What Is ☝️Buyer Intent? Explain It Like I’m 5

Buyer intent is the level of interest that a potential buyer has in a product or service. It can be influenced by a number of factors, including advertising, word-of-mouth, and previous experience.

Understanding purchase intent is essential for businesses because it can help them target their marketing efforts more effectively and generate more sales.

Buying intent can be categorized into two main groups: active and passive buying intent.

Active Buying Intent

Active buyer intent characterizes the proactive steps a potential customer takes to learn more about a given product or service. In other words, they’re in the market for the solution you offer.

This type of buyer intent helps companies measure how likely a consumer is to make a purchase in the near future. It can be determined using a variety of factors, including search engine queries, website visits, and social media engagement.

Passive Buying Intent

Passive buying intent is when a potential customer is not actively searching for a product or service but may be open to purchasing if they encounter it.

There are a number of factors that can influence passive buying intent, including social media usage, online activity, and even offline interactions.

In the real estate industry, passive buyers make up 66% of the market, with a similar situation across all industries. Regardless of the industry you’re in, active buyers make up only a tiny sliver of your target audience.

That’s why businesses must be aware of the various ways that they can reach potential customers with passive buying intent. By understanding this concept, businesses can more effectively market their products and services to those who are most likely to make a purchase.

How Does Buyer Intent Work?

When someone visits a website, enters a keyword into the search box, or pulls up their mobile app, there is usually a purpose or goal in mind (also known as buyer intent signals).

Let’s use search engine queries as a running example to show you one of the ways buyer intent can be categorized using aggregated behavioral signals.

When it comes to search engine queries, all of them can be categorized into four different types: informational, navigational, commercial, and transactional.

  • Informational intent is when the user is looking for information about a topic without necessarily intending to make a purchase. For example, someone might visit a travel website to find out the population of London for their marketing project.
  • Navigational intent is when the user is looking for a specific website or webpage. For example, someone might enter “Facebook” into a search engine in order to be taken directly to the Facebook homepage.
  • Commercial intent is when the user is interested in researching a product or service before making a purchase. For example, someone might search for “best laptops” in order to find reviews and compare prices before buying a new computer.
  • Transactional intent is when the user is ready to make a purchase and is looking for the best way to do so. For example, someone might use the keyword “Expedia discount code” in order to find a coupon that will save money on their hotel booking.

By understanding buyer intent, businesses can better target their advertising and content marketing strategies, resulting in more website visitors who are interested in what they have to offer.

How Is Buyer Intent Data Collected?

There’s a special type of B2B and B2C data that helps companies decode buyer intent. It’s called buyer intent data, and it’s generated from a variety of online and offline interactions, including:

  • Website visits
  • Bidstream intent data
  • Search engine queries
  • Social media interactions
  • Downloads of digital content (e.g., eBooks, white papers, etc.)
  • Attendance at webinars or other events
  • Interactions with sales representatives

Behavioral intent data can be separated into three distinct groups based on the sources of that information:

  • First-party intent data: This is data that’s collected by the company itself, such as website visits, interactions with sales representatives, and downloads of digital content.
  • Second-party intent data: This is external buyer intent data that’s collected by another company and then sold or licensed to the company in question. An example of this would be if Company A collected website visit data from its own visitors and then sold that data to Company B.
  • Third-party intent data: This is data that’s collected by a company that specializes in intent data and sells it to multiple companies – also known as intent data vendors.

Note: On top of merely supplying companies with data, the best intent data providers offer advanced data enrichment, analytics, and management capabilities to boost the ROI of the information they provide even further.

How Do You Use Buying Intent to Improve Business Outcomes?

Understanding and capitalizing on buying intent can be the difference between success and failure.

So how can you use buying intent to improve business outcomes?

1. Enhanced Predictive Lead Scoring

Buyer data can be extremely useful for predictive lead scoring. By understanding the intent of a lead – what they are looking to buy and when – businesses can better align their sales and marketing efforts to the chances of making a sale.

Once this data is collected, it can be used to create models that predict the likelihood of a lead converting into a customer. These models can then be used to score leads and prioritize follow-up efforts to close more sales and retain customers.

2. More Effective Content Marketing Campaigns

B2C and B2B intent data provides marketers with the ability to see which topics and keywords are associated with users who are further along in the purchasing process.

By understanding what questions potential and existing customers are searching for answers to, businesses can create content that directly addresses their needs, from blog posts to lead magnets.

Additionally, changes in buying intent over time can provide insights into when customers are likely to be ready to make a purchase.

For example, if there is a spike in searches for product reviews shortly before a holiday, retailers may want to adjust their marketing campaigns accordingly.

Armed with this data type, businesses can create more targeted and effective content marketing campaigns that are better aligned with customer needs to identify early buyer interest.

3. Better Personalization

Personalization is vital to providing an excellent customer experience and generating a higher ROI. By definition, personalization means tailoring content, products, and services to match the specific needs and wants of an individual.

This can be done in a number of ways, but one of the most effective is through the understanding of buying intent.

For instance, if you know that someone is in the market for a new car, you can provide them with information on the latest models, special offers, and test drives. This type of personalization helps to create a more seamless customer experience and can ultimately lead to more sales.

4. Better Segmentation

To segment your target audience in an effective way, you can use buying intent to identify when users are further along in the decision-making process. By understanding the factors that influence their decisions, you can cater your messaging and offerings to match their needs.

Identify the different stages of the buyer journey, from awareness to decision. Once you know where your customers are in their buying cycle and have successfully identified customer success triggers, you can tailor your marketing and sales strategies to fit their needs.

Customers in the awareness stage may need more education about your product or service, while those in the decision stage will be more receptive to special offers or discounts.

By understanding and leveraging buying intent, you can ensure that your sales and marketing teams are always aligned with your customers’ needs, leading to better business outcomes.

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Daniel Smith
Daniel Smith
Daniel Smith is automation consultant with a passion for technology, data, AI, and machine learning. Daniel loves to learn about new technologies and how they can be applied to solve complex problems. He is also a big fan of productivity hacks and enjoys finding ways to automate tasks to make organizations more efficient.