Having covering the basic definitions of intent data, together with its key uses and limitations for B2B Sales and Marketing, one thing should be clear by now: intent holds great potential — but only if used as part of a much wider data intelligence strategy.
For a start, it’s no silver bullet. You’ll never be able to know 100% what a prospect is thinking about at any given moment. In B2C, online purchases are usually made within a very short time frame, which makes it much easier for the likes of Google, Amazon et al to ascertain precise intent based on your current online activity.
For example, if you search “flights to Paris,” it’s fair to assume you’re interested in buying tickets at some point in the near future — perhaps even at that very moment.
But B2B buying decisions typically take several weeks at best, and more often drag on through months of consideration, “process” and bureaucracy. So if you’re searching for “B2B intent data providers” today, that doesn’t mean you’re even in a buying cycle at all; you could simply be trying to learn more in order to make an informed decision on whether to even bring the topic up at your next team meeting. (And that’s assuming you’re even a potential customer and not someone with an interest in the topic, but no actual buying intent.)
There’s another reason why intent data is hard to leverage — one which will sound very familiar to anyone who’s carefully observed the rise and fall of “Big Data” hype: there’s simply too much data out there to make sense of it all.
3rd party intent vendors like Bombora provide an incredibly vast array of intent signals, from “2-D Animation” to “Workforce Optimization”. This is the kind of real-time insight B2B marketers and salespeople have dreamed of — but the question is, where do you even start?
Here are three important tips for activating intent data, and practically applying it for Sales and Marketing success:
1. Be picky — but think laterally
Rather than spreading a wide net, it’s important to spend time thinking about the handful of signals which are most relevant/useful for you to monitor.
To do this effectively, you need to think a little out the box. For example, if you’re selling a sales enablement tool, you’ll obviously want to monitor “Sales Enablement” signals. But there are numerous other related topics which could equally signal buyer interest, like “Sales Acceleration” or “Sales Effectiveness,” or even “CRM Software.”
2. Context is key
Just like any other data set, context is key to getting real value from intent data.
Let’s take the use case of leveraging intent data to identify potential net-new prospects.
Say you’re seeing high interest a relevant topic; that alone isn’t enough to extrapolate any buying intent. For example, that person could be an industry analyst with no intent to buy whatsoever. They could equally be a student, or a competitor, or someone else whose interest will never translate into any kind of buying decision.
To translate intent signals into an actual indication of buyer interest, you need the context behind the interest. To do that, you need accurate, relevant underlying data.
Of course, since B2B intent data is account-focused, account-level data is particularly important to gauge initial buying intent. But you’ll obviously need equally accurate, relevant data on the individual level as well in order to identify the right people within those accounts (aka buying centers/Demand Units) for effective engagement.
3. Combine Intent with Predictive and Persona Modeling
Again, just like any other type of data, intent can only provide consistent, scalable value with the right technology to process it into actionable intelligence.
For example, many of our customers use their Intent scores alongside their custom Predictive and Persona scores for a full view of their audiences, taking into account both the “who” (lead & account-level modeling) with the “when” (intent signals) to accurately predict who their best prospects are right now.
On the flip side, intelligence solutions like Predictive and Persona modeling can help weed out precisely the kinds of misleading intent signals mentioned earlier. If there’s a surge of intent from a source that’s scored low by your predictive models and/or which doesn’t fit any of your buyer personas, it’s probable they aren’t actually interested in buying anything. Of course, that’s not to say they definitely aren’t worth looking into, or that you should be automatically rejecting every such signal — but it does give you a strong level of context to start with.
Intent Data can help B2B marketers and salespeople to improve productivity and results — by reaching the right people, at the right time. Read more in our free white paper: