Intent classification, in a nutshell, converts data into action so that you convert prospects into sales. With the right tools and techniques, go-to-market teams can leverage intent classifications every step of the way, and even gain the ability to recognize and approach white-hot leads. The next wave of sales strategies is all about intent signals, with intent classification bringing order to chaos.
What Is Intent Classification?
Users of intent data want insights into their prospects’ wants, so they:
- Get their IT people to generate it from their own websites and media properties
- Subscribe to a SaaS company like Salesforce to collect and process this data
- Buy it from intent data providers
What they end up with is lots of information that needs to be put in some kind of order. Intent classification is the practice of sorting intent data into categories that are useful to sales and marketing. The idea is to rank the data according to the chance of a pending purchase, and then send it to sales or marketing for appropriate treatment.
Isn’t that the Same as Lead Scoring?
Not exactly. Lead scoring is “the process of numerically ranking incoming leads on a scale that shows their potential to translate into sales.” The important part here is “incoming.” Intent data includes this type of information, but goes beyond it with intent signals that are not incoming.
For example, a sales team might buy third-party profiling data such as firmographic (company information) and technographic (technology use information) signals that would be classified as weak. The companies covered in this information may have never even heard of you. But they are intent signals nonetheless, and can be turned into intent-based targeting strategies that do amazing things.
Being Clear about Sales Intent Classification
There’s a bit of confusion out there around the phrase “intent classification.” For some, intent classification is a technology mix of natural language processing (NLP) and machine learning (ML) that channels an interaction into certain categories.
So, for example, if a customer types “I want to talk to a person” while using a chat bot, intent classification will route their request accordingly. The problem is that “intent” in the world of sales is short for “intent to purchase,” but many of the applications for the NLP type of intent classification are not related to sales.
How Does Intent Classification Work?
You need two things to operate a classification system: data and a grading standard. As we mentioned, data is gathered from first-, second-, and third-party data providers.
A grading standard is a way to translate the facts and figures of data into something workable. A very simple grading standard can categorize intent data into marketing or sales information. In this case, a signal that involves somebody downloading a white paper – and while doing so, entering their contact info – will be sent to marketing. This can lead to a follow up in the form of emails that notify the prospect of new white papers.
On the other hand, if the signal is categorized as sales, a different treatment follows based on data insights. Let’s say that the data point has been classified as a strong buying signal, like a direct contact filling out a demo request. This goes to sales, where they might arrange a pre-demo call to figure out the prospect’s needs.
Back to that NLP/ML Thing
One interesting crossover between the “NLP” interpretation of intent classification (see above) and sales intent classification is the ability to immediately exploit very recent contacts. Normally, an automated intent classification system reviews the data and sends a report to sales and marketing, perhaps once a day. But an NLP-based system can be set up to go a step further by recognizing high potential leads.
For example, if somebody sends an email to a salesperson asking for a demo, NLP can be programmed to understand the meaning of the email and notify the salesperson immediately. As the Harvard Business Review explains, very rapid responses to customer queries – within an hour – greatly improve the chances of success. So NLP does not need to be part of an intent classification setup, but it can provide valuable sales opportunities in some cases.
How to Use Intent Classification
Making the most out of categorizing intent data relies on the source of the information, the grading system in use, and the data insights that your team creates. Here are a few examples:
1) First-party data with a web analytics-based classification system. You can translate certain customer interactions with your website into type and level of intent. If somebody views one of your product pages, you can classify it as direct contact with strong intent, but perhaps not ready for a sales pitch (because otherwise they would have asked for a demo). In this case, you would categorize it as a marketing contact. This interaction would then trigger a data insight such as activating targeted advertisements.
2) Third-party data with a customer data platform service. Third-party data is often delivered in masses, but these providers tend to have consulting teams that work with clients to filter the information and derive insights. If you are running an ABM strategy, you can use the service to define your ideal customer profile and then classify the third party’s intent data according to the profile. Get a “hit”? Finding a potential lead because it matches your intent category is only the beginning. When working with third-party providers, they’ve also possibly given you the contact data for the organization, if not the individual, that triggered the hit. All you need to do is call.
3) Second-party data with manual sorting. For a limited amount of information, there’s no need to apply technology. Simply creating an Excel spreadsheet might do. An example is a product review site that sells contributor data to companies, which is a classic second-party deal. In this case, a company can look at negative reviews of their product as a signal of potential churn. With the second-party data, salespeople can contact disgruntled customers and do a “welfare check.”
Key Takeaways
- Intent classification is a way to categorize intent data so as to handle prospects in the most appropriate manner.
- To use intent classification, companies obtain intent data and then rank it according to a grading standard that divides leads based on level of intent.
- The exact follow-up of categorized intent data depends on how it was obtained, the nature of the intent, and the approved sales/marketing approach.
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