With Predictive Analytics, Earlier is Better
The sooner your predictive analytics can predict an action, the more effective your marketing efforts can be.
The most prominent use cases for predictive analytics have typically focused on identifying the imminent likelihood of some event, be it fraud, intent-to-buy, or customer churn.
Even so, predictive analytics is an extremely valuable tool for detecting the earliest signals that can be correlated with the coming of these and other events. Think of what you could do if you could identify an instance of likely customer churn or intent-to-buy days, weeks, or even months before it happened. Early warning of this kind is a powerful advantage.
It's a no less powerful advantage in the world of business-to-business (B2B) sales and marketing. B2B marketing strategies such as account-based marketing (ABM) are especially dependent on early intelligence about a customer's likely intent-to-buy. The earlier you can get this intelligence, the better according to the logic of ABM, which focuses on custom-tailoring sales and marketing efforts to appeal to specific stakeholders with likely B2B buyers.
The Earlier, The Better
Early warning of a customer's intent-to-buy can make the difference between closing the sale or losing out. Just as likely, it can result in a diminished (or damaged) relationship with an existing customer or a missed opportunity to establish a connection with a new account.
Early recognition of a buyer's long-term intent-to-buy is much more valuable to a B2B seller than detection of imminent intent-to-buy, argues Rajeev Kapur, president and CEO of 1105 Media, a publishing and research company that markets Prophyts, an ABM-oriented predictive analytics service. [Full disclosure: 1105 Media is parent company to both TDWI and Upside.]
"If you go to [a marketing team] and say, "This company is going to buy in the next 20 to 30 days," many times that's way too late. Is there value in that? Certainly, but for account-based marketing, it's a matter of really focusing on the bigger fish, which is the predictive nature of the algorithm and what you can do early in the buying stage. That's the true value," Kapur says.
He adds: "A lot of times, [imminent detection] might be too late for a B2B salesperson."
In fact, detection of imminent intent-to-buy is the analytical equivalent of low-hanging fruit. The events or patterns that signal imminent intent-to-buy are easier to detect, if only because they tend to occur in close (temporal) proximity to one another. Early detection, by contrast, poses significant technological and logistical challenges because the predictive algorithm must be able to correlate events that are spread out over time -- over days, weeks, or even months. The ability to do this assumes a capacity to both collect and process massive amounts of data.
Detecting intent-to-buy early in the buying cycle is much harder than going after the low-hanging fruit, Kapur points out, but it is a prerequisite for successfully executing on an ABM strategy.
"If you're a [B2B seller], would you rather focus your limited resources on someone who's probably already committed [to a seller] or someone who's early in the process? Someone [with] whom you can pinpoint your sales and marketing activities, focusing on growing the relationship. I think the bigger ROI is going to be early in the buying cycle."
The Promise of ABM
Account-based marketing is a tool for combating two frequent problems in B2B sales engagements.
In the first case, sales cycles tend to vary from product to product. A sales cycle for a business intelligence (BI) or data integration product could span three to six months. The longer a seller has to cultivate a relationship with the buyer, the better positioned it will be to close the sale.
In the second case, B2B sales are likewise extraordinarily sensitive to poaching by competitors. A seller could invest several months in building a relationship with a would-be buyer only to lose that sale to a competitor claiming to offer the same features at a lower price. This is where ABM's emphasis on cultivating focused, targeted relationships with the individuals who are most likely to make buying decisions for a company is critical.
"What marketing now has is the ability to go to the [chief revenue officer] and say, 'We just got a whole bunch of accounts from Prophyts that are predicted to buy. These are people who are in-market. Let's focus on those folks and target them properly," he explains.
"Where marketing has an important role to play here is they have to be careful when they give those companies over to sales. Marketing has to do a much better job of nurturing those [relationships]. If marketing takes a pinpoint approach to going after these companies, building up that brand awareness [with influential stakeholders] in those companies, now sales can engage. That's going to drive the close ratio even higher."
The success of a solution such as Prophyts is a function of both the volume and the quality of the data that's fed to their predictive algorithms. To this end, Prophyts sources its data from data providers including B2B intent data provider Bombora, a company that specializes in collecting, processing, and syndicating data for the B2B market. Today, Prophyts is working against a data set of billions of B2B page views. This data set will continue to grow, Kapur points out; as it does, the accuracy of the predictions Prophyts makes will improve, too. In the same way, and for the same reasons, the types of predictive insights available to Prophyts subscribers will also grow.
"Right now, we have billions of page views of data, and I think that's just going to keep increasing. We can keep enhancing the algorithm over time so that it just keeps getting better and better, and the predictions it's able to make keep getting better and better," he concludes.
[Editor's note: For more about how Prophyts uses predictive analytics, see How Predictive Analytics Can Transform Marketing.]