As data-driven marketers, we can actually prove ROMI — the trick is having systems in place to actually do this. Proving ROMI doesn't just happen; the process put in place for tracking and showing ROMI needs to be well understood, agreed upon and diligently executed within leadership, marketing and sales.
At Mojo, we use a dashboard that we've named the "Results Runway" to track ROMI. Simply put, the Results Runway measures forecasted results to actual results from website visitations to customers over a 12-month period. Here's an image of everything that we're actually tracking on a monthly basis:
Are we measuring too much? Why not just track visits, leads and customers? The biggest reason is because if we were only reviewing and measuring those metrics, there would be really no way to get any meaningful information for identifying potential issues or areas of opportunities. Wouldn't you want to know what your close rate was on proposals to customers versus just seeing how many leads turned into customers? If the close rate was low, why is that? Is the proposal strong enough? Or is the definition of a Marketing Qualified Lead (MQL) strong enough?
When a new client comes aboard to Mojo, we initially create the forecasted numbers based on best guesses. During the first year, we're documenting the actual numbers and initiating conversations surrounding how to strengthen those numbers. Going into the second year of our engagements, our forecasted numbers should then be much closer to reality. We should then better be able to put in stretch goals for both Marketing and Sales.
Here's a detailed look at everything we're measuring here and why:
What are we expecting in terms of a percent increase in driving website visitors through our brand building activities (blogging, SEO, social media, media outreach, email)? This really ranges based on multiple factors stemming from the current online presence of the client and how many resources are available to us right from the start. Starting with no contact lists, no social following and no created content to leverage will take longer to increase visitations versus a client with more "fuel in the tank."
How many of those website visitors are filling out a form field on our clients website? This is a critical number; if the conversions are lower than we expect, it begs the question: why? Using a variety of tools, we then dive into what's working and what's not. Are CTAs being clicked on? If so, are the landing pages converting? What is the behavior when a visitor is on page? Our team utilizes a slew of great tools designed to give us the insight we need to make suggestions to "optimize" our efforts.
Lead-to-Marketing Qualified Lead Conversion (MQL)
How many of those leads become MQLs? Before we dive into this, I'd like to discuss MQLs. What the heck is really an MQL? For us, an MQL is whatever is defined and agreed upon between Mojo and the client with total buy-in from the sales team. This is wildly different for every client. The trick is define this early in the engagement and align it with what is asked as a lead becomes more engaged in our brand (aka progressive profiling). As marketers, are we really asking the important questions needed to define someone as an MQL?
Once we define it, we measure it. Why? Are we doing a good enough job at nurturing our leads, building a relationship with them and providing relevant enough information that they are willing to give us more information in return? At Mojo, when a lead becomes an MQL, they are passed on to the sales team.
MQL-to-Sales Qualified Lead Conversion (SQL)
How many of those leads that we consider marketing qualified actually turn out to be sales qualified? Again, the definition of an SQL will be unique to each customer. Why do we measure this? An example is if we notice a high conversion rate from MQL to SQL we can feel good that we're qualifying leads well. If it's low, maybe we need to tighten up the definition of an MQL and figure out ways to better qualify those leads. After all, a salesperson's time is valuable and we want to provide high quality leads with a higher propensity to close.
SQL-to-Opportunities Conversion Rate
At Mojo, we define "Opportunity" as an actual revenue opportunity — something that the SQL can say "yes" to, such as a proposal of some sort. So, how many SQLs make it to Opportunities? Hopefully a lot, but what if not many do? This would then beg the question of what is happening in the sales process between the salesperson and the SQL? Could something be improved? That's likely to be the case. Oh, and you might be thinking: could marketing support sales during this phase? The answer is likely yes!
Opportunities-to-Customer Conversion Rate
Phew, we've made it. Finally, the last step to measure in order to track ROI! And, just like the other stages, we're asking: how can we improve the conversion rate here? Is pricing too high? Too low? Is the proposal or service offering not strong enough? Without measuring, how would we know where the improvement areas are?
It's important to stress that this truly does take a committed effort for marketing and sales. If sales is not engaged and isn't providing the MQL-to-SQL, SQL-to-Opportunity and Opportunity-to-Customer information, there's no way for us to get it — and no way to measure ROMI. Even if there's a full integration of the marketing platform to the sales CRM, if it's not being used properly, it doesn't work.
Numbers are nothing to shy away from. Are we a smart team that makes the best decisions possible given what we know? Yep. Do we knock results out of the park each and every time with our marketing efforts? Ah, no. The trick is to learn and get smarter by measuring realty versus living in a fantasy. As Mike Rose, Mojo CEO, often says, "One day the data we provide our clients will be as valuable or more valuable than the results we deliver." A strong statement, but I tend to agree!