Intent data’s popularity has spiked over the last few years, recently hitting an all-time high. But, on its own, intent data, can be problematic for businesses and requires an additional filter to achieve the biggest results.

Intent data is, by definition, any data that has been collected to help make the assumption that a business has the intent to purchase. First party intent data has been harvested for nearly a decade in the form of engagement data from platforms like Marketo, Eloqua and Pardot, revealing who has visited your website and what they were doing while there.

Today, it’s third-party intent data that’s rising in popularity and getting more attention. This data shows the browsing behavior of anonymous visitors tracked back to the company they work for via reverse IP lookup. The added benefit is that it shows the consumption of content across a network of publishers (think Business Insider, Forbes, etc) in aggregate across an entire business. A baseline of normal consumption is set, and intent vendors (such as Bombora) look for spikes or “surges” in research, indicating an intent to purchase.

Also Read: Why Data Orchestration Is a Big Deal for B2B Marketers

Intent Data is Raw

While these vendors cannot de-anonymize visitors to pinpoint exactly who is doing this heightened levels of research, it can give you an indication of a project building within a broader organization.  And, although you may not know that Tom, Dick or Harry from Microsoft is looking for a solution in a specific category, the power is in knowing that multiple employees are researching, which can be used as a signal that an evaluation is in fact, imminent, and not just errant late-night internet browsing.

Net-net you find out which companies are looking to buy your solution. Sounds pretty great, right? Not so fast. The issue with intent data of this type is that it is a raw feed of all companies performing heightened levels of research. It does not address your ideal customer profile and will give you results that include accounts that are, in fact, not a good fit for your business. Simply passing this data on to your sales team and saying “Go!” could significantly damage trust between sales and marketing.

Also Read: Marketers Must Leverage Unstructured Data

Pairing Fit and Intent Data

So what to do? This is where Fit as filter criteria layered in front of intent data is critical. Fit is defined as all the characteristics that make a business a good fit for your product or service. Fit takes into account any demographic, technographic or recent changes to a business that would make them an ideal account for you to sell to, even going so far as to show the level of similarity that exists between that account and your current customers.

Pairing Fit and Intent data together ensure that you are marketing or selling to accounts that are a great fit for your product or service and are actively looking for a solution like yours. Fit and Intent data can be pushed directly into your CRM and Marketing Automation platforms so both your sales and marketing teams are operating off of the same data to ensure there is alignment around which accounts to target and when to target them.

As a marketer, this gives you the freedom to spend your time, money and effort marketing to accounts that your sales team actually wants to sell to and have a high probability of closing. As a sales professional, this means you will only be selling to accounts that meet your qualification criteria and are looking to make a purchase.

Also Read: Strengthening Predictive Analytics with Intent Data