How to Boost Sales with Artificial Intelligence

New data management methodologies now allow retailers to take advantage of even the smallest piece of information to generate valuable insights that help optimize their marketing and customer loyalty strategies.

  • What promotions do we do to get more customers to the point of sale?
  • How do we make them stay longer in the store? 
  • How do we improve the customer experience so that they buy more at each visit?

These questions are frequently asked by the sales teams of all retail companies, regardless of their industry. Increasing turnover, increasing the number of customers in physical stores, building customer loyalty so that they come back and also buy online, are part of the challenges that companies face today.

New techniques for mining and analyzing hundreds of thousands of pieces of data, coupled with the ability to use technology to model customer behavior at the point of sale to predict specific actions, is part of what retail companies can do today to better understand their customers and optimize their marketing and sales strategies at the point of sale.

How to increase conversion at the store?

Before increasing the conversion rate of potential cusom, it is necessary to measure foot traffic; to know how many people are in the store, in which areas and their potential interests.

Also read “Foot traffic analysis: Home Depot vs. ACE Hardware in California, USA“.

With mobility data, for example, it is possible to identify what the people who enter the store are like, what interests they have, their behavior, their socioeconomic level, among other data. With this information it is possible to profile the consumers who enter the stores and use this information to develop promotional strategies adapted to these groups or clusters of customers.

Foot traffic analysis is derived from location intelligence, helping retailers better understand how people move around a physical location and providing new visibility into consumer behavior patterns by presenting an aggregated picture of the movement of people within a physical area, even segmented by the types of consumers passing near a store.

See “Big Data to Understand Consumer Mobility.”

Through analysis of consumer behavior patterns, store design is improved and marketing strategies are created to attract more consumers at the right time and place.

Predictive models and AI (artificial intelligence) also help to renew aspects of stores, products and services, based on future trends, in order to stay one step ahead of competitors and keep customers interested and engaged.

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