Are Your Sales Metrics Deceiving You?

sales

7 Mar

Deal Cycle Time and Win Rates are critical metrics that help explain the cost of customer acquisition. When they measured on assumptions that are incorrect, any efforts to make the best decisions for the business will be inhibited. In this article we’ll discuss how this can happen, what the potential consequences are and how to avoid it.

What we uncovered revealed a problem that came as a complete surprise to them and was shocking because all their assumptions about the length of their cycle times, who the best performers were and even their actual win rates were proven to be erroneous. This is about that experience and what we uncovered.

Background

We’re going back probably 7 years or so - before the days of ChatGPT, LLMs and Agentic AI. I did some data analysis for a client with the aim of helping them understand more about what was really happening in their sales operation. I met with two members of their sales operations team and we discussed what pressures they were under as a business. “We have lots of separate sales teams that work independently from one another, but there’s huge variation in performance between teams”, they tell me. They go on “Some teams have win rates that are higher and their cycle times are a fraction of other teams. We want to know what best performing teams are doing so we can standardise it across the organisation.” So with that pretty clear brief I set to work and starting analysing their sales data.

If it looks too good to be true it probably is

Now it’s funny - when you look at the data of an organisation it tells you something about what the culture. Sure enough as soon as I started looking through their opportunity data it was clear that they were obsessed by cycle times. Now, it’s normal for large enterprises to have lots of fields configured in their CRM system, but what was different about this system was what those additional fields were doing. So many were tracking time: how long since the account was a prospect; how long has the opportunity been in the current stage; how long has it been since it was in a higher stage. I mean we’re talking like maybe 15 fields just to track an opportunity in different parts of the sales cycle.

I like to get an idea of what I’m dealing with before starting to dive into any analysis too far, and so the first thing I wanted to check was how the age of an opportunity affected the the win rate.

The image above shows what I saw and I’ll explain what it means. We look at all opportunities that have closed and group them according to their age ( ie how long it took for the opportunities to close after being created). The bars represent the counts of opportunities and the line represents the win rate percentage. So the first bar is a group of 17,500 opportunities that had an age of 0 to 14 days and a win rate of 88.3%. The general trend is that as the age increases the volume of opportunities decreases and so does the win rate. This was consistent with what I was told so what could be going on?

The truth of the matter

Now Salesforce can be a bit awkward to do analysis that involves looking back at the history of things as it doesn’t retain a complete history of changes, however it is possible to work around these limitations. The next thing I wanted to do was look at how long the opportunities were staying in each stage as I believed that could help me identify where the bottleneck was and this was where I started to notice a problem. We were missing huge numbers of opportunities in the early and mid stages of the sales cycle, but somehow they were present in the late stages. A bit more investigation and I had confirmed what I had feared: that opportunities were being created in the CRM system late on in the sales process just before they would be closed.

After discussing with the Sales Ops team we knew what was going. Reps were simply managing their deals outside of the CRM and only entering them late on. Thousands of them had been entered at the very last minute and that was why the cycle times looked so short. It wasn’t they cycle times were that short - that was just an illusion. To make matters worse - we realised that who in their right mind is going to record an opportunity at the last minute if it had already been lost? No-one. So not only were their cycle time assumptions all wrong, but so too were their win rates. We wouldn’t know what their real win rates were if they weren’t recording lost opportunities. All along their top performers had just not been entering their data at the right time and no-one had noticed. The metrics they had been using were all wrong.

Consequences

So here are a few consequences of such as problem.

Your teams waste time on wild good chases chasing problems that don’t exist while being oblivious to a problem that does exist.
You lose all the history of deals that you thought you had settings you back months or years and having to start your data collection process all over again which will take time to build up to a point where you have useful information on which to base decisions on.
Suppose that important decisions were made on these false metrics and imagine that you had increased your product prices because the win rates were so high. In such a scenario, with the sales rep behaviour unchanged, you would see that fewer opportunities would be closed, because the lost deals would never make it into the system, but the win rate would remain high. In such a scenario you might think that there was an issue with lead generation or nurturing and start looking into marketing activities, or you may possibly think that existing accounts were not continuing to purchase your products and that maybe there is a product quality issue.

How to Avoid

Well obviously we need to ensure that the data that is in the CRM system is a true reflection of what’s happening on the ground and how opportunities are being processed, but how can we achieve that in practice. Well here’s a few things that I think might help:

Get your data analysed to ensure that issues with adoption and usage are identified early.
Ensure that there are clear working procedures that describe how your sales process interacts with your CRM system and what you expect from your team in order to ensure that data is captured consistently.
Automate the data entry process as much as possible. Use tools that can automatically integrate with emails, calendars, social media media messages, video calls and can take much of the leg work out of highly repetitive tasks that are often a duplication of effort.
Simplify your CRM implementation to make it easier to use so that manual data entry tasks are quicker and less onerous. Track only the really important facts of deals and where possible remove restrictive validation rules that prevent data from being saved if certain conditions are not met.
Use reports from the CRM system to drive meetings with your team. Reports that can support things like Pipeline / Deal reviews can easily be generated.

Conclusion

Ultimately for our customer they realised that data quality was a major problem for the organisation and set on a path to improve it. With a few changes it is possible to improve the quality of your CRM data. Ultimately, good quality data is the lifeblood of insights from analytics and machine learning and I firmly believe facilitate logical data based reasoning that will lead to better business decisions.