A Story Of Expensive Leakages
It is estimated that an average of 30% of water is lost in North America's supply due to pipeline leakages. This accounts for non-revenue water loss costs at an estimated USD$15 billion per year to the US govt.
There’s another “revenue leakage” that I want to highlight, the one that is the subject of this blog: revenue lost to missing potential leads in your funnel.
When you realize that the industry average cost per lead stands at about $198, you already know B2B lead gen is expensive. However, what’s more expensive is not knowing you have potentially hot leads unattended or going entirely after the wrong ones - things that bleed your marketing budget and the opportunity cost of building revenue.
How Did We Get To This Point?
No org wants to lose a prospect. No org wants a prospect in waiting. So what leads to this leaky funnel where potential leads are missed out?
The answer is two-fold: poor business processes and poor analytical systems.
Poor business processes:
What is a qualified prospect? If you dig into this question, you realize the question is an onion with peeling layers of more questions.
It means your organization has put in place a documented methodology of defining lead scoring; it means your org has capacity planning in place to ensure there are enough reps to manage the generated demand. It also means your org has very clear definitions of what gets transferred to sales and when.
Far too many orgs suffer from siloed data to poor business processes. The good news is this can be easily fixed. I cannot say the same for the next problem.
Poor analytical systems:
When your campaign data is on a spreadsheet, your funnel conversions for last Qtrs leads on another spreadsheet, your opportunity conversion data on Salesforce, and your MQL to SAL metrics on another spreadsheet - you are less Business Analyst and more “Spreadsheet Manager.”
If sales & marketing meetings had one system that ingested all the data from marketing automation tools to sales automation tools to see holistically what campaign is leading to a dollar in the bank, what ICP is no longer working in a specific region, what insights lie buried deep in your terabytes of org data, the problem of leakages was solved.
You need to move from spreadsheets to a proactive analytical system.
Do you really know your ICPs?
Sofia joins Meridian, a B2B SaaS startup that builds solutions for Salesforce users, as an SDR for the Europe region. Meridian has grown 3x in the past 14 months and is topping revenues north of $13M ARR. Most of Meridian’s revenue has come from the US and the leadership now wants to make a foray into Europe. Sofia was hired as their first SDR in the EU market.
As part of onboarding, Sofia is taught that their Ideal Customer Profile(ICP) is RevOps teams. She learns from Aiza, the top performing sales rep in the company, that “going after companies with revenues above $10M ARR with a Director of RevOps” is Meridian’s sweet spot.
Sofia is excited and doubles down on what she’s taught on the company’s ICP: RevOps. Two weeks in Sofia has built the list of every single European startup with a RevOps Director and over $10M ARR to go after.
She is ready to go after the prospect list and will reach out to nearly 500 companies she has shortlisted for the next two weeks. To her great surprise, she discovers DemandGen Directors are being looped into some of the conversations she has had. In a few cases, she has been routed to speak to InfoSec Directors too.
Sofia does an experiment of reaching out to DemandGen Directors on another campaign for the next few weeks and soon discovers that DemandGen is a strong ICP. She goes with this data to her leadership, who tested this hypothesis in the US market, and to their great surprise, it is an ICP they did not start out with before.
Did you notice what happened in the above story? A stellar SDR discovers a new ICP. It took her months to discover it worked. After a few convincing conversations and a few months later, the org realizes the ICP is new and goes after it. This is all unbelievable but possible. This is all possible but limited.
The average sales cycle is 100-120 days in B2B SaaS.
Time lost experimenting by a rep and time lost convincing the leadership is not something a sales rep would sign up for. Then there are thousands of variables across systems that are humanely impossible to connect and measure. A Business Analyst in your RevOps who does these analyses will also need a co-pilot and an app to measure it all. Which, unfortunately, has not existed for far longer.
This means there are ICPs that are not “documented by the org” that SDRs will drop coz they are not trained to go after them.
There are prospects marked as ICP by the marketing, but they truly aren’t in a different market the SDRs and Sales will pursue because that is what has been documented and trained on.
This opacity in leads to opportunity conversion plagues organizations. More so, given Demand Gen teams are signing up for the “marketing-generated revenue” goal. More so, coz SDRs are being compensated on “Stage-2 of the opp” and not on just converting it and parking it in Stage-1 for sales to pick up.
The Cost Of Acting On Incorrect Metrics
December is a month of planning for the next year for a lot of companies out there.
Suppose John, your RevOps Director presents that your SQL to Pipe conversion has been 20% for the year in the planning meeting. Your leadership takes a goal to have this conversion jump to 30% next year.
However, what if we told you that, analyzing the data, your SQL to Pipe is actually 27% except for two industries which brings the average down to 20%?
John is not at the mistake here, given even RevOps could not dissect the data down to this level manually. This is a systems problem. And if John still did, your decisions would be operated on spreadsheets that are looking at months-old data and not live information.
Wouldn't you be signing up for an incorrect goal next year? You would be signing up for an improvement of 3% while continuing to spend campaign money on industries that are pulling your conversions down and your revenue opportunities.
The leakages in your Lead to Stage 1 conversion are an outcome of the opacity of what happens in your funnel. What you need is the ability to see through what exactly converts in your funnel, what ICP is not working out, what campaigns are sucking your dollars with poor ROI, and what personas to go after. And so much more, and you need all this shared with you live now.
Gartner’s State of Marketing Budget and Strategy 2022 highlights a few important things in Marketing:
Marketing channels are adjusting as post-lockdown customer journeys recalibrate and CMOs seek to balance awareness and performance.
61% of CMOs reported they lack the in-house capabilities to deliver their strategy even with 25% of the marketing budget spent on technology solutions.
We are spending millions of dollars on a marketing tech stack and setting up a team to deliver the CMO’s vision. Each of these systems generates data at the speed of light. In the absence of a system to measure it all and share recommendations, nearly two-thirds of all CMOs lack in-house capabilities to deliver on their goals.
CMOs know this, and Gartner’s study closes it well:
Longer-term, the majority of CMOs know that they must make large-scale changes to marketing, with 71% of respondents agreeing that they need to reevaluate the role the function plays to achieve their long-term vision. To achieve that vision, CMOs must lean into their role as change agents, reimagining and reappraising the components required to drive the marketing machine.
CMOs needs a solution to measure their marketing strategy in real-time down to the atomic level of every outreach campaign and the data it generates.
The famous statistician Edwards Deming was largely responsible for the revolution in Japanese manufacturing management that led to the economic miracle of the 1970s and 1980s. His profound faith in good data to make decisions is encapsulated in these words he famously said:
“In God we trust. All others must bring data.”
Gladly our trust in real-time data analysis led us to build RevSure!
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