POS Analytics based on Big Data
When we talk about POS, we refer to the places where a customer transacts payment for a product or a service. 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 devices.
These places 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 stores can provide retailers with new opportunities to improve the promotion and marketing of their products, obtaining a better influence impact on consumers’ purchasing decisions.
You may be interested in: “Location analytics can drive retailers to success: Case Study Home Depot Vs. Ace Hardware.”
What extra 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 stores with the greatest sales potential.
- Maximizes business profitability by reorganizing the commercial and distribution strategy in the traditional channels.
How and with what tools is it done?
Mobility data consists of a group of anonymized historical records of different positions of a mobile device. Using mathematical algorithms, it’s 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.
This data generates valuable insights, 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.
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 Site Selection for New Outlets.”
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.
Useful layers of information for more detailed analysis
By adding different data layers to the analysis, such as socio-demographic censuses or information from retailers’ points of sale, it’s 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 competition billing, making possible to identify and predict which particular product or service the consumer are acquiring in each sector.
Several models can be used to maximize accuracy in predicting stores with higher and lower billing potential stores. 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.
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