We build and deploy enterprise machine learning via a proprietary platform and expert operators.

A lifetime value machine learning model forecasts the future value of a customer to a business. It can take into account factors like the customer's likelihood to make future purchases, the amount they are likely to spend, and the length of time they are likely to remain a customer. It seeks to answer the question: “How much is a user worth over their lifetime?”.

Lead scoring models use lead behavior and demographics to predict their likelihood to complete a conversion event (e.g., a purchase). We do this by assigning a quantitative score to each lead, with higher scores indicating a higher likelihood of conversion.

A customer retention model forecasts a customer's likelihood to continue doing business with a company over a given time period. It seeks to answer the question: ‘How long will the customer stay with the company?”

These models can use data on customer behavior, demographics, and other specific engagement factors, including customer's past purchases, their engagement with the company's marketing efforts, and their satisfaction with the products or services.

Outreach timing models predict the optimal time to contact potential customers in order to maximize the likelihood of success. They seek to answer the question: “When should I reach out to a customer for maximum impact and efficiency?” These models use data like past response rates to marketing and sales efforts, time zones, and preferred communication channels.

These models predict the degree to which offers (or incentives) are likely to be effective for a given customer. An offer affinity model might predict a customer is more or less likely to respond to a discount on a product they have purchased in the past, rather than a free trial of a new product. At their foundation, they seek to answer the question: “How effective is each promotion or offer for this customer?”

A propensity model can predict the the next products a customer will purchase. Advanced applications might prioritize moving customers to a preferred action based on its relatively high ROI and low cost as compared to other available actions. This model, and models tangential to it, seeks to answer the question: “Of available products, which one is the customer most likely to purchase?”

A media recommendation model recommends videos, articles, or songs based on a user's stated or implied goal. These models often use data like the customer's past interactions with media content, their demographics, and their engagement with the company's marketing to predict which content is relevant; they often look at the behavior of other, “look alike” customers as well.

An intervention recommendation model recommends “interventions” (or actions) that may positive impact user or customer experience. These models can radically improve the ROI of high cost touch allocations (e.g., a 1:1 sales call) as well as the full system of interventions across the full customer population.

A product recommendation model predicts which product should be suggested to a customer given a set goal. Classic applications of this model include search results in customer shopping experiences as the now ubiquituous checkout widget that highlights products other customers were interested in.