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Customer segmentation for email marketing systems using unsupervised machine learning.


Machine Learning

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Calder partnered with a US provider of IT solutions for restaurant networks to improve its legacy email marketing system introducing customer segmentation 

with ML.


Client: leading US provider of IT solutions for restaurant networks with a focus on marketing.


Industry: Information & Communication, eCommerce

Case Highlights:

  • Determine appropriate product pricing.

  • Develop customized marketing campaigns.

  • Design an optimal distribution strategy.

  • Choose specific product features for deployment.

  • Prioritize new product development efforts.

  • Customer segmentation ML model introduced

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Business Analysis

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Quality assurance

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UX/UI Design



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Technical support

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Project management


Challenge We Faced:

​​As a restaurant, you have some basic data about your customers like age, gender and spending score. You want to understand the customers like who are the target customers so that the sense can be given to the marketing team and plan the strategy accordingly. It is a significant strategy as a business can target these specific groups of customers and effectively allocate marketing resources. 

  • Front-End

  • React.js/ Redux

  • HTML

  • SaaS

  • PostgreSQL

  • MongoDB

  • Cassandra 

  • REST

  • Node.js

  • Nest.js

  • AWS

  • Docker

  • Git

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Calder’s Process

Calder assembled a team of seasoned professionals, including a certified Project Manager and several senior-level developers. The team adhered to an agile, predictable process tailored to the client’s business needs and transparency standards.


During the development cycle we have adopted additional requirements from the product owner’s side. Trying to be flexible with changes and achieve client goals at the discussed date and budget.

Customer Profitability

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Multiple Charts Sample Present PPT

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Delivered Solution

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Calder conducted a research of the business needs of the end business for the email marketing system and proposed using machine learning to automatically analyze user data and segment customers. 


This backend part of the system was implemented and tested as well as the frontend part with advanced data visualization practices.

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As a result, the client's business focused on the more profitable groups of clients and specified marketing strategies for them. Also, product owners had a focus on more prospective areas of software development.

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