Data is everything—especially in account-based marketing.
Whether you’re analyzing fit, tracking engagement and intent signals, or measuring where your accounts live in the funnel, the right data unlocks the most effective plays for your marketing team.
But collecting the data is just one step in the process. To put that data to work—and earn efficient wins—marketing leaders must know the difference between the data that demands their full attention and the data they can afford to deprioritize.
Here are four ways marketing leaders can get more from their data.
1. Create a data strategy
Running the numbers without a well-thought purpose muddies the decision-making process.
To realize the true benefits of ABM—prioritizing accounts with the highest chance of closing—marketing leaders need a plan. That means putting data in context, not taking it at face value. It also means thinking about how you’ll:
- Judge the success of programs via conversion rates and KPIs
- Gauge the quality of marketing analytics data output
- Determine the success of marketing analytics
- Analyze marketing campaigns
For marketers with big tech stacks, this will also mean making sure your systems can talk to each other enough to make data collection count.
Remember: Realizing the full potential of your marketing technology (including fostering alignment with sales) isn’t sustainable unless all your systems play nice. Plus, running through the same platform makes budget allocation and reporting a lot less time-consuming.
But integrations can’t do all the work. To make effective data-driven decisions, consider how you’ll manage data from the start. If you create and stick to a specific approach, you can leverage your data over time to measure which programs are the most successful.
The key is to always have a plan for how you’ll use your data before you request it or pull it from your platform. With the end goal in mind, it’s easier to know how and when to invest your resources and turn data into wins.
2. Prevent data overload
As the number of data sources continues to grow, interpreting data quickly is becoming even more important—especially for marketing leaders who have tons of data thrown at them every day.
To confidently steer your team towards wins, you don’t just need the right information at your fingertips, you need it in a format that’s easy to digest.
Here are three ways to make your data more visually appealing:
- Use charts and illustrations to frame challenging or overwhelming data sets.
This will help you and your team process the information more efficiently. For example, your team might group data based on certain criteria (like current accounts grouped by funnel stages), enabling you to quickly decide on investments, budgeting, etc.
- Use simple graphs to spot outliers.
A simple line graph can make a point in seconds that might otherwise take an entire meeting to explain. If a stage of the buying journey is causing friction for your customers, for instance, examining engagement metrics could reveal a gap you didn’t see before.
- Choose formats based on topic and audience.
Diagrams are useful for describing complex processes and mapping out the flow of data to identify problem areas.
Charts showcase meaningful data for comparing accounts or uncovering hidden correlations.
Infographics work best for overviews of a given topic, providing top-down perspectives that quickly communicate complex information.
But before you can get creative with data visualization, you’ll need a way to perform quick (but holistic) data analysis. The solution? Integrate data sources into one account-based platform.
Instead of sifting through data on various platforms to manually combine it, unite disparate data sets under one roof to process the information more efficiently.
For example, you might gather demographic data on leads from Salesforce and then target that demographic through Facebook and Instagram ads—or analyze information from your CRM alongside website visitor information from Google Analytics.
3. Track data by funnel stage
Tracking data by funnel stage is another way to prevent data overload. Beyond that, it’s also an effective way to sort through and prioritize accounts in your pipeline.
How does this actually happen? Through a combination of static and dynamic data.
Static data: Are these accounts a good fit?
Static data refers to information gathered at a fixed point in time, typically related to the profile of accounts. It includes key elements such as firmographics or technographics—the core data that drives the backbone of your ABM strategy, ideal customer profile (ICP), and target account list (TAL) building.
Static data drives key strategic decisions around which accounts you should target (based on firmographic and technographic factors) and ultimately informs the long-term strategy as you begin to layer in dynamic data.
Dynamic data: Are these accounts engaged or ready to buy?
Dynamic data refers to a dataset that continually changes over time based on behavior—and it’s crucial for understanding how accounts move through a buyer’s journey. For instance, dynamic engagement data for an account constantly updates to reflect changes in on-site engagement or off-site intent.
From there, dynamic data continually funnels into key decisions, influencing who you target, when you target, and what offers you target them with.
It provides insight into account activity that helps you prioritize which accounts are the most ready to buy versus which accounts need extra nurturing.
Mapping journey stages with fit, intent, and engagement data
Here’s how the typical ABM funnel breaks down:
- Open Opportunity
- Won Opportunity
- Post-Won Opportunity
These stages map where an account or customer is in their buying journey and they’re better defined by the dynamic and static data you’ve collected—namely fit, intent, and engagement signals.
Customizing these journey stages will benefit from as many channels as possible, including:
- Advertising data
- Website data
- Email engagement
- Content engagement
- Sales activity (such as Salesforce, HubSpot, etc.)
With a robust ABM platform, you’ll have access to an overview of your accounts and their distribution throughout the funnel, which will enable you to:
- Identify in-market behaviors
- Find and engage high intent accounts
- Prioritize accounts based on buying stage
Segmenting these accounts will pave the way for new account-based strategies you can tailor to multiple customers in similar stages of the buying journey. Ideally, this will save both time and money while providing the insight needed to move the most valuable targets down the funnel.
4. Embrace machine learning
Machine learning isn’t just a buzzword.
If you want to make smart investments and find customers who are a perfect fit for your company at scale, machine learning (ML) opens a slew of new opportunities by leveraging your data into a predictive model that scores and prioritizes accounts.
Here’s how that might look in practice:
- Build a custom training model based on data from your best closed-won customers.
- Grade your target account list based on those parameters, scoring the accounts using fit data and factors like company size, revenue, tech stack, and industry.
- Score your TAL and assign grades to accounts to tier them based on “most likely to close” versus “less likely to close.”
- Work hand-in-hand with sales to prioritize your highest-scoring, data-validated target accounts.
But don’t expect machine learning to handle everything. Leveraging account-based data collection means continually refining your ICP and TAL based on dynamic, cross-channel insights.
Connecting the dots
Through machine learning, integrated systems, and a thorough understanding of how your customers make their way through the funnel, marketing leaders can use data to do what ABM helps marketing teams do best: prioritize the accounts with the highest chance of closing.
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