I was going to say Demandbase came to town last week, but in fact the San Francisco-headquartered ABM platform has had a New York office in the Chrysler building for some time now — one that’s set to outgrow its existing base. Perhaps it’s better to say that CEO Chris Golec was on the east coast, to talk about the broadening of the platform to encompass not only targeting, but engagement and conversion in the same solution — as well as the intriguing strategic of educating the market about ABM through agencies.
Agencies first. With the profile of ABM as a key B2B startegy on the rise, not only have Forrester and Gartner started to take an interest: “We’ve started to see agencies jumping in,” Golec told me. “We’ve seen Accenture and Deloitte’s forming practices” — especially as ABM has expanded beyond the high tech sector and become significant for a range of verticals. Demandbase became an official Merkle partner back in August. “Clients are looking for help [with ABM],” said Golec, “and we’re not a services business. We’ve built training and certification, but we’re not going to scale that.” Working with agencies, however, means Demandbase is front of mind when the agencies’ clients need ABM as a strategic component.
Last week, Demandbase revealed the results of a survey of 400 advertising professionals: 66% said their agencies use ABM; 70% of the remainer had plans to use it in the future; but perhaps most significantly, all those surveyed had clients who use ABM, but identified lack of education about ABM as an obstacle for those who don’t.
As for the new platform, Golec sketches five components:
- A common data model supporting audience creation
- An AI layer across all parts of the solution
- The integration of what were previously distinct Demandbase applications, joined by APIs
- Best practices to measure ROI, and
- An easier to use, self-service UI.
On the latter, “You can turn on the lights and see what’s working,” he told me. I asked whether the use of AI for ABM wasn’t limited, for many companies at least, by the relative scarcity of B2B — in comparison with B2C — data. He agreed that this could be the case, but pointed to the quantities of data which they can “harvest” for insights: three billion visits per month across the Demandbase customer network; 400 terabytes of web pages crawled; and 50 billion impressions through their advertising stream.
Even so, the Demandbase offering — which I have heard described as expensive — is primarily aimed at large, data-rich companies (over $50 million in revenue). But with 30-35,000 such companies in the U.S. alone, the advanced ABM space is just getting started.
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Since AI is in the house, let’s look at Pegasystems’ latest move to crank up the AI and robotics in customer service. The Boston BMP and customer engagement vendor announced three augmentations to Pega Customer Service on the Pega Platform, coming end of month:
- An automated AI agent to assist live agents by interpreting messages in real-time (using NLP) and suggesting best response. Machine learning, of course, means the robot agent gets better each time
- A virtual assistant for email, which triages incoming messages, opens service cases, and/or routes them to the right resource. That’s NLP again, of course
- Interactive Voice Response which replaces static option menus for calling customers with personalized choices based previous interactions, account information and context.
Our options have recently changed.
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Back in February, I described the work Infutor was doing to provide “completed identities” — the kind of aggregated physical-email-landline-mobile profiles which help make personalized marketing possible and worthwhile. Now come news of the first of a suite of consumer intelligence solutions layered over the Infutor database — this one aimed at the auto industry. Applying analytics to robust completed identities should permit identification of active auto-shoppers and propensity scoring, before they show up at a dealership. The Auto In-Market solution appends transactional data and demographics to identities, and makes predictive intelligence available for five in-market categories: new vehicles, used vehicles, auto insurance, auto finance, and parts and services.
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The advanced location data analysts at Blis are also pulling data-sets together. Today came the announcement of a series of partnerships aimed at bringing physical and digital environments even closer together for targeting and attribution purposes. The new partners are Oracle DataCloud, RSI’ Ansi (store analytics), IRI (consumer BI), and PushSpring (mobile audiences), and the aim is to integrate Blis location insights with purchase histories, store memberships, and app usage, to build a holistic view of in-store and online behavior for target audience segments.
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It’s easy to get the impression — and I have — that organizations are at very different stages of preparedness for GDPR, the data protection regulations which will impact any company handling the data of European residents, whether they’re handling it in Europe, in the U.S., or on the moon. One analytics vendor trying to smooth the path to May 2018 (GDPR D-Day) is SAS, the veteran analytics, BI, and data management company. The company recently launched the SAS Solution for Personal Data Protection, a set of tools designed to help businesses identify and extract personal data, and monitor, protect, and audit it. But of course, you already have a plan for May 2018. Don’t you?
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