Customer Data Analytics

SessionM strives to make data actionable in real time, with the goal of allowing marketers to create engagements powered by data, that facilitate personalization at an individual customer level.

Benefits

  • Liberate data into actionable information

    Gain insights into the metrics most meaningful to your brand - how often customers visit and how much they are spending - to create the foundation for intelligent marketing campaigns

  • Sophisticated modeling, simple reporting

    Track and capture data from all channels in your customer journey and combine each source to provide robust customer-level analytics, eliminating blind spots or separate instances in reporting

  • Confidently trigger the perfect engagement

    Our robust product recommendation engine uses historical purchase behavior to predict the probability of “liking” a specific product or group of products

$0

increase in LTV across over 1M customers for a retail client

0%

reduction in churn for retail client

0%

increase in customer lifetime value

RFM Metrics
RFM Metrics
RFM Metrics
RFM Metrics

RFM is an acronym for Recency, Frequency, and Monetary Spend. The SessionM Platform calculates RFM for three distinct time periods: 7 day, 30 day, and lifetime. Using RFM, it is easy to compare the historical performance of one customer to the full customer base.

Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV)
Customer Lifetime Value (CLV)

Customer lifetime value is a forward looking metric that calculates the projected revenue each customer will generate over the course of their lifetime with a brand. The metric can also be seen as the present value of future cash flows attributed to the customer relationship.

Churn Assessment
Churn Assessment
Churn Assessment
Churn Assessment

The platform’s churn model looks at the historical transactions at an individual customer level to understand the “normal” behaviors and cadence for each customer. As long as that “normal” pattern is maintained, churn probability will remain low. Once the model senses a negative deviation in that “normal” pattern, the churn probability will increase.

Recommendation Scoring
Recommendation Scoring
Recommendation Scoring
Recommendation Scoring

SessionM's robust recommendation engine provides scoring at scale for large data sets. The platform’s engine uses machine learning to deconstruct historical purchase data into latent factors that can inform estimates of previously unknown customer/product affinities.

Proven Results

“SessionM garners praise from references for the quality of its team, hands-on involvement with deployment and integration, responsive support, and robust reporting capabilities. Several references note that SessionM is instrumental in helping drive incremental revenue and improving customer experience.”

Gartner Magic Quadrant for Mobile Marketing Platforms, July 2018
Related Resources
Consumer Goods Case Study
Case Study
SessionM: Unlocking Data for Deeper Engagement
Videos
Executing Data-Driven Loyalty for More Personalized Engagement
Product One Sheets

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