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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.

Use cases

Efficiently acquire new customers

We build state-of-the-art customer acquisition solutions by combining first- and third-party customer data, creating advanced statistical models for lead scoring, and integrating models with transactional systems, marketing automation software, and external advertising platforms. Our clients often achieve double-digit improvements in conversion rates using our customer intelligence platforms. The systems we create using big data analytics and actionable methods for decision automation not only improve customer experience but also provide your business the ability to hit your sales goals.

Retain customer and prevent churn

Our customer intelligence solutions help enterprises to manage and optimize all stages of the post-acquisition customer lifecycle. Our models for one-time buyer conversion, customer retention, and churn prevention not only help to improve targeting decisions but also provide deep insights into churn drivers, customer lifetime value profiles, customer microsegments, and much more. These customer insights are critical for building strong customer relationships, improving customer lifetime value, and achieving strong competitive advantage.

Optimize next best action

Predictive analytics models provide useful customer insights to marketing teams, but many enterprises struggle to reconcile dozens of scores and efficiently operationalize all these signals. Our prescriptive solutions take it to the next level by automatically optimizing actions and even sequences of actions based on all available information. The overall business process, even at the enterprise level, will be improved, so you can focus on management while our system focuses on customer growth.

Dynamically personalize content

Traditional marketing models rely on historical data to make future decisions, but this approach falls short in dynamic environments with steady flows of new users, products, and pieces of content. We leverage the latest achievements in reinforcement learning with real-time customer data to actively explore the environment, continuously learn, and manage various aspects of customer experience even when historical data are not available.

Personalize B2C and B2B recommendations

Our recommendation solutions work in both B2C and B2B environments because of a rich set of algorithms that accounts for content similarities, user similarities, behavioral patterns, market trends, and textual and data visualization. A wide range of recommendation and personalization models creates a strong foundation for building excellent user interfaces, improving sales metrics, and increasing customer satisfaction.

Our clients

Retail
Hi-tech
Manufacturing
Finance
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.
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We develop customer intelligence platforms for enterprises from many industries including retail, telecom, video games, and finance
Retailers and brands

Personalized customer experience is the key success factor in retail. We help our retail clients to acquire new customers, improve customer loyalty, personalize experience, and retain high-quality customer base using the most advanced machine learning technologies available in the industry.

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Technology, media, and telecom

Customer retention is critically important for all subscription-based businesses such as telecom. We work with leading telecom companies to build state-of-the-art customer analytics and churn prevention solutions.

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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.

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Financial services, banks, and insurance

Insurance companies have several important touchpoints with their customers, such as insurance purchase, claim filing, and insurance renewal. We help insurance companies to optimize customer experiences across these touchpoints using data science methods.

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Learn more about solutions for your industry
Solutions for retailers Solutions for technology, media, telecom Solutions for video games Solutions for finance & insurance
Related card image
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.

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.
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.
Related card image
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.

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.
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.
Related card image
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.

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.
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.

We provide flexible engagement options to design and build a customer intelligence and analytics solution for your enterprise. Contact us today to start with a workshop, discovery, or POC.

Workshop

We offer free half-day workshops with our top experts in marketing technology and customer analytics to discuss your  customer intelligence strategy,  challenges, optimization opportunities, and industry best practices.

Proof of concept

If you have already identified a specific use case that can be solved using data science and other customer intelligence technologies, we usually can start with a 4-8 weeks proof-of-concept project to deliver tangible results for your enterprise.

Discovery

If you are in the stage of requirements analysis and strategy development, we can start with a 2-3 weeks discovery phase to identify right use cases for customer intelligence and personalization, design your solution using industry best practices, and build an implementation roadmap.

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