How to Use Intent Data and AI to Predict Buyer Behavior

How to Use Intent Data and AI to Predict Buyer Behavior

You know what’s high up on the list of daydreams we have at work? 

Friday’s two-hour meeting being canceled? The clock jumping forward two hours? Your boss bursting in with a cake and a world’s best employee award? Using tea leaves to predict what your prospects are going to do next? 

Hang on a minute…that last one doesn’t sound that much like a daydream. Minus the tea leaves, of course. 

Here’s the thing: in the wonderful modern-day world of B2B sales, we can rely on intent data to figure out what our prospects are thinking and whether they might be considering a purchase. 

That’s an amazing ability – but the downside is that analyzing intent data and trends manually takes quite a long time, particularly when you’re working at scale. And in a world where reps like you spend just 30% of your time selling, the last thing you need to add to your list is yet another time-consuming task. 

And that, our friends, is where AI comes in to save the day. Nowadays, you can use AI to predict buyer behavior by analyzing intent data. You’ll be able to process large volumes of data quickly, see patterns and trends at a glance, and become a master at scoring and prioritizing leads. If you want to learn more, you’re in the right place: 

By the time you’ve finished reading, you’ll be ready to enjoy an improved lead qualification process and shorter, smoother sales cycles. 

Ready? Chuck those tea leaves away, and we’ll get started 👇

What is Intent Data?

Ok, what actually is intent data? 

In a nutshell, it’s data based on online behavior that indicates a buyer’s interest or intent to make a purchase. You typically gather intent data from: 

  • Website visits and content engagement 
  • Content downloads (like a whitepaper or eBook, for example) 
  • Search activity and keywords
  •  Social media interactions 

This data isn’t to be sniffed at: 90% of B2B decision-makers start their buying process with online research. If they’re doing one or more of the above, it’s fairly likely that they’re at some stage of the buying process.

How AI Enhances the Use of Intent Data

So far, so simple. But how can we use buyer intent data to its very best advantage? 

As with many things in this day and age, the answer lies with AI. But that doesn’t mean developing an army of AI salespeople. AI allows you to: 

  • Processing large volumes of intent data quickly (helpful if your website gets a lot of traffic or visitors each month). 
  • Identifying patterns or trends (helpful if you don’t want to spend all day with your head in a spreadsheet). 
  • Scoring and prioritizing leads using predictive analytics (helpful if you don’t have plenty of spare time to add up lead scores or figure out who to go after full-throttle). 

Use AI to help manage your buyer intent data, and you’ll enjoy:

  • Improved lead qualification: 67% of lost sales are a result of sales reps not properly qualifying the lead. In other words, 67% of lost sales could have been easily avoided, and the time spent on them saved. 
  • More personalization opportunities: what sounds better – “Hey, any chance you want to book a demo?” or “Hey, I noticed you checked out our pricing page – want to chat ROI through on a call?”. Exactly. 
  • Quick decision-making: use AI to alert you when a buyer shows high purchase intent, and you’ll be able to quickly get in touch or re-prioritize your list. 

Next up – let’s put it into practice. 

Predicting Buyer Behavior with Intent Data and AI

Before we talk about exactly how to use AI to speed up your sales processes, what behaviors actually indicate buyer intent? 

If you count every buyer action as buyer intent, you’ll end up doing nothing but annoy your prospects. Let’s take an example: say a prospect visits your homepage and hangs about for 30 seconds. Or perhaps a lead totally not on your radar downloads a useful piece of your content but then doesn’t interact with anything else. 

In both of these instances, it’s probably not too appropriate to pull them to the top of your list and jump in with a hard sell. Sure, they’ve engaged with your company, but in a very light-touch way – at most, you’ll want to add them on LinkedIn, maybe to a nurture campaign, something like that. 

Now let’s say a lead suddenly downloads every case study you have in their vertical. Or perhaps multiple team members from the same company sign up for your webinar. Maybe they visit one of your product pages five times in half an hour. If you think about the intent behind these actions, it’s far more likely that they’re considering a purchase. In this case it would be far more appropriate to start moving them down the funnel. See what we mean? 

Now, these behaviors are all pretty easy to keep track of when you’re working prospect-by-prospect – but unless you have a very narrow ICP, it’s far more likely you’ll be working at scale. And that’s exactly where AI can help: 

  • Combining past purchase patterns with current intent signals. 
  • Suggesting the best next step – is it more appropriate to send an AI generated email, or try to schedule a call? 
  • Predicting churn risks (has a client totally disengaged with your website, for example) and identifying upsell opportunities (has, say, another client visited the page of a product they don’t yet have multiple times).

How Your Peers Use AI To Improve Their Processes

Want to steal some clever ways we’ve seen businesses implement into their day-to-day? 

Duh. Here you go: 

Lead Scoring and Prioritization

Implement a lead scoring system, and you’ll increase deal close rates by 30%, and company revenue by 18%. Why? Well, by making sure you’re focussing on the leads most likely to convert, you’re increasing your chances of closing and decreasing your chances of wasting time. 

Lead scoring can be a pretty manual, repetitive task. Fortunately, this type of task is exactly where AI shines – leaving you to act on the lead scores it delivers. 

Sales Cadence Optimization 

We’ve all been there: trawling through six months’ worth of email campaigns to try and figure out whether Monday is a better day to send than Thursday, or whether your prospects are more responsive first thing in the morning or last thing at night. 

No thanks – leave this type of task to AI. All you need to do is then set the day and time according to its insights. 

Team Collaboration 

We all know being best friends with marketing is paramount – but when everyone’s so busy, staying aligned can be tough to keep on top of. 

If best practices like the above keep on slipping to the bottom of your to-do list, AI can help – and you could outperform your peers by 1.9x if you take advantage. For example, if you regularly run out of time to share insights with marketing, get AI to write you a report. The more you share this type of thing, the more they can create campaigns that’ll drive the quantity, and quality, of your leads up. 

Challenges To Keep An Eye On

Let’s talk about the big one first: data privacy and compliance. Before adopting any new AI tool, or integrating AI into your workflows in a new way, make sure you’re still respecting data usage laws like GDPR. Don’t skimp on this step. Trust us, not doing so isn’t worth it. In the same vein, you want to make sure that you’re collecting and analyzing intent data in an ethical way. 

AI is only as good as the data you give it, so make sure your sources are accurate. The more datasets you use, the more complete the picture – and the better the results you’ll get. 

Also, keep in mind that while you might be an AI guru (or an aspiring AI guru, at the very least), not everyone on your team might be. Train everyone to use the tools effectively to make sure your activities – and results – stay consistent. Involving the team in the adoption process from the get-go also gives you an opportunity to work through any resistance to change, and make sure they don’t revert back to traditional methods. 

Quick Summary and What to Do Next

Did you skip ahead to this section? Can’t blame you for being efficient – sounds like you’re a perfect candidate for AI. 

Here’s what to do once you’re finished reading: 

  • Start collecting intent data:  make sure it’s from reliable sources and gathered in an ethical way. 
  • Implement your AI tools: prioritize gathering real-time analytics and insights for the best results. 
  • Train your sales team: keep your buyer experience consistent by ensuring everyone knows how to 1) interpret AI’s insights and 2) act on them.
  • Continuous monitoring: make sure that your new activities are actually getting you results – and if they throw up any room for improvement, too. 

 

Let’s Wrap It Up!

Hang on a minute…where’s our cup of tea gone? 

Fair enough – we can’t blame you for getting excited and throwing away our tea leaves. You’re now such a pro in using AI to analyze buyer intent that traditional future-predicting techniques are a thing of the past. 

Hey – fancy telling us when we’re going to hit target? 

Surfe is trusted by 30000 sales people wordwide

Want to practise your new AI skills on your prospects? Of course you do.

Better find some prospects – we know just the tool to help with that.

FAQs About Using Intent Data and AI to Predict Buyer Behaviour

What Is Intent Data In Sales?

Intent data refers to information gathered from online behaviors that indicate a potential buyer’s interest in a product or service. This might be an action like visiting a website or downloading a certain type of content. By analyzing intent data, sales teams can identify prospects who are likely to be in the market for their offerings and in turn prioritize leads and personalize outreach. With 90% of B2B buyers starting their research online, intent data is a valuable tool for predicting purchase intent and shortening sales cycles.

How Does AI Improve Intent Data Analysis?

AI enhances intent data analysis by automating time-consuming tasks like processing large datasets, identifying patterns, and scoring leads. This allows sales teams to quickly spot high-priority prospects and act on opportunities. For example, AI can predict which leads are most likely to convert based on their behavior or alert reps when a prospect shows strong purchase intent. By integrating AI into their processes, teams can make faster decisions, improve personalization, and ultimately increase revenue.

What Are The Key Benefits Of Using Intent Data?

Using intent data offers several benefits:

  • Improved lead qualification: sales reps can focus on high-intent prospects, reducing wasted time
  • Personalized outreach: sales reps can tailor messaging based on a prospect’s specific interests
  • Shorter sales cycles: sales reps can quickly identify and act on ready-to-buy signals.
  • Better collaboration: sales reps can share insights with other teams, like marketing for example, to refine campaigns.

What Buyer Actions Indicate Purchase Intent?

Buyer actions that signal high purchase intent include downloading case studies, visiting a product page multiple times, or having multiple team members attend a webinar. High-intent actions suggest readiness to move further down the funnel, making them a priority for follow-up. Light-touch interactions, like a quick visit to a homepage, typically indicate lower intent and may require nurturing. Keep an eye on the context and frequency of these actions to accurately determine the level of intent.

How Can You Use AI To Predict Buyer Behavior?

AI can predict buyer behavior by analyzing past purchase patterns alongside current intent signals. For example, it can identify when a prospect is likely to convert or flag churn risks in existing clients. AI tools can also recommend the best next steps, such as whether to send an email or schedule a call. AI is incredibly helpful when you’re working at scale, as it takes away a lot of the manual requirements of analyzing and combining these data insights – leaving you to focus on the actual selling bit.