Spanish Portuguese (Brazil)

segmentation with artificial intelligence

success story


Two concrete examples of companies that have managed to make the most of the vast amount of information from their customers.




Two concrete examples of companies that have managed to make the most of the vast amount of information from their customers.

customer segmentation

How to identify the most strategic customers by applying artificial intelligence in the analysis of purchase tickets


A network of stores with more than 1900 locations, some of them own but the vast majority licensed and franchised with different administrative systems.

Many stores had below-average sales and generic network promotions did not deliver the expected result, generating frustration and trouble retaining the Franchisee.


Integrate the information of all stores with information of unit tickets for sale to consumer, in different formats and systems.

Organizational capacity to develop multiple proposals in parallel without increasing the operating cost.


4 totally different segments of stores were obtained, depending on the shopping and consumption habits of their customers. Multivariate analysis considered aspects such as day of the week, time of day, ticket value, purchased products, number of units per product, packaging format, price and unitary margin.

Processes, organizational structure, profiles and digital management tools were redesigned to multiply the capacity of the core team.

As a result of the work, it was possible to optimize the portfolio of products, promotions and even the use of space within stores, substantially improving the performance by location and the satisfaction of the franchisee.


Airlines collect a wealth of demographic, financial and travel usage information, such as dates, destinations, number of passengers, reason for travel, time of stay, among other data.

The segments and offers used in the marketing of their products were however defined a priori (e.g. holiday offer) and then tried to fit customers into the offers.


Define higher value and profitability consumer segments by period of the year with their profiles, habits and preferences without bias from the marketing team.

Develop the organizational capacity of implementation of multiple value propositions per period of the year (one for each segment), WITHOUT increasing the cost of operation.


Customer groups were identified 8 times more attractive than average, per season of the year. These customers traveled for both business and pleasure.

Redesign of all internal management processes and hiring models of agencies and service providers, organizational structure, digital profiles and tools of management and project control.

These segments are now described in detail to facilitate the development of more attractive Value Proposals than as a result, they are easier to sell without discounts.

Do you have a lot of transactional information with your customers?

Do you want to increase the effectiveness of your promotions?


With Artificial Intelligence we can customize the offers by segment in order to increase the relevance of Value Proposals.

Want to increase the profitability of your business?


Optimize Trade Marketing’s investment across all physical and digital channels through the analysis of your sales data per customer with Artificial Intelligence.

Artificial Intelligence Application in Customer Segmentation

Illumr helps us uncover secrets in “black boxes” of data from big manufacturers and retailers

  • Illlumr leverages its accumulated expertise from Machine Learning academic research and deep neural network development to deliver solutions tailored to the needs of our customers.
  • It manages to open the so-called “Black Boxes” where countless isolated data is hidden, which can only make sense by relating them to each other in specific contexts.
  • Targeting Shoppers and Customers
  • Developing Effective Promotions
  • Retention Programs – Loyalty
  • Optimizing Demand Planning
  • Reduction of inventives
  • Loss Reduction and Fraud
  • Reducing Shopper’s waiting time in paid rows
  • Optimizing the assortment at specific days and times
  • Determine the actual cost of each product, considering all direct and indirect expenses.
  • Maximize alignment between online and physical channels.
  • Optimize distribution and delivery logistics equipment on peak days and times.

Spanish Portuguese (Brazil)


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