The right ways to use intent data: How to pivot strategies in 2021

When it comes to B2B marketing, intent has emerged as one of the hottest buzzwords, next to more established strategic considerations such as ABM.

In less than a decade, B2B marketers have been taxed with becoming data scientists, data analysts and most importantly, stewards of one of most important assets to success of their marketing initiatives… all in the service of illustrating a path for marketers on what action to take.

To date, most intent data has focused on the digital research activity of B2B consumers. The methodology of data gathering has been the primary difference among data sets. Ultimately, the industry has boiled down to three different categories.

The first are those that listen to the ‘exhaust’ across programmatic advertising exchanges. Obviously, the exchanges have a tremendous amount of scale, but that scale includes a lot of noise.

Less than 2% of that traffic will be B2B specific as the inventory that can give true signals of B2B buyer behaviors/intent is often not available through the exchanges. Not to mention privacy concerns or the fact that publishers can’t monetize their own data.

Next are data co-ops. The data co-ops bring a network of publishers together, and since they do not solely focus on Owned & Operated properties, they can corral a more specific, targeted suite of inventory sources which provide better quality data for (Read more...) marketers.

This model enables publishers to monetize their data and allows the co-ops to still provide sufficient scale in their data models to ensure their effectiveness. While they can still provide scale, every provider still has different strengths and weaknesses.

Finally, there are niche providers. These providers have a narrower and highly granular targeted set of inventory sources, which provides very high-quality intent signals. Generally, these do not possess the same level of desired scale but exist as a higher quality source. Higher quality ensures a marketer can get the most effective data asset for their needs.

These sources are all focused on a single classification of buyer behavior data. This results in a data asset that is basically one sided. A true supply chain analysis requires analysis of both buyer and seller behaviors to understand what is going on in the market and how to address it.

So, what are B2B marketers supposed to do? What type of data is right for them? What other data sources should they consider?

What should a marketer look for?

While a static data point on current intent trends is interesting, the trend over time gives you a better perspective on the direction the research activity has been taking. The current score is critical, yet the trend over the last 6-12 months tells the story and enables a marketer to understand the research patterns that align to their sales cycles.

It’s important to remind ourselves that every intent source has gaps in coverage. It is unrealistic to expect any one source to excel in each need; integrating data from multiple sources yields the best opportunity to ensure you have the best coverage across all your segments and accounts.

Finally, for a marketer to differentiate themselves in an already crowded market, they need to look beyond the traditional research signals. Marketers need to find other signals that indicate market interest, market saturation and opportunities to develop the most effective account and content strategies.

What do I do with all this data?

Trends and insights are great, but a marketer needs to be able to turn the data and insights into an actionable company and content strategy to influence their ability to convert more accounts, faster. (We call that the 3 C’s of B2B Marketing!)

After validating the markets and companies to engage with, understanding the level of saturation from other sellers is a great tool to inform the content strategy. For example, oversaturated markets require a strong message around product differentiation, but in undersaturated markets one should focus on industry challenges.

I’ve built my company and content strategy, now what?

Once those strategies are developed, a marketer must not only build their execution plan that leverages the dominant paid media channels, but most importantly, having the ability to measure and optimize towards the effectiveness of their data-driven strategy is critical to success.

If a marketer can’t measure the effectiveness, then they have no way of optimizing their efforts. How should one measure effectiveness? Sophisticated marketers have moved beyond legacy lead conversion rates and are looking at three key measures of effectiveness of their ABM investments, which include reach/engagement, pipeline and revenue.

In terms of the funnel, reach/engagement is the best leading indicator. This is the first measure of one’s ability to influence the accounts sales teams are looking to work with. And running data-driven, multi-channel approaches has shown significant increase in a marketer’s ability to increase that engagement.

However, just measuring the engagement is not enough, without using the measurement to drive optimization of the companies and content to continuously increase engagement. Optimization is a common theme here, across all three measures of success. If the measurement isn’t being used to optimize the strategy, then it’s not actionable.

Next comes pipeline, the mid-line indicator. Optimizing the companies and content based on pipeline activity, is how a marketer can further influence the pipeline volume, value, and velocity (or, the “3 V’s”).

Finally, revenue is the trailing but most important indicator for a marketer to focus on. By identifying the composition of companies and content that make up their pipeline and revenue, marketers can optimize their strategies to convert their accounts faster. That is the marketers funnel; from engagement through pipeline and revenue.

Through efficient use of multiple intent signals and prime measurement tools and tactics, intent data can be the holy grail to take marketing and sales to the next level.

Sonjoy Ganguly is the Chief Product Officer at Madison Logic, with over 25 years as a veteran product management executive. With extensive experience in the media, financial and professional services industries, Sonjoy came from Deloitte in their Strategy, Brand & Innovation division, managing the definition and execution of new product strategies to identify and cultivate new ways the firm’s practices and its clients can adopt new technologies to create innovative, market leading solutions at scale.