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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ReachForce Buys SetLogik: One-Stop-Shopping for B2B Marketing Data Plus Database

B2B marketing data vendor ReachForce today announced its purchase  of SetLogik, which provides technology to build cloud-based marketing databases and do predictive modeling against them. (See my post from last October for more on SetLogik.)

There’s an obvious peanut butter-meets-jelly type of logic to this match. Reachforce’s core business is assembling data on marketing prospects, which it then sells for as many uses as possible: appending to Web leads, enhancing existing databases, and buying as lists. The SetLogik acquisition takes this a step further by letting them build databases to hold their data, thereby expanding the market beyond people with a database already in place. Conversely, having a readily-available data source encourages marketers to build their own database. SetLogik’s predictive modeling features make it even easier for marketers to get a return on their investment once the database is in place. Everybody wins!

The two products will be combined in what ReachForce calls the “Connected Marketing Data Hub”. The name is frightfully generic, but the key points are:

• cloud-based system, making it easy to deploy
• comprehensive customer view including data from marketing automation, CRM, transaction systems, and ReachForce’s own sources
• continuously updated and cleansed
• connectors available for Salesforce.com, Eloqua, and Marketo  

In other words, the ReachForce solution supplements rather than replaces your marketing automation or CRM database. As I wrote in my earlier SetLogik review, one particularly attractive result is the ability to match sales revenues with marketing leads, always a challenge in measuring the value of marketing programs.

ReachForce has just begun to offer the combined system, which is currently deployed at one pilot client. Pricing is based on data volume, whether the client wants a one-time append or continuous cleaning, and on the data sources included. Minimum is $625 per month for continuous cleaning on 50,000 records.

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Marketing Automation Beer Goggles: What I Think I Learned at Dreamforce

I’m writing this on my way home from Dreamforce, the Salesforce.com user conference that has become the primary industry gathering for marketing automation vendors. With a reported 90,000 attendees (I didn't count them personally), the show is fragmented into many different experiences. My own experience was mostly talking to marketing technology vendors in the exhibit hall, private meetings, and maybe a party or two. I did attend the main keynote and the “marketing cloud” announcement, but neither contained  major product news and the basic story – that social networks change everything – was true but far from novel.

So what did I learn? On reflection, there were two themes that hadn’t expected when I arrived.

The first was data. I generally think of marketing systems as relying primarily on data from the company’s own marketing, sales, and operational systems. But the exhibit hall was filled with vendors offering information – mostly from Web crawling or social media – to supplement the company’s internal resources. Of course, this isn’t new but it seems that external sources are becoming increasingly important. The main reason is so much valuable public information is now available. A lesser factor may be that there’s less internal information, at least for sales and marketing, because so many prospects engage indirectly and anonymously until deep in the buying process.

But there’s more to data than the data itself. The theme includes easier connectivity to external data, via standard connectors in general and the Salesforce.com AppExchange in particular. A closely related trend is real-time, on-demand access to the external data: say, when a salesperson views a lead record or a lead is first added to marketing automation. This requires immediate matching to find the right person in the supplier’s database, and, sure enough, matching was another popular technology on the show floor. I also saw broader use of Hadoop to handle all this new data: as you probably know, Hadoop effectively handles large volumes of unstructured and semi-structured data, so it’s a key enabling technology for data expansion. A final component is continued growth in the reporting, analytics, and predictive modeling systems that make productive use of the newly-available data.

Some products combine all these attributes, others offer a few, and some just one. Obviously a single integrated solution is easiest for the buyer, but as Scott Brinker recently pointed out in an insightful blog post, platforms like Salesforce.com may actually make it practical for marketers to mix and match individual products without the technical pain traditionally associated with integration. It therefore makes sense to view the data-related systems as a cluster of capabilities that will develop as parts of single ecosystem, collectively raising the utility and importance of external data to marketers.

The second theme, considerably less grand, was lead scoring. I suppose this is really just a subset of the analytics component of the data theme, but I saw enough new lead scoring features from enough different vendors to treat it separately. In particular, predictive modeling vendor KXEN announced a free, cloud-based service to automatically score a new Salesforce.com lead’s likelihood of converting into a contact. (If you’re not familiar with Salesforce.com terminology: contacts are linked to an account, while leads are not. The conversion usually indicates the salesperson has deemed the person a valid prospect and is thus a critical stage in most sales processes.)

The KXEN service requires absolutely no set-up; users just install it from the AppExchange. KXEN then reads the data, builds a predictive model based on past results, and returns the scores on current leads. From a technical standpoint, the modeling is nothing new, and indeed the people I met at the KXEN booth seemed to feel the product was barely worth discussing. But I’ve long felt that an automated, predictive-model-based scoring service was a major business opportunity because it would replace the time-consuming, complicated, and surely suboptimal lead scoring models that most companies now build by hand, usually with little basis in real data. Of course, there are plenty of other predictive modeling systems available for marketers, but I’m excited because I don’t think anyone else has made model-based lead scoring as simple as the KXEN offering. Maybe I need to get out more.

Speaking of which, I met SetLogik at a loud party after several glasses of wine, so I may have been wearing the marketing technology equivalent of beer goggles. But if I understood correctly, it tackles the really hard part of revenue attribution by using advanced matching technologies to connect the right leads and contacts to sales (reflected in closed opportunities in Salesforce.com). Once you’ve done that, determining which marketing touches influenced those people is relatively easy.  It’s a unique solution to a huge industry problem. Come to think of it, correct linkages are also critical for building effective lead scoring models, which it turns out that SetLogik also does. (I'll admit it: I Googled them the next day.) So they're part of that theme as well.

As I mentioned earlier, data and lead scoring were themes that emerged for me during the conference. I did have some other themes in mind when I started, which are also worth sharing. I’ll do that another day.

Finally, it’s worth noting that the conference itself was tremendously well run. It sometimes felt that one-third of those 90,000 people were Salesforce.com employees hired to stand around and answer questions. Where they found so many cheerful people outside of the Midwest I’ll never know. Congratulations and thanks to the Salesforce.com team that made it happen.

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