Executive Reports
October 1, 2023

Out with the Old: Why Traditional Buyer Intent Signals No Longer Cut It and How AI-Powered Visitor Behavior Analysis is the Future

Greg Wolfe

Executive Summary

When I was the COO at Marketo, the concept of finding ready-to-convert prospects in real-time was unheard of.

Instead, we relied on “lead scoring” and “in-market intent signals”, which were systems of qualifying prospects in the market based on some activity across the internet, databases of contact information, and simplistic engagement metrics on our own website. 

We did this because it was the only way, and it has become the standard for marketers ever since. 

However, the results of this traditional “buyer intent” framework did not significantly change the game (causing conflict between sales and marketing, which I wrote about here) because:

  • The lead scoring and in-market intent signals are based on human hypotheses, which means they are best guesses subject to bias and inaccuracy 
  • The signals are too few in number to accurately determine intent (in reality, they need exponentially more signals) 
  • Their intelligence is not provided when it matters most (real-time)
  • Some of the website engagement metrics are too simplistic and isolated, such as if website visitors attend a webinar, viewed a pricing page, filled out a form, etc
  • Similarly, their on-site intent signals only work with identified visitors and accounts, so they miss on average the 70% of website visitors who are completely anonymous and can’t be ID revealed yet have tremendous hidden opportunity

So, these tools gave us a rough idea on what companies and accounts might be in the market to buy, providing a list of potential opportunities for our sales team to follow up with.

But these tools couldn’t narrow down to individual buyers who were on our website in real-time.

That’s until a far more precise way to measure buyer intent was introduced called Lift AI.

Lift AI is the only buyer intent technology that uses artificial intelligence to score the buyer intent of website visitors in real-time with over 85% accuracy, based solely on their behavior (no account information required). 

In other words, it finds buyers on your website in real-time, ready for conversion. 

So, the concept of “buyer intent” in modern marketing has become confused.

And it’s causing marketing and sales teams to miss a huge number of potential buyers.

To explain why, we’ll need to take a few steps back and understand the wider picture, including:

  • What is buyer intent, really?
  • How do current buyer intent tools work?
  • Why are current buyer intent tools missing buyers?
  • How many hidden buyers are being missed on average?
  • What is the solution for better buyer intent?
  • What kind of results can you expect from solving the gap?

What is buyer intent, really?

Let’s start by looking at the definition. 

Put simply, buyer intent (also known as purchase intent) represents how likely someone is to buy a product or service. 

Today, the concept of buyer intent is usually referenced within the context of digital or website marketing, and less so in the physical world.

If someone has high buyer intent, that means they have a high probability of being close to buying. Contrarily, low buyer intent means there is very little chance of a closed purchase. 

So, buyer intent is a way to find out where your prospects are in their buying journey.

However, intent in any form is a complex thing to measure.

And therein lies the problem with current buyer intent tools.

How do current buyer intent tools work?

In order to determine a prospect’s buyer intent, current tools perform a calculation using “buyer intent data”, which is often interchangeable with the term “buyer intent signals”.

At a basic level, current tools determine buyer intent by hypothesizing a list of signals they believe represents “intent”, assigns a numeric score to each signal, then adds up the signals to provide an intent score. 

These signals are gathered across a variety of internet activity - not just on your website, but across other websites. 

It’s important to note that these signals are generally only calculated and stored when the intent tool is able to reveal who someone is against their database of contacts and company records, or your own contact records - this is called “ID match” (more on this later). 

This is the traditional way of calculating buyer intent, and it is commonly known as “in-market intent”.

In-market buyer intent signals typically fall into two main categories - first party and third party - with sub-categories underneath.

First-party (internal) buyer intent signals

This is buyer intent data collected through your own channels, such as your website or CRM  system, or ABM tool:

Marketing and sales engagements

If you send a marketing or sales email to your database of recipients, and a user opens that email, it might serve as a signal that they are interested in your product or service and a score is added (e.g. +10). If they then click on the link inside of the email, it may indicate another level of intent (e.g. +20). The score for each step is arbitrarily decided upon by humans, then added automatically to the intent score of the prospect inside of your CRM.

Similarly, if someone comes to your website and fills out a contact form, or a demo form, then that engagement could be considered as a strong signal of intent, and a weighted score will be provided accordingly. 

Basic website engagement metrics

If a visitor comes to your website and goes to the pricing page, this may indicate some level of intent. Additionally, if they spend a long time scrolling through your site, visiting multiple pages, and coming back to the site often, these signals may also demonstrate intent. Again, an arbitrary score is given to each signal (e.g. +20 if a user is on the site for more than 2:00) then added up to represent a level of intent. This score can only be assigned to a user if they are able to be ID matched.

On-site content consumed

Similar to the basic website metrics, if a visitor comes to your website and reads specific types of content including certain ebooks, videos, and articles, then it may represent more intent. For example, if you have recently launched a campaign to sell a meeting scheduling feature to your software, and the visitor is consuming content on your website related to that campaign, they may be demonstrating more intent. This only works if the visitor is able to be ID revealed.

Third-party (external) buyer intent signals

This is buyer intent data collected through channels outside of your own. This typically means the data is purchased from a company that houses a huge amount of aggregate data on a database. Imagine that database as a big spreadsheet, with each row being a different contact with any known information. 

Database ICP (ideal customer profile data) fit

When a visitor lands on your site and is ID matched, your ABM tool can look up information on that visitor to see if they fit your criteria for an Ideal Customer Profile. For example, if your ideal prospect is in the software industry, located in Los Angeles, and is in a company with more than 50 people, your tools can provide scores for each criteria met to add to their buyer intent level

Off-site “similar content” consumed

Similar to the on-site content consumed, intent tools can “read” the content of a web page to guess what the core topics of that page are, then store those high level topics visitors against a record of known contacts. For example, the intent tool may recognize that someone is looking at “project management” topics or “work management” topics, and add a score to their record on your side if those topics coincide with what you sell. The scores are enhanced if many people from the same company/organization are researching the same topic, and/or if they’re reading it frequently. 

Off-site “relevant keyword” search terms

Third party intent tools often purchase or partner with data from other companies to build large databases of consumer information. One element which can be purchased is relevant search terms used by your prospects. For example, many advertising tools allow you to bid for keywords (such as Google keywords) and generate ads if a user searches for those keywords. Those keywords can then be logged against contacts in a database if an ID match is found. If the database shows that a visitor on your website has been searching for keywords relevant to your product or service, it may indicate intent and an arbitrary score is provided.

Why are current buyer intent tools missing buyers?

There are four problems with current buyer intent tools and the in-market intent signals that they measure:

  1. The scores are best guesses and hypotheses 

The scores provided for each signal are decided by humans, and are therefore subject to bias, inaccuracy, and incomplete information. 

For example, someone has to decide how important it is if a visitor goes to the pricing page, then assign an arbitrary score to that signal. Their biases may lead them to assign a higher score for this page versus other pages, based on assumptions. 

However, recent data has shown that 64% of high buyer intent visitors don’t even go to your pricing page - and that’s just one example of how common misconceptions and biases may lead to incorrect or incomplete estimates of intent. 

Those human-based scores are then added up algorithmically to represent “intent”, but it’s simply not enough.

  1. They can’t understand nuanced behavior 

Traditional intent tools use oversimplified views of intent which only track a relatively small number of signals. However, understanding ‘behavior’ is far more complex.

For example, imagine a highly experienced salesperson working at a retail store. They eventually build an intuition for what a high intent buyer looks like based on the shopper’s behavior, which is processed automatically and compared internally against what previous likely buyers did. 

Some of their cues may include time of day, current season, compelling dates, body language, facial expression, where they are in the store, eye movements, who they’re with, and a variety of other contextual signals. 

These signals usually come together to form an instinct that represents likely intent, but it’s not a basic addition. Instead, some signals may offset the others, or interweave with each other in complex ways. For example, someone may display body language that isn’t likely to buy, but because it’s the week before Christmas they’re still likely to buy due to an obligation rather than a desire to purchase.

Traditional buyer intent tools can’t see this kind of behavioral complexity, nor can they calculate in real-time which signals interweave with others, which are mutually exclusive, and so on.

More importantly, traditional buyer intent tools don’t measure enough signals, relying on too few to build an accurate picture of visitor behavior and their intent.

  1. Their intelligence is not provided when it matters most

Because traditional in-market intent tools rely on a lot of off-site and third party data, their intelligence is typically not updated in real-time.

That means that signals demonstrated by your website visitors or prospects may not turn into actionable insights for a period of time - ranging from 30 minutes to 24 hours (or more).

However, in a day and age where our attention spans are close to zero and the first company to respond to customer requests close 50% of the sales, having instant and real-time insights is exceptionally important.

For example, would you rather know that someone from Coca Cola was on your website showing in-market intent signals last week, or that someone from Pepsi is on your website showing strong intent right now

The saying “strike while the iron is hot” rings true for sales, and in this case it would make the most sense to engage Pepsi with conversion-oriented experiences.

  1. They only work with identified visitors, so miss 70% of your traffic

Traditional in-market buyer intent tools rely on attaching intent data to known website contacts in a database.

The problem is that known visitors on your website who can be matched to a database typically represent only 30% of your website traffic.

That leaves the remaining 70% as completely anonymous with no meaningful intent data being provided from traditional tools. When you have a website with tens of thousands of visitors per month, that’s a huge number of missed opportunities and hidden buyers. 

Additionally, the intelligence provided by traditional in-market intent tools for the 30% of known visitors is still not highly meaningful, accurate, or timely, as demonstrated in points 1 through 3.

The solution - behavioral buyer intent powered by AI

If traditional buyer intent signals don’t truly represent intent, what does?

The answer is behavioral buyer intent, which can only be predicted accurately, and at scale, in real-time, by artificial intelligence (AI).

It’s similar to the example given of the experienced salesperson working at a retail score. Based on observing behavior alone, the salesperson is able to ascertain the buyer intent of the customer.

They don’t need to know who the visitor is to determine their intent, just their behavior. 

That’s exactly what Lift AI does for websites. It’s the only behavioral buyer intent AI available, and it’s also the only intent tool that does not require the website visitor to be ID matched, meaning it can provide buyer intent data on every single visitor - including the 70% of your completely anonymous visitors.

It works by tracking the behavior of each visitor as they navigate your website, tracking hundreds of variables simultaneously and cross-referencing them with each other based on billions of pre-trained data points built into the AI model. 

Lift AI’s model is proven to be over 85% accurate for each score which is provided in real-time - this process can only be done by AI, as it’s far too complex and nuanced to be done by humans or a simple linear algorithm. 

Let’s paint a picture of how this looks in practice.

A visitor comes to your website and Lift AI immediately begins scoring their behavioral intent.

That intent is represented by a score number between 0-100, where 100 is a certainty to buy/convert. 

With that data in hand, the visitors can be determined as low intent, medium intent, or high intent.

Lift AI’s buyer intent scores are then streamed in real-time to your existing tools. Now you can tailor your conversion experience for each individual visitor based on their intent score and category.

For example, high intent visitors can be set up in your chat tool to trigger a playbook, connecting ready-to-buy visitors directly to your live sales agents (who have the best chance of converting a visitor).

You could also set up your content personalization systems to show different content to high intent visitors, such as call to actions, versus nurturing content for lower intent visitors, such as ebooks or learning resources.

You can even layer in your intelligence from other tools such as CRM, ABM, and more. Now you can know the intent of the visitor in real-time and know who that visitor is according to your ICP fit and trigger additional actions as needed to convert.

What kind of results can behavioral buyer intent AI yield? 

Given that Lift AI can provide accurate predictions of buyer intent for 100% of your traffic, the results are game-changing:

“Our goal for ARR from chat for the first year was to generate $50-$100K. In the first year, we were able to attribute over $1M in incremental recurring revenue to Lift AI."

  • John Walker, Director of Demand Marketing, PointClickCare

Want to see results like this for yourself? Get in touch with Lift AI  and the team will set you up with a free high intent playbook (if you have more than 20,000 visitors per month). The majority of Lift AI free trials start seeing a lift in revenue before they’ve spent a single cent. 

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