On a good day, a sales executive can direct a salesperson to hone in on the best lead in the customer-relationship-management system such as Salesforce.com, and a deal might or might not come through. But getting the most out of sales and marketing staffers every day is the sweet spot. A startup named Infer is emerging from stealth mode with $10 million in venture funding to help more companies get to that sweet spot with a tool for identifying the most promising leads based on a user’s historical deal-making tendencies and external data about potential leads.
Redpoint Ventures led the Series A round, and Andreessen Horowitz, the Social+Capital Partnership, Sutter Hill Ventures and others also contributed.
Based in Palo Alto, Calif., the company focuses on sales operations that keep their leads in Salesforce and other cloud-based tools such as Eloqua and Marketo, although Infer Co-founder and CEO Vik Singh said it’s also possible for the software to hook in to on-premise appliances.
Once signed up with Infer — Box, Jive Software, Tableau Software, Yammer, Zendesk and other companies are already paying customers — the system inspects historical sales information to check which deals have been sealed and which fell apart. That becomes training data for a model that scans “hundreds of signals of external data” to get a sense of which potential leads the company stands a chance of closing. Inputs include news articles, social-media accounts, website-traffic data, industry data, financial data, legal data, trademark data — “anything we can get that can give us more of a complete picture on who the customer is,” Singh said. Users can determine the weight of certain types of information.
Users also set priorities for the scoring of leads. “Do you want a model where the higher the score, the more likely (you are) to win (a deal), or do you care more about conversion, or do you care more about lifetime revenue, or deal size? We build the model based on that,” Singh said. It’s not just a neat way to prioritize leads; Singh said many Infer customers have boosted conversion rates with the tool.
Before starting Infer, Singh spent some time at Google, where he focused on machine-learning methods for automatically providing answers to questions users type into the search box. Another former Googler, one-time chief information officer Douglas Merrill, co-founded a different company that uses lots of external data to make determinations: ZestFinance, formerly known as ZestCash, extracts information from 70,000 sources to figure out if a lender should make a loan.
The broader strategy the two companies have in common — making predictions based on data — has become more popular in recent years, as companies merge and grow data sets to create more than the sum of their parts. In this case, it seems that the approach could garner wide adoption as a few companies optimize the time of their sales and marketing employees and pull ahead of their competitors, and other companies might want to do the same thing to catch up.
Of course, if Salesforce or Oracle acquires Infer, then adoption could come even faster.