3 “t-i-o-n-s” of Third-party Intent Data Project Failure

Aug 27, 2019 | Author Yvonne Haendel

TL;DR - Third-party data can deliver significant impact with a great ROI. Sometimes, however, the results are disappointing. Extracting the full value requires appropriate expectations, rigorous tactical activation and visionary data orchestration. Here’s how they all fit together, and a quick look at what can go wrong.

Sometimes Third-Party Data Implementations Fail

We like to talk about intent data successes. They can be remarkable. We often share the statistics of 82% reduction in sales cycle and 50% close rates. That’s serious.

Nevertheless, it’s important to be realistic. Not every company gets those results.

In our experience, there are three reasons for disappointing results — they’re the three “tions” of purchase intent data. You might even remember this from days gone by.

The three “t-i-o-n-s” of third-party data are:

  • Expectation
  • Activation
  • Orchestration

In other words, companies should have appropriate expectations for short and long-term results, as well as for the work they’ll have to do to fully operationalize the data.

Let’s dig into each of these.


Sometimes companies expect a silver bullet. That’s not intent data.

We might refer to it reasonably as the Rosetta Stone or Holy Grail of marketing — it’s the important signal that we’ve always yearned for. Now we know who’s currently active.

And these leads are the perfect basis for programs to generate opted-in leads and scheduled sales meetings.

However, these “leads” aren’t like folks that came to your tradeshow booth, brought an RFQ, and told you they were buying from someone next week. Rather, these leads are somewhere in their buying journey and are often taking action early in their research.

If you’ve built an optimal bespoke algorithm for your third-party data, you will have a variety of signals. Some are ready for sales outreach. Others will require nurturing and patience while watching for confirming signals. Most will benefit from paid social ads, and first-party data intersections will indicate which are potentially the highest priority for your team.

It will take work to sell these leads. They’ll close faster and at a higher rate than random outreach by far. But they may not even know your company, much less appreciate the value you can bring.

Expect the work. They don’t sell themselves.


The work of selling these leads will involve marketing operations and sales operations, in addition to your AEs (account executives). Some of it will be familiar and some will require many companies’ teams to stretch.

The leads will have to be sold using a robust sales process. This probably means 10 to 15 touches across channels including email, phone, SMS, social, direct mail and paid targeted ads. That process will have to be adapted for several categories (job title, industry, stage in buying journey, specific area of focus, active competitors, etc.), and it will have to be continuously measured, tweaked and improved.

Each use case will build on the same data set but will use different analysis and tactics. Therefore each must be planned and executed separately. Often teams start with one — for instance, churn reduction, ABM, sales intelligence, demand gen, event marketing or retargeting. Then once they’ve established and begun to refine the process, they pick the next.

Even in situations where the data is intended for a single-use case — often demand generation — it’s an easy extension to trigger alerts regarding activity from known accounts. Alerting success teams to potential churn risk and up/cross-sell opportunities is common, as is pinging sales teams when there’s third-party data activity from target accounts, those with open opportunities, or when there are intersections between first- and third-party data.

These simple alerts still require context and training for recipients, and they’re best delivered with alert messages that provide context and tips for the suggested follow up.


Fully operationalizing intent data across the organization takes a vision. Contact-Level™ Intent Data supports use cases across marketing, sales and success functions. Often KPIs and incentives differ, as do organizational priorities.

Additionally, most companies have multiple data sets — from their first-party intent data, ABM target lists, customer renewal information, and multiple sets of third-party data including buyer intent, industry and technographic.

This mashup of data and competing priorities means that partial, single-use case/dataset implementation is the far more likely outcome than a fully orchestrated approach. That’s not necessarily bad. The right data matched to the right use case with proper activation can deliver an astounding ROI. Nevertheless, casual implementation carries a substantial opportunity cost.

The reality is that often orchestration follows activation. One department subscribes to data based on its own justifiable use case. Departmental leadership then begins to socialize their success, as well as opportunities for them to assist colleagues. Senior management becomes intrigued and then collectively they build the plan, and the will, to orchestrate data across the enterprise.

The Outsourced Alternative for Partial or Complete Data Orchestration

Anticipating these challenges, some companies choose to bundle intent data with other services. This can range from tactical support (e.g. paid social and display ad management) through full-service programs to deliver opted-in leads and appointment setting. Outsourced email nurturing, content syndication and omnichannel sales-as-a-service (BDR) programs are other options.

These can be appealing to companies with constrained resources and for specific campaigns like targeted event marketing.

The reality, however, is that while this sort of IDaas (Intent Data as a Service) program reduces the internal workload of implementing a program, it’s not the same as full-data orchestration.

Typically, these programs support the demand generation function with a flow of opted-in leads and meetings and they overlook other use cases. In fact, often the third-party data used to support these programs is never even seen by the company.

Current customers engaged with competitors continue to fly below the radar. Other members of complex buying teams unknown to sales reps continue with their own research in the shadows. Contacts at target accounts demonstrate purchase intent, and no one ever knows it.

That highlights a subtle difference between data orchestration and demand orchestration.

Understand What’s Possible and Define Your Goals

Third-party data can improve results across the entire customer lifecycle.

Not every company has the vision to unlock that full value, and limited application can still drive impressive ROI.

The key is for companies to make deliberate decisions about the scope and pace of data implementation, and to recognize that outsourcing demand orchestration, while convenient, may unwittingly cost them data opportunities elsewhere. delivers IDaaS services ranging from data delivery with activation and orchestration consulting, through content syndication and paid ad management, to fully outsourced lead management to deliver opted-in leads. This range of capability means we can build on unique contact-level third-party data to deliver the impact you need.


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