Where Industry Meets Innovation

  • Contact Us
  • sign in Sign In
  • Sign in with certificate
mit campus

Resources

Search News

  • View All
  • ILP News
  • MIT Research News
  • MIT Sloan Management Review
  • Technology Review
  • Startup Exchange

April 24, 2017

Winning Back Unresponsive Customers with Artificial Intelligence and Machine Learning

Alan Ringvald, CEO and Co-Founder of Relativity6, utilizes proprietary behavioral listening algorithms to predict when and what a company’s most profitable lapsed customers will repurchase with a greater than 80 percent accuracy rate.

Daniel de Wolff



Alan Ringvald
Cofounder and CEO
Relativity6


Though Relativity6 was launched less than a year ago, CEO Alan Ringvald attributes much of their early success to the fact that for he and co-founder and CTO Abraham Rodriguez, decoding unresponsive customers’ behavioral economics is a longtime obsession. While both had prior success separately in the start-up realm, Ringvald as a co-founder of Superdigital and Rodriguez as a co-founder of Northrend Centinela, the roots of their current venture Relativity6, dates back to their time as students at MIT where they co-wrote their master’s thesis on reactivating unresponsive customers through machine learning. While in school they were also the recipients of the MIT Sandbox Innovation Fund, which not only provided them with their first funding, but also allowed them to pitch their product for the first time, and was, as Ringvald puts it, the springboard for what would eventually become Relativity6.

Relativity6 utilizes proprietary behavioral listening algorithms to predict when and what a company’s most profitable lapsed customers will repurchase with a greater than 80 percent accuracy rate. “The technology behind Relativity6 is quite streamlined at this point,” he says. “Initially we ask for purely behavioral data from a company. We don’t want names, we don’t want emails, nothing personal, we just need a unique customer identifier because what we are doing is analyzing internal past purchasing behaviors.” This focus on behavioral data means that stringent privacy policies have no bearing on what Relativity6 does. In fact, Ringvald is quick to point out that they take privacy very seriously, and there are several cybersecurity PhD’s on staff. Not only do they not want or need personal information such as names, email addresses or credit card numbers, but that type of information doesn’t help their model. “All we need is a unique customer identifier and we are good to go,” he says. The raw data is then run through their machine learning algorithms, and what emerges are predictions of which lapsed customers will repurchase and which product or service they are most likely to repurchase. In addition, the process allows them to predict through which channel they are most likely to reengage, be it email, phone, or catalogue. The client is provided with these predictions and then uses them to reengage former customers. The truly elegant aspect of the model as designed by Rodriguez is that, as Ringvald puts it, “Whether we are right or wrong, the model retrains itself; it is the beauty of machine learning. It learns whether it was accurate or not and is able to retrain and be more accurate next time around. And that is the process that repeats itself until we reach the 80 percent accuracy rate.”






In terms of customer base, companies that have participated in Relativity6 pilots vary greatly, from those that have only 1,000 customers to those with upwards of 50 million. Ringvald stresses that that company size and customer base are not key predictors for what Relativity6 does so successfully. Rather, all he and his team of MIT professors, data scientists, behavioral economists, and business strategists need is 18 months of back-data for their algorithms to understand past behaviors and thereby predict future behaviors. The numbers: Relativity6 finds out who will repurchase with a 95 percent model accuracy rate; when they will repurchase with an 80 percent match rate between predicted and actual lapsed customer future purposes; and what they will repurchase with 2-5 percent monthly conversion rates of total lapsed customers.

Thus far, Relativity6 has worked with companies of various sizes from a wide a range of industries. For example, Nutraclick, a technology driven company that provides leading health and wellness products, engaged Relativity6 to reactivate customers from their subscription service, and tripled their ROI in just one month. Other case studies done with companies including Zipcar, Coachup and Magellan Jets have yielded similarly positive results. “In terms of an ideal customer,” says Ringvald, “Relativity6 can help any organization that has been around for more than two to three years, has customers that haven’t purchased in a long period of time, and has behavioral purchase data. Literally any organization that has kept their data and has enough customers for us to be able to analyze.” This includes financial institutions, insurance agencies, hospitals, and retailers, but extends to political organizations, universities and nonprofits in terms of gifting and donations.

And with a seemingly endless list of potential clients looking to benefit from working with Relativity6 and their machine learning algorithms, the future looks very bright for Alan Ringvald and his team. They recently joined AI world leaders NVIDIA’s AI Inception program, and have even partnered with them on an external basis. Ringvald is also excited that Relativity6 has joined the ranks of STEX25, and cites the partnership with MIT ILP as particularly fruitful. “ILP has been an instrumental part of Relativity6,” he says. “We have gotten incredible support from the staff, and have already started working with several companies in the network.” And people are paying attention. Ringvald, on behalf of Relativity6, presented at the MIT Consumer Dynamics Conference (January 2017). Most recently, they were tapped to present at the MIT Silicon Valley Showcase (February 2017) hosted by Google. Relativity6 is picking up a significant head of steam. The accuracy of their algorithms and the success rates of their clients are proof of their achievements thus far. Ringvald says the next frontier is being able to automate the process of predicting why a customer defected in the first place. And Relativity6 is already hard at work on this next piece of the puzzle.





About MIT Startup Exchange, STEX25, and MIT’s Industrial Liaison Program (ILP)
MIT Startup Exchange actively promotes collaboration and partnerships between MIT-connected startups and industry. Qualified startups are those founded and/or led by MIT faculty, staff, or alumni, or are based on MIT-licensed technology. Industry participants are principally members of MIT’s Industrial Liaison Program (ILP).

MIT Startup Exchange maintains a propriety database of over 1,500 MIT-connected startups with roots across MIT departments, labs and centers; it hosts a robust schedule of startup workshops and showcases, and facilitates networking and introductions between startups and corporate executives.

STEX25 is a startup accelerator within MIT Startup Exchange, featuring 25 “industry ready” startups that have proven to be exceptional with early use cases, clients, demos, or partnerships, and are poised for significant growth. STEX25 startups receive promotion, travel, and advisory support, and are prioritized for meetings with ILP’s 230 member companies.

MIT Startup Exchange and ILP are integrated programs of MIT Corporate Relations.