As marketers, we face the daily challenge of proving our ROI.
Marketing teams still have to change the perception that all we do is ask for money to spend on fancy campaigns, conference exhibits, and schwag to give away.
We are experts at driving messages externally to prospects. That skillset needs to be driven internally as well by illustrating how marketing and lead generation metrics are the key to predicting future revenue.
Let me illustrate how you make the results of your marketing activities get the attention of the CEO and CFO—and allow them to confidently predict future revenue for their board of directors.
Most companies don’t begin to predict future revenue until a formal opportunity is created in their CRM platform. A weighted average of each opportunity then is added up to predict what revenue will look like in the current quarter, next quarter, and fiscal year.
The problem with that approach is that opportunities are not usually created until a prospect is well down the pipeline, often 2-3 months into the sales cycle.
A smart data-driven approach at the lead generation stages can help eliminate this 2-3 month lag on revenue prediction.
Big Data has been around long enough that people cringe at hearing the term. However, this data overload has enabled a clear opportunity for marketing departments. We now have access to our own set of Big Data, but the key to data-driven marketing is to find the “micro-data” needed to make decisions. Identifying this micro-data is the key to predicting future revenue well before those formal Saleforce.com opportunities start showing up.
Let’s start with the typical prospect stages in the lead generation process. Though yours may be different based on industry or maturity, it could look something like: Prospect > Marketing Qualified > Sales Accepted > Sales Qualified > Contract. Most opportunities are opened once they reach the Sales Qualified stage. However, by tracking the right metrics, the Marketing Qualified stage will give you a very good idea of what the revenue outlook looks like.
The key to starting the prediction process is getting a baseline understanding of how the typical prospect moves through your particular funnel. Best-in-class conversion rates depend on a variety of things such as your industry, how you sell the solution, your annual contract amounts, and your criteria for defining each of the stages. That said, typical B2B conversion rates fall in the ballpark of:
- Prospect to Marketing Qualified—5-10%
- Marketing Qualified to Sales Accepted—50-60%
- Sales Accepted to Sales Qualified (Opportunity Entered)—40-50%
- Sales Qualified to Contract—20-30%
For top-to-bottom funnel conversion, Marketing Qualified to Contract, expect between 5-10%. Getting these baseline metrics is just a start. To start predicting revenue, you need to track these metrics over time by cohorts.
Predicting future revenue amounts is one thing, but the other is predicting the timing of the revenue. Herein lies the importance of cohort analysis.
Traditionally, you could count the average time between when an opportunity was opened to when it closed through standard fields in your CRM. But as mentioned earlier, this takes 2-3 months longer to predict than if you were to look at it from an upstream stage like MQL (marketing qualified lead).
Also, sales reps often have their individual comfort levels of when an opportunity shows up on the executive pipeline dashboard. That makes the opportunity to close time highly variable by sales rep. Tracking it at the MQL stage eliminates this.
One way to define your cohort is by month. You start by grouping all the MQLs into each month when the MQL was generated. You then track how each of these cohort groups move through the funnel to begin adjusting your original baseline metrics. What you should notice over time is that the conversion rates between stages improve over time. For example, your January cohort may have shown 52% of MQLs turned into SALs. You may have had a few initiatives that turned that into 61% for your April cohort.
You also continue to track the time between stages, as that number should shorten as you optimize your funnel moving strategies. You could see something like:
In this illustration, January’s 3.5% conversion rate means that 29 MQLs would need to be generated to close 1 deal in 193 days. If you were just starting out, you would expect both the conversion rate and the time to contract to improve as you test, learn, and implement new approaches.
Once we have established a benchmark, in this case 29, we can track that over time to see if it’s getting better or worse and whether there are any seasonality effects. We can also track the metric by the source of the MQL—whether it be inbound driven, a marketing campaign around a piece of thought leadership, or inside sales. You can also understand the stage where leads are falling off and pinpoint where you should be optimizing.
However, you will begin to level off at some point, in this case around 7.5% and about 6 months to close. This is where you can see the definitive value. Now, you need only 13 MQLs to generate the same closed contract, and you can predict that it will happen 6 months from the date of the MQL. You no longer need to wait 2-3 months for an opportunity to be added; your data-driven lead generation is out in front of it.
This MQL/ Close ratio is critical for tracking and is an easily digestible metric for people internally to see the benefit of optimization and how marketing performance can be the new predictor of future revenue.
In addition, you can apply your average contract size to quantify the value of each MQL. In this case, if you have an average contract size of $ 65,000, each MQL is worth $ 65,000 / 13 MQLs = $ 5,000.
You have now turned marketing from a cost-center measuring “costs per lead” into its true purpose—revenue generation, by quantifying “revenue per lead.”