Everstring Offers Fast, Flexible, Account-based Predictive Models for B2B Sales and Marketing

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.

Lots of Vendors Can Help You Find Leads on the Web

Few people would suggest you learn salesmanship from the play Glengarry Glen Ross,* but its central message rings true: good leads are the lifeblood of a sales organization.** That’s why scanning the Internet to find new  prospects is such an exciting opportunity. At least a dozen firms are now following that path.

These firms scan company Web sites, social media, news sites, directories, and other sources to identify companies, extract attributes like revenue, growth rates, and technologies used, and flag events that might indicate a sales opportunity, such as opening a new office, launching a new product, or hiring new management. Of course, there are plenty of important differences which impact which might make sense for you.  Some of the more important ones include:

• Specific data sources, scanning techniques, and analytical methods. Evaluating these in the abstract is interesting, but what works well for one purpose in one industry might work poorly for something else. So buyers really need to run their own tests to see what works for them.

• Types of predictive models available.  Some vendors only rank leads while others build multiple models for different purposes.

• Use of the client's internal data for model scoring, and whether this extends to sources beyond CRM.

• Whether the vendor sells prospect lists or only enhance names provided by the client.

• Whether the vendor provides lists of individuals as well as companies.  Since Web scanning is usually at the company level, the individual names usually come from other sources.

• Coverage outside the United States

• Information returned beyond names and lead scores, such as recommended treatments and social profiles.

• Whether the company maintains a permanent database on all businesses or only scans when clients request information about specified businesses or segments.  The permanent database costs more to maintain but stores history and trend information that is otherwise unavailable.

Here are brief profiles of the vendors I’ve identified in or near this space. There are probably others.  I’ve grouped them based on how much information I have available.  This correlates to some degree with market presence.

Vendors I’ve Reviewed

• Mintigo both returns new prospects and applies scores to prospect lists provided by the client. It is currently stressing uses of predictive modeling beyond traditional lead scoring and making it easier for clients to set up new models on their own. I last reviewed them in June 2013.

• Lattice Engines runs different types of models against names provided by the client. It provides recommendations for customer treatments in addition to scores. I wrote about them in April 2013.

• Infer runs multiple models against leads provided by the client. It originally returned only lead scores, although they are now adding multiple applications that create different scores for different purposes. I wrote about them in August 2013.

• Fliptop returns scores and some summary data on names provided by the client. It stresses quick model building. I reviewed them in June 2014.

• LeadSpace scans for data on demand, rather than maintaining its own master database.  It can find new prospects in specified segments and enhance names provided by the client.  It returns individual names as well as companies. I wrote about them in June 2013.

Vendors I’ve Spoken with But Not Reviewed

• Growth Intelligence is a relatively recent UK-based startup that provides lists of companies and associated contacts that are likely to become customers.  It draws from Web information, government lists, and similarity to the client’s current customer base.

• Kemvi is just emerging from stealth and plans to launch formally late this year or early 2015. It expects to focus on finding trigger events and advising salespeople about the best ways to approach each prospect.

• 6Sense finds new prospects using behavioral data gathered from a network of "several thousand" Web publishers rather scanning public sources like others in this list. So it doesn’t quite belong here, but it’s interesting nevertheless.

• Radius finds small business prospects that resemble current customers and deploys them to Salesforce.com, along with key profile information and lead scores.


Vendors I’ve Only Seen on the Web

• Avention (formerly OneSource, now part of D&B Hoovers) scans an eclectic collection of data sources to find prospect companies based on attributes and signals. It can rank companies with scoring but the scoring formulas are built manually.

• Gagein sends alerts on trigger events in media, social or public Web sites. It can track companies named by the client or build prospects lists for client-specified segments. It’s primarily a sales tool, with other features such as social selling and apparently without any predictive modeling.

• RealSociable is another sales-oriented product that tracks social media for trigger events related to target accounts. It appears to let users decide which events are important without using predictive models.  But it seems to have some clever technology to extract the trigger events from unstructured social streams. That (presumed) semantic filtering is the only reason to include it on this list -- otherwise, the limit to social sources and lack of predictive models would rule it out.

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*and the one person who admitted to it now makes his living as an arts critic.

** Also, coffee really is for closers.