POS Analytics based on Big Data

Knowing and understanding the geographic distribution of points of sale in the retail channel can provide retailers with new opportunities to improve the promotion and marketing of their products and better influence consumers when making purchasing decisions.

Tuesday, August 10, 2021

When we talk about point of sale (POS), we refer to the place where a customer transacts payment for goods or services. It can be a physical store, where terminals are used to process card payments, or a virtual POS, such as a computer or mobile electronic device.

These are the foundation of success for retailers, as consumers tend to make purchasing decisions on high-margin products or services at these strategic locations. Knowing and understanding the geographic distribution of retail outlets can provide retailers with new opportunities to improve the promotion and marketing of their products and better influence consumers' purchasing decisions.

You may be interested in: "Geomarketing: What every retailer should know."

What added value do retail companies get from this analysis?

  • Allows to optimize the marketing strategy to focus on the points of sale with the highest turnover potential.
  • Facilitates the identification of outlets that may be over- or under-served.
  • Allows reorienting marketing efforts and product positioning towards the points of sale with the greatest potential.
  • Maximizes business profitability by reorganizing the commercial and distribution strategy in the traditional channel.

How and with what tools is it done?

Mobility Data

Mobility data consists of a group of anonymized historical records of different positions of a mobile device. Using mathematical algorithms, it is possible to generate classifications to differentiate between records coming from cars and pedestrians, thus achieving a very precise analysis of the mobility of people in the surroundings and at the specific point of sale.

With this data, valuable insights can be obtained, such as, how many visits a specific point of sale receives, how the flow is distributed throughout the day, how mobility compares between one store and another, among others.

"The image shows an analysis carried out by PREDIK Data-Driven, on the mobility patterns of consumers in the surroundings of a POS".


Geospatial Analysis Techniques

Once the points of sale of interest are identified and cross-referenced with the mobility data, questions such as:

  • How many people pass through my POS and at what times?
  • How long do customers stay near or inside the stores?
  • Where were customers after or before visiting my point of sale?

Also read: "Geospatial data for the selection of zones for new points of sale."

With this information it is possible to generate marketing strategies that capture the attention of consumers at the most suitable time and place for each POS.

"This image represents an analysis performed by PREDIK Data-Driven about the distribution of mobility data segmented by time of day, day of week and day of month."


Useful layers of information for more detailed analysis

By adding different layers of information to the analysis, such as socio-demographic censuses or information from retailers' points of sale, it is possible to identify the number of people, segmented by age ranges, socio-economic level and gender that move around the points of sale, estimate the turnover of the competition and thus be able to know and predict spending on a particular product or service in each sector.

"Image where a specific floating population is identified within a particular socio-demographic area."


Predictive Models

Several models can be used to maximize accuracy in predicting stores with high turnover potential and stores with lower potential. All the information collected for each particular store is used to train a machine learning model that generates predictions of the sales potential of each location.

"Image representing the estimated visits prediction per block".


Aware of the challenges faced by mass consumption companies when planning their distribution and marketing strategies in the thousands of stores that operate in this channel.

At PREDIK Data-Driven we develop information solutions that help companies analyze the traditional point-to-point channel to optimize their strategies and maximize profitability.

Contact us for more information about our solutions with geospatial data and predictive models for the retail sector









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