Company websites, Social Media, News Media, Government Datasets, Weather Datasets, Proprietary Datasets.
Tuned against internal data to deliver customer insights and behavioral predictions.
Data is continuously ingested, cleaned, structured, normalized and streamed through Relativity6 proprietary machine learning APIs.
Identify customers' propensity to purchase.
Recognize accurate cross-sell and upsell opportunities.
Know when each customer is most likely to purchase.
Recommend most efficient sales channel per customer.
Through research at MIT, Relativity6 concluded that in order to generate the most accurate predictions possible, we needed to tune our algorithms strictly into objective, unbiased data.
This is why we focus our data inputs on internal customer data, specifically the actions and behaviors taken by your customers in the past. Our technology is able to seamlessly ingest both structured and unstructured data sources and filter out only the features that are directly related to purchasing activity.
We call our proprietary system of algorithms 'behavioral listening algorithms' because we've trained our models to tune into strictly action based variables, leaving subjectivity to the side.
Relativity6 worked with Coachup to identify and reactivate customers who hadn't purchased in at least 6 months.
Utilizing Coachup's existing data we ran our automated machine learning algorithmss and pinpointed lapsed customers with at least a 60% probability of repurchasing in September.
Nutraclick provides leading health and wellness products. We worked with them to identify and reactivate former customers of their subscription service.
Utilizing our proprietary machine learning algorithms, we were able to identify which inactive customers had the highest probability of subscribing again. These predictions included the most likely product recommendation per customer.
We worked with Magellan Jets to identify the buying patterns and signals of lapsed customers willing to re-purchase a jet membership or book a charter flight.
Our predictions achieved above 90% accuracy and our insights helped Magellan's sales team prioritize their efforts and brought significant incremental revenue with minimal effort.
Relativity6 worked with Zipcar to identify which inactive members had the highest probability of making a booking and analyzed the type of vehicle these customers were most likely to request.
Using Relativity6's proprietary machine learning algorithms, we launched a targeted campaign to these members to increase their incentive to make an immediate purchase.