How Predictive Analytics Technologies Can Transform Marketing
Account-based marketing can profit from scalable data processing and predictive analytics, says the CEO in charge of Prophyts.
One domain that's especially ripe for innovation is marketing. Innovative strategies such as account-based marketing (ABM) emphasize the coordination of sales and marketing efforts to optimize messaging, grow relationships, and target contacts with pinpoint precision.
On paper, ABM looks like a slam-dunk use case for predictive analytics. ABM is at its best when marketers are able to identify intent-to-buy as early as possible. If that sounds like a problem for predictive analytics, it's because it is. Until recently, however, ABM's enabling technology didn't exist. We didn't have the data-processing capacity to aggregate, index, and deliver massive volumes of data at scale. We likewise didn't have the predictive tools and techniques to mine this data, at scale, to identify signals that could be correlated with intent.
This is no longer the case. Not only are scalable data processing and predictive analytics technologies available, they've even been commoditized -- as services.
A good example of this phenomenon is Prophyts, a new ABM-focused service from 1105 Media. [Full disclosure: 1105 Media is parent company to both TDWI and Upside.] Prophyts is a creation of 1105 Media's own intellectual property (IP): the publishing and research firm spent 18 months developing the IP that underpins it, according to president and CEO Rajeev Kapur.
"What the team [that developed Prophyts] has been able to do is to take a firehose of data and run it through our algorithm. We've been able to measure the buying behaviors of many companies over the last few years and get a pattern of what that [intent-to-buy] data looks like. Ninety-five percent of the companies that we measure had the same kind of buyer journey from awareness to consideration to decision," he explains.
For Prophyts to deliver valuable insights, however, Kapur and 1105 Media needed something other than an algorithm. In the first case, its algorithms would need training data on a massive scale. The data scientists working on Prophyts needed to feed their predictive models input data -- e.g., page requests and other clickstream information -- to "train" them to reliably detect signals that could be correlated with intent to buy. In the second case, once it was up and running, Prophyts would need a source for fresh B2B data. If its enabling algorithm were to be useful, it would require the freshest, most accurate data possible. The earlier that marketers can identify likely intent-to-buy, the more time they have to optimize their ABM strategies.
Collecting, processing, and managing this data wasn't something Kapur felt the Prophyts team could or should do on its own. Instead, they enlisted the services of Bombora, a company that specializes in collecting, processing, and syndicating intent data for the B2B market.
"Having actually come from the [personal computer] world, I look at this as a kind of 'Intel-inside' relationship. Dell and these other companies weren't going to build their own processors when they could just resell processors from Intel. We weren't going to build our own network when we had an option like Bombora," Kapur explains.
For quite some time, argues Bombora CEO Erik Matlick, "there wasn't a scalable source of third-party B2B [intent] data. There were just all of these data silos at companies that are basically point solutions, selling contacts or selling media. We provide [a service] that targets the data and makes it relevant so that companies can build really innovative products such as Prophyts around very large data sets." In point of fact, Matlick argues, companies that aren't Google, Facebook, Netflix, or others wouldn't have the resources to build a similar network.
Once you have a prediction, what do you do with it? That's the other part of Prophyts' value proposition. ABM is a strategy for early engagement and highly focused interaction with prospective buyers. In the first case, Kapur argues, customers who subscribe to Prophyts don't get insights. They get predicted in-market companies and contacts -- high-quality contacts that represent decision makers, stakeholders, and influencers who are all part of the buying process.
"Effectively, subscribers are going to get net-new, in-market contacts [to specific people at] specific companies every week. We're going to give them companies that are in-market that are predicted by our algorithm to have a high likelihood to buy [a product or service the company is selling]," he explains.
The accuracy of the in-market companies and contacts that Prophyts generates is significantly greater than that of traditional sales and marketing demand-gen techniques, Kapur stresses. "From [marketing's] perspective, these [companies and contacts] are gold. Right now, the [strength of the] prediction is at 25 percent. If we get them early in the buyer journey, one in four companies that we're providing to the customer is going to buy a product in their category," he says, noting that the duration of the sales cycle (or buyer journey) varies by technology category.
"What we're providing is a list of companies that are predicted to buy. That [prediction-to-buy] means that the algorithm is calculating that there's a one-in-four chance they're going to buy in their category. Typical content-type converts at much lower levels," he notes.
Prophyts wouldn't be an ABM solution if it were simply a means for detecting and correlating intent-to-buy with specific companies. With each prediction, subscribers also receive contact information about key influencers or stakeholders who have responsibility for purchasing decisions. Prophyts also offers one-stop custom content services -- whitepapers, ebooks, case studies, and other collateral -- that subscribers can use to augment (and more precisely pinpoint) their marketing efforts.
"Once we provide that information, we don't walk away. We provide all of the contacts that go with that, too. Hypothetically, let's say that the algorithm is predicting that Disney is going to buy, for example, enterprise backup software. We go to our contact database, which is one of the largest in the B2B industry, and we provide that contact information to the customer," he explains. "We then go to the customer and say, 'If you want to target these folks, you can target through our own ad network or you can work with us and we can help you develop targeted marketing services to reach those contacts."
Prophyts, which launched in June, is a focus of ongoing development and improvement, Kapur stresses. "A year from now, I think Prophyts will be viewed as one of the dominant platforms to help marketers and sales teams drive more revenue in their business. It's the first [service] to make account-based marketing a realistic possibility for companies of all sizes," he argues.
"The algorithm is going to continue to evolve and get better, in part because we keep ingesting more and more data. The more data we have, the smarter the algorithm becomes. The value Prophyts is providing is that it's basically a one-stop shop for account-based marketing, and the cost to get into this is really attractive."
Bombora's Matlick sees Prophyts as a good example of how predictive and prescriptive technologies will transform B2B sales and marketing. "We're in a fortunate-enough position to see sort of what the whole marketing and advertising world is doing in the B2B space. Rajeev and his team realized early on what a lot of publishers still don't realize: with account-based marketing and with enough data, you can for the first time target not only accounts, but target accounts that are actively in search of your products," he concludes.
"That was never done before because, really, how could it [have been]? How would you as a vendor know that someone was interested in you? This is a new era for sales and marketing."
[Editor's note: For more about how Prophyts uses predictive analytics, see For Predictive Analytics, Earlier is Better.]