Where do we win and lose? Why? How will this shape our future performance?
Let’s be honest, we’re all using Salesforce and co. much better these days. Data is going in more regularly than ever before. And if you’re not there yet, no doubt you’re working on it. With this, we open ourselves up to an incredible opportunity. That is, learning from our history to shape our future.
Don’t worry, I’m not going to get all philosophical. Rather, I’ll delve into the world of win/loss analysis, where to start and how to action the information – or insights as we call them these days – you extract.
Let’s look at a few areas we can apply win/loss analysis to:
- Deal Size
- Deal Age
- Vertical / Sector
- Lead Source
- Product / Business Line
- Opportunity Stage
- Key sales events
- Opportunity Engagement
Next, let’s think about the structure with which we want to approach this. I’ll offer examples as we go.
- Where is win and loss most prominent
- Why do we think this is?
- How should we change what we do to mitigate or capitalise?
So, let’s get into it…
Step 1 – Which method?
There are two win/loss analysis methods here. The method you select will come down to your understanding of how your salespeople use their CRM.
Method one is win % = Number of opps closed won / Number of opps closed (won & lost)
This is the traditional method and to be relied up, it requires your salespeople to have serious opportunity admin, closing deals as soon as they are lost… ahem.
Method two is Pipeline Conversion % =Number of opps won / Total Number of opps open with a close date for that period
Less widely used, but often a far more accurate indicator of success, pipeline conversion cuts through the poor admin. This method is simple – “Of all the pipeline we had open for December, what proportion did we actually win?”. Personally this is my preferred method. But again, this will ultimately hang on your preference and sales function’s CRM discipline.
Step 2 – Where to start?
Often this can be a stab in the dark. However, there is some safe ground we can start on that will apply to most businesses. Let’s take a look at win/loss by vertical first:
The above chart details the win % across the various industries’ Company X targets. Now, while we consider where win/loss is most prominent, we need to consider how complete the data-set is from which to draw sensible conclusions. Typically, if you’ve created 20 or more opportunities within one vertical, we start to get an accurate picture that we can rely upon.
In the figure above, Company X has a 47% win rate in Energy & Utilities, way above the company average of 38%. From this, we can conclude that if we were to source 100 opportunities within Energy & Utilities versus our typical spread, we’d close 47 opportunities rather than 38, right?
Which brings us onto why. The why is the crucial element that needs to be thoroughly explored before any new initiatives are rolled out. This will involve speaking to our salespeople, our marketers, and any other stakeholder who may have had some influence in this space. Here, we are looking for stories that either back-up the date, or discredit it.
In the case of Company X, it may well be that Energy & Utilities prospects are structured in such a way that closing a sale there is just quicker. Less stakeholders to navigate and assemble, for example. But it could also be that the majority of our content marketing goes out to that market and therefore our credibility is such that closing becomes far easier. Either way, the why has to be established before we throw ourselves into a niche market in the hope of more £.
Then we’re onto the how. How are we going to capitalise on this data insight? For example, is this market big enough to exploit with all or a proportion of our sales resource? How might we adapt our language and content for our hot industry? How might we source the relevant businesses and prospects? And how can our sales processes or style be adapted to suit?
Step 3 – Recording the results
When putting win/loss analysis into action, it’s important we consider the framework within which we will explore our “hypothesis”. We may want to consider:
- How long will we run the test for?
- What results would deem it a success or a failure?
- What resource shall we attribute to it?
Once we have agreed upon the above, the test can begin and so too the proof that our new found data insight is a £ changer!