Just Give Me a Simple Answer

-3%!

Gartner’s CMO spend survey finds that MarTech’s proportion of the marketing budget has fallen from 29 to 26%. Maybe it’s not a cut – perhaps MarTech stayed steady while budgets increased. What’s clear is that the reflexive reach for newest software may have slowed.

So it’s natural that as companies consider implementing or improving an intent data program that they budget carefully. Many marketers wonder what intent data should cost.

We all know that the real answer is “that depends.” But you need some guideline. So let’s look at this from various angles.

But first, the short (not so simple) answer to the question “How much does intent data cost?” is this.

Enterprise intent data typically costs between $30,000 to $100,000/year for a data source subscription. On the low end are subscriptions for account level data which are embedded in other software including account-based marketing (ABM) packages. At the upper end are bespoke 2nd party data programs.

Most IntentData.io contact level™ intent data clients pay less than $50,000/year.

With that basic answer, let’s dig into the factors that you should consider when budgeting for intent data and comparing options.

Total Cost/Value/Commitment for a Single Data Set

Most intent data is sold based on a monthly subscription cost.

That can be misleading.

Factors to consider include:

• Value of the data – how granular, detailed, and accurate it is. For instance, does it only provide account level indications? Or does it include contact details? Will it include details that will support personalization at scale? Is it sourced around the entire web or only a limited range of sites? Is it based on search? or action? Or less accurate bidstream data? Does it include access to additional contact information?
• Total contract cost – three year and one year contracts are industry standards. A three year intent data contract in early 2020 requires you to commit for a period almost as long as intent data has even been a thing! That’s a long time, and a big commitment. There’s a significant cost factor there beyond the monthly subscription rate. (IntentData.io, Inc uses quarterly contracts as our standard option. We believe the data needs to continually resell itself. We recognize that technology and your markets/buyers are changing quickly, and the structure and resources of marketing and sales teams is as well.)
• Unlocking the value – different vendors provide different levels of coaching and consulting as you get started. On the one hand account level, surge type data is pretty self explanatory and might not need much explanation. On the other, more complex solutions including contact level data and predictive models require more insight. If it’s up to your team to figure it out on their own, you’ll have a cost associated with gradual, iterative learning rather than rapid implementation.
• Contact count costs – when starting with intent data most people are eager to maximize the number of signals they receive. Then they’re often surprised by the volume, and shift their focus to filtering. Therefore additional cost considerations include the ability to filter, and delivery options. Can data be filtered to the ideal customer profile (ICP?) Are there options for multiple deliveries so that certain signals (e.g. current customers) can be delivered directly to the marketing automation or CRM, while others are available as CSV files or for access via AWS? Be sure to consider marketing automation platform (MAP) contact count costs. A typical contact-level data algorithm may generate 3,000-5,000 signals/week. Generally half will be new unique contacts which means you might add up to 10,000 new contacts/month to your platform!

Cost for Full Intent Data Stack

Most companies that effectively optimize intent data incorporate multiple sources. Different methods provide different insights, and different industries have different options.

In the technology space, for instance, it is common for companies to have data stacks including:

• 2nd party intent – Publisher lead programs
• 3rd party intent – embedded Bombora surge data in ABM software, G2 software research data, and IntentData.io contact level data for granular, detailed insights. They can be used in different ways at different points in the buying journey and for different purposes to aid in anticipation, prediction, marketing intelligence, sales intelligence, custom audience retargeting, competitive intelligence and more.
• enrichment data – companies often rely on IP lookup tools (like Clearbit) to identify anonymous 1st party intent data and enrich known 1st party signals. Depending on your product/service, you might need technographic enrichment (like Datanyze.) Physical addresses are becoming important again as 3D and flat direct mail are increasingly back in vogue and direct dial phone numbers are valued by outbound sales teams.
• validation – even if you use separate outbound email domains, you’ll still want to validate email addresses prior to any large scale outbound email campaigns
• contact databases – Challenger research shows that today’s complex buying teams include 10.2 members. No intent data source will reveal all of them. Therefore you’ll need additional tools. These may well be free (e.g. LinkedIn and Seamless.ai) but you may also use paid versions like Zoom/Discover.org and LinkedIn Sales Navigator.

Do you need a full intent data stack? The short answer is “no.” Many companies operate with just one or two sources and achieve great success. As more companies turn to data though, keep in mind that the edge you’ll gain in the future will be based on sophisticated integration and orchestration of various data sources – rather than simply having one. So your plan should account for the likely need to build a robust data stack over time.

Cost to Activate Intent Data

The data itself doesn’t accomplish anything. You have to put it to work.

That means resources – budget and time.

In the simplest implementations, data signals are passed to a sales team. Sales people will have to digest and analyze the signals. They’ll probably have to do research, and spend time using social selling best practices in addition to common outbound sales approaches. This may slow them down. The payoff will be realized as they focus on the right people at the right accounts and become more efficient. Initially, however, they’ll work slower.

That’s just one example. Other resource requirements include the following:

• Marketing operations / sales operations – maximizing the data will require some creative minds and data operations
• Paid ads – whether you’re simply using embedded surge data in your ABM software or creating very carefully segmented custom audiences for narrowly personalized messaging, you’ll have paid ad costs to maximize the data. You’ll also have marketing operations resource requirements to create and dynamically manage the custom audiences.
• Syndicated content – some companies use intent data to focus syndicated content on specific prospects to create opted in leads for mass marketing
• Copywriting / content creation – contact level intent data which provides detailed information on stage in buying journey, problems to be solved, competitor engagements, and role and seniority create amazing opportunities for personalization at scale. That requires more content for email cadences, call scripts, chatbot experiences, video clips, paid ad creative, and website experiences. If you really want to unlock the power of intent data, plan on creating content for each role on a buying team, at each stage in the buying journey, with variations for problems/outcomes and competitor engagements.
• Sales enablement training and content – as you start to unlock the amazingly powerful insights of intent data you’ll want to put them in the hands of your sales team. That takes thought, playbooks and training (and can run off the rails quickly if you cut corners here.) You’ll need to train the team on how to find, interpret, and incorporate data into their sales playbooks. That means patient, structured coaching. It also means workflows that help to connect the dots for them, and even suggest questions to ask, actions to take, emails to send, and enablement content to use based on the inferences you can draw from the data signals.
• Product marketing – all of the amazing contact and account level insights will also hold important information in aggregate. Plan on additional work for your product marketing team. They’ll gain persona, competitive, and market insights from the data, but they’ll have to learn how to access and analyze the data.
• Success – intent data can provide important churn reduction and up/cross sell signals. Just as with new account sales you’ll have to create the playbooks, workflows and training to help your success team incorporate intent data into their processes.

Is all this required? Of course not. Just as some companies only use one intent data source, some also use it only in a simple form for one use case – often handing it to sales teams for them to digest.

But as you budget for an intent data program, understand what’s possible so you’re aware of progressive resource requirements to unlock additional levels of value.

Cost to Unify and Orchestrate the Intent Data

Really maximizing intent data involves a lot of moving pieces. Each of those has cost implications as we’ve listed above.

There’s another, higher level consideration as well.

Typically MAPs and CRM systems are incapable of fully integrating data sources and orchestrating the complex playbooks necessary to deliver very personalized buying journey experiences.

For instance, weaving together account-level and contact-level insights is often impossible with contact centric systems. Artful personalization requires an accurate and constantly updated single customer view (not to mention managing compliance of all marketing data, including intent signals!)

Dynamically orchestrating experiences, at scale, requires the constant observation and evaluation of multiple signals (first-party and third-party data) and relationships between them. Inferences which can be drawn from those signals must then be translated into action which anticipates the prospects likely journey. Examples include:

• dynamically evaluating all first/third party, individual/account level, and known/anonymous activity to discern where there are specific, actionable and likely opportunities (this is much more complex than it sounds!)
• adding individuals (even multiple email addresses) to custom audiences
• adding accounts/domains to paid social targeting
• adding individuals / accounts to lists for dynamically delivering appropriate website content, chatbot experiences
• triggering personalized outbound sales cadences (e.g. through SalesLoft or Outreach.io) and 3D mail
• incorporating omnichannel approaches into integrated playbooks (paid ads, email cadences, 3D mail, personalized content, etc.)
• notifying BDRs, AEs, and Success teams when there are signals they need to know of and act on – and further providing insights on what can be inferred, how they should respond, and which assets they should use

MAP and CRM systems often sync information. They don’t unify it, and they are don’t offer other critical CDP capability. Leadscoring models can trigger handoffs from marketing nurturing to sales engagement, but don’t begin to incorporate all the elements required to fully orchestrate intent data.

That requires a customer data platform (CDP.)

And CDPs carry software and implementation costs of their own. This may well be part of your marketing budget independent of your intent data program, but it’s an important tool to fully leverage intent data.