Remember how much simpler life was back in 2010? Among our quaint notions, we thought that B2B companies couldn’t build predictive models because they didn’t have enough data about their customers and prospects. The Internet has changed that, providing oceans of relevant detail from company Web sites, social media, job boards, and other sources. Today, at least a dozen vendors are offering predictive models for B2B lead scoring, sales intelligence, and customer success management.
Many of the original scoring vendors specialized in a single application. But today, most are broadening their products to serve multiple purposes. This lets the vendor charge more to each client and blocks out potential competitors. Marketers also benefit since they only have to buy, learn, and integrate a single system.
Everstring is relative newcomer to the B2B predictive modeling arena, founded in 2012 but only seriously entering the market after it received $12 million in Series A funding last August. The late start has let it adopt a broader scope from the beginning, offering both lead scoring and new prospect identification. The company plans to extend its offerings later this year to include real-time treatment recommendations.
But what really sets Everstring apart are two other factors: it works at the account rather than individual level, and it builds models really, really fast – as in, six minutes for a new model once data connections are in place. The two factors are related: Everstring can work quickly because it only imports a client’s account list and sales activities, saving complicated data mapping and analysis, and because it has preclassified its master database of six million U.S. businesses into clusters based on similarities in products, technologies used, hiring patterns, news events, social data, and other factors. This means that building a new model only requires using activity history to identify the client’s responsive accounts and finding which segments have the highest concentrations of those accounts.
That’s pretty light work compared with loading individual level data and identifying which attributes are most predictive for each client’s business. Matching against six million companies rather than 100 million individuals speeds things up too. The approach also lets clients score anonymous leads if IP address or similar information can identify their company. Models for different products can be based built by selecting only accounts that purchased that product.
Once a model is built, Everstring can score any new leads by just by identifying the segment their company belongs to and applying that segment’s score. Lists of new prospects require simply taking names from the highest-scoring segments.
Sounds pretty simple, eh? That’s because I’ve over-simplified. The data gathering and actual math are actually quite complicated. Beyond that, Everstring does more than provide segment scores, which measure the fit between a new account and the client’s previously responsive prospects. Specifically, it also measures purchase intent by based on more than 1 billion clicks per day on third party Web sites and emails. And it measures engagement by analyzing visitor behaviors on the client’s own Web site, gathered through a tracking pixel, plus other data imported from marketing automation. The combination of fit, intent, and engagement will guide the real-time treatment recommendations and can support additional scoring applications. Fit scores alone are much more limited..
So, how do you deploy all this? Everstring has standard integrations with Salesforce.com, Marketo and Oracle Eloqua, which can send data for the initial model building and score new accounts as they are added to those systems. A real-time API can integrate with other CRM and marketing automation systems.
Pricing for Everstring is based on the types of models and volume. Lead scoring usually runs from $60,000 to $100,000 per year. New prospect names is additional. Pricing for real-time message selection isn’t yet set. The system currently has about 25 clients, nearly all added since last August.
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