Author's Note: CaliberMind, in conjunction with Demand Gen Report, will be hosting a marketing measurement webinar on Tuesday, July 10th. During this webinar, marketing leaders Rishi Dave (former CMO of Dun & Bradstreet, Kayla Kirkeby (VP Marketing at Dizzion), and CaliberMind's own Chris Nixon (VP Marketing) will explore why ABM is a data and measurement problem and how you can use data to execute and outperform. You can register for the webinar here.
While a majority of marketers consider ABM an important part of their strategy, only a handful are experiencing stellar results with their current programs. In fact, only 20 percent of marketers say their ABM programs are exceeding or greatly exceeding organizational expectations, according to a report by DemandGen.
And while you might think you’re not performing as well as you’d like because of your campaigns, targeting or positioning, the real reason goes deeper — right down to your data. Because if you don’t have the right data to run your ABM programs, you’ll never really know if an account is the right fit for your business, how close your prospects are to a purchase, or how they’re engaging with your company.
Here we explore how companies have determined fit, intent and engagement over the years, and how today’s technology can help you make the best use of your data so you can get the best results from your ABM program.
Target Account Fit: Then and Now
When you determine fit, you’re really defining which companies you want to target with your ABM efforts. The premise of ABM, of course, is to spend most of your resources on prospects that are closest in line with your ideal customer profile (ICP). To determine fit, you can evaluate everything from company background information to details about the individuals that work for that company.
Let’s take a look at how fit has evolved over the years.
Then: If you’ve been in marketing for a while, you may remember those quarterly meetings in which you sat down with the sales team to determine your top accounts, and then checked the right box in Salesforce to flag those accounts. Years ago, without any data to back up account selection, sales would choose the accounts they thought were most promising based on gut feeling, friendships, past interactions or some other factor.
Once marketing automation came into the picture, collecting leads became more efficient, but to this day requires manual processes to associate those leads with the right accounts and opportunities. Currently used by many companies for ABM, these systems bring in leads based on email addresses, and later sync them with your Salesforce contacts and accounts. This is problematic because:
If someone visits your site but doesn’t leave you their email address, you’ve missed a potentially viable lead.
Every time a new lead comes in, you have to manually match that lead with your key account in Salesforce.
Because marketing automation and CRM systems were designed to store data based on those email addresses, if that key piece of data is missing, you won’t be able to store any details at all about that visitor.
Now: Customer data platforms can help you identify who has visited your site, whether people give you their email address or not, because those systems can identify anonymous site visitors based on their IP address. Once a lead comes in, the platform automatically can tell whether the prospective customer is a good fit and alert your marketing and sales team to their activity, cutting down on a lot of manual steps.
Also, because these platforms are designed to store a variety of data, like campaign, budget, geographic and other information, they can help you get the most from your marketing automation, CRM and even third-party systems by pulling all of that data together so you can get a more complete picture of who your key accounts and contacts are, and whether they’re the best fit for your company.
Here are a few characteristics to consider when building out "fit" for your company:
Intent Signals: Then and Now
Intent measures how likely your prospect is to make a purchase. Ideally, you would consider behavior like what people are searching for on the internet — and not just your own website.
Then: When evaluating intent, many companies started out looking at page views and downloads from their own website using Google Analytics or a marketing automation system like Hubspot, Marketo, or Pardot. But since today’s customers have a lot more options to gather information, web analytics only tells you a tiny part of the story.
Now: With a customer data platform, you can not only pull in behavior on your own site, but also from third party sources like keyword search, industry publications, online review sites and public forums. For example, if a prospective customer visits your "category" page on a review site, a CDP can pull in that data from the third party source and aggregate that with any other account and lead level intent signals to give you a real picture of whether that account is in market for a solution like yours.
Account-Level Engagement: Then and Now
Account Level Engagement pulls together all the activity from people who interact with your company at the account level so you can strategize around next steps for each account.
Then: Marketing automation, CRM and other systems typically sat in silos, making it difficult and time-intensive to piece together email engagement, webinar and meeting attendance, product trial signups, ad clicks and other interactions. In addition, most companies didn’t even consider product engagement data, which is a leading indicator of whether or not someone will purchase.
Now: As with intent, a customer data platform saves marketers valuable time by automatically aggregating and ranking lead-level engagement from a wide range of sources, including social media, product usage, live chats, email and others. It then takes the lead engagement and surfaces it at the account level, allowing you to report on engagement across the entire account, not just at the individual lead. This gives you exact insight into how "engaged" the account is with your organization.
It’s time to evolve your data
In the end, ABM is about knowing who your customers and accounts are so you can provide them with personalized service that will make them want to buy from you and stick around in the long run. While manual processes worked in the past when customers had limited options to source information, today’s customers move too quickly and expect a lot more from the companies they interact with.
Once you have the right data, systems and processes in place, you can better meet, and even exceed your ABM expectations, without the extra effort.
To learn more about how you can make the best use of your data to fuel your ABM efforts, read ourGuide to Account Scoring.
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