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.

6Sense Finds B2B Prospects Using Web Site Activities

I mentioned 6Sense briefly in a recent post about vendors who help companies find prospects on the Web. Since then, I’ve had a more detailed briefing, which clarified that their scope extends well beyond prospect lists to predictive models applied across all stages of the purchase cycle. We also clarified that users can extract company-level profiles including attributes (industry, revenue, etc.) and key activities (Web site visits, topics researched) and scores at both company and individual levels.

The extraction features are important – at least to me – because they determine whether 6Sense qualifies as a “customer data platform” (CDP), a type of system I see as fundamental for future marketing. As a quick refresher, CDP is defined as “a marketer-controlled system that supports external marketing execution based on persistent, cross-channel customer data.” The part about “supports external marketing execution” is where data extraction comes in: it means that external systems can access data within the CDP for their own use. 6Sense wouldn't be a CDP if it merely displayed its data on a CRM screen without letting the CRM system import it.  If 6Sense exposed model scores but no other data, it would qualify as a CDP by the thinnest margin possible.

Of course, there are more important things about 6Sense than whether I consider it a CDP. Starting at the beginning, the system imports a list of each client’s customers and sales opportunities from CRM and marketing automation systems. Standard integrations are available for Salesforce.com, Oracle Eloqua and Marketo.  APIs can load data from other sources, potentially including other CRM marketing automation products, Web logs and tags, order processing, bookings, call centers, media impressions, and pretty much anything else.

The system standardizes and deduplicates this data at the individual and company levels. It then matches against company profiles that 6Sense itself has gathered from the usual Web sources – public social media, Web sites, job boards, directories, etc. – and from a network of third-party Web sites. The Web site network is unusual if not unique among B2B data providers; the most similar offerings I can think of are audience profiles from B2C site networks, from owners of large B2B sites, and based on other B2B activity such as email response. The advantage of Web site activity is it finds companies early in the buying cycle, when they are most open to considering new vendors. The system can map known individuals to individuals on partner Web sites, using hashing techniques to avoid passing personally identifiable information.  .

The result of all this is a database with deep company and individual profiles including both attributes and activities. 6Sense uses this to build company and individual-level predictive models.  Company models score each company’s likelihood to buy from the client.  Individual models predict the individual’s likelihood to be the best sales contact. Models are built by 6Sense staff using automated techniques and take about three weeks to complete.

The system can also estimate what product each company is most likely to purchase, when it will buy, and what stage it has reached in the buying process. Stages are defined in consultation with the client. Assignment rules might use purchase likelihood or a predictive model trained against a sample of companies in each buying stage.

Outputs from 6Sense can include lists of likely new prospect companies (not in the client’s existing database), contacts at those companies, current prospects organized by purchase stage and ranked by purchase likelihood, current contacts within each company, and key indicators that drive each company’s score. The key indicators can be very specific, such as searches for competitors’ names, visits to product detail pages, or activity by known leads.

Users can define segments based on these or other attributes and export their related data to CRM, marketing automation, ad targeting, or Web personalization systems via file transfers or API calls. 6Sense can also display the information on screen to help guide sales conversations and is now testing an extension to recommend specific talking points.  

Pricing for 6Sense starts at more than $100,000 and is based on factors including the number of models created and volume of new net contacts provided.  The company was founded in 2013 and released early versions of its product that same year. Formal release was in May 2014. It has ten current customers and more in the pipeline.

Fliptop: A Customer Data Platform for Predictive Lead Scoring, Pure and Simple

It’s been a while since I wrote about Customer Data Platforms, but only because I’ve been distracted by other topics. The CDP industry has been moving along nicely without my attention: new CDPs keep emerging and the existing vendors are growing.

Fliptop wasn’t on my original list of CDPs, having launched its relevant product just after the initial CDP report was published. But it fits perfectly into the “data enhancement” category, joining Infer, Lattice Engines, Mintigo, Growth Intelligence (which I’ve also yet to review) and ReachForce. Like all the others except ReachForce, the company builds a master database of information about businesses and individuals by scanning the social networks, company Web pages, job sites, paid search spend, search engine page rank, and other sources. When it gets a new client, it loads that company’s own customer list and sales from its CRM system, finds those companies and individuals in the Fliptop database, enhances their records with Fliptop data, and uses the combined information to build a predictive model that identifies the likelihood of someone making a purchase. This model can score new leads and classify existing opportunities in the sales pipeline.

So what makes Fliptop different from its competitors? The one objective distinction is that Fliptop is publicly listed on the Salesforce.com App Exchange, meaning it has passed the Salesforce.com security reviews. Not surprisingly, the company’s Salesforce connector is very efficient, automatically pulling down leads, contacts, accounts, and opportunities through the Salesforce API and feeding them into the modeling system. New clients who import only Salesforce data can have a model ready within 24 hours, which is faster than most competitors. But data from other sources may require custom connectors, slowing the process.  Fliptop is also able to model quickly because it defaults to predicting revenue: in other systems, part of the set-up time is devoted to deciding what to model against.

Once the model is built, Fliptop scores the client’s entire database and assigns contacts, accounts, and opportunities into classes based on expected results. A typical scheme would create A, B, C, and D lead classes, where A leads are best. Reports show the percentage of records in each group and the expected win rate, which in turn relates to expected revenue. A typical result might find that the top 10% of contacts account for 40% of the expected revenue or that the top 40% of contacts account for 95% of the revenue. Clients can adjust the breakpoints to create custom performance ranges. Reports also show which categories of data are contributing the most to the scoring models: this is more information than some systems provide and is presented quite understandably.  (Incidentally, Fliptop reports it has generally found that "fit" data, such as company size and industry, is more powerful than behavioral data such as email clicks and content downloads.)

Fliptop scores are loaded into a CRM or marketing automation system where they can be used to prioritize sales efforts and guide campaign segmentation. There are existing connectors for Salesforce.com, Marketo, and Eloqua and it’s fairly easy to connect with others. New leads can be scored in under one minute or in a few seconds if the system is directly connected to a lead capture form. Clients can build separate models for different products or segments and receive a score for each model. The system automatically checks for new sales results at regular intervals and adjusts the models when needed.

At present, Fliptop only sends scores to other systems. (Infer takes a similar approach.) The next release of its Salesforce.com integration is expected to add top positive and negative factors on individual records.  The company is considering future applications including campaign optimization, pipeline forecasting, and account-level targeting. But it does not plan to match competitors who offer treatment recommendations, sell lists of new prospects, or provide their own behavior tracking pixels.

Pricing for Fliptop is based on data volume and starts at $2,500 per month. The company offers a free 30 day trial – unusual in this segment and possible because set-up is so automated. After the trial, clients are required to sign a one-year contract. The system currently has about three dozen paying clients and a larger number of active trials.

Bottom line: Fliptop does a very good job with predictive lead scoring. Marketers looking for a broader range of applications may find other CDPs are a better fit.

Infer Keeps It Simple: B2B Lead Scores and Nothing Else

I’ve nearly finished gathering information from vendors for my new study on Customer Data Platform systems and have started to look for patterns in the results. One thing that has become clear is that the CDP vendors fall into several groups of systems that are similar to each other but quite different from the rest. This makes sense: most of the existing CDP systems were built to solve specific problems , not as general-purpose data platforms. Features will probably converge as vendors extend their products to attract more clients. But right now the groups are quite distinct.

One of these categories is systems for B2B lead scoring. I found three CDPs in this group: Lattice Engines (which I reviewed in April), Mintigo (reviewed in June), and Infer, which I'm reviewing right now.

Like the others, Infer builds a proprietary database of pretty much every company on the Internet by scanning Web sites, blogs, social media, government records, and other sources for company information and relevant events.  It then imports CRM and marketing automation data from its clients' systems, enhances the imported records with information from its big proprietary database, and builds predictive models that score companies and individuals on their likely win rate, conversion rate, deal size, and lifetime revenue.

The models are applied to new records as they enter a client’s system, creating scores that are returned to marketing automation and CRM to use as those systems see fit. The most typical application is deciding which leads should go to sales, be further nurtured by marketing automation,  or discarded entirely. But Infer customers also use the scores to prioritize leads for salespeople within CRM, to measure the quality of leads produced by a marketing program, assess salesperson performance based on the quality of leads they received, and even adjust paid search campaigns based on the quality of leads generated by each source and keyword.

Infer differs from its competitors in many subtle ways: the scope of its data sources, its matching processes to assemble company and individual data, the exact types of scores it produces, its modeling techniques, and reporting.  It also differs in one very obvious way: it returns only scores, while competitors return both scores and enhanced profiles on individual prospects.  Infer gathers the individual detail needed for such profiles, but has decided so far not to make them available. Its reasoning is that scores provide the major value from its system and profiles would detract from them – perhaps because sales people might ignore them scores in favor of profile data. Focusing on scores alone also makes Infer simpler to set up, operate, and understand.

Infer might be right, but it’s hard to imagine they'll will stick with this position once they start selling directly against competitors that offer scores plus profiles.  They will surely lose many deals for that reason alone.  On the other hand, Infer’s initial clients have been companies where free trials versions generate huge lead volumes, including Box, Tableau, NitroPDF, Zendesk, Jive and Yammer. Scores that accurately filter non-productive leads are more important to those companies than individual lead profiles.  Perhaps there are enough such firms for Infer to succeed by selling only to them.

Whether or not Infer expands its outputs, it faces another challenge: convincing buyers that its scores and data are better than its competitors. This might well be true: based on the information I’ve gathered, Infer seems to have a richer set of data sources and more sophisticated identity matching than at least some competitors. But my impressions may be wrong, and most buyers will won’t dig deeply enough to form an opinion.  Instead, their eyes will glaze over when the vendors start to get into the details, and they’ll simply assume that everybody’s data, matching, and modeling are roughly equivalent.

The only real way to measure relative quality is through competitive testing of which scores work better.  Each buyer needs to run her own tests since results may vary from business to business. How many buyers will take the time to do this, and which vendors will agree to cooperate, is a very open question.

That said, I did speak with some current Infer users, who were quite delighted with how easy it had been to deploy the system and with results to date. This is hardly a random sample – these were pioneer users (the system was only launched about a year ago) and hand-picked by the vendor. But their experience does confirm that performance is solid.

Infer pricing is based on the number of records processed and connected systems.  The vendor doesn’t reveal the actual rates but did say it is looking at options to make the system more affordable for smaller clients.