CASE STUDY

Assessing FMCG point-of-sale potential – how location intelligence helps boost sales

FMCG

About the client

The FMCG company conducts sales activities through sales representatives, but has not seen an increase in sales value for some time. This is because the locations chosen for sales and marketing campaigns have low potential.

Challenge

Our client’s company lacks objective, centrally defined criteria, making it easy to overlook points with high sales growth potential. Sales representatives:

  • Rely on subjective opinions instead of data - An example is measuring the number of customers for a given point of sale by counting cars in the parking lot and then calculating the results. Such opinions are often flawed and fail to account for many factors, which is why even
    after standardization, they remain inaccurate.
  • Use inconsistent evaluation criteria - Each of them looked at different, subjectively chosen indicators, making the point-of-sale analysis inconsistent and impossible to compare across the entire network.
  • Focus on the wrong locations - They focus on places where sales are already high – and often cannot go any higher. Instead, they should be identifying stores with growth potential; only then can a real increase in sales value be achieved.

Solution

Dataplace.ai created a comprehensive point-of-sale scoring system for the company, analyzing all specified addresses
based on key parameters:

Key analysis parameters:

  • Foot traffic - Precise analysis of visitor numbers at different times of the day and week
  • Competitive landscape - Distances from competitor locations and the probability of local residents choosing a specific point of sale
  • Demographic profile of the area - Number of residents within range, their purchasing power, and total monthly food expenditure

Implementation results:

  1. Objectifying sales decisions - Sales representatives gained easy access to objective data on retail outlets instead of relying on intuition.
    ‍
  2. Route and activity optimization - Route planning began to be based on the sales growth potential of visited locations, maximizing the efficiency of every visit.
    ‍
  3. Identifying new business opportunities - Additional research was conducted on potential points of sale for specified addresses where distribution is currently absent, analyzing them against the same variables.

Results

Practical application of data

The analysis results were presented in a clear summary with weights assigned to individual variables (e.g., a weight of 5 for foot traffic around the store and a weight of 2 for the purchasing power of local residents). Using sorting from highest to lowest values and color-coding enabled quick identification of locations with the greatest potential.

This created a comprehensive system for evaluating stores in the region, allowing the company to precisely align its business goals and enabling sales representatives to focus on locations with real growth potential.

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