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Today’s marketing and advertising leaders have more data than they know how to manage. Over the last five years, advancements in machine learning have equipped marketing teams with the ability to reach customers with on-target, precise messaging. But these same strides have the potential to bring chaos to the martech industry. Around the world, data regulations are becoming more stringent. In response to heightened needs for security and privacy, ad platforms are revamping their core technologies. Rising retail fragmentation makes it tougher for brands to strengthen and fortify customer relationships.
With this challenge comes the need for disruptive leadership. It is this reason why 50% of CEOs see their CMO as the primary driver of growth for their organizations, according to Accenture Strategy: “If CMOs focus on disruptive growth, rather than on traditional growth avenues, they have a chance to impact the bottom line in a heretofore unseen way—earning the key to that corner Chief Growth Officer (CGO) office.”
But only 36% of marketing leaders surveyed rank launching new business models as important priorities. This stat likely stems from a general trend, according to Accenture, that marketing teams are also likely to take blame if targets aren’t met. Even though marketers have a wide open door to unlock new growth channels for their organizations, they’re gridlocked. One way to overcome this challenge is to capitalize on deep funnel data which can help better predict a buyer’s intent.
Reach the right customer with the right message at the right time in the buyer journey. This goal is fundamental to all marketing—and the primary driver for how companies choose effective advertising channels. Deep funnel intent data tells marketers what their buyers are searching for and how to develop tailored, high-performing campaigns. At the same time, intent data “causes a great deal of perplexity for many marketers,” writes Sean Zinsmeister for MarketingLand. “Simply put, intent data is information collected about a person’s or company’s activity.”
Deep funnel intent data, also known as first-party data, “contains highly predictive buying signals, since the content is relevant to the purchase decision — for instance, which pages a prospect touched, links they clicked on and how long they spent on each page.”
External intent data, also known as third-party data, comes from publishers, social networks, and other data aggregators such as Bombora, The Big Willow, IDG, Connexity, and TechTarget. This information helps marketers gain contextual information about their audiences.
Deep-funnel intent data enables enhanced prioritization and outreach. With an understanding of an audience’s needs, marketers can develop systems for prospect prioritization, outreach, nurture programs, and more effective measurement.
But these frameworks only scratch the surface of what is possible in marketing. Intent data also enables personalization and targeted advertising. According to Viktor Mayer-Schönberger and Thomas Range in their work in the Harvard Business Review: “To compete against digital champions, [most business leaders] will have to overcome not just scale and network effects but especially new, data-driven feedback effects.” In other words, deep funnel intent data is more than a targeting tool. It provides a mechanism for building in-depth customer profiles through data-driven attribution. Intent data helps marketers distinguish between rudimentary clicks and the buying decisions that those clicks represent.
While it’s easy to collect a variety of data points from different sources to inform your bid strategy, deriving actionable insights from them is the real challenge. Online advertisers and search marketers need to be able to quickly merge and analyze downfunnel data from various sources, such as call tracking or CRM.
Despite these challenges, the value of deep funnel data to identify and better target high quality leads is simply too high for online advertisers to continue ignoring. Using a blanket strategy to bid equally on all kinds of keywords using a simple CPA model can lead to lost opportunities and wasted ad spend. A simple portfolio strategy can cause search marketers to overspend on cheaper keywords or overspend on expensive keywords. Instead, they should be using a customized strategy to reallocate funds to target the most valuable leads.
The information leads provide after they click through to your site can tell a lot about their value and better inform keyword bidding strategies. For example, businesses typically capture good indicators of quality in their lead capture form that can be used to identify trends in where the best qualified customers come from. Businesses can derive insights from metrics like company size, address, etc. Finance or insurance companies can use credit rating, income, address, and other indicators of lead quality, for example.
While most marketers capture this information to inform lead nurturing strategies, the same data can be used to improve ad targeting. Every data point represents an action from a human or machine. The future of marketing precision is built on an ability to make sense of it all, tying signals to user behavior. In this way, deep funnel intent data can help bridge connections between digital and physical touchpoints, allowing marketers to better understand their customers and increase their relevance so they can market to them better.