Mintigo InterestBase Harvests Web and Social Data for Marketing and Sales

Every marketer recognizes that the Web and social media could be rich sources of information about customers and prospects. But harvesting that data has been frustratingly difficult.  Doing it yourself  takes multiple tools to gather different kinds of information, and then patching the result together into personal profiles. Most tools do little more than keyword searches, which only capture a fraction of the potential information and only cover keywords that marketers know in advance are important.

More advanced technology does exist. Semantic engines can extract information such as executive changes and product announcements from press releases and social media profiles. Sentiment analysis can (with limited reliability) detect the attitudes that individuals express. Identity aggregators can link email, social media, and other addresses for the same individual. Predictive models can show how different attributes correlate with targeted behaviors such as purchasing a product.

Few marketers have the skill or resources to pull all these tools together for themselves. Vendors are another matter: there’s inherent scale economy to scanning the Web and social media once and applying the results to many different clients. I recently wrote about Lattice Engines,  which has assembled these pieces to create prospect lists. Infer starts with your own customer data, enhances it with information mined from the Web, and generates predictive scores.

Mintigo has also been mining Web and social data to build prospect lists, starting in 2011. This week it announced a new platform, InterestBase, that gives clients an interface to define target groups, analyze group members’ interests, push prospect lists to marketing automation and CRM systems, and enhance individual lead records.

The foundation of InterestBase is a central repository of 30 million names and 3 million companies (and growing), built by scanning Web sites and social media for job postings, product and technology names, group memberships, accounts followed, hashtags, Javascript calls, and other information. The system uses this data to assign individuals and companies such attributes as job title, company size, technologies used, hiring plans, and interest scores for products and topics. Marketers can use titles and other attributes to define their own target groups, called personas.

Lists containing members of a persona can be assigned to marketing campaigns and sent to external marketing automation or CRM systems for execution. Connectors are currently available for Marketo and Salesforce.com, with an Eloqua connector due soon. A campaign list can include the entire persona universe or a quantity specified by the user. Once the campaign is run, responders are loaded back into Mintigo and the system will identify attributes that distinguish them from non-responders.

Clients can also upload their own lists of customers or campaign respondents.  Mintigo will determine which attributes correlate with group membership, display the most important ones in reports, and use the findings in predictive models that score the entire database on likelihood of purchase or response. Clients can also upload other lists for Mintigo to enhance with its own information. This enhanced data can be used in lead scoring or to help guide salespeople.  External systems like Web sites can also accessed the data in real time via API calls.

The features are interesting, but what really matters about Mintigo is the data: fresh, powerful, and unique information about a large share of the business universe. Richer information lets Mintigo clients identify new prospects they’d otherwise miss, distinguish strong prospects from weak ones, and target messages to each prospect’s interests. The result is substantially more effective marketing and sales operations, finally letting marketers use data the Web has so tantalizingly exposed.

In case you're wondering, I do consider Mintigo a Customer Data Platform: it assembles a persistent customer database, uses predictive models to classify the members, and makes the data available to external systems for marketing execution.  

Pricing for InterestBase is based on the number of names in the client’s prospect pool, based on automated analysis of their actual customers. An average client starts around $3,000 per month.

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How to Get the Most from Social and Behavioral Data: Webinar, March 19

Raab Associates has been gradually relocating from New York to Pennsylvania over the past two weeks. I won’t subject you to a post like “what B2B marketers can learn from moving companies”, which is one of my least favorite ploys for repackaging old advice in a “fun” format. In fact, I only mention it to explain why I haven’t been writing with my usual frequency and why this post is relatively brief.

Still, I did want to let you know that I’ll be giving a Webinar next Tuesday, March 19 at 2 p.m. Eastern on “Making the Most of Social and Behavioral Data for B2B Marketing”. It’s sponsored by Mintigo, a hard-to-classify vendor with technology to scan the Web for prospects and predict their interests. You can register here.

The chaos of moving has slowed down my slide preparations, which are made even harder by the fact that our 100-year-old house has such uneven floors that my chair keeps rolling away frrrrom mmmy desssssk. But I did finish my research before they packed up our computers, so the content itself will be solid. Without giving away all the goodies, some of the more interesting things we’ll cover include:

- where social and behavioral data are used in the marketing process. This actually matters quite a bit: there are some things that social and behavioral sources can provide, and others they can’t. You have to be sure you’re using them correctly and supplementing with other sources where appropriate.

- what to do once you capture the data. Traditional marketing data was pretty easy to manage because there wasn’t that much of it.  With social and behavioral, you’re surfing a flood. We’ll talk about how to keep your head above water.

- how to deal with the ephemeral nature of much social and behavioral data: without belaboring the flood analogy, conditions change rapidly and marketers must react quickly. We'll discuss what this means and how to do it.

- which data elements are available from different sources.  It isn’t news that each social network works differently, but it’s still eye-opening to see just how distinct they are. We'll talk about which network is best for different purposes.

- what all this looks like from a sales person’s viewpoint. Most marketers will try to swim in this data despite the rough surf. Sales people are more likely to leave the water and have a hot dog. We’ll talk about ways to keep them immersed.

I’m more curious than anyone to see my final slides, but have no doubt that the session will be useful and interesting. It’s an important topic: join me if you can.

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