Whether you’re already doing it – or thinking about doing it – you’re probably one of an increasing number of B2B companies transitioning to an account-based approach to sales and marketing.
The effectiveness of this data-driven, “flipped funnel” approach has been proven many times over, but many professionals struggle with this transition.
Peter K Herbert, VP of Marketing at Terminus, an Atlanta-based company helping sales and marketing teams execute ABM campaigns, was just such a professional. (In fact, prior to joining Terminus, he was VP of Marketing at VersionOne – a client who used DiscoverOrg to find and engage their target accounts. Small world!)
In Herbert’s own words, here’s the story of how he made the leap. (Look for the full story here at Terminus).
Watch the video series: A True Story of Account-Based Everything from DiscoverOrg
Origin Story: Terminus’ Formula for Account-Based Marketing
If you’re trying to get an account-based marketing (ABM) program off the ground, Terminus offers a simple formula: Fit + Intent + Engagement.
I became interested in target account marketing, which I later learned to call ABM, while tying to efficiently grow a software business.
The hypothesis was that if all of our time, energy, and money was spent on a focused sales and marketing effort to convert a list of target enterprise accounts, we could grow our average sales price (ASP) – without breaking the bank.
Based on this, in 2015, I began building an ABM program focused on selecting a list of target accounts and finding ways to promote specifically to those accounts, with the help of Kristen Wendel. Kristen is a marketing operations and #MarTech guru who is pretty much “the boss of operationalizing ABM.” We have worked together at three companies, and together we architected the SiriusDecisions 2017 Program of the Year for ABM.
Our first experiments used basic firmographic information and a presence on lists such as the Global 2000 to identify our target accounts. By the time we launched a full-scale account-based marketing program, we’d spent a month with the sales team, scouring spreadsheets and lists to determine our target accounts. We adapted their input to create a three-tiered list of 600 target accounts.
At the end of the day, we did a good job.
We saw dramatic spikes in a number of engagement metrics, such as website visits from target accounts. Within the first 30 days, the first sales appointments were set and opportunities were opening.
But we immediately felt we had some shortcomings with account-based marketing.
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Our selection process was unsustainable. We worked too hard to create endless spreadsheets of accounts and answer innumerable requests to manipulate the data. During the account selection process, sales reps spent too much time researching and in meetings wrestling for accounts, which took them off the front line and distracted them from selling.
Our data was limited. We relied on basic characteristics downloaded from several well-known data platforms. Revenue, geography, basic technographics, and rep knowledge were not enough to answer whether a company was a fit, and certainly didn’t tell us whether the company was actively buying or aware of us. Five or six data points simply didn’t get us there.
Our accounts were static. Even with our planned cadence to update accounts each quarter, our sales leader described this was a “big bet.” We immediately saw the need to prioritize which accounts were currently being worked and to change our accounts as they were converted and disqualified.
We started to think about how we could eventually use engagement data and other signals to dynamically target prospects. As we went about solving these ABM challenges, we identified that we needed a solution that:
- Was predictive: Using artificial intelligence to consider more variables than we, as humans, could calculate
- Signaled about who was “active” in the market
- Used engagement data as a primary signal that an account should be actively worked. (We were already tracking account engagement, but we needed to operationalize this data as a trigger rather than just a measure of success)
In essence, we were building our version of what SiriusDecisions later described as the Demand Unit Waterfall. We wanted to understand our total addressable market, active market, and engaged market – and then prioritize those accounts as dynamically as possible.
A formula for account-based marketing
Today at Terminus, we use this data-driven formula for account-based target account selection, dynamic prioritization, and smart campaigning.
We primarily operate off of this formula for account-based Fit + Intent + Engagement versus operating off inbound lead conversions.
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Fit refers to accounts that fit the Ideal Customer Profile (ICP) of who your company is trying to market and sell to.
Think of ICP as the firmographic, technographic, geographic, etc. filters you apply to your data to narrow your accounts to your target market, plus an incredibly powerful machine that considers thousands of variables to see if those accounts are similar to your healthy customers, open opportunities, or whatever you decide is best for your business to build your model from.
Intent data shows what people at companies are searching for and consuming on the internet (not just your website). “Intent” is based on prospects’ current interests ; for example
Engagement data aggregates activity from all the people who interact with your company at the account level. It helps marketers understand and act on behavior.
Ideally, you want to focus on high-value activity. For example, I prioritize companies that spend time on my product pages more than those that visit high-level blog posts, because it’s a better signal of who may be evaluating my product.
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Use Cases for a Fit + Intent + Engagement Data-Driven Approach
Fit, intent, and engagement provide the core data you need to start, optimize, operationalize, and measure your account-based marketing program. Many steps of the ABM journey are dramatically easier with this data at your fingertips, and significant milestones to operationalizing ABM will come sooner for you and your organization.
So how exactly can you use this data?
5 key scenarios to use Fit + Intent + Engagement data
1. Qualified market-building – How many accounts fit your ICP and are scored as a high fit using a predictive model? How much more efficient would you be if sales and marketing only worked on high-fit accounts?
2. Select target accounts – How should you select and tier your ABM target accounts? How do you go beyond basic data about companies to more intelligently, and easily, select your target accounts beyond the strategic accounts your sales team already is working on?
3. Sales Insights – Do your sales development and sales teams have the right data under their noses so they can create the most relevant and personalized messages? Do they have data-driven scoops at the account-level to take the right actions?
4. Dynamic Targeting & Active Prioritization – How can you dynamically target accounts based on data and trigger the right actions in our sales development and sales teams? Should your target accounts and ABM campaigns be static or intelligent and dynamic? This one is the most exciting to me!
5. Smart Campaigning – What if your account-based advertising was triggered automatically off of intent signals and engagement data? How much more effective would your campaigns be if you were triggering the right message at the right time to the right account? Thanks to an increasing number of partner integrations, you can run multi-channel account-based marketing campaigns with consistent messaging.
Feel free to share your thoughts in the #FlipMyFunnel Slack channel – and tag me (@peterh)!