B2B Marketing and Sales leaders are constantly looking for the next innovative method to give them a competitive edge – particularly in driving revenue for their business.
One method that continues to pique interest is Intent Data.
Having created numerous products that leverage massive volumes of data in the marketing and sales space, I continue to hear many questions on this subject.
But it’s clear to me that the most fundamental problem is this: the majority of B2B professionals still don’t actually know what Intent Data is!
So, what is intent data?
To answer this question, let’s first break down the components that comprise “Intent Data”.
“Intent” is behavioral information collected about a person’s activities online which combines “topic” and “context” data.
What do I mean by “topic data”, and where can I get it?
When you search for something or visit a website, you are expressing an interest in that topic. For example, people who read this article are expressing some level of interest in the topic of “Intent Data”.
Types of topic data:
There are several different categories of topic data:
- Anonymous 1st Party Behavioral: People visiting your website identified by their IP address. The IP address is then mapped to company name. Vendors like Demandbase and Marketo website personalization leverage this information to personalize the content displayed.
- Known 1st Party Behavioral: People visiting your website who have also filled out a web form online. By providing their email address, they are considered “known”. Marketing Automation Platforms like Marketo and Eloqua then track each page view associated with this person’s email.
- Anonymous 3rd Party Behavioral: People visiting other websites that the marketer doesn’t own but indicate some relevance, for example Forbes.com. The IP addresses of people browsing that site is collected by vendors like Bombora and Big Willow.
- Known 3rd Party Behavioral: People visiting other websites and also fill out a survey on that site providing their email address. In this context they become known to the website owner and vendors like TechTarget make that information available to marketers.
But there’s another layer. Topic interest alone is not all that actionable without knowing the context of the individual person reading the article.
What do I mean by ‘context’?
Context is all about gaining a human insight into who the person is that’s taking the action in question.
For example, if someone reading this is a marketing professional, it’s possible they are in the process of evaluating a product that leverages intent data. But if the person is an industry analyst, it’s more likely they’re writing a white paper and looking for people to speak with on the subject.
[Shameless plug alert: If you’re evaluating Leadspace – feel free to contact us when you’re ready. ;-)]
Levels of context range from higher-level, more general information (Which company does this person work for? What is their official role within the organization?), to really granular, personal insights (Does this person have expertise in using technologies or best practices associated with my product? Does this person and company combination match with my buyer or could they be an influencer?)
Without this context, you’ll be wasting your marketing and sales energy and budget engaging with companies and leads that may be making all the right behavioral signals, but will never become customers (for example, trying to sell your product to the industry analyst).
How can B2B marketing teams most effectively leverage intent data?
When people visit your website before they’ve filled out a form, their activities are considered “anonymous”.
This term is a bit misleading because the visitor is not, of course, completely anonymous. Using website personalization technologies like Marketo web personalization or Demandbase, a marketer can identify the company and/or industry a visitor represents based on their IP address alone.
However, as explained above, that visitor is still “anonymous” on a personal level. You don’t know who they are or where they fit within their given company or industry. They could be the CEO or CMO – but they could just as easily be a junior intern or a janitor.
Once the visitor has been identified “anonymously” on the website and viewed a collection of webpages, they can be served customized content to incentivize them to take a specific action. In most scenarios, the anonymous personalization is a means to get the visitor to identify themselves via a form fill so a sales rep can engage with them (or not).
With the adoption of marketing automation platforms and lead lifecycle management, many companies are already using 1st party behavioral data (context of the individual) to track progress within a lead scoring model.
This scoring model attempts to quantify the intent of the visitor based on a culmination of activities. For example, when someone visits the product overview page their lead score will increase by 5. If they visit your pricing page, indicating an even greater interest in buying, it will increase by 10, etc.
When that person’s score reaches an agreed upon threshold (or becomes marketing qualified) an alert is sent to sales to reach out to that person.
The value – and challenges – of 3rd party behavioral data
While it may not be strictly true that most customers make the decision to buy before even speaking to sales, what is clear is that more and more people are at least being strongly influenced in that decision before reaching out to a vendor or even visiting a website. People review content within their newsfeed on social platforms such as Facebook, Twitter and LinkedIn, or read reviews on G2Crowd, which can help push them in one direction or another. These activities are considered 3rd party behavioral data.
3rd party behavioral data is highly unstructured and the volume is massive. As a result, very few companies possess the budget or expertise to integrate such data into their existing marketing and sales processes.
Marketers have increasingly turned to predictive analytics platforms, to help sift through the noise to determine which 3rd party topics are actually relevant and integrate with marketing automation and CRM platforms.
Anonymous 3rd party topic data can be incorporated into predictive account scoring models to determine prospective accounts’ likelihood to buy. This information is used to identify target accounts for outbound initiatives as well as prioritize new inbound inquiries from people within high scoring accounts.
Known 3rd party topic data can also be incorporated into predictive persona scoring models.
The most common mistake B2B marketers make with Intent Data
The first use case for persona scoring models is to prioritize inbound leads and the handoff to sales. When a person has researched the relevant topics related to your product on other websites, you don’t want to wait for them to then hit the behavioral thresholds (like downloading your whitepaper) before passing them onto a sales rep. If they’re clearly interested, you want to strike while the iron is hot.
The second use case for persona modeling is to deliver personalized content based on the user’s persona.
It’s at this point in particular that many marketers stumble and fail to actualize the full potential of intent data.
Nascent personalization and nurture efforts leverage job titles to segment inbound leads. The problem? Job titles in the B2B space particularly are not standardized, change frequently, and often give no real insight into the seniority, buying power or even specific functions which that lead serves within their company.
This often results in improper categorization of people, leading to unqualified leads being sent to sales, and “personalized” content being delivered to leads for whom it is in fact irrelevant.
To ensure your vendor is properly tying topic data to buyer context (i.e. intent), be sure to ask the following questions:
- What level of context can you provide me about my buyer? (Company only? Person only? Ideally, you want both.)
- Can you deliver the context (i.e. company, person and topic attributes) used in your modeling process, so I can use the information in my existing lead and account scoring workflows? (Steer clear of “black boxes” – i.e. predictive models which don’t let you look “under the hood” and view the data which informed the model.)
- Can I get setup with a quick win and then grow with your offering into more sophisticated uses of intent data?
If your vendor cannot deliver on the 3 above items, it will be difficult for you to leverage intent data to it’s fullest potential. Instead, you’ll risk putting yourself and your organization at risk of buying a solution that doesn’t help you achieve your business objectives.
Are you leveraging intent data today? If so, I’d love to hear how you’re using it and what you think in the comments section below.
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Picture by geralt on Pixabay | CC0 Public Domain