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How to Use Intent Data For Market Research

Jun 11, 2021 | Author Ed Marsh

Tl;dr - Contact level™ intent data can provide powerful market research data for creative internal use AND as the basis for insightful proprietary research published by brands. This use case demands a creative understanding of how to structure and collect data, and how to extract impactful insights from intent data which is often thought of as merely demand generation fodder rather than a cutting-edge market research resource.

Market Research for Internal Decision Making

Large, broad data sets are an important asset for companies trying to understand high-level market trends. While qualitative market research is a wonderful adjunct and better for certain specific requirements like building effective personas, quantitative research is important to understand the big picture.

Sourcing the right data can be a challenge. Even companies with large budgets for market research data and services are often stuck trying to extract insights from stock data sets, or relying on surveys with low response rates and inherent inaccuracies. As a result, they often rely on relatively small sample sizes and anecdotal data for their research. Between sources that don't provide exactly the detail they need, and small samples, they risk making important decisions based on inadequate data.

Market Research for External Promotion

Published research is often used for marketing and promotional purposes, PR, and thought leadership. That's also based on market research, but typically from different sources and obtained on an expensive bespoke basis. The firms which produce these commissioned reports often affix their brand as an imprimatur of impartiality and they rely on the captive audience of their publishing subscribers lists to conduct the research. 

In short, these two types of market research are often sourced in different ways, for different purposes.

There's an interesting alternative approach. 

The Power of Contact Level Intent Data

Intent data has the potential to help with both. In fact, this is an example of the breadth of use cases of intent data for marketing that are often overlooked since it's mostly thought of as a demand generation tool.

First, a quick caveat. You might not be able to do this with your current intent data. This use case relies on two important data capabilities:

  • Contact level data - signals which include at least the job title of the person taking the action, in addition to the company
  • Granular activity data - specific key terms, competitors, etc. (not topics based on opaque taxonomies)

These are important because neither the rich insights that support internal use nor the ability to extract specific trends which support proprietary published research can be gleaned from data that omit either of these.

The huge potential power, though, is in the aggregation of thousands of detailed observations of actions people are taking which are then associated with the related company and job title data. Rather than surveying a few hundred contacts (a naturally limited and biased sample by the scope and original source), you're able to truly observe what's happening across a market with a very wide capture, yet very deep event and activity insight. Therefore, from the same data set you're able to collect meta-level market observations AND very specific insights into buying teams, buying journeys, and more.

An interesting example of this is the way that a number of IntentData.io clients in the technology space were able to recognize the higher education opportunity very early in the pandemic last year. Before many schools had decided to shift to a remote model, it was clear from intent data that the higher ed market was changing. Clients who detected these trends as they were developing were able to quickly react - even those for whom higher ed wasn't an ICP industry, but who saw the opportunity.

It's not a market they would have normally thought to query, nor would normal research methods have allowed them to identify the opportunity so early and react.

Three Tips to Use Intent Data For Market Research Requirements

"OK. Sounds intriguing," you might say. "But how do we actually do this?"

Good question. Here are three keys to success.

  1. Build an algorithm that will collect data in support of your likely requirements.
  2. Answer unasked questions to help people anticipate the future by understanding the nature and vector of change.
  3. Guide consumers in digesting the data AND the insights

Let's look at each in a bit more detail.

Build an Appropriate Algorithm

Many intent data sources are built on a pool of standard information which is then sliced and sold incrementally. That may not be adequate. If you're trying to understand, for instance, how the buying journey is changing for cybersecurity software to combat ransomware then you're going to need to think about the range of job titles, the names of threats and high profile cases, the competitors who might claim to offer solutions, the departments, and roles involved in the analysis, etc. And you're going to have to collect data on a bespoke basis to reflect those details.

Knowing simply that there's been a blip in Company Surge™ data might help to understand the ratio of companies that are active in the space, but won't illuminate the difference in CTO/CIO activity in the weeks around high profile events, how other CSuite officers are involved, or when research transitions from the manager and director levels to the VP level.

Similarly, understanding which top of the funnel key terms are most closely associated with the shortest path to specific competitor engagement could be helpful but wouldn't be clear from most intent data.

Finally, observing fluctuations in ownership level engagements in lower-middle market companies could help to identify the right time/conditions for sales outreach if that's your target market.

These are obviously only examples, but they're intended to illustrate the need for a specific key term and competitor detail, the importance of job description (roll, seniority, and function), and the anticipation of helpful questions as the data collection schema is planned.

Answer Impactful Questions

You're going to want a good analyst to help with this, and to collect suggestions from across departments, functions, and seniority to understand the type of questions, and the reasons for them, that will resonate with various people and buying roles.

Marketers who often control intent data will have specific questions in mind, and those may be largely adequate for internal use cases like product marketing and persona research. However, projecting those assumptions on a full spectrum of buyers and influencers is risky. A product marketer will look at the world very differently than a CFO - yet the CFOs desire to understand trends in ransomware protection, and how that informs their own choices and options for the board's consideration - will be important to answer for the proprietary research to have significant value.

It will certainly be possible to add additional data parameters later to help answer new questions that arise. Nevertheless, that will then mean a lag time in collecting data and a longer runway to identifying trends. So anticipate as many creative questions as possible at the inception of the data collection.

And collect data that enables you to offer potential answers which can be gleaned from the data.

Make the Data Accessible

The answers may be fascinating, but will they be meaningful?

It's important to push the dots close enough together to help busy, preoccupied people digest the important insights. After all, how many times have you skimmed through a published report with 15 pages of pie charts showing the data in various ways...without taking much actionable insight away?

This is an important point for market research whether based on intent data or more traditional survey methodologies. The granularity and depth of insight from contact level intent data mean those insights can be more complex and nuanced, though. Therefore it's particularly important to help those who are consuming the data - whether internally or externally - understand why certain questions might be important for them to consider. And therefore why the answers might be important to incorporate into their own mental models.

Two Key Takeaways for Marketing Communications Marketing Agency Folks

First, don't wait for someone to come up with funding for a project with a research publisher in your market. You've got an affordable and creative way to generate your own proprietary research insights for publication. And the richness of the contact level purchase intent data set means that your insights might well be correspondingly richer than even the high-priced publishing sources. After all, the data is possibly already there within your marketing department, and this application would be a free byproduct that could generate a large return.

Second, this is a real opportunity for agencies since many marketing departments are under-resourced and probably not eager to undertake a completely new sort of project like this. If you bring market familiarity and a vision (even a productized package to produce, distribute and promote this proprietary research) for how to present their insights and how it benefits them in their efforts, you can probably use the project as a profitable one that simultaneously gets you into an account without going head-to-head trying to displace a competitor. In other words, sell them on a completely creative approach that then pays you to do your business development to win all the business later!

Traditional Intent Constrains Creativity

The bottom line is that traditional limitations of intent data (inaccuracy, vague topic taxonomies, account-level only) naturally limit the creative applications. That's a function of specific data models, not a limitation of intent data in general.

Market research, whether for internal use in product marketing, or external use in publishing innovative proprietary research is an example of interesting opportunities for intent data for marketing - akin to using contact level intent data for public relations and other applications.

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