I’ve been writing a series looking, ultimately, at pipeline management. I’ve written about Win Rates, Pipeline Integrity. In this post, I’ll cover two more aspects of pipeline management, Average Deal Size and Average Sales Cycle.
To understand our qualified pipelines, we have to know our win rates, average deal size, and sales cycle. Before we start analyzing the pipeline we have to make sure we have a high integrity pipeline. Pipelines filled with garbage are meaningless–none of the analysis we provide will be accurate or helpful.
Average deal size is pretty simple, it comes from an analysis of your past wins. I tend to like to look at trailing 18-24 months of data. It’s calculated very simply, it’s the average value of those deals. There are a lot of ways we might look at average value, but it’s important to makes sure we have a consistent way of looking at the average deal size, not mixing different methods of measuring the average deal size.
We can look at the average purchase price, if you sell products that are purchased outright. It could be ARR if you sell subscription based product. It could be bookings or revenue. It could be lifetime contract value.
Each of these has subtly different meanings, depending on your offerings and business, you may actually have multiple versions of these. It’s important in understanding your pipeline that every opportunity represents the same type of deal. For example, mixing ARR and revenue from Professional Services in the same pipeline can produce some misleading insights and gaming from the seller.
Likewise if we are selling very different products/services, with very different average prices, we may want to separate the pipelines to better understand their health. For example, if we have an offering with an average deal size of $10K, and another with an average deal size of $1M, combining them in a single pipeline with an average deal size of $505K can produce meaningless results. (These may also have very different win rates and sales cycles.)
One error people make is they take the average value of deals in the current pipeline, not the historical average of deals won. To know if we have a healthy pipeline, we have to make that judgement based on the historical average deal size that we’ve won.
I’ll come back to how we use this to determine pipeline health in the next post.
Let’s move to sales cycle. There are a lot of people that look at something called pipeline velocity. Frankly, I’ve never liked it. First, it’s a calculation of a number of factors (# of opportunities, deal value, win rate, sales cycle length). I’ve not found it very useful because it’s not immediately obvious in a pipeline report and it just shows things are going slower, faster, or normal, but doesn’t provide any insight into specific deals and overall pipeline velocity, based on historic wins.
The easiest “velocity” metric is to look at sales cycle. To determine the average sales cycle, which we use to compare current deals and pipelines, we look at past wins and the number of days it took to close them.
When we start counting the days is critical. It starts with the day the opportunity was first qualified and ends with the day we receive the order. If we look at past wins and losses, we take the average of the days for each deal. For example, if we historically had 4 wins, one at 100 days, the second at 200 days, the third at 125 days and the fourth at 250 days, the average sales cycle is 169 days.
A mistake that too many organizations make is they start counting from the very first meeting we might have with a customer. As I’ve discussed, in one of the earlier posts, we may nurture opportunities for a very long time, until they are qualified. For example, many of our opportunities are nurtured for over a year, sometimes several years. But when we qualify them, they tend to move pretty quickly (an average of 275 days.) The only metric that makes sense is to count the elapsed days from first qualification.
Sometimes, qualified deals are moving through the process, perhaps into proposal, then become unqualified. We work on those to re qualify them. moving them to closure. While there are differing opinions, I don’t restart the count on sales cycle in this case, I always use the first qualification date. I do this, because deal becoming “unqualified,” lengthening our sales cycle is a more realistic representation of performance than starting this over.
For extra credit points, you may want to look at time in stage. This is the number of days in each stage, the sum of these is average sales cycle. This is a refinement on pipeline metrics, but since so few are doing this well, starting with total days from qualification to to closure is very easy to do and give you a lot of insight on the health of your current pipeline.
Let’s imagine you have an average sales cycle of 180 days. Then as you are doing pipeline reviews, if you see deals that are projected to be significantly under this, say 45 days, or significantly over, say 1100 days (These come from a real example). Because these deviated so significantly from your past experience, you should question them. Sometimes deals close very quickly, some take a very long time, but you want to understand what’s happening, and what you can change (for the long deal) to improve performance.
While I’ve stated that your healthy pipeline metrics should be based on the average value of your wins and the average sales cycle of those wins, I also like to look at the average value of losses and the average sales cycle. These can give you additional insights about sales performance. For example, I often find that people take 2-3 times longer losing a deal than they take to win a deal (ask if you have a question about how this happens).
Now we have the fundamental elements to understand and manage our qualified pipelines. We know how to build a high integrity pipeline, we know our win rate, our average deal size and our average sales cycle. All we need now is to know our expected quota to be able to analyze our pipelines and drive performance. That comes next.
Some of you might be wondering about your unqualified pipelines and key metrics to track there. Again, stay tuned, right now we focus on the qualified pipeline. Once we understand the metrics and key performance, we can start to establish metrics and goals for the unqualified pipeline