Struggling with a bad sales forecast? That’s not surprising. According to one survey, nearly 59% of sales forecasts are wrong.
Inaccurate sales forecasts are deadly for growing companies. Failing to hit the number causes people to lose their jobs. Dealing with inaccurate forecasts eats up a sales managers’ time. That is time that could be spent developing reps & building the team. And without predictable revenue projections, your company will lose the trust of investors. That can be the difference between raising another round of financing or burning all your remaining cash.
A lot of advice for improving sales forecasts focuses on examining sales process & using well defined stages. But one area that is frequently neglected is human element. It is easy to complain about sales reps sandbagging deals or having “happy ears”. But it is much harder to show why human error creeps into forecasts again and again. And more importantly, how to remove this error & make your reps better forecasters.
To really improve your sales forecasts, you need to understand the cognitive biases that cause your sales reps to consistently overpromise and underdeliver. With a knowledge of these biases in mind, there are many simple tricks you can use to overcome these irrational tendencies and improve forecast accuracy.
In this article, we’ll walk through five of the most common cognitive biases that are impacting your sales forecast, and provide a checklist of actionable recommendations you can take to make your forecasts better immediately.
For better or worse, people struggle to be completely logical and objective in our decision-making. Researchers have shown in multiple studies that we demonstrate clear deviations from rational behavior.
Sales people are no different. Think of all the stress a sales rep must deal with – chasing down deals at the end of the quarter, a big quota looming, dozens of prospects to track and manage. It is the sort of pressure cooker that makes it hard to think objectively.
The unique challenges of the sales role make certain cognitive biases appear more frequently. Here’s five biases you need to be looking for when managing the sales team.
Summary: Events that are more easily called to mind are presumed to be more important. This leads to an overweighting of more recent information.
Why It Matters: Salespeople are extremely busy. When managing multiple deals and tracking several prospects across different stages of the sale cycle, it is easy to let some deals slip through the cracks. Your sales reps may neglect to follow up with prospects that they spoke to earlier in the month versus those that they’ve spoken to much more recently. During the weekly forecast meeting, the projected deals in the sales rep’s pipeline may be missing some “hidden gold” that just requires a bit more followup. On the same token, recent prospect discussions may get added to the committed pipeline only because the conversations happened more recently.
Base rate fallacy
Summary: People tend to ignore general information about a situation and focus on the specific instead.
Why It Matters: Sales reps are talking with prospects and guiding potential customers to a purchase. So it is natural that you would expect them to have the best understanding of how likely a deal is to close.
But this proximity can be deceiving.
Let’s say your sales team has an average 30% opportunity close rate. If one of your sales reps, who is an average performer, consistently forecasts deals with greater than 50% likelihood to close, you need to make a decision. Will you trust the sales rep, or do you trust the average?
You are likely to be better off applying the benchmark close rate to the rep’s pipeline if the sales rep doesn’t have a track record of overperformance. In fact, if your rep’s projected close rate is consistently higher than the average (without actually attaining that success), it is likely the rep suffers from the overconfidence bias – more on that later.
Summary: People tend to be overly optimistic in predictions. This results in overestimating the likelihood of positive outcomes and downplaying the likelihood of negative outcomes.
Why It Matters: One issue many new sales reps struggle with is “happy ears” – being way too optimistic about a deal based on limited positive signal. This is driven by poor qualification – not digging into a prospect’s hidden objections or uncovering sufficient urgency needed to move a deal forward.
The fact is, prospects may tell lies and omit facts. People want to be polite. They want to avoid conflict. And if they aren’t interested in buying your solution, the quickest way to get off the phone may be saying “yes” a lot. Don’t be fooled.
Summary: People tend to be overly confident in their likelihood of being correct.
Why It Matters: This may sound a lot like the optimism bias, but it is actually different in one important ways. The overconfidence bias is focused on perceived certainty of outcomes – being sure that it will happen as envisioned or that our knowledge of facts is correct.
This issue is the heart of sales forecasting woes. Let’s say a sales rep tells you that each of her 4 deals has a 50% chance of closing before end of the quarter. If her estimates were “properly calibrated” to the actual probabilities, then you would expect exactly 2 of those deals to come through. If none of those deals actually closed, you would know those estimates were far too optimistic.
If sales reps could be trained to accurately assess a deal’s likelihood to close, that would solve so much. Luckily, there are some simple techniques you can use to do just that.
Summary: People tend to underestimate completion times
Why It Matters: Everyone has dealt with the deal that drags out for months beyond the initial close date. Perhaps a hidden requirement or objection revealed late on requires additional selling. Maybe there is a procurement process hadn’t been considered. Either way, a lack of information drives us project unrealistically short timelines.
Techniques for Better Forecasting
Now that you know the cognitive biases that stand between you and an accurate forecast, let’s talk about what you can do address the problem.
First, we suggest you spend time “calibrating” your reps to make better forecasting estimates. That doesn’t mean turning them into statisticians. Instead, there are a set of simple steps you can take to provide them feedback on the quality of their forecasts so they can actually improve.
Here’s what the process looks like:
- For each sales rep, ask them to provide a likelihood to close on each deal during your regular forecast meetings. Note the deals and probabilities in a spreadsheet.
- Track how many of the deals actually close over 2 sales cycles. If your sales cycle is longer than 3 months, you may need to limit this to a single sale cycle. As deals close (won or lost), track the reasons why in your sheet.
- Compare the actual deals won to the sum of probability weights. Did the rep predict more deals coming through than reality? Fewer deals?
- During a 1-on-1 meeting with the sales rep, walk them through the findings of your analysis. Work with them to identify patterns in their forecast predictions.
- At the end of the 1-on-1, have the rep draft a short forecast action plan. In this plan, the rep will identify sources of forecasting inaccuracy and list steps they will take to improve future forecasts.
My next suggestion is taking humans out of the equation entirely. By feeding CRM and other relevant data into a predictive model, you can take human biases out of the equation entirely. Using a machine learning approach, you can consider hundreds of factors impacting the deal. That’s way more than a human could do with pencil & paper (or even a spreadsheet). And this predictive forecasting can be automated and improved with time as more data is fed into the system.
Sales forecasting may seem more voodoo than science, but there are ways to improve forecast accuracy. Understanding the cognitive biases that impact forecasts allows you to create processes that account for these behavioral quirks. One way to address these issues is calibrating your sales reps’ forecasts by evaluating their accuracy and providing them feedback on how to improve. Another way to enhance forecasts is to use predictive models that estimate likelihood to close using a broader set of factors.
The secret to improving your sales forecast isn’t a crystal ball. It’s just a bit of math.