When it comes to today’s average sales performance, the numbers aren’t pretty. From 2011 to 2019, average sales quota attainment has dropped from 63% to 43% by some estimates. The average close rate is around 19%. And more than 50% of the deals that are forecasted to close don’t
When we considered the reasons behind such poor results, a fundamental theme emerged: B2B Sales strategies most often fail because they aren’t executed well. You know you’ve got an execution problem when you’re struggling with all-too common gaps like poor pipeline quality, inaccurate forecasts, and hot deals that go cold. We call these execution gaps, and the good news is that they’re fixable. But why do so many sales organizations have them in the first place? In our view, it’s because for too long, sales was considered an art, not a process that needs continuous optimization.
Traditionally, sales has been all about the power of persuasion and the strength of the relationship. It has somehow evaded the management rigor and professional process discipline that emerged in departments like Finance or Manufacturing. As a result, still to this day, sales processes have remained unstructured and undefined, leaving it up to the sales managers’ talent and expertise to know what opportunities are really worth their time and effort.
Sadly, CRM technology hasn’t done anything to solve that problem and, in fact, it has created new ones. It’s not even the CRMs’ fault — after all, they’ve never been more than “simply a dumb repository of information,” as sales legend John McMahon puts it. It’s the wrong expectations sales teams had for their CRMs to begin with.
Rather than defining formal sales processes, sales forces focused on designing reports. Rather than asking how the technology should support their critical sales activities, they assumed the technology had its own inherent value. (For a deeper dive, read our ultimate guide to sales execution that has a look at the limitations of current sales and analytics tools.)
Today, many sales organizations have come to realize that digitizing unstructured processes was never the solution. It just automated their own chaos. If they really want to improve sales execution and, by extension, sales outcomes, they need to tackle the underlying process problems. Here’s what that can look like.
Imagine two hypothetical sales managers looking at their early stage pipeline. Both need to answer the same question — “Do we have enough pipeline to hit our targets?” — but have a very different approach.
The “interrogation/gut feel” approach: The manager in the first team will crawl through the CRM for insights such as deal size and expected close date, or look at activity data such as opportunities created, meetings booked, calls made, etc. A method that’s not only time-consuming but useless when it comes to driving actual outcomes. Because measuring reps by activity will simply tell him how “busy” his teams are, not how deals really advance. Then he’ll probably talk to his sales reps and RVPs in weekly 1:1s to figure out where the (what he sees as) the most promising or most shaky deals stand. But doing this across hundreds of deals and reps just isn’t scalable. On top of that, even if he finds a pipe gap in time, he has to rely on his own gut feel and experience to close them.
The (real) data-driven process approach: The sales manager in the second team, on the other hand, leads a data-driven sales organization and can forecast confidently and accurately. Leveraging historical and real-time data to assess the strength of current opportunities, she knows how big her pipe gap is and which deals need higher attention, and can therefore ask a different question: “Which actions do we need to take today to close these deals in the next quarter?”
No prizes for guessing who’ll hit their target at the end of the quarter (and the one after that). If you take a process perspective, you move from reporting on outcomes to influencing them at every step of the way. But for that, sales teams need to look beyond the different sales stages and understand what happens in between them.
A quick caveat before we go any further. This isn’t a rule that applies to every company’s sales organization. If you’re selling billion-dollar power plants, for example, and each deal takes years to develop and negotiate, then you’re not dealing with the kind of process-related problems we’re discussing here.
There’s a certain threshold of sales volume and complexity you must break through before patterns and best practices emerge, and a process perspective becomes necessary. Here’s another way to think about it: If you’re dealing with transactional, high-volume deals and believe that the way you execute your sales process has an impact on the outcome, then you should adopt a process perspective.
Since process-related sales problems are the kind that emerge when you reach a certain level of process complexity, it’s no surprise they can be extremely difficult to solve. In fact, they’re basically impossible to solve without a critical piece of the sales tech puzzle.
So what’s the missing piece in your sales tech stack?
It’s an intelligent layer that brings order to your process chaos. An umbrella technology that enables an end-to-end view of your sales process, combining and analyzing data from all systems touching your sales process in real time.
This is where Process Mining comes in. It extracts all sales relevant process data in form of event logs — giving you a clear picture of how your sales activities are interconnected and influence each other.
Adding Machine Learning algorithms to the mix, you finally know which patterns in your sales process have the biggest impact on your KPIs and the right actions to close them. In sales, that can mean adding an executive sponsor at the POV stage, coaching a specific rep whose close rate is significantly below target, or sharing a successful sales motion across the entire sales org.
In the best-case scenario, this is the moment where intelligent automation steps in, taking the right actions directly in your source systems, or sending alerts to the right people.
If you’re a sales ops leader trying to improve sales performance, the first step is recognizing that a CRM is not enough. We’re not saying you don’t need one. On the contrary, your CRM is a foundational data collection tool — but its role shouldn’t be overestimated.
If you want to do more than just report on sales performance, and actually figure out why you have the outcomes you have and how to improve them, then you’re going to need to adopt a process perspective and the right technology mix:
Process Mining to give you visibility into process problems
Machine Learning to know which gaps need your attention the most and the best way to solve them
AI-enhanced automation to trigger action directly in your source systems or alert the right stakeholders (be it sales reps or executives) to act at the right time.
The good news: All of these technologies come together in the Celonis Opportunity Management Execution App. Acting as a single layer across the entire sales tech stack, it reduces the complexity of data management, without reducing the richness of the data itself.