To succeed with ABM, get data-driven with Fit + Intent + Engagement to select accounts, prioritize what needs to be worked now, and promote effectively and intelligently.
Executing account-based marketing with excellence and efficiency requires moving to a data-driven approach. Today, however, adopting data-driven processes and ABM platforms that power targeted programs are easier than ever, promising to streamline your marketing, sales development, and sales processes in pursuit of your revenue goals.
One of the main purposes of account-based marketing is to align sales and marketing on pursuing the accounts most likely to buy or expand (and of the highest potential lifetime value). This means getting focused and efficient, and you need to target the right accounts in the right way.
In addition to top-tier target account selection, many businesses can benefit from defining their qualified market, which is a pool of accounts that could potentially be targeted. Rather than open prospecting or grinding through MQLs, marketers can help sales, and the whole business, to be more efficient by only working on accounts (inbound and outbound) that the data says matches your company’s ideal customer profile.
The aligned account-based process should be efficient and orchestrated, rather than the typical inefficient marketing and sales handoffs in a lead-based system. And because this effort within the go-to-market team is more focused, our goal is to create a better experience because we are communicating in a more relevant and personalized way. Gone are the days of waiting on form fills. Templated emails and generic content experiences are becoming history.
[Tweet “Template emails and generic content experiences are becoming history says @PeterKHerbert #ABM”]
But how should you select and prioritize the right accounts? And how can you use your data to intelligently promote to them?
Origin Story: “The Formula” for Account-Based Marketing
I became interested in target account marketing, which I later learned to call ABM, while looking to grow a software business with efficiency. 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) and do it without breaking the bank.
Based on this, in 2015, Kristen Wendel and I began building an ABM program focused on selecting a list of target accounts and finding ways to promote specifically to those accounts. 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 before I joined Terminus as VP of Marketing in the summer of 2017.
There is power in collaboration. We used our ability to think and act together to improve quickly and get an ABM program running.
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 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 a number of engagement metrics, such as website visits from target accounts, dramatically spike. 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.
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 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. As our sales leader described, this was a “big bet,” even with our planned cadence to update accounts each quarter. 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. As we went about solving these ABM challenges, we identified that we needed:
- A “predictive” solution that used artificial intelligence to consider more variables than we, as humans, could calculate.
- A solution that would give us signals about who was “active” in the market.
- Engagement data as a primary signal that an account should be actively worked. We were already tracking account engagement, so we just needed to operationalize this data as a trigger rather than just a measure of success.
In essence, we were building our version of what was later described in the SiriusDecisions 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.
The Formula for ABM and What It Means
So what is the formula for account-based marketing success? The ABM tech stack and processes we created are what we came to refer to as the Fit + Intent + Engagement model for ABM. 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.
Here’s what the Fit + Intent + Engagement formula means and the technology we use at Terminus to see success with account-based marketing.
Fit
Fit refers to accounts that fit the ideal customer profile of who your company is trying to market and sell to. Preferably, fit refers to your ICP + AI-assisted scoring. 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.
FIT TECH: I use EverString for fit. At two companies, I have measured a preponderance of closed-won deals as having a high fit score based on models built with this AI-assisted platform. It works. At Terminus, we actively prioritize and find new accounts to focus on through EverString.
Intent
Intent data shows what people at companies are searching for and consuming on the internet (not just your website). This is a signal of possible “intent” based on their current interests relative to historical data.
INTENT TECH: I use Bombora for intent. Intent, when coupled with fit, is a very strong signal for identifying the active market that you would like to market and sell to. The best Terminus ad campaigns my teams have run have been intent-based marketing campaigns that use keywords to create tailored ads, tailored content portals, and tailored/personalized collateral and video. At Terminus, we use intent data to alert sales to which accounts they should actively work and to trigger intent-based, account-based ad campaigns.
Engagement
Account-based engagement data aggregates activity from all the people who are interacting with your company at the account level so you can understand and act on this response and 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 course, of who may be evaluating my product. Having the ability to track people who are not in your database (and are therefore anonymous) is an important capability.
ENGAGEMENT TECH: In terms of account based marketing solutions, I use Terminus for engagement data. Terminus, in addition to being an account-based advertising solution for promoting to people at target accounts, provides highly accurate web engagement data on the number of visits, visitors, pages visited, and dates of those visits so I can understand the impact of my account-based advertising and overall ABM program. At Terminus, we use engagement on high-value pages to prioritize accounts for follow-up. And it also helps us find new accounts to target!
Use Cases for a Fit + Intent + Engagement Data-Driven Approach
Fit, intent, and engagement provide the core data you need to get started, 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? The key scenarios you can use Fit + Intent + Engagement data for are:
Qualified Market Building – How many accounts fit your ICP and are scored as a high fit based on an AI-Assisted model? Wouldn’t it be nice to know whether there are 20,000 accounts or 1,000 you can really go after? How much more efficient would you be if sales and marketing only worked on high-fit accounts?
Target Account Selection – 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?
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 triggers at the account-level to take the right actions?
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!
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 multichannel account-based marketing campaigns with consistent messaging.
I’ll dig into each of these use cases for account-based marketing in my next blog post. In the meantime, feel free to get a preview by checking out the recent webinar I did with Kristen Wendel from LeanKit, Deborah Holstein from EverString, and Dale Durrett fromBombora.
