What is Real Purchase Intent Data and Where Does it Come From?

Rebecca Kitchens
Rebecca Kitchens

Executive Publisher, SVP of Market Development

More than in other B2B categories, Martech and Salestech present an overwhelming number of product choices and trending buzzwords (we’re all familiar with Scott Brinker’s  landscape chart!). A big one that’s near and dear to me is the term “Intent”. Short for “behavioral data that provides substantive indication of an impending purchase”, Intent Data’s become a kind of badge that vendors have started adding to their branding. That’s why, as a B2B publisher, it makes sense for me to weigh in on where real purchase intent data comes from and how it’s made.

Better intent data comes from better content

Better purchase intent data describes data sources that are better than others at identifying actual active and capturable demand present in a market. Actual demand is very different from general interest in a topic, and that’s where many sources fall down.

For both their personal and professional needs, people are constantly searching for and reading an endless stream of material. It only takes a passing familiarity with the various B2B-relevant news cycles to recognize that most content consumption has very little to do with impending purchases. Surges on a topic at an account usually only tell you that they’re consuming news relevant to their company or their job.

These general topic surges simply mimic the popularity spikes you’d see if you analyzed content consumption trends across similar content-consumer roles industry-wide. For example, if there’s been a recent data breach, you’ll see a spike in security-related content consumption among those companies and roles that are especially sensitive to the topic. Or consider all the noise around the current pandemic and its associated business process implications. Lots of people are reading about issues related to Work-from-home.

Account-level surges do tell you what’s popular with a set of personas right now, but in most cases, they tell you relatively little about who might actually be making a purchase.  News-based surges tell us something about the mood of the market, but they actually explain very little about impending purchases except in the most macro sense. News-based surges may give you something to talk about with your target accounts or your ABM list, but they don’t tell you enough about actual buying motions to warrant aggressive prioritization.

Weak intent data is that data that reports on trends which aren’t true indications of purchase. Stronger intent data provides a better indication of purchase precisely because it is derived not from general information content but instead from the consumption of content that is created to be more supportive of an impending purchase.

So what defines better content when it comes to purchase intent?

When people shift from general information consumption to trying to solve a business problem, they start probing more deeply. They seek out materials that aren’t part of their daily feed. While they may still continue consuming news, they’re now using real work time to read materials directly related to what has for them become a critical new  “job to be done”. They need to read up on specifically that content which will help them navigate their way to the right purchase.

Clearly, for these imminent buyers, better content is content that is particularly useful to them as they move through their buyer’s journey.  They will seek out and consume material that’s far more granular than they do at other times. And when the content has been analyzed and tagged with the end-goal in mind, this consumption can generate very specific signals about buyer needs, preferences, considerations and the like. Therefore, for marketers and sellers on the opposite side of the impending transaction,  better content is content constructed and tagged to generate such clear, information-rich and actionable signals – signals that truly indicate a purchase is likely to occur — real purchase intent.

Of course most content is not like this. Instead of going deep into a topic, it’s developed to be broadly appealing. That’s because most content is written to maximize ‘eyeballs’ in support of a general media business model. Most content is used to generate traffic for advertisers rather than to identify the relatively infrequent purchases hidden within the noise of normal consumption fluctuations.

Why it’s unlikely that real purchase intent can be sourced effectively on the open market

To reiterate, the best content for Intent data creation is content created both to support purchase decisions and to render the end-to-end buying process visible to those who would leverage it to help their customers. To deliver useful insights, it must be tagged, organized and analyzed in ways that make it super actionable for marketing and sales. This is difficult to do for two reasons.

First, it requires the ability to create very specific, granular materials in significant volumes. Most organizations struggle with the process because of the focus, skills and resource it demands. Even most large media organizations have avoided focusing on this type of content because it doesn’t generate the volumes of traffic per asset that their business models require. The best content for generating intent signals therefore typically comes from highly specialized teams or as an infrequent output from generalist – compare Consumer Reports for example to a general publication like Forbes.

Will real buyers also consume content from general publications even as they pursue a buyer’s journey? Certainly they will, but the problem for marketers and sellers is that in a surge derived from general media, there’s no real way to tell who’s a real buyer from everyone else.

Media outlets that make their money primarily through advertising must necessarily maximize their overall traffic.  To do this in the era of programmatic adtech, they participate very broadly in ad networks open to all. Conversely, because their own model is so expensive, specialists like Consumer Reports and our own TechTarget sites, depend on a registration or subscription model that enables us to control access in return for value captured in other ways, such as through the intent data business.

Data suppliers who attempt to deliver real purchase intent data without owning the content face a number of insurmountable challenges.

Since they don’t make the content, they can’t design it to help buyers or to produce truly valuable signals.

Secondly, since they don’t actually possess the content, they can’t accurately analyze what it’s actually about. Nor can they tag it to make it more meaningful and actionable. Thus their ‘signals’ generally lack the precision necessary for the pin-point personalization salespeople rely on.

Isn’t AI a way to do all this automatically?

You can use predictive analytics approaches to scour your own historic successes and build a model from what your own data shows. You can then train AI to look for profiles that match your ICP. When these profiles are surging on news stories from open media sources that everyone is reading, on the surface, the patterns look like signs of a purchase — the right profiles are interested in relevant news. Right?  AI is doing what it was trained to do: to scour the web looking for activity and bring it back to sell.

The problem is that the activity which your AI can see is mostly around news consumption, not buying behavior at all. The power of AI in this case is in automating a massive scouring process. But because the content AI can scour was not created to generate real intent signals, there’s no clear signal there for the AI to find.

And even if your AI were powerful enough to aggregate signals from the very few granular decision-support pieces published to the open web, it won’t have access to the contact information you require to productively engage.

Yes AI can be trained on the search terms you have used successfully to attract customers to you. It can scour the internet looking for similar search strings and the companies researching that way. But it can’t tell you where that search led, what the content served up was actually about, or, most importantly, who is doing the searching. Because of the content-based realities underpinning the creation of real purchase intent data, AI applied independent of content control simply generates volumes of weak, un-refinable – and largely false positive — signals.

AI is very good for accomplishing analytical tasks at scale. We use it ourselves to determine what content to serve to our 20 million subscribers. But AI can only be as good as the data it ingests. So as you study potential sources of ‘Intent’, make an effort to understand what AI actually has access to.

By understanding the role of content in ‘intent’, you can make a more informed decision

For these reasons and more, when you and your team are investigating different potential sources of behavioral data to add to your stacks, going the extra step of investigating exactly where the data come from — how it was made — can be a great way to understand exactly what you’re getting and how confident you can be that it will deliver for you.

Better content, built to support the different stages of the buyer’s journey, informs your teams of how best to engage – what types of assets to offer up to help the buyers – and through which channels.

Better content addresses the different needs of the various personas in a buying group – it identifies more of the key players earlier in the process, so you spend less time trying to find the influencers and decision makers you know will be critical in moving the opportunity forward.

And most importantly, because it’s useful to the buyers when they’re doing their purchase decision making, better content inspires them to willingly register and provide permission for future outreach from you, and that makes all the difference in the actionability of this data.

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B2B publishing, b2b purchase intent data, content, decision support content, demand generation, real purchase intent data

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