Marketing organizations are not exactly known for their desire or ability to measure the outcomes of their activities. As a former director of marketing ops I ask myself: Is it ability? Is it lack of desire? Or, is it simply that marketers just do not know where to start? As with any complex organizational discussion, it is likely a mix of the three. My experience leads me to believe that most marketers just do not know where to start. And those who have started are likely choosing all the wrong key performance indicators and metrics.
When I was still in my marketing operations role, I would frequently hear from the marketing teams about how much win revenue we got from our marketing programs. We were very fortunate that our organization had standardized our reporting; provided analysis capabilities to our program managers and the management team with standard dashboards for a single version of the truth. Managing your business via lagging indicators is fine when you hit your target which we frequently did.
Still, something troubled me. We knew what had happened and why but still could not answer what WILL happen. Essentially, we could do an autopsy on a dead guy but could not predict what would kill him…Something had to change.
As a B2B marketing organization we subscribed to Sirius Decisions demand waterfall. In spite of this, our key performance indicators, metrics and business intelligence platform continued to reinforce the behavior that win revenue is king. Our CMO was no longer content with seeing the results at the end of every quarter. He wanted to know within a reasonable margin of error (+/- 10%) where we would end up each quarter. We needed to change our behavior to do this, we needed to change our metrics…moving up the waterfall all the way to inquiries…and beyond.
Applying Predictive Analytics to the Problem
We were swimming in marketing data – contacts, inquiries, marketing qualified leads, sales qualified leads and win revenue. What we needed to do is figure out what patterns lead to a person progressing all the way through the demand waterfall. By applying predictive analytics, we were ultimately able to predict within +/- 5% how much win revenue would come from our marketing activities.
We took our contacts, marketing outreach data (emails, web, live events), inquiries, pipeline and win revenue and brought this data together in a predictive model. This predictive model identified those contact job titles, marketing offers and marketing channels which would yield a successful marketing inquiry. From here, we were able to extend the model to be able to forecast which contacts would lead to inquiries and which of those inquiries would convert to marketing and sales qualified leads. With this knowledge in hand, we were able to conduct “what if” analyses and predict the optimal mix of contacts, tactics, and channels to meet or exceed targets.
Now that we were able to predict the mix of contacts, offers and channels, we decided to change the way we measured marketing moving WAY up the demand waterfall. We implemented the following metrics:
- # of contacts within each segment (industry and geo)
- # of net new contacts added to the database (as determined by email address)
- # of inquiries
- Conversion % from inquiry to marketing qualified lead
- # of marketing qualified leads
- Net new pipeline created ($)
- Conversion % from marketing qualified lead to sales qualified
- Acceptance % by sales rep
Notice – no win revenue. As a marketing organization we believed marketing is responsible for finding new names and delivering net new pipeline while sales is ultimately responsible for revenue.
We updated our reports and dashboards I spoke about in a previous post to reflect these new metrics and assigned targets to each marketing team based on our predictive models.
My thought: I would rather know early on that I was off target versus having to do the post mortem…what are your thoughts? Send me your thoughts @BrendanRGrady on Twitter.