Optimize actions strategically
We help companies to improve long-term customer engagement and lifetime value rather than temporarily boost metrics such as click-through rates. Our models and decision engines evaluate and optimize sequences of actions in a strategic context creating a personalized communication strategy for each customer.
Provide prescriptive guidance
Predictive analytics provides useful insights into marketing and customer care teams, but many organizations struggle to reconcile dozens of scores and efficiently operationalize all these signals. Our prescriptive next best action solutions take it to the next level by automatically optimizing actions and even sequences of actions based on all available information.
Leverage textual, audio, and video data
Unlike traditional personalization models that use mainly interaction data, our solutions are able to extract useful signals from virtually any source — call transcripts, customer reviews, camera footage, and many others. These signals are automatically converted into meaningful attributes and tags and then used in next best action optimization models and analytical processes.
Get deep insight into customer behavior drivers
We build solutions that analyze customer event histories and quantify the contribution of individual events into outcomes such as customer churn or conversion. This helps to reveal and visualize hidden event patterns, and design better, more relevant actions for individual customers and customer cohorts.
Extend your marketing automation software
Our solutions can be integrated with marketing automation software such as Adobe or Salesforce marketing clouds, so that Next Best Action recommendations can be immediately operationalized.
Automatically personalize offers and customer experience
Our personalization engines can be integrated with eCommerce systems, mobile applications, and other touchpoints to optimize offers and content in real-time and learn from feedback events.
Create advanced decision support tools
Our analytics models and decision engines can be integrated with tools used by customer care specialists, financial advisors, doctors, and other professionals who need to be advised on the optimal customer communication and prioritization strategies.
Advanced customer data analytics and data driven personalization are expected in modern businesses, but how do you get there? Most solutions on the market right now are based on methods and methodologies developed decades ago and can’t keep up with the growing need for AI powered personalization. In this white paper, we review the six most common challenges and limitations of traditional personalization solutions and discuss how you can solve these challenges with the new generation of personalization solutions.
A completely automated next best action platform was created for a leading video game company in just 8 weeks and delivered 25% customer ROI improvement.
Next Best Action is a versatile paradigm that can be applied in many B2C and B2B industries, and we have experience helping clients from different verticals to adopt it.
- Product recommendations
- Marketing communications
- Personalized offers
- Personalized offers
- Direct-to-consumer messaging
- Loyalty programs
- Decision support tools for patient support specialists
- Personalized patient notification
Finance and Insurance
- Decision support tools for financial advisors
- Personalized client notification
- Churn prevention
- Web portal personalization
- Personalized offers
- In-game personalization
- Special offers
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).
How to build and evaluate a Next Best Action model for customer churn prevention
It is challenging to develop a model that automatically prescribes the next best action for each customer because we need to quantify the true impact of various actions. In this article, we describe the design of the next best action model that we commonly use in practice and elaborate on the methodology for offline efficiency evaluation.
Customer churn prevention: A prescriptive solution using deep learning
The ability to identify and interpret churn patterns and prescribe correct solutions is as important as achieving churn prediction accuracy. In this article, we discuss how to build a solution that helps to quantify, investigate, and fight customer churn, complaints, and other issues related to customer dissatisfaction.
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