Relay42 predicts customer conversion likelihood to power personalized journeys
Relay42Delta LakeRelay42, a Customer Data Platform (CDP), uses customer data on the Databricks Data + AI Platform to predict how likely a shopper is to convert; the prediction feeds a decision engine based on anonymized PII to build customer profiles that inform next-best-action messaging. For example, a major Singapore telecommunications client uses this to determine what messages to send shoppers researching phone plans and when. The migration from Hadoop/Cassandra to Databricks cut time to deliver complete customer data from 20-24 hours to 1.5 hours and real-time analytics from 8 hours to about 15 minutes, saving over €180K/year in storage costs.
Overview
Relay42, a Customer Data Platform (CDP), uses customer data on the Databricks Data + AI Platform to predict how likely a shopper is to convert; the prediction feeds a decision engine based on anonymized PII to build customer profiles that inform next-best-action messaging. For example, a major Singapore telecommunications client uses this to determine what messages to send shoppers researching phone plans and when. The migration from Hadoop/Cassandra to Databricks cut time to deliver complete customer data from 20-24 hours to 1.5 hours and real-time analytics from 8 hours to about 15 minutes, saving over €180K/year in storage costs.
This entry has 15 published fields tied to exact passages in an immutable source capture.
Inspect the highlighted sourceThe challenge
Relay42's legacy Hadoop infrastructure struggled with the tremendous amounts of data to be stored, processed and analyzed, creating stability issues that impacted data accessibility and time-to-insight for their customers.
The solution
As a Customer Data Platform, Relay42 gives digital experts complete control over customer data to create real-time personalized customer journeys on all channels; with Databricks Data + AI Platform and Delta Lake, Relay42 can now deliver reliable data to its customers quickly and consistently.
Reported business value
What used to take 20–24 hours to make complete customer data available now takes 1.5 hours, real-time analytics that took upwards of 8 hours can be done in almost 15 minutes, and Relay42 is saving over €180K in data storage costs per year.
Sources
Open any source and check the claim yourself — that is the point of the register.
This record was researched and written with AI assistance, and its claims were checked against the sources above. (EU AI Act art. 50 transparency notice.)
Other technology & software entries in the register.
HP crafts marketing campaigns that resonate with customers using Databricks and Uniphore
HP centralized first-party customer data on the Databricks Data + AI Platform with Delta Lake and Unity Catalog, and connected it to Uniphore's HybridCompute for federated query pushdown, cutting campaign setup from 2 weeks to 2 hours and processing 400 million records in seconds.
Building a safer and more sustainable world
Novade, a construction management software company, partnered with Databricks to modernize its analytics and ML infrastructure, moving from a homegrown Apache Airflow stack to the Databricks Data + AI Platform with Delta Lake, Unity Catalog and MLflow. The move reduced total cost of ownership by 60% and supported a 100% increase in new clients, while enabling ML models for predicting incident risk affecting worker safety and project delivery schedules.
SafeGraph optimizes geospatial data processing with Databricks and Delta Sharing
SafeGraph used the Databricks Data + AI Platform, Delta Lake, Delta Sharing and MLflow to process petabytes of geospatial data and feed predictive models, achieving 2x-10x faster spatial querying, indexing and partitioning and a 50% reduction in peak memory consumption compared to other platforms, while reducing data access time for partners from months to minutes via Delta Sharing.
Transforming Weather Forecasting with Lakeflow Jobs
AccuWeather migrated from on-premises infrastructure to Databricks and Lakeflow Jobs, working with Datadog for observability, to unify diverse weather data formats and orchestrate 4,500+ weekly jobs. Lakeflow Jobs coordinates the ingestion of multiple weather models, triggers machine learning processes that weight and blend different forecasts, and manages complex job dependencies for reinforcement training workflows used in AccuWeather's proprietary forecasting engine. AccuWeather reports 3x faster dataset development (three months to one month per dataset), a 50% reduction in unactionable alerts, and 50% cost savings on serverless job usage.
AccuWeatherLakeflow Jobs · Delta Sharing · DatadogWas this helpful?
Your feedback helps us improve our use case database