SessionM strives to make data actionable in real-time, with the goal of allowing marketers to create engagements powered by data that facilitates personalization on an individual customer level.
Every customer engagement begins with data and how it can be leveraged to make each customer more loyal and each engagement more profitable. Using purchase and activity history as well as other latent factors, the SessionM Platform is able to predict customer preferences that influence affinity, motivation, and intent to purchase.
Customer recency, frequency and monetary spend (RFM) are historical metrics calculated for each individual customer for more personalized and impactful targeting
Customer lifetime value is a forward-looking metric that calculates projected revenue each customer will generate with a brand over the next 12 months – giving brands a valuable snapshot of each customer for relevant action
Risk of Churn is determined by looking at an individual customer’s past purchase behavior over time. Establish triggered messages with targeted offers to customers when they reach a certain percentile to entice them to come back into the store
Send relevant messages to customers at the moment of impact to drive incremental spend. Target customers who have not purchased in 30 days with a special offer on his or her favorite product to get them back in the door, and at the same time send everyday purchasers an upsell offer to drive basket size
Knowing each customer’s lifetime value helps marketers drive retention rates by driving targeted actions that are both relevant and timely. This calculation can be used in audience segmentation for special rewards or offers to motivate your most valuable customers to perform specific behaviors
SessionM’s churn model looks at an individual’s historical transactions to understand their “normal” behaviors and cadence. As long as the normal behavior is maintained, churn probability will remain low. Once the model senses a negative deviation in that “normal” pattern, the churn probability will increase
Using matrix factorization techniques, SessionM’s recommendation engine generates personalized scores on a per-customer and per-product basis, without requiring extensive knowledge on either customer demographics or product attributes.
Making your data actionable so you can make your customers more loyal and profitable.