Tl;dr - Early intent data adopters were in industries that are well funded, invest in marketing and have rigorously executed marketing and sales operations. That doesn't mean it isn't appropriate for other industries. In fact it can work in a wide variety of technical and industrial fields too. Here's an overview.
"Our Industry is Different. Will Intent Data Work for Us?"
Digital marketing and sales approaches prompt this question frequently. While most companies agree that a website is required, there's a huge spectrum of philosophy on how much information to share, how dynamic the site should be, and even the fundamental goals of the site.
Similarly, while some companies use a very crude CRM system (e.g. contacts in individual Outlook files) others have very robust data systems including marketing automation, sales force automation and a customer data platform - even with built-in AI to improve customer experiences.
Each company, like its founders, owners, and executives, is different, therefore it's risky to generalize too much. Working with a wide range of companies of different sizes and from different industries gives us some perspective though. We generally observe that companies from more traditional industries (e.g. industrial manufacturing vs. SaaS technology) are more skeptical of the value of intent data. Generally, they also invest less in marketing (although their margin structure is very different) and are slower to adopt technology.
But is that skepticism warranted? How much is mindset? And how much reality?
And are there industries for which intent data is especially well or poorly suited?
Let's Focus on B2B Intent
First, let's scope this conversation to B2B industries. There are various B2C intent products available, but many of the major intent data products folks reading this article will consider are focused on B2B applications.
Within the B2B space, there are some industry considerations that impact the potential effectiveness of intent data.
- First, longer buying journeys offer more opportunity for observation of relevant intent data signals, while very shot sell cycles often occur too quickly for meaningful signal accumulation.
- Second, the transaction value or ACV needs to be significant enough to merit investment in the data as well as the resources for activation and orchestration. Generally, we find that >$5,000 is appropriate.
- Third, increasing the size of the target company and complexity of the purchase (size of the buying team) create more vectors for applying data insights. Businesses that target very small companies where one person wearing many hats will make many decisions single-handedly and often careening from one priority to another may find less value in the data.
Beyond these high-level considerations, there are details to consider with some possible industry-specific implications.
Topics vs. Key Terms
Different third-party intent data collection methods deliver different degrees of granular signal insight. Those insights can contribute to an understanding of how strongly a signal may be indicative of intent and how effectively it covers different industries. The question of category topics vs. specific key terms is one example.
The publishing coop model of intent data collection typically relies on topic categories aggregated from a small universe of collaborating publishers. While there are thousands of topics, they tend to focus on common information technology and professional services areas and overlook important industrial niches.
Further, topics also necessarily aggregate a wide variety of signals under an umbrella without differentiation. This means the data customer has no insight into the relative importance of various signals. Some might be very powerful and others irrelevant, but without detailed insight there's no way to know. The common workaround is to build a matrix of topics and to look for intersections that may be more indicative of a project. Nevertheless, it's inherently vague, and for many industries, it's simply not specific enough. (More on topics vs. key terms here.)
Data Propriety and Privacy
Another topic that impacts different industries (and companies) differently has to do with risk tolerance and privacy considerations. Intent data is often seen as problematic from a privacy perspective (until companies are clear on specifically how it's collected), and different industries will weigh that differently.
Some data solutions use their inherent inability to identify individuals as the solution to the privacy issue. In other words since they can't identify individual intent signals, they suggest that the account level data is naturally innocuous from a privacy perspective. Nevertheless it's often based on observations which a user (e.g. a subscriber to a site which is a member of a publishing platform) might reasonably consider private (scrolling down a page) and certainly doesn't expect to be monetized.
Bidstream data collection boasts of key term level insight, and harvests data from the internet broadly. However, the accuracy is typically low as it's based on a tenuous series of assumptions predicated on someone having an ad flashed in front of them. And of course, bidstream data carries potentially significant questions of data propriety that have attracted the attention of the Senate Committee on Finance.
IntentData.io solves for both of these issues by collecting data based on observations of public actions. In other words, our signals report on specific actions that individuals take publicly, for the whole world to see. While this addresses data propriety and privacy, it also has some other implications.
It means that the signals are more directly indicative of intent - not simply scrolling down a page or having an ad flashed before you - but actual, demonstrable action. It also means that for a narrow range of industries/requirements it may not yield significant signal because it can be so specifically tailored to a narrow set of personas and activity types.
This is a common question we receive since people tend to project their personal behavior preferences on the market. It normally sounds something like "I browse but don't take public action. I can't believe there will be signal for our buyers." Certainly, certain buyer personas and certain industries are more active than others. Greater activity can mean more signal, but more noise whereas lower levels of activity can make titrating signals easier.
In fact, there's only been one industry/requirement where we found functional limitations of this model. It involved an application of a consulting firm that works with pre-IPO firms and which wanted to use intent data to identify CFOs evaluating firms to help them prepare for IPO. Generally, CFOs are not as demonstrative publicly as, say, CEOs or perhaps mid-level finance folks. Further, companies are private about impending IPO plans and hesitant to take public action that hints at preparation. In that case there was minimal signal. (Of course while other models may have generated more signals in that situation, there's no assurance there were actually indicative of that specific intent.)
Sales & Marketing Alignment and Sales Process
Now circling back to where we started, certain industries are more sophisticated in their marketing and sales movements and more rigorous in following a nuanced sales process (or more accurately, enabling the buying process!)
With the same caveat about generalizations, we normally see that recent, funded startups in the SaaS space are better positioned to activate and orchestrate data than more traditional and industrial companies.
That's not insurmountable for teams that are willing to work hard to adapt. But it's a factor that generally correlates to an industry's ability to optimize the data.
Will Intent Data Work for Your Company
Using the right data collection methods, and specific key terms for granular, actionable insights means that third-party intent data can work for nearly any B2B industry where deal size and sell cycle are appropriate.
The more important question is often whether it will work for a specific company.
Mindset, marketing resources, alignment, sales enablement, and management emphasis will all play a role in the outcome - whether intent data will work or not. There are some industry generalizations that give us a starting point, but with clients in numerous durable goods, capital equipment, and professional services industries - in addition to various technology including cybersecurity, AI, chatbots, cloud hosting, managed services and more - we know that the right intent data activated and orchestrated properly can work well in a wide range of industries.
And because we can and do support industries well outside of technology, we're particularly tuned to specific requirements and approaches.
