Modern-day Decision-making Fatigue
An average adult makes about 35,000 decisions per day. At the onset, it feels ridiculous and unreal - but the number is accurate because it also accounts for a variety of micro-decisions. From “should I sleep a little longer today” to “should I unmute and speak” to “why is the pipe coverage poor for Enterprise this Qtr” - they all account for everyday decision-making.
And as such, we build mental fatigue when it comes to paying attention to make an important decision. Hence the corporates came up with the idea of “middle management” to delegate decision-making. And rightly so.
However, some decisions we make are entirely wrong owing to the lack of information or foresight. The case of the restaurant seafood restaurant chain Red Lobster is a classic example of lousy foresight in a marketing campaign.
The Curious Case Of Snow Crabs Leading A Company To Bankruptcy
In 2003, Red Lobster ran an extravagant promotion enticing customers to come and enjoy an all-you-can-eat snow crab experience for $20. That was a pretty low cost, given snow crabs are very expensive. The restaurant underestimated its customers' appetite to consume and had diners come in and eat more and more to the point of pushing the company to the edge of bankruptcy owing to the losses from the misfired marketing campaign!
This story of poor campaign management resonates with many B2B SaaS startups that fail to measure campaign success. It gets worse with B2B because the result of a poor campaign is not known immediately. And by the time it is measured (if it is measured rightly!), marketing has lost the budget, sales have lost Qtrs & commissions, and the org has lost revenues.
We have extensively spoken about how to think of ICPs and Campaign management in our blogs. Here’s a good primer on the art of ICP refinement. We have also written about the demo-to-deal journey here.
In this blog, we wanted to dispel the myths around “Pipeline Health”. There’s so much content on this subject and hardly any actionable takeaways on “what do I need do to improve the pipe” - what metrics, how to dissect, how to visualize it to the leadership and the board and help their decision making. This blog will answer it all - in words and charts!
Metrics & Influence Factors That Matter In The Pipe
When it comes to the sales pipeline - it feels like a bridge - at one end, events are happening at the lead level that decides what converts into the pipe - and at the other end, the events happening in the pipeline decide how much of that converts into revenue. No wonder the health of the pipeline decides the fate of the organizations.
When you think of the health of your pipeline, here are the metrics you should focus on.
Lead to Opportunity:
- MQL to SQL%
- Lead to Opp velocity
- Lead mix by source, industry, persona, etc.
- Lead leakage rate by stage, by industry, by persona, etc.
Opportunity to Closed Won:
- SQL to Pipe %
- Pipe to win % (Win Rate)
- Loss Rate (segmented by reasons)
- Sales Velocity
- Pipe coverage
- ASP by region
- Sales forecast
- Avg. Sales Cycle
- Total Pipeline Value by Stage
- LTV: CAC Ratio
- Opportunity to Closed Velocity
- Pipeline mix by source, new vs existing biz, pipeline by Target Segment, etc.
- Pipe leakage rate by stage, by industry, by persona, etc.
Focusing on the funnel metrics clearly shows your pipeline’s health. But it is not only the metrics that matter, but an understanding of the underlying factors that drive the metrics is as important to help drive interventions and refinements.
- MQL to SQL%
Marketing Qualified Lead(MQL) to Sales Qualified Lead(SQL) conversion decides the quality of your MQLs. Suppose you ran a Super Bowl ad that attracted all the wrong kinds of persona for your product; it will show an inflated MQLs but a poor pipeline downstream. This metric is a leading indicator of your pipe that will be created perhaps months down the line. When your MQL conversions drop, you can guess your pipeline volume will take a hit shortly.
It is important that your DemandGen team is able to dissect the Top of the Funnel (ToFU) by a variety of splits like Industry, Lead Source, Titles, and Campaigns and see how each of these segments on a chosen cohort converts downstream and tie that insight into the active and new campaigns.
Let’s do a role-play in a sales-marketing meeting for a startup.
Danny (Marketing Manager): “The LinkedIn Ad Campaign we started in October has worked tremendously well, generating about 509 MQLs so far! In comparison, the Facebook Ad Campaign has generated only 121 MQLs so far.”
Sophia (DemandGen Director): “Danny, what are the conversions for both these campaigns?”
Mike (CMO): Also, how long are MQLs from each of these campaigns taking to convert? The ones that get dropped, what persona are these? What percentage?
Danny: “LinkedIn has seen a 42% conversion to SQL, and Facebook saw 37%. At this point, we do not have data on how long it takes to convert into SQL but it should be easy to pull that. On what persona gets dropped, we will need some time to analyze the data.”
Sid (SVP Sales): “What are their contributions to Pipe (SQL to Pipe%)?”
Danny: ”We have the info for Facebook shared with the leadership on a gsheet but are yet to tie for LinkedIn. Let me publish that by tomorrow.”
Tomorrow is the enemy of hustle. Tomorrow is the enemy of strong Operations. If only Danny was enabled with an application to see the entire funnel analytics, he would never have started the conversation the way he did and instead would have said this:
“Danny: The LinkedIn Ad Campaign we started in October has not given the expected returns. While it generated 509 MQLs, much higher than the Facebook Ad Campaigns, which generated about 121 MQLs in the period, the downstream conversions into pipe are poor for the LinkedIn Campaign. It generated a $1.1M pipeline, while Facebook generated $950K with much lesser MQLs but better conversions into the pipeline so far. When we further break the pipe stages by the campaign, we notice opportunities from LinkedIn have a higher drop at the POC stage while Facebook ones continue to lead up to Contract and win with a higher ASP than any other active campaign.”
✅ Danny and Sophia from Marketing walk out of this meeting confident about how they are reallocating the budget.
✅ Sid is much happier on marketing, doubling down on Facebook Campaigns to bulk up the pipe for his team.
✅ Erica (CRO) is clear on her vision of how the revenue strategy is working out and what her updates and recommendations to the board are.
2. SQL to Pipe %
Sales Qualified Leads (SQL) to Pipe indicates your lead scoring is wrong, and what marketing thinks is the right ICP is not what the sales see converting.
Knowing the volume and amount of your pipe by business type (Enterprise v/s SMB) and downstream revenue generated from each of these markets is a good example of analysis that SalesOps and GTM leadership sees immense value.
As you step into the last month of the Qtr (and fiscal ending for many organizations), you need to know what opportunities were carried over from the previous Qtrs, what opportunities got added newly in this Qtr (addition), what are the opportunities that got dropped (leakage), the ones that moved stages(progression).
I would not be surprised if many organizations don’t do this deep analysis. Wonder why? It is not that the Operations leadership does not see the need for this. It is simply because they have not been armed with an application that did this out of the box for them. The manual effort of getting this data on sheets using hundreds of hours of the company’s time and having it stale by the time it is delivered makes no sense and is not worth the time for organizations.
3. Win Rate
Win Rate indicates the ratio of opportunities you close versus created. Far too many companies fail to dissect the win rates by segments: regions, reps, campaigns, etc.
On the face of it, an overall win rate tells you how many opportunities you make revenues from versus what was created. But like they say: the devil is in the details!
Suppose your win rate is a healthy 30%. Let's say starting the last two Qtrs, and your marketing has invested significantly in LATAM markets. However, when you dissect, you discover that while the Americas have been contributing greatly, it is the organic growth from EMEA and SEA that is driving the win rates. LATAM shows no improvement from the campaigns.
Here’s what is happening: your marketing is losing money on the campaigns run in LATAM. Your SDR teams are forced to work MQLs generated because these are “costly leads”. Your sales team only realizes in the pipeline “it wasn’t worth their time,” and most LATAM deals get dropped, say to budget.
The ability to break what is contributing to your wins, knowing what your current win rates are and what the projection says, is a critical operational advantage.
The ability to project where the pipeline is generated from is an arduous task of putting dozens of spreadsheets together for weeks in many organizations. The problem is challenging because the data is siloed and out of context.
If only marketing knew where their campaigns are landing in the pipe, they would have an edge on what campaigns are working and not and restructure and experiment better. They would have a solid claim to budget when marketing budgets are being shelved in the current macroeconomic environment.
In the current macroeconomic environment, where the board decides to cut costs, marketing is given a goal of cutting, say, “half a million dollars” per month. Imagine if only marketing retaliated by saying:
“We will cut the cost by $350K starting this month. Cutting the cost of ongoing paid ad campaigns in EMEA for the upcoming Christmas season would be a major loss of pipeline generation for Q1 2024, given historically, we have done 42% of revenues in Q1 of the new fiscal from the Christmas campaigns run in the Q4 of the ongoing fiscal year.”
See how powerful an insight can be! It literally gives you the confidence to back your decisions with data and the ability to project revenues into the future. That’s what real “Funnel Analytics” means!
Knowing where your leads, MQLs, SQLs, or Pipeline started for a specific cohort and how they moved during that time frame is such a power play to execute what is working and what is not. The ability to dissect this all by segments like industry and titles allows for re-architecting the very marketing campaign strategy.
The ability of a B2B to generate the required quantity and quality of a “qualified pipeline” to meet its revenue targets is a competitive advantage. Every org needs this “Sales Pipeline Readiness” - the pipeline predictability advantage.
There are innumerable variables at play in a marketing and sales motion. Every single stage generates its own data, and every single action by the reps adds more data. Over the decade of my experience in Ops - I have seen this getting complicated by the turn of every Qtr.
The ability to tie all this information together and generate insights on the exact list of leads or opportunities to after - makes the GTM hustle in its truest sense. It practically cuts down your deal age and has a direct impact on your sales velocity and revenues.
It is hard to grasp the collective improvements at a scale when you have the insights to act on, predictive analytics to caution you ahead of time, and the right metrics to measure. The fact is “operational advantage” is a real differentiator.
In a world where humans have reached the ability to change four tires of an F1 car in under 1.82 seconds, in a world where across a race weekend, over 1 terabyte of data is generated per car - you need a cockpit of your own to monitor, measure and strategize what’s happening in your racing pipeline.
Gladly we built that revenue cockpit at RevSure!