The digital revolution has raised customer expectations massively. People want much more than just a product—they crave a complete experience.
The success of your marketing campaign no longer depends on the number of leads you generate. It rests on your ability to maximize your customers’ experience.
That’s why forward-thinking marketers are prioritizing account-based marketing—determining together with sales which accounts to target, why they should be targeted, and when they should be targeted. In fact, 87% of companies report a higher return on investment when adding account-based marketing (ABM) to their engagement strategy than traditional lead-based only marketing initiatives.
As we covered in our overview blog, a successful ABM strategy has people and experience at the center of it instead of random, individual interactions.
In this blog, we’ll take a closer look at the first step of how to create epic account-based experiences (ABX) with the right account insights & profiling.
With the right data—and a platform powerful enough to use the data effectively—you can gain highly accurate account insights and effectively profile your targets to create hyper-personalized account-based experiences that your customers and prospects will enjoy.
The first and most important challenge to overcome when wanting to truly maximize your ABX is knowing who your ABX should be for. What data should you be gathering? And how can you make the most of it?
Leveraging account insights in the new age of data
We’ve come a long way from sales-based account selection. Previously, sales teams would create a target account list based on the sales data they had available in their CRM. The process was time-consuming, took the sales team away from selling, and relied heavily on gut instinct.
But since the digital revolution, marketers have gained access to huge pools of data that can be used to help sales create more accurate account lists. Using these three key data sources, you can have better insight into which accounts to target:
Fit data
Fit data often includes firmographics that detail a company’s location, number of employees, size, and other basic details. While this information is useful for gaining a high-level view of who your ideal account is for targeting, it needs to be combined with other data types to become useful.
Intent data
Intent data identifies accounts that are actively searching or engaging with content related to your service or products. Whether it’s from paid media advertising data sources or opt-in data cooperatives, intent data can help you prioritize WHEN you should focus on certain target accounts over others and what messages to include in your content.
Engagement data
Engagement data helps you take account targeting to the next level by adding a third layer of insights to help hyper-prioritize which accounts to allocate resources on immediately. By identifying which accounts have already engaged with your content in the past—whether by opening emails, reading blogs, or registering for an event—you can get greater insight into which accounts have an even higher propensity to turn into closed-won accounts as well as the best way to reach them.
Great experiences aren’t just created from one data type—they come from combining all three to help you select the right accounts to target. But with so much data at hand, how do you effectively manage it?
Ideal customer profiles and predictive target account lists
Assuming your account data is clean and accurate, having lots of it can be both a blessing and a curse. It can bless us with the ability to create robust ideal customer profiles (ICPs) that help us select accounts with confidence, but it can also curse us with the daunting burden of having to manually analyze it so that we can actually use it. This is where AI can step in and help us speed up the process in a scalable way. However, AI is only as powerful as the amount of data that it is trained on and the quality of that data. It can help us leverage ICPs to create predictive target accounts lists that support your ABM strategy.
For example, if your ABM strategy is to cross-sell existing customer accounts with a specific product, you first need to know which of your existing customer accounts are ideal to target. With AI-powered look-alike models and the three data sources mentioned above, you can upload a list of customers who recently purchased a specific cross-sell product along with even having a high deal velocity. The model will analyze all the fit data and engagement data, along with enriching intent data to learn common attributes and even weigh them differently based on unique importance to you and your business to create your ideal customer profile. Next, you’ll want the AI to use your ICP to scan your existing customer base to pick out the best-fit accounts to cross-sell.
Here are a few common examples of predictive target account lists:
Predictive best-fit lists: List of prioritized best-fit target accounts based on recent high-value, closed-won deals
Predictive quick-win list: List of prioritized best-fit open opportunities with accounts that have high propensity to close faster than others
Operationalizing your predictive target account lists
Once you have your predictive target account lists, it’s time to actually do something with them. Successful account-based marketers adopt marketing automation software that can operationalize their predictive target account lists.
With a best-in-class marketing automation platform, you can use AI to identify your ideal customer profile and match it to those in your account list. But identifying and prioritizing accounts is just the first step. The real magic of ABM comes when you put that data into practice by strategically discovering specific contacts within each target account
While most marketers initially think the next step is to start engaging with the accounts on their target account lists, the next steps inevitably become hydrating those accounts by discovering net-new contacts within those accounts through the art of contact discovery. Remember, account insights and profiling uses mass amounts of data to primarily help you identify what the “perfect account” is, but it doesn’t help you identify people within those accounts.
Paving a path to step two of creating epic ABX: Contact discovery
Gathering accurate account insights and profiling your ideal customers is only the beginning of your exciting quest to create epic ABX. In our next blog in the series, we’ll look at how to discover contacts in your existing target accounts—as well as net-new accounts—to ensure you’re targeting the right people.