AI vs. purchase intent

Comparison of AI-derived signals and directly observed intent data from TechTarget

Steve Niemiec

AI versus purchase intentWith about 10,000 RevTech solutions out there, it’s become super hard to understand who is really doing what, and most importantly, whether what’s being said really helps deliver substantial value to your business at the end of the day. At TechTarget, we realize that while there is little real mystery in what we do, there is absolute magic in how it affects our customers’ bottom line. To help you and ourselves navigate the fog of functionality that sometimes threatens to obscure what it really takes to make steady progress, we’ve put together a series of pieces examining recent claims and presenting our case for a more transparent and pragmatic way to understand the issues involved.

Claims of “the best AI in the industry”

According to McKinsey’s latest research The state of AI in 2021, B2B marketing isn’t even in the top 10 use cases for AI today for the 8 features they explored. If we look specifically at the sales and marketing functions, the two main use cases for AI are Customer Service Analytics and Customer Segmentation, with only 17% and 16% of all companies surveyed trying to apply AI to their work. For us, this begs the question of why RevTech companies are actually touting AI when the experts are just beginning to see its usefulness, and the cases are largely coming from the high volumes, high costs, or high costs. transaction worlds of customer service and consumer products? We believe that suppliers promote AI for two simple reasons: because it makes them sound smart and because it is difficult for potential customers to inspect. We do not believe that AI in the way it is currently deployed in the field of contact identification can provide material value to our customers compared to other more transparent methods.

What they say, try to claim and hope you accept it.

The claim that we have the best AI in the business is easy to make because none of us can evaluate it. The question you need to ask yourself is: what is this AI good at? And how can I determine that it is better than other methods?

What data do they use for this in the first place? Importantly, AI needs a relevant dataset to run on. For our purposes, that means one that must necessarily relate to people and behavior. If a supplier doesn’t have such information, they need to get it from you to run their models. But as recent history has shown, modeling B2B customers’ first party data yields little or no improvement (the companies trying to build a business around this have all either been absorbed by others with more successful business models, or they have potentially more impactful opportunities for their portfolios).

So what’s going on here? Like anyone, an “AI-powered” offering can scrape “open” (and therefore low-value) domains off the Internet, or they can buy data from sites that want to sell it to them (which begs the question of why a site we honestly think that it’s because it’s weak in the first place and these are sites that struggle to monetize themselves due to quality or scale issues).

Black boxes you can’t look into to evaluate yourself

The theory seems to be that they will pick up enough of this low-level, low-quality traffic, put it all together using a data lake, analyze it with an AI model, and get a pretty good estimate of what you should prioritize. But these super-weak signals they collect are often false as to the existence of demand, because they assume a very weak inference in the first place — an inference that often just uses a general topic keyword as the connection between a search query and your solution. And besides, such signals aren’t additive — in data, just like in mortgage-backed securities (you still remember 2008, don’t you), two weak things can’t be added together to make one strong.

At best, this method only gives an indication of a weak general interest in a topic, which is why they so often have to add feeds from G2/TrustRadius as an account data source – it’s to give the appearance of some sort of verification of their initial ” peak”. As you probably know, these two suppliers make their money from suppliers who pay for promotion (which even casts doubt on the veracity of their signals), but at least with them you know that the account was looking for a solution like that yours. Unfortunately for you, an AI based vendor could also use this to go back to their other signals and then claim that any peak corresponding to the G2/TrustRadius or Gartner signal must therefore be real. Again, a false positive from G2 used to verify that a false positive from AI is real should not convince you in our opinion.

TechTarget’s approach, our solutions and directions

Years of academic Research, and both our own analysis and personal observations of the markets around us, have shown that buyers largely focus on locations where they can easily meet their needs. (In our category, this comes in the form of the decision support information they need to make business tech purchases). This is why we built our business – to create such clusters for our customers (and ourselves) and then make money. Thanks to our massive content volumes and SEO power, we can continue to reliably attract these technical buyers over other suppliers of similar material. We are by far the #1 publisher of enterprise technology buyer decision support: one million 1st page organic rankings leading to massive engagement and click-throughs! With our scale and precision, we reliably intercept the vast majority of research on business technology buyers taking place on the Internet at any given time.

We know the content so we know exactly what each person really cares about

And because we create all editorial content and can therefore successfully use an opt-in membership model, we know exactly who is reading what and why. This means that you can then adapt to the specific person, resulting in better conversion, more meetings and better conversations.

At TechTarget, we do not interpret arbitrary distribution of signals and ask you to believe. We deliver dozens of relevant interactions per user and active purchasing team; interactions that you can easily inspect and understand as the results of a transparent process. And with BrightTALK signals now added, the breadth and depth of our data can literally mean: hour of involvement in extraordinarily detailed material about your category and products. There really is no other source of insight that comes close to this strength of the signal, its accuracy in showing the journeys of real buyers, or the precision in understanding each individual making up the buying group.

Directly perceived intent: a transparent method you can inspect, understand and evaluate

As a marketer, you are well aware that every element within this data concept can deliver critical benefits in your efforts to successfully maximize go-to-market performance – from strategically understanding your markets and goals, positioning them and moving towards them directs, and executes, from your product concepts to your content, from the click to the end. Despite everything you may hear, in the category of business technical marketing and sales data, no data-generating process can currently produce an output stronger, more accurate, or more accurate than Priority Engine Prospect-Level Intent™.

This blog is a small part of my ongoing effort to both provide helpful information and clearly explain why we are so confident that Priority Engine will deliver fewer false positives, fewer false negatives, and many more opportunities into your pipeline, faster and more reliable at scale. As always, my team and I are here to assist you and discuss this material at your leisure. I can be reached here† I hope to speak to you soon – Steve

actionable purchase intentAIb2b purchase intent dataintent dataIntent data signals

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