AI-driven Marketing: Multivariate or Single Variable Analysis?

AI and Intent Data Take B2B Leads from Anonymous to Fabulous

According to Forrester, most B2B buyers prefer anonymity while researching, rather than reaching out to sellers and turning up as B2B leads. No surprise there. That’s been the challenge for brands that depend on lead-based demand generation. In other words, for just about every B2B marketer.

There’s a difference between buyers actively evaluating solutions or topics, and someone consuming content to stay up to date with trends or to learn something new. Content interaction can mean many things, so you have to work to figure out what’s going on. Someone could be browsing a website for several reasons apart from purchasing a product or subscribing to a service. That’s where artificial intelligence (AI) comes into the picture.

With capabilities like Natural Language Processing (NLP) and Machine Learning (ML), AI sorts through intent data to understand buyer behavior beyond just content consumption. While typical intent companies primarily deal in basic data, next-level partners bring more AI-driven value by pulling more information out of a rich data set. (We’ll cover more on that later.)

Equip your B2B revenue programs with AI-powered intent data to:

  • Target more precisely 
  • Tighten focus to most relevant prospects
  • Provide the most relevant content

When you are more empowered to do these things, you increase the probability of getting to those interested, highly qualified B2B leads all marketers want.

Attribute Intent and B2B Leads to Individuals in Accounts

The value of AI-driven data targeting is organizing large audience segments of individuals actively emanating intent around similar topics relevant to your industry. Reliable data algorithms screen out inaccurate records, so you can get on with your selling and go-to-market strategies. No more time wasted on invalid contact data. It’s time to execute campaigns.

The ability to attribute intent signal activity directly to an individual in the buying organization is a game-changer for shortening sales cycles and winning revenue.

Segment and Target at Scale

Because there’s so much data out there, “analysis by paralysis” is a risk. AI helps marketers concentrate on the right B2B leads. A Natural Language Processing algorithm allows Google-like search capabilities, making it possible to find appropriate topics for your goals and personas. A skilled data partner uses algorithms like these helps you build your ideal customer around these qualities.

One example of AI organizing intent for marketing use is the True Influence Relevance Engine®. Its sophisticated analytics study web search behaviors and page content that contribute to identifying intent. The process gets quite complex, which is why it’s taken AI to take it to this level.

The advanced analytics engine in this example uses variables for some 7,000 topics (hardware, software and service solutions) essential to B2B marketers. To analyze intent signals, data is assigned to relevant topics and then mapped by AI with information like:

  • Company domain
  • Location (headquarters, branch)
  • Firmographics (industry, employees, revenue)
  • Full contact record (for respondents)
  • Installed Technology (for that location)
  • Demographics (Title, location, phone, email)
  • Customer base
  • Type of organization

Partnering Around Targeting

Many intent companies typically collect some data on these anonymous buyers by tracking visitors to specific websites and syndicated content assets. The list of companies showing interest in a relevant category is sent to the brand marketers. That used to be all there was to it. Now with AI-driven analysis, B2B marketers have more contextual data about anonymous buyers. 

A typical intent data company tracks online prospect interaction around specific topics during a finite period. This data delivers somewhat one-dimensional results. If the goal is a list of in-market B2B leads — companies and decision makers — who might be helped by your business, this data only takes you part way.

Go for Deeper Insights into Audience Behavior

The AI-driven approach to market intelligence offers deeper insights into audience behavior than the averaging used by some intent monitoring solutions. 

  1. Spiking Contacts: These individuals match an ideal customer profile and exhibit high levels of purchase intent. Prioritize them for sales qualification and outreach.
  2. Demand Units within Buying Organizations: Extensive contact and firmographic data to augment database and identify-inferred contacts, individuals in active accounts who are peers to Spiking Contacts and will influence the buying decision. Target them with engagement and nurturing programs.
  3. Intent Signal Content Sources: Not all web content is created equal. A solution like the Relevance Engine weighs the credibility and source of signal activity in its analysis, (including contacts and intent signals from True Influence Content Syndication.)  
  4. Market-wide Topical Interest: Research the interest activity in your solution category in any industry across millions of companies, locations and personas. Get the clearest possible picture of your Total Active Market.
  5. Segmentation Performance: Track overall intent trends for unlimited target segments based on your segmentation criteria for contact information, firmographic data and topical interest. Some solutions can project the number of net new contacts it will deliver, based on your ideal customer profile, intent topics and other criteria.

Sometimes potential buyers are even left out altogether because they don’t exhibit the behavior intent data companies are tracking. Maybe they’re using different search terms than what the brand targeted. Maybe they don’t match the persona. 

The greatest asset for any sales and marketing team is accurate data, yet it’s also one of the most difficult to capture in most cases. Bringing in an “intent-tech” partner to coach and launch campaigns is often preferred. 

Data Is a Marketer’s Best Friend

Many companies provide intent data. The difference comes in how partners handle the data itself. Quality, not quantity is the goal, but with AI working at scale, you can pretty much have both. Quality data saves time that would have been wasted chasing the wrong B2B leads, and it helps you find new prospects genuinely interested in buying your product. 

Services from a well-developed intent partner will include email verification, tele-verification, and often a B2B / B2B2C contact replacement guarantee. Also ask whether out-of-target contact records are replaced without question. 

For a half day of learning about B2B leads, AI and intent data, register for the True Influence Spring Summit: Accelerating Revenue with Artificial Intelligence. 

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