Signals of purchase intent — what you can glean from behavioral and contextual data — indicate a prospect's interest or readiness to buy a product that solves their problem (ideally your product).
Intent is a key inflection point because these people are ready to buy, but there's still a chance they could buy from your competitors instead. Don't let that happen. It's prime time for marketing and sales to step in and pay extra-special attention to these prospects, providing them with the information they need to make their decision (or an offer they can't refuse?).
As a point of comparison, the B2C world takes intent signals very seriously. Think of the "abandoned shopping cart." If you put an early Weird Al Yankovic vinyl record you've always wanted into your checkout cart in an online store, and then you close the browser window before buying, the company won't let you forget about it. Weird Al will be following you around the internet in the form of retargeting ads for weeks. You might even get automated emails to remind you that you left a record in your cart, and that time is running out before it's snatched up by someone else. And who wouldn't want those greatest hits?
In the B2B world, understanding signals of intent helps marketers go after the people who are ready to learn more and talk to sales. It helps them create better, more relevant experiences and convert prospects at higher rates. But the purchase cycle for B2B SaaS products is typically longer and involves more stakeholders than buying a vintage vinyl record from an online retailer. It's just not as straightforward, which is why marketers need as much intent data as they can get if they want to paint the full picture.
Intent data broadly comes from the actions customers take before they make a purchase. Classic examples include filling out a lead form to sign up for a product trial, asking for a demo from sales, attending a webinar, and downloading an ebook.
They're "classic" because most marketers already track those behaviors in a CRM or marketing automation platform and use them to prioritize leads. They are demand gen bread and butter. Demand gen marketers have traditionally used these actions as their go-to conversion metrics, to determine the point at which sales and marketing can spring into action and start actively courting the prospect, through automated or manual outreach.
But notice that all of these "classic" examples require prospects to provide their email or phone number, by filling out a lead form, say, or sending an email. They need to make the first overt move for marketers to notice them in the first place. And in the past, these folks were the only ones that gave marketers actionable data.
Today, web analytics — and data in general — is much more sophisticated, so marketers can use more than just a hand-raise as an intent signal. There are more covert (yet valuable) signs of interest, such as browsing product and pricing pages on a website, visiting implementation docs, and doing online research. There's gold in them thar hills!
|Actions where a prospect reveals their presence or interest by providing email or other contact information. Typically tracked in a CRM / Marketing Automation tool.
||Actions that don't require self-identification. Typically tracked via website analytics, using a reverse IP lookup tool to identify anonymous traffic. Some research-related intent scores can be purchased from a third party.
Items in the left-hand column (overt actions) are those traditional, "classic" intent signals, and the ones on the right-hand side are those that marketers need to start taking more seriously.
There is so much more intent out there that traditional overt intent signals don't capture. Plenty of qualified and interested leads have reasons not to fill out a lead form. Maybe it's too high friction. Maybe they want to do more research or need to coordinate with numerous other buyers on their team. Maybe they simply get distracted.
So form fills are useful, yet incomplete. Contemporary marketers can get a much richer picture when they supplement overt intent signals with quieter user behaviors, especially combined with how frequently they happen.
To illustrate, think of shoppers visiting a jewelry store at the mall.
Some shoppers will make a beeline for the sales clerk to ask whether there are any gold necklaces in stock. (Overt.) Others are browsers who don't immediately interact with the sales clerk as they poke around in the display cabinets. They might say "I'm just looking," even if they're nearly ready to buy too. (Covert.)
Consider these types of shoppers, but in the B2B world:
How a Beeliner shops for B2B products:
The head of sales ops at Acme registers for your webinar on Monday, a salesperson on their team downloads two whitepapers on Tuesday, and on Wednesday they sign up for a free trial of your product. Another colleague also responds to your automated marketing emails to ask a question about your product. Since they filled out forms and replied to an email, you can see pretty plainly that this company may be interested in buying, and you know exactly which individuals are doing the shopping and the research.
How a Browser, or Window Shopper, shops for B2B products:
You look at your website's Google Analytics (with Clearbit Reveal) and notice that two visitors from a company, Acme, visited your site five times last week. They browsed one of your product pages (but not the others), and viewed the product demo page, and read through the implementation logs. But they didn't fill out a form.
Acme is a net-new account for your marketing team, so you wouldn't have known they're interested without Clearbit. This week, you see that they've visited your pricing page twice and ramped up their pageviews. It could be a great time for marketing or sales to reach out to some contacts at Acme — and since you know that they were researching one specific product, you know which one to promote in your outreach.
The moral of the overt-covert story? Don't let high-value prospects fly under the radar just because they don't fill out a lead form. You can track website and browsing actions and use them as clues about where a prospect is in the buyer's journey.
Remember, some of these actions, which on their own are just behavioral data, may not actually correspond to purchase intent. This is why it's important to consider contextual data, fit data, and timing in a holistic way, to help you accurately determine intent before you barrage a visitor with marketing and sales outreach. You also want to be appropriate, helpful, and strategic in your follow-up, remembering that if a shopper in a store says "I'm just looking," they'll be put off if a sales clerk still follows them around and butts in every few minutes. That's the worst.
As we've said, you probably already track your overt intent signals.
Meanwhile, your own website is a gold mine for covert signals. People show their intent all the time in the way they browse, so web analytics tools can provide rich background data and nuance to your understanding.
Most B2B web traffic is anonymous, but don't let that stop you. You can de-anonymize your traffic with a reverse IP lookup tool like Clearbit Reveal, which takes a visitor's anonymous IP address and looks up which company it's affiliated with to provide a firmographic profile. That's how you can see that someone from Oracle, or Google, or Hubspot has been poking around on your pricing page. It's the blacklight to an invisible ink pen.
Intent data can also come from third-party data vendors.
For example, G2 is an app review site where people write reviews of business applications and others can browse through them. When someone looks at reviews of a certain software or software category (such as "top CRMs"), it's a strong signal of intent, which is very useful for marketers. G2 sells purchase intent information about which companies are browsing these app listings.
Contrast G2 with some traditional third-party intent data vendors, whose data isn't especially actionable because the signals aren't very strong or clear. They may share big lists that are essentially a "black box" of information that doesn't provide much nuance about actual purchase intent and timing. If an intent data vendor tells you that Company A is interested in CRM software, does that mean that someone from Company A read an article about CRMs on some unknown site, somewhere, some time ago? Or does it mean that dozens of people at Company A have been conducting research to purchase a new CRM in the last year?
This "black box" issue is why we place so much emphasis on mining your own website analytics to round out your understanding of intent. You get more context about browsing activity (i.e., which products companies view, and when), and that gives you greater clarity on how to shape their experience.
The very fact that prospects are spending time on key parts of your site is the biggest signal — they're literally inside the store.
We've split intent signals into overt and covert actions — Beeliners and Browsers — but many prospects exhibit a combination of the two.
This is why your website is the linchpin of understanding intent: it fleshes out so much more detail about visitors, whether they announce themselves through forms or not.
Today, B2B SaaS buyers tend to do a lot of decision making while looking around on their own. 67% of the buyer's journey is now done digitally, according to SiriusDecisions. And that's a big deal. It means that marketers have a huge opportunity to influence the buyer's journey and shape a prospect's experiences and interactions, based on what they can learn from intent behaviors.
The way B2B SaaS sales used to operate mirrors the way consumers used to shop before the internet was a thing. Folks buying a car were more likely to start the process by going to a dealership (as opposed to reading online reviews first). Dress shoppers didn't have thousands of fashion bloggers to consult before going into a clothes store. And people were more likely to purchase flight tickets to Hawaii from an airline, without using Kayak.com to comparison-shop.
That's not to say that people didn't do research. There were still resources for consumers. It's just that today, there's far more opportunity to do that solo research online. And because it's digital, it's also more measurable.
It can become impossible to grow a company if sales and marketing spend too much time and money chasing low-quality leads. Leveraging intent data helps you find and target prospects who are more inclined to buy and give you the highest returns.
Double down on this by prioritizing the "VIP customers" who show intent and fit your ideal customer profile.
Finding and prioritizing those high-intent VIPs is a cost-saving tactic that is becoming more necessary for B2B SaaS companies. Outbound sales used to be the bread and butter of B2B sales, but it's not working as well as it used to. Email's not dead yet, but it takes more than a spammy email blast to get people's attention.
Meanwhile, there's a shortage of B2B sales talent; in fact, ManpowerGroup found that sales representatives are in the #2 most in-demand role globally, beating out even engineers and technical workers. This usually means that salespeople are getting more expensive for companies (and that maybe you should apply to be on the Clearbit sales team — we're hiring, wink wink).
So, B2B companies have more expensive employees reaching out to less responsive prospects — and you don't need an MBA to know that's not an efficient way to grow.
This is where companies can use intent and fit signals to see where marketing and sales can step on the gas.
Let's say you have a list of target accounts you're pursuing, a subset of Fortune 1000 companies. You see that people from Oracle have browsed your Homepage, Solutions page, and Pricing page multiple times in the past two weeks, so you know you can pursue the Oracle account with more gusto. And when email@example.com signs up for one of your general newsletters (typically a relatively weak sign of purchase intent) and her job title fits your buyer persona, you know you can launch right into a more specific sales pitch and use middle- to bottom-of-the-funnel content.
And what if a chatty Cathy account comes in and reads all your blog posts, chats up a storm on your chat widget, and visits that all-important pricing page ten times? That's all well and good, but if you know these visitors come from a ten-person startup with a revenue well below budget, it would be a waste of time and money to pay attention to them in the same way.
Using the quality filter of matching fit and intent focuses the efforts of your sales team, because they won't get bogged down and distracted by prospects who have neither the intention, means, nor position to buy. Who's got time for that?
And it gives you the data you need to be able to tailor the buying experience to maximize conversions — with proactive outreach from your sales team and a personalized marketing approach to help prospects with research earlier in their journey.
Use behavioral data and contextual data to interpret a prospect's interest in buying your product or service. When we put them together, we call these signals intent data.
Intent signals are traditionally thought of as form fills and other overt actions that "announce" a prospect's interest. These are useful, but they don't tell the full story. Your company's website is an important watering hole for buyers with purchase intent, and tracking its visitors and the way they browse your pages can reveal plenty of covert intent as well.
This insight gives marketers a way to gain control over the buyer's journey by detecting intent — and reaching out — early. It also gives them clues for how to make the outreach relevant, helping them create a holistic, consistent experience, which leads to happier and higher-value customers.
Combine intent data with fit data for a powerful double-whammy. When intent and fit overlap, it means you want the same customers that want you. Knowing who those customers are lets you focus your sales and marketing energies on VIP customers who are more likely to buy and provide high ROI on sales and marketing costs. This type of prioritization is necessary for a B2B SaaS company to scale up while maintaining a relatively small team and lower customer acquisition costs. Boom!