurate and effective.
The new store openings allow quick assignment to a segment or readjustment of stores to another segment due to changes in purchasing habits or shopper profiles. That is, the system is highly dynamic, automatic and without human bias.
Conclusions
The commercial functions of Retail, Trade or Shopper Marketing now have ML technologies at their fingertips that will allow them to take advantage of existing information to segment stores, be they physical or digital, in order to focus initiatives and strategies. of the brands where the target Shopper is and the predominant purchase mission.