More Than a Leads List: How Data Impacts ABM

We all know that ABM campaigns rely on quality data to work well. But not every marketer clearly understands exactly how data translates into ABM results and, more tellingly, a lot of ABM practitioners don’t know which data points to monitor and measure.

In their 2018 ABM Benchmark Study, ITSMA says ABM strategies continue to mature, as the number of documented cases of successful ABM campaigns keeps on growing. The report finds that 77% of companies applying ABM see 10% higher ROI from their ABM programs than from their traditional marketing efforts.

77% of companies applying ABM see 10% higher ROI

But, as ABM strategies increasingly become part of the marketing mainstream, many ABM users still haven’t fully learned the difference between what true ABM really is versus what ITSMA calls “M2A” (or “marketing to accounts”).

M2A is simply using a sales leads list and “targeting your marketing collaterals at buyers on the list.” In other words, it’s just traditional marketing under a different name.

With M2A, you’re not achieving one-on-one, personalized messaging that’s geared toward both account and lead-level needs. This goes against what ABM should be since ABM requires carrying out tailored and customized campaigns.

I think a huge factor behind the ABM-vs.-M2A divide boils down to two things: knowing which types of marketing data really impact ABM and understanding how these data points drive the ABM process. Without a good grasp of ABM data, it’s practically impossible for you to:

Understand your target accounts’ characteristics and behavior
Get to know and connect with stakeholders who make the buying decisions in each account

To achieve both ends, you need the right data, and you need to use it in the right way. That’s what we’ll learn in this post. We’ll take a look at the different ways data impacts real-world ABM campaigns. We’ll find out exactly how quality data contributes value to actual ABM programs.

Alongside recent trends in ABM data, I’ll also share a story of one of Callbox’s clients that managed to build a solid ABM-focused marketing database almost from scratch.

When I was planning out this article, the things I initially wrote down reminded me of that company, and I think their experience will help make the points in this post even clearer.

Anyway, this client is a mid-sized U.S.-based company that provides precision manufacturing solutions to customers across the country. Before working with Callbox, the company had been struggling with sluggish growth in a market segment they were trying to penetrate.

This client had a (nearly) non-existent marketing data process. All they had was a saturated leads list and a patchwork of separate analytics trackers. This made it hard to scale their marketing program into newer segments.

But after implementing a major overhaul in their marketing and sales process, the company slowly put together a data-driven ABM program from the ground up. How they did it is a great example of how quality data can make a difference.

With that said, here are four important ways that marketing data impacts ABM:

1. Better account selection (40% higher deal value)

A study from Sirius Decisions finds that quality ABM data increase average deal size by as much as 40%. That’s because better ABM data improves account selection effectiveness and targeting accuracy.

The right ABM data can help uncover high-value accounts and remove unpromising companies from your target marketing database. The main types of data needed to identify potential accounts include:

Firmographics: Company size, industry, location, annual revenue, funding
Technographics: Software in use, complementary technology, technological maturity
Intent: Topics researched, content accessed, ads clicked
Interest: Past purchases, rep activity levels, decision makers mapped, interaction with campaign

How we supported the client: To help our client acquire the data they needed for effective account selection, we ran a research and direct outreach campaign that:

Narrowed down their pool of potential accounts based on their ideal customer profile (ICP)
Collected firmographic and technographic data
Gauged initial propensity to buy and level of interest

2. Clearer roadmap of key decision makers (16% shorter sales cycle)

According to research made by the Aberdeen Group, having a clear idea of what constitutes a qualified opportunity accelerates the sales cycle. The study estimates that selecting and prioritizing the right decision makers can cut sales cycles by around 16%.

That’s why effective ABM relies on granular data about the key decision makers involved and how they affect the buying process. To map out each stakeholder’s influence in your target account, your marketing database needs to include the following fields for each decision maker:

Job title
Role in the purchase process
Years in the company
Place in the org chart
Goals and pain points
Prospect-level intent (content viewed, resources downloaded, topics accessed, etc.)
Response to your campaign

How we supported the client: We expanded and enriched our client’s leads list by identifying relevant stakeholders for each target account. We used their buyer persona definitions to pinpoint the right decision makers and collected contact details, demographics, professional info, decision-making hierarchy, and insights on prospect activity.

3. Deeper audience interaction (4.7x to 7x higher engagement rates)

The 2019 State of Account-Based Marketing from Demand Gen Report shows the power of high-quality data in driving tactical and operational results for ABM campaigns. One of the survey’s key takeaways is that using deep insights (knowing what each account’s target prospects need) to craft messaging and execution significantly lifts engagement rates (7x better ad response rates and 4.7x higher email CTRs).

ABM is all about personalization. You need a good understanding of both account-level and prospect-level characteristics to design ABM programs that truly connect with the right people. To achieve this, eMarketer suggests:

Combine multiple data sources to paint a clearer picture of fit and intent
Match intent signals with account characteristics and buyer profiles
Leverage third-party data sources alongside your in-house marketing database

How we supported the client: Callbox helped set the stage for the client to maximize ABM personalization through highly targeted data at both account and decision maker levels. This would later prove to be very useful in expanding the client’s targeting and segmentation capabilities.

4. Higher conversion and close rates (57% to 285% increase in win rates)

Numbers from different sources show that ABM boosts close rates anywhere between a decent 57% to a whopping 285%. While those are certainly very impressive results, the main driver behind these figures is (you guessed it) quality data.

ABM increases closed-won deals because of targeted, one-on-one messaging and relevant delivery. That requires going beyond fit and intent data. It involves collecting information and segmenting based on a target account’s readiness, willingness, ability, and potential success:

Ready: The target account is currently facing an urgent problem or opportunity that your solution can address.
Willing: The company is open to exploring possible solutions to the problem or opportunity.
Able: The target account can make the needed commitment to implement the change.
Success potential: There’s a high level of fit in terms of technical, functional, resource, competence, experience, and culture.

How we supported the client: By doing much of the data collection legwork in the early stages of the client’s ABM program, Callbox helped the client focus on qualifying, nurturing, and following up on prospects further down the ABM process. This also enabled the client to prioritize tier 1 accounts and tailor their campaign accordingly.

Conclusion:

ABM needs a robust marketing database that offers fit and intent insights, not just a leads list. These four key roles of data in ABM should serve as your guide to create and refine a solid ABM data management strategy.