As marketers, we measure conversion rates. Some argue that it is the most important metric that we track. We measure marketing conversion rates by channel, by segment, and through time to understand where we are successful and where we need to improve. The problem is, conversion rates can be tricky to measure on a routine basis. This blog post provides a framework for how to best measure your marketing conversion rates.
Marketing conversion rates are used to measure the percent of people who successfully transition from one stage of your marketing funnel to the next. A typical marketing funnel might be:
Prospect → Marketing Qualified Lead (MQL) → Sales Accepted Lead (SAL) → Sales Qualified Lead (SQL)
At each stage, we as marketers provide content that is designed to move a person to the next stage. As we test new content, we want to know if it is more effective at moving people down the funnel and we use conversion rates to do it. In addition, we measure conversion rates through time to see if our marketing efforts are improving.
Typically, we look at our marketing automation platform to find the data we need to calculate marketing conversion rates. We download the data to an Excel file and after many hours of manipulation and pivot tables, we finally arrive at something that can be pasted into PowerPoint for your CMO. There has to be a better way! This process should be automated and routinely available to you and your CMO. To do this, you first need a good framework to measure conversion rates.
The best framework for measuring marketing conversions rates through time uses rolling averages. One of the biggest problems with measuring conversion rates is the time it takes people to move through your funnel (funnel velocity).
Consider a case where you acquire 1000 new prospects in a day. If you measure conversion rates on that day, none of them would have a chance to move to the subsequent stages. You would understate your true conversion rates.
Similarly, imagine a case where you are measuring monthly conversion rates. Suppose that you have only a few new prospects, but many people who convert to MQL and SQL. In this case, you would overstate your conversion rates.
Typically, a 12-month rolling average is a good framework to use, but it is really dependent on your funnel velocity. Some businesses would be better served using 3-month rolling averages while others might choose to use 24-month rolling averages.
Here is an example of how to calculate a 3-month rolling average.
- Raw Data – Count the number of people who moved into a given stage each month. Note, you can use all of your leads, or you can use just a subset. This is where you would divide your marketing database between channel, segment, or business unit.
Number of People Jan Feb Mar Apr May Jun Prospect 1,000 1,200 900 1,500 1,100 800 MQL 250 300 250 250 300 250 SAL 125 120 150 130 120 160 SQL 75 50 50 75 60 70
- Rolling Average – Take a 3-month rolling average for each stage at each month. This is the mean of the current month and the two previous months. Note that you need November and December data to get an average for January and February.
3-Month Rolling Average Jan Feb Mar Apr May Jun Prospect 1,033 1,200 1,167 1,133 MQL 267 267 267 267 SAL 132 133 133 137 SQL 58 58 62 68
- Conversion Rates – Calculate the stage-to-stage conversion rates. You do this by dividing the current stage average by the previous stage average. For example, MQL / Prospect, in March is 267 / 1,033 = 26%.
Conversion Rates Jan Feb Mar Apr May Jun Prospect → MQL 26% 22% 23% 24% MQL → SAL 49% 50% 50% 51% SAL → SQL 44% 44% 46% 50%
Now that you have a good framework to calculate marketing conversion rates it is time to put it to work. You should find a way to automate these calculations, so they are readily available to you and your team. At ORM Technologies, we provide Optimized Analytics for sales and marketing. This includes easy-to-use and robust reporting on leads and marketing conversation rates. If you have questions, or would like help calculating conversion rates, don’t hesitate to drop us a note at email@example.com.
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With over a decade of experience with advanced technologies, Pete leads both the Sales and Marketing teams at ORM Technologies. Previously, as the VP of Engineering, he led the technical development of ORM's advanced analytics and optimization solutions.
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