Last summer we had an unexpected hiccup in sales forecast accuracy. No big deal, right? Same product. Same market. It’s summer and everyone is on vacation. Well, the comfort of that explanation didn’t last long. We missed our sales forecast again the very next month.
Only by identifying a benchmark, following the data trail and taking corrective action quickly were we able to regain our sales forecasting confidence. Oh, and we increased our win rate by 50% at the same time!
Read on to see how we did it.
1. Identify Relevant Sales Forecast Benchmark
We have reasonably good Salesforce.com adoption, so the quality of our sales data was considered reliable. Fortunately, some time ago, we took the decision to create two types of probability – ‘forecast’ probability and ‘win’ probability. We found that having only one probability assignment confused the subjective practice of calling a deal to close by a certain date with the distance the deal was from our sweet spot (benchmark). Where forecast probability represents the sales rep’s call on how likely a deal was to happen by the Close Date, win probability is automatically calculated based on weighted qualification question values.
Of course, the problem we faced was a divergence of these two probabilities over the prior months.
2. Follow the Data Trail to Develop Insights
When looking at data, we tend to start testing simple ideas first before diving deeper. Since there was a divergence in the probability assignments, either the reps were becoming less tuned into where they were in a deal or some assumptions behind our win probability calculations were no longer valid. We took a look at six months of new customer Opportunities and mapped our qualification criteria to won/lost deals, looking for shifts. While most were unchanged in their expected impact, one of our qualification criteria – ‘Met With VP Sales’ – had rocketed up in significance. Over the prior six months, we were 3-times more likely to win a deal if the prospect’s VP of Sales was actively engaged. And most of our losses (slips too) had no VP Sales engagement. While this may not have been the only thing that had changed, it was pretty clear we needed a response.
3. Use Insights to Adapt Sales Process, Skills or Practices
Once we isolated ‘Met with VP Sales’ as a more critical sales forecast risk factor, we adjusted the win probability calculation to weight it more heavily. We also wanted to measure and validate the impact, so added a risk filter to our sales forecasting application model. After adjusting how we were tracking ‘Met with VP Sales’, we went a step further to examine best practice VP Sales engagement. What was it that our best reps were doing to engage the VP and how could we help drive this best practice more across the team?
We had gone through a subtle shift in market positioning to embrace a broader sales forecasting agenda, pushing analytics and reporting to a secondary message. While this has better aligned our value with market demands, as it turned out it also resulted in a change to the ‘Decision Group’ dynamics. Getting first-hand exposure to the VP Sales requirements put our best reps in a much more valuable position with respect to our Sales Ops coach on any given deal. To help standardize the practice of engaging our VP Sales early in the cycle, we created a set of interview questions for the reps that would help guide a mutually valuable conversation. These were introduced, validated and put into practice within a couple of weeks.
The Result – 50% Higher Win Rates
Through the second half of the year we steadily improved our conversion rate on forecast deals, driving accuracy back up to within 15% (+/-) tolerance of the initial call. More interesting are the benefits to our customers. As we invest more time early in the cycle on senior, multi-touch-point engagements our team’s value to the customer increased dramatically. We uncover management inconsistencies that were not visible to senior execs, we’ve identified best-practice silos and we’re a stronger partner in the Ops/Sales/Vendor relationship. Yes, it’s added more time to the selling cycle and the Account Exec’s workload goes up a bit; but we’re driving more value, larger deals and 50% higher win rates. Well worth the adjustment.
While sales forecast accuracy fluctuations will not hurt a private company the way it might for a public company, it remains one of the best indicators available for sales process health. A sudden change in forecasting accuracy signaled fundamental shifts in the way our customers were buying and how our sales team was running the process.
No matter what your business or what you are selling, you’ll be able to cut through the Data Fog and transform your own business using this simple process:
- Identify Benchmark Data
- Follow the Data Trail to Develop Management Insights
- Use These Insights to Inform How You Should Change Process, Skills and Practices
How do you stack up against your peers?