Predictive analytics in ABM won't always deliver perfect results but the clues are often strewn around and, taken together, they act as a strong indicator of intent.
Predictive analytics is one of the great hopes of modern IT. CEOs have always wanted their CIOs to tell them what’s coming next, whether that’s an emerging, game-changing product, a potential block to business, a risk factor or an opportunity that’s ripe to be exploited. In Account Based Marketing (ABM), predictive analytics also has a big (and still growing) role to play.
“Predictive to my mind is really simple,” says Agent3’s Dan Sands. “It’s looking at previous data to call what’s coming next and it’s a way to say: you should sell this product to this segment in this way and not go near other routes.”
Predictive analytics risks getting a bad reputation today. Every time a Big Data project tries and fails to predict the outcome of a sports contest or political poll, the luster of data-driven analytics is tarnished. That’s because there are so many factors and moving parts that it’s tough to call a football match, presidential race, national referendum or general election. Predictive analytics in ABM won’t always deliver perfect results but the clues are often strewn around and, taken together, they act as strong indicators of intent.
Predictive analytics can come across as an esoteric and quasi-magical solution in some marketers’ hands, with promises sometimes outstripping reality. But Agent3’s Sands takes a pragmatic view, saying that you can get a strong idea of what’s coming down the line if you simply correlate activities such as what content decision-makers and influencers are consuming and creating, what they are saying in public statements about corporate strategic direction, combined with a contract up for renewal and who they are recruiting. “And if you can get more data later it all adds up, and you can have real analytics-driven decisions,” he adds. At network services giant Tata Communications, chief marketing and innovation officer Julie Woods-Moss uses ABM in association with IBM’s Watson machine learning to weigh profiles of decision makers and influencers in key accounts.
“We’ve significantly ramped up and I’ve put in one of my best young leaders to drive this,” she says. “We’re looking to deliver ABM to our top 30 accounts and we have built up an analyst community, but when we apply Watson bots to that process of analyzing personas, that takes us from 14 hours of analytical time to 30 minutes.
“You can call it artificial intelligence or you can call it machine learning but we’re finding real value in using machine learning to tune in to how certain parts of our industry are being talked about. In unified communications the conversation moved from ‘WebRTC APIs’ to ‘UC as a service in the cloud’, and you want to be in the right place in the mall and having the right display so knowing the language that’s being used really helps. If you’re not hitting the momentum in these ways then you are highly disadvantaged.”
Using that combination of ABM and AI, Tata can see which vendors are winning mindshare or social sharing, Woods-Moss says.“You can see how much mindshare Cisco has versus Microsoft and then tilt by geography and you can use analytics to approach key people in the right way. That could be as simple as saying there’s a high likelihood this person will be receptive to breakthrough ideas even if you don’t know them very well, versus people who would want specific endorsements.”
For Tata Communications, that combination is creating a double opportunity to see what’s going on today… and what will happen in the future.
Click here to download the full ABM Insight Report and learn about more ABM best practices across the following fundamental areas:
- Selecting the right accounts
- Identifying the right channels of engagement throughout the customer lifecycle
- Targeting the right decision-makers
- Aligning sales and marketing
- Proving the value of ABM
- Managing change to ensure ABM success