Customer Behavior Analysis & Prediction

At Xyonix, we use AI to predict which of your customers will be loyal and which will lapse. Our approach will help you understand what makes a great or bad customer so you can take action.

  • You know an off the shelf customer analytics platform won’t tell you much

  • Top down reports of your customers’ behavior are dated or lack insight

  • You want to better understand what exactly makes a great customer

If your company is like most businesses, some of your customers will become amazing, loyal, long term customers, and some will churn, or stop using your product or service. If you know ahead of time a specific customer will be loyal, you might offer them perks, and if you know a customer might lapse, you can reach out to that customer and try to understand what is wrong.

check out Some of our Recent Projects to get a taste of our capabilities
Manufacturing Engagement Optimizer, A Case Study

Manufacturing Engagement Optimizer

We worked closely with a large industrial parts marketplace provider to help them better understand their current customers and partners. We developed AI models trained on their customer and partner site usage, profile and external attribute data to analyze engagement. We were able to successfully forecast which customers and partners were destined for high and low engagement. Perhaps more importantly, our models were able to explain why customers were likely to be high or low engagers. These deep, data driven insights, when used to power a living intelligence dashboard, are able to direct sales and marketing activities so they are more efficient and impactful.

Customer Behavior Analyzer, A Case Study

Customer Behavior Analyzer

We worked very closely with executives of a major dental insurance company to help better understand their customers. We used historic data to predict future customer behavior and purchase selections. Our models predict which customers are likely to lapse, and which are likely to remain loyal. Our models are also explainable, so it is easier to understand which attributes of a customer are indicative of loyalty or lapse tendencies. In addition to understanding high level customer characterizations, our explainable models describe why a specific customer is predicted to be a lapser or loyalist. [Read more]

To illustrate, consider the following example. We recently analyzed millions of customers for an insurance company, and were able to point out which specific factors predict both loyalty and lapse. We went much further, and built a machine learning model that automatically predicts what the chances are that a given customer will be loyal or churn in the next few months.

Teaching machines to predict customer behavior, and communicate which customer attributes predict specific behaviors, allows us to help you build your organizational playbook for acquiring and keeping happy customers. For example, your client success organization can reach out before there's a problem, your marketing department can reach new customers that are less likely to churn, and your sales organization can bring these better customers on board.

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Matt Leibel

“We talked to 15-20 different companies. We chose to go with Xyonix because they understood our problem better than other firms. Deep is an expert in his occupation. He knows what he's doing and clearly understands this world. ”

Matt Leibel, CTO @ Xometry [more]

 

check out one of our recent articles to learn more

blog
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Explaining a Passenger Survival AI Model Using SHAP for the RMS Titanic

In 1912, the RMS Titanic hit an iceberg in the North Atlantic Ocean about 400 miles south of Newfoundland, Canada and sank. Unfortunately, there were not enough lifeboats onboard to accommodate all passengers and 67% of the passengers died. In this article, we walk through the use of SHAP values to explain, in a detailed manner, why an AI model decides to predict whether a given passenger will or will not survive.

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Inside the Black Box: Developing Explainable AI Models Using SHAP

Explainable AI refers to the ability to interpret model outcomes in a way that is easily understood by human beings. We explore why this matters, and discuss in detail tools that help shine light inside the AI "black box" -- we wish to not just understand feature importance at the population level, but to actually quantify feature importance on a per-outcome basis.

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more of a podcast listener? check our your ai injection below

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AI and the Ethics of Influence: Exploring Synthetic Respondents & Decision-Making with Avi Yashchin of Subconscious AI
AI and the Ethics of Influence: Exploring Synthetic Respondents & Decision-Making with Avi Yashchin of Subconscious AI

Could AI be the ultimate tool for mass manipulation? In this episode, host Deep Dhillon interviews Avi Yashchin, CEO of Subconscious.ai, about how AI reshapes our understanding of human decision-making through synthetic respondents—AI models that simulate human choices with surprising accuracy. Together, they explore whether these tools will democratize causal insights or enable new avenues for manipulation, questioning if technologists have a duty to regulate AI innovation when ethics lag behind advancements. [LISTEN] - [SUBSCRIBE: YouTube, Spotify, Apple Podcasts]

Transforming Workforce Management with AI and People Analytics with Adam Binnie
Transforming Workforce Management with AI and People Analytics with Adam Binnie

In this episode of "Your AI Injection," host Deep Dhillon interviews Adam Binnie, the Chief Innovation Officer at Visier, discussing the revolutionary role of AI in enhancing people analytics. Adam illustrates how Visier uses AI to transform traditional HR into sophisticated, data-driven approaches, integrating diverse data sources to improve decision-making across management levels. He also explains how their platform interprets complex data, aiding managers in assessing current performance and predicting future trends. [LISTEN] - [SUBSCRIBE: YouTube, Spotify, Apple Podcasts]

Explainable AI: Data Scientists Discuss What's Inside the Black Box
Explainable AI: Data Scientists Discuss What's Inside the Black Box

AI is increasingly used to make important decisions in our lives like predicting the onset of a disease, the suitability of a job candidate, the likelihood of a criminal to reoffend, or the next president of the United States. In this episode, Xyonix Data Scientists engage in a robust discussion where we explore model interpretability and discuss why you should care. [LISTEN] - [SUBSCRIBE: YouTube, Spotify, Apple Podcasts]