Santa Monica, CA-based retention marketing startup Retention Science launched its big data platform to the public today, along with $1.3 million in seed funding from Baroda Ventures, Mohr Davidow Ventures, Double M Partners and several angel investors, including Paige Craig and several Stanford Ph.D. data scientists. The company, a graduate of Los Angeles startup accelerator MuckerLab, helps ecommerce companies re-engage with customers by analyzing customer behavior through its Customer Profiling Engine, which uses algorithms and statistical modeling to build retention strategies.
CEO Jerry Jao and his co-founder Andrew Waage previously built two ecommerce-centered companies. Based on those experiences, Jao said he learned that most companies focus on getting new customers, rather than engaging with the ones they already have. Retention Science is based on the idea that it’s more cost-effective to increase the value of existing customers than to acquire new ones, so the company focuses exclusively on improving the value of existing customers.”We really learned and realized that at the end of day, there’s not enough solutions out there focused on churn and customer retention,” Jao said in an interview. “There’s very little focus around once you acquire a customer, what exactly do you do to continue to building that relationship and getting them to spend more.”
Retention Science aims to help companies understand and predict consumer purchasing behavior based on their customer profile and shopping patterns. Instead of just tracking purchase data, the company gathers social and demographic data to give companies a comprehensive view of who that person is as a customer – whether they are likely to buy again, and what factors they care about when purchasing. “Our core technology is really built around being able to leverage different data points that we can both collect from the ecommerce companies themselves, as well as collecting third-party data around a customer,” Jao said.
They help companies predict campaigns that will increase sale conversions, by using intelligent pricing to predict how price-sensitive customers are. For example they can help an online retailer understand that Customer A doesn’t care about sales or deals, all they really care about is free shipping, while Customer B won’t purchase an item unless it’s on sale. Or they will predict the projected annual spend for a customer, monitor whether they’re on track for that number, and target deals accordingly. Companies can also use Retention Science to identify online influencers and provide incentives for them to share product details, for example providing a discount to all customers who have a Klout score above a certain number for sharing products with their networks. “We actually want to provide an actionable marketing strategy for them,” Jao said. “We’re not giving you analytics, we’re actually giving you actionable marketing strategy. We don’t tell you information about your customers, we tell you what to do with them.”
Right now the company is focusing on large online retailers, and is working with 10 beta clients, some of them paying customers (the company is currently planning to charge on a subscription-based model, but the pricing isn’t finalized). Though they aren’t able to disclose customer names, Jao said they’ve been able to show positive impacts on their bottom line – the company helped one client, a bike and accessories store, improve its sales per customer by 133 percent.
Many large online retailers like Amazon have their own in-house data analytics tools and statisticians, but Jao said it’s not feasible for the majority of online retailers to build these capabilities in-house. He said companies are often focused on marketing and merchandising, so the biggest challenge isn’t collecting the data, it’s doing something meaningful with it.
Jao said the funding will help build automation into Retention Science, so instead of his team having to help onboard clients, they would be able to get set up and perform their own analyses. Currently for a client to get set up on the platform they have to provide them with what Jao calls a “data dump,” and then Retention Science’s team gets them set up on the back-end. “We want to be able to automate the entire platform, we want to be more of a plug-and-play platform,” he said.
Jao said the funding will also go to hiring and product development. Since right now they only make recommendations for clients rather than actually executing on lifecycle marketing campaigns, Jao said they plan to build out their third-party integrations with push notification companies and email marketing providers to close the loop.
Since companies are handing over their most sensitive customer information, privacy is obviously a concern. Jao said that their technology doesn’t give them access to the customer’s personal information, rather they just analyze behavior to build a profile of a given customer. They also try to make sure that their clients are approved to collect customer data in a “legal and very honorable method,” so they’ve been approved to drop a cookie on a customer’s browser so they can monitor browser activity. “From our end in terms of privacy concerns, how we become smarter in terms of predicting customer behavior is all through using data that is being appropriately collected from the ecommerce site.”
With social CRM companies trying to help companies attract and retain customers by leveraging their social profiles (Dynamic Signal and Sunnytrail are just two we’ve written about this week), it only makes sense that big data would enter into that equation to tie a customer’s social profiles together with their purchasing data. Ultimately it’s all about predicting a customer’s behavior, and if Retention Science can help companies do that, they might be a hit with ecommerce companies.