Most organizations today struggle with pipeline forecast accuracy. In fact, the average win rate of forecasted deals is just under 47%. And when deals do close, only approximately 48% close as originally forecasted, according to a CSO Insights report.
Why the discrepancy? Because most models are built off lead-based metrics that really don’t give you the whole picture. You’re essentially trying to forecast and set pipeline goals based on a constantly moving target.
It’s akin to going to the grocery store to prep for a dinner party. At first, it seems like a pretty straightforward task. But as you’re walking through the aisles filling your cart, the number of guests on your list keeps changing. Maybe other events were canceled, the weather cleared up, or several guests finally found babysitters –– either way, you’re now dealing with a fluctuating list that’s being impacted by numerous factors. You’re shopping strategy has suddenly become inefficient and chaotic.
This is similar to most of the pipeline revenue forecasting models currently in play across a number of companies. It’s no wonder most are unhappy with their current models.
An account-based marketing (ABM) approach can bring more clarity and accuracy to your forecasting. Let’s take a look at how.
Get Started by Setting a Baseline
We’ve previously discussed the 7 strategies needed to jumpstart a successful ABM program and how to show the revenue impact along the way. But if you’re still in the early stages of implementing ABM, you won’t have a baseline to compare your organization’s past performance. To understand how your programs might perform in the future, you have to gain an understanding of how they’ve performed in the past.
To do this is simple: analyze the last two to three quarters of pipeline conversion rates across your entire company. Specifically, look at win rates and the conversion rates between each stage of the account lifecycle. These metrics, though based on the performance of other marketing and sales initiatives, will give you a target to build from. With your ABM program, you should expect to at least match this baseline performance, if not see it improved.
As you implement your ABM program, you’ll gain data to compare and can fine tune your baseline numbers over time.
Identify and Analyze Patterns in the Data
With your comparison data, the next step is to analyze it to see if patterns emerge –– specifically patterns in different conversion rates with ABM. To do this, we track six stages of account engagement to see how our marketing activities are progressing toward opportunity and revenue goals. The six stages include:
- No engagement
- Low engagement
- Brand engagement
- Surging engagement
- Open engagement
- Customers
While pipeline and revenue influence are critical to monitor, they’re actually lagging indicators of success. Why? Take form fills, for example. Prospective customers typically won’t fill one out until after they’ve already researched your solution extensively. By the time they’ve performed this action, you’ve missed out on key opportunities to build a deeper relationship and potentially accelerate the sales cycle. And at the same time, consider how rare it would be for multiple people from one buying committee to fill out that same form.
This is another reason why engagement data is so valuable. As the ultimate leading indicator of success, meaningful engagement is defined by critical interactions a person has with your company, online or offline. Engagement data clearly demonstrates your future customer’s interest and intent and helps your organization identify when opportunities are heating up or cooling down to take the appropriate action. When there’s a meaningful increase in the number of people from an account’s buying committee engaging with content on your website, it sends a very clear signal there’s interest. Your team can then act fast to prioritize outreach.
Tracking surging engagement and the conversion rate of surging accounts directly enables you and your team to start forecasting and planning based on the surging accounts you currently have in your pipeline. Measuring this data not only helps you forecast revenue performance, but it also helps stakeholders understand the impact of ABM activities, and conduct an influence-based analysis of your ABM campaigns.
Reassess if You Don’t Meet Your Baseline
At this point, you’ll have determined your initial baseline based on data from previous quarters, and analyzed patterns to inform your pipeline forecast. But what if you fell below expectations and didn’t meet baseline?
Most organizations see better results with ABM than with other inbound and outbound marketing strategies. But, if you did fall short, it’s simply a signal to reassess and see what went wrong. Because ABM is a prescriptive approach to marketing, and you’re tracking engagement data, you should be able to easily detect where the breakdown occurred.
ABM allows your team to gauge progress at every turn to pivot quickly when needed before you get too far off course or to the end of the quarter and realize you won’t hit the forecast.
Set Goals for The Future
Once you’ve met your baseline, don’t stop there. Identify ways to improve your conversion rates and set new goals to increase by certain percentage points each quarter, based on overall business goals.
Keep in mind, account-based pipeline forecasting is exactly that –– a prediction or estimate. The goal is not to reach 100% accuracy. Though you may hit it from time to time, the intention is to help you create a predictive business model by coming as close as possible to your forecast.
For more on metrics, get the ABM Reporting Template. This simplifies the metrics to track and includes customizable scorecards, dashboards, and reports to make communicating the value of your target account strategy simple.
