What if you had a brick-and-mortar store and exhaustively interviewed every unwitting soul who walked in to get a granular-detail understanding of what they were looking for? Including where they’d looked for background information (if that were applicable), what they’d learned about it from talking to friends and neighbors and checking out recommendations on Amazon, what they expected from your products in terms of pricing, how they felt about the overall user experience in your store, and other seemingly pertinent tidbits. 

You’d certainly make them wonder what you were up to, as that’s waay over the top. But you’d probably also be able to confidently discern exactly what they want — their true intent — and whether this benchmark trove of offered-up data overlapped with the benefits or functionality of a product or service your company happened to provide.

Continuing on…imagine that you’re satisfied that you’ve spent the last hour picking their brain, and you’ve released them to browse your in-store wares (if they’re even interested in doing that after that little ordeal).

You set up a baseline-level spreadsheet and start playing around with your makeshift intent prediction model and trying to make correlations in the hopes of stumbling on some natural ways to do user intent prediction: to put your finger on just what they want.

Finally, based on that insight, you do some optimization of your site to market to them in the most attractive way possible, and you work on upgrading your customer experience to meet their expectations.

This clunky form of forecasting is the equivalent of modern information retrieval and validation of purchase intent, whether your shoppers are consumers or business buyers.

Intent data can be collected courtesy of computer-science advances such as natural language processing and semantic search, thanks to the fact that 97% of consumers research products and services before making a purchasing decision. With software augmented with artificial intelligence and advanced algorithms, researchers can turn up exactly what users are looking for, and in the case of ecommerce, whether they’re likely to click Buy anytime soon.

What is intent data?

Buyer intent is one thing; buyer intent data from the unique buyer journey — the vital proof documenting that intent — is another.

Intent data is info about a potential buyer’s online behavior that indicates what they could likely do next or buy: their interfaces illuminate their intention to purchase. It’s collecting information using identifiers such as IP addresses and browser cookies, plus clicks on keywords and content. Someone’s content consumption might include reading online reviews, perusing blog posts and clicking links to land on other content, watching videos, gaining access to gated material, and comparing products, for instance.

Intent data also typically revolves around particular topics, products, and services. And it zeroes in on the (positive or negative) sentiment associated with topics, supplying a more comprehensive view than traditional profiling or behavioral data can produce.

What’s the difference between predictive analytics and intent data?

Predictive analytics (also known as predictive intent) is mostly tied to things that have happened in the past. It uses Big Data to make predictions about who will purchase what and when. Information comes from various data sources, such as customer profiles and sales, and this method incorporates elements like data mining, data modeling, machine learning models, historical information, and AI.

Intent data is arguably superior in that it’s focused on tracking and recording shoppers’ actual explorations online. It can therefore do a better job of helping marketers and sales reps identify potential prospects and pinpoint when they’re ready to buy, such as when they’re entering relevant keywords in a search bar, watching a webinar, or downloading related content.

It’s worth noting that a company can certainly use these two approaches in tandem to get a complete picture of the data. However, gathering buyer intent data is less complicated (with fewer integrations needed), and it immediately supplies specific real-time insights, unlike predictive intent.

B2B intent data generates major leads

When it comes to collecting and analyzing intent data, the B2B buyer is of exceptional interest. Aggregate business buyer intent data is, in fact, so intriguingly valuable that Gartner has referred to it as “the future of B2B lead generation.” And Gartner predicts that by the end of 2022, more than 70% of B2B marketers will utilize third-party intent user data to target B2B sales prospects on the buying journey or engage groups of buyers in selected accounts.

Two kinds of intent data

Intent data breaks down into two types: topic and context.

A topic is similar to what it would be in a generic sense: a subject. In this case, it’s the subject of content online. Topics are formally identified through the application of natural language processing (NLP) and deep learning models. A topic could be a concept, place, person, product, company, or other entity.

When you do a search for something, you’re indicating interest in whatever that topic embodies. 

However, with compiling intent data, it helps considerably to understand the context of the person’s role and their search in order to make the data genuinely relevant. To get accurate buying signals, real-world context is key. For example, maybe the searcher is a student doing research, or someone looking for data to present at an international conference, as opposed to a business buyer with the authority to purchase for a large company.

When you have a clear topic coupled with clear context, you have a pretty good picture of true intent and your marketing folks can confidently move forward.

Three types of intent data

Data is grouped according to first-, second- and third-party-supplied information.

First-party intent data

This is visitors’ intent signals gathered from your own site. Businesses collect first-party data on their target markets by employing analytics tools or marketing automation platforms.

First-party data may be anonymous, or you might be able to use IP identification or forms the person has filled out in order to dig up their specific details. 

Data could be pulled from forms that people fill out on your website, downloads they complete, search terms they enter, pages they land on and look at, items they put in their shopping cart (whether or not they then buy them), anything activity based. 

More broadly, first-party intent covers any information you collect in interaction with your site visitors, prospective buyers, and existing customers, including what you can glean from any email marketing campaigns and social media placements.

Second-party intent data

Second-party data can be thought of as essentially another organization’s first-party-gathered data. With second-party information, data is collected by a secondary company, such as a review site (think Yelp) or a publishing network or collection of sites where your products or services are linked. When users register on these types of sites, they grant the site owner the right to share and sell their contact details and any intent data that they leave behind.

Like first-party data, this gathered material could include customer engagement data from activity on websites, apps, and social media, in-store purchase history, survey responses, and more.

Search engines are one type of second-party customer intent data collector. For example, if you advertise with Google, your keyword data is technically second-party data, not first. People searching provide keywords to Google, not directly to you on your site. You contract with Google in an informal partnership to gain access to those keywords.

Third-party intent data

This last type of intent data encompasses information collected from another site. As with first- party data, it may be gleaned from filled-in forms and IP addresses, interaction with chatbots, and other activity. 

Third-party data is usually gathered by companies that are in the business of gathering intent data. These intent data providers can typically offer a series of researching and shopping touchpoints embarked on by a user before visiting your website. 

Which type of intent data is best for your business?

First, second, or third: Which should you pick?

It depends on factors such as the size of your marketing campaigns. If you have a limited budget for collecting intent data, here are some things to consider:

Pros of first-party intent data

• The data is all your own: a unique dataset illuminating your buyers’ interests and pain points (by contrast, if you choose to buy third-party intent data, your competitors will have access to it as well)
• You control and can segment your data in whatever ways best meet your needs
• Your marketing team can use the data you collect to strengthen your sales processes and marketing strategies

Cons of first-party intent data 

• First-party data constitutes only one segment of the shopping journey
• You could miss out on data on potential prospects who aren’t visiting your own site, for example, those who are only researching the competition on third-party sites

Pros of third-party data

• You get a broad and a detailed view of a prospect’s activities, the entire path they follow and each step they take.
• Third-party data is available the earliest of any type. That means it can help a company plan how to work out the kinks, proactively beat the competition, and offer a more personalized experience when the shopper sets virtual foot on their own site.

Given these benefits and limitations, companies often combine first- and third-party data to get the most complete picture of their prospects’ journeys.

How can you leverage intent data?

So you now get why intent data is so key to predicting user behavior and increasing your revenue. Essentially, if you can identify a prospect’s position in the sales cycle, you can then appropriately feed them the relevant content they want in order to nudge them closer to making a purchase, potentially leading to big revenue increases.

It all comes down to using your intent data to identify prospects who are ready to buy, and then fine-tuning your marketing campaigns accordingly. 

Most businesses start utilizing intent data to assist Sales in prioritizing a list of target accounts to approach. Sales teams can then intuitively reach out to account-based marketing (ABM) prospects right when they’re likely to be interested, conceivably leading to a higher conversion rate. 

Intent data can also be used for the related work of future prospecting as people reveal their ongoing shopping and buying patterns.

Want to take your intent data utilization to the next level? You can use the data to create unique groups and targeted lists based on specific activities, then let Marketing do audience outreach in more-personalized ways.

Use case: leveraging search intent data

If you have a retail website, you’re undoubtedly concerned about the quality of your search intent data.

You can immediately start collecting search intent data from your site visitors’ actions in order to provide state-of-the-art results. The Algolia API platform will let you predict your users’ intent and deliver the right search results, both on your site and in your apps.

In addition, your decision makers can use our out-of-the-box search analytics to pore through the data and adjust their content marketing efforts. You can model users’ behavior by breaking down their searches into metrics such as popular searches, number of searches with no results, and filter usage. 

Companies including Lacoste, Slack, and Medium rely on us to meet the search needs of their potential customers in their unique buying processes.

Want to join these winning sites and enjoy a likely bump in retention or conversion? Contact us to find out how you can use intent data to improve your customer satisfaction with search, with all the attendant rewards we feel it’s sure to bring.