Customer intelligence and personalization

Automatically optimize billions of micro-decisions related to customer experience, personalization, message targeting, and timing using machine learning and advanced analytics. From small business to enterprise, enhance the customer experience and achieve tangible results using real-time, personalized components.

Our clients

Retail
Hi-tech
Manufacturing
Finance
Healthcare
How our customer intelligence technologies work
Advanced customer scoring

Our customer intelligence platforms generate a wide range of advanced signals that help marketers focus on high level business goals related to their customers:

  • Propensities to channels and brands
  • Estimated customer lifetime value
  • Propensity to try new products, increase consumption, or churn
  • Estimated replenishment cycles

These scores and metrics can be used by enterprise business analysts or consumed by downstream enterprise systems. Top-of-the-line feature and data management systems and advanced analytics signals created by our data engineers and data scientists can improve your overall business revenue.

End-to-end decision automation

We build systems that understand top-level business objectives, and optimize marketing actions accordingly through:

  • Contextual personalization and scoring
  • Dynamic targeting and budgeting decisions
  • Campaign arbitration

These smart decision-making components can be integrated with a wide range of marketing analytics software and systems including eCommerce platforms and advertising platforms.

Dynamic optimization

Our consumer intelligence solutions use advanced machine learning methods for the online experimentation and dynamic optimization of marketing actions based on real-time feedback data:

  • Reinforcement learning
  • Multi-armed bandits
  • Learning to rank

These algorithms can efficiently handle consumer data with a large number of new users or frequently updated pieces of content. We collect all sorts of business intelligence to be used to help with the business processes, despite only customer data being used at the end.

Introduction to Algorithmic Marketing
Book
Introduction to Algorithmic Marketing
Would you like to learn more about the theoretical foundations of customer experience optimization? We have published a 500-page book on data science that is available for free download, and there are chapters on data-driven customer acquisition, customer retention, promotion personalization, and product recommendations in it.

Industries

We develop customer intelligence platforms for enterprises from many industries including retail, telecom, video games, and finance
video game industry icon
Entertainment and video games

It is critically important for technology and video game companies to understand in-app and in-game customer behavior to improve user experience as well as find monetization opportunities. Our customer analytics and personalization technologies help companies to accomplish these goals.

Learn more about solutions for your industry

Read more

Building a next best action model using reinforcement learning
In this article, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). These techniques can be applied to various business problems such as customer retention, customer experience optimization, and promotion targeting.
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Cross-channel marketing spend optimization using deep learning
The analysis of event sequences using deep learning methods is one of the latest innovations in data science. We explore how deep learning methods can be used to analyze sequences of customer interactions in the content of channel attribution problems and how the insights gained from such analyses can be used for spend optimization.
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Customer2Vec: representation learning for customer analytics and personalization
We focus on the learning of useful semantic representations (embeddings) for products and customers using neural networks. These representations can be used for multiple purposes within machine learning pipelines: they can be utilized as features in downstream models and analytics software to improve the accuracy of propensity scores and recommendations, they can be used to cluster and analyze embeddings to gain deep actionable insights into the semantics of customer behavior, or they can be used to perform other personalization and analytics tasks that use embeddings to measure the proximity between products and customers.
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