All sales leaders must “count their deals before they close,” and this makes many of you nervous. It should: With less than ⅓ of businesses classifying their sales forecasting models as effective, it’s easy to see why many sales teams regard forecasting as a guessing game that they’re bound to lose.
The good news is that sales forecasting doesn’t have to be nerve-wracking. Rather than making educated guesses and predictions, overcome anxiety by using a data-guided process for a solid sales forecasting model.
Replace guesswork with data
Don’t rely on the optimism of your sales team or upper management when sales forecasting. Doing so only results in discouragement when forecast numbers aren’t met. Your sales targets should be realistic and achievable.
To do this, develop an objective foundation early on for your sales forecasting model:
- Establish a clear sales process: Have a clear, standardized sales process in place for your sales reps. Sales stages and the steps for each stage should be repeatable and clearly defined, so your reps know how to take a customer all the way through the sales pipeline. You will then have consistent and uniform data to refer to when sales forecasting.
- Focus on accurate data: Forbes found that “84% of CEOs are concerned about the quality of the data they’re basing their decisions on.” Use a CRM to minimize data entry (data will only be entered into one system) and increase accuracy. Data should also be entered in real-time by your sales team to avoid forgetting key information.
- Utilize historical data: Use historical data as a guideline to review what’s worked in the past to predict the future. While historical data should not be the only factor in your forecasting decisions, it does provide a look at past scenarios where sales increased or decreased. Replicate activities that have been effective in the past, and identify areas that need improvement.
Assuming you have the above foundation in place, use the following three scientific strategies to create an accurate sales forecasting model:
- Adjust your scoring strategy to ensure you understand which deals will actually close.
- Analyze stage duration to identify which sales stages are taking the longest and why.
- Measure lead and opportunity performance at each stage along the sales pipeline.
1. Give your forecasting scoring strategy a tune-up
To help prioritize prospects and gauge their likeliness to convert, companies score leads based on criteria that they have determined signifies purchase intent, such as content downloads and website visits.
This is one way to lead score, but another method enables businesses to actively seek out and identify high-value prospects based on profile similarities with previously won deals. This method
- enables your sales reps to actively identify high-value prospects based on profile similarities with previously won deals,
- gives your reps confidence that the leads they qualify will eventually turn into paying customers,
- gives you real, data-driven evidence as to which deals you should count on to actually close and include in your forecast.
For example, after analyzing your recent data, perhaps you discover that the CTO was the decision maker in nearly 65% of wins. In that case, score the leads higher where CTOs are the main point of contact, and increase their win likelihood once they enter your pipeline.
You can also follow the same methodology for lost or unqualified deals: If 80% of deals where the CMO was the decision maker were lost, then score leads with this point of contact lower. The same goes for similarities in industry, company size, location – the list goes on.
Once each deal is scored, you have a strong indicator of which deals are going to be won and which will be lost for your forecast.
Taking this smart lead scoring approach not only helps you be more sure that the leads you qualify will eventually turn into paying customers, but it also gives you real, data-driven evidence as to which deals you should count on to actually close and include in your forecast.
2. Incorporate stage duration into your sales forecasting model
As any sales leader who has fallen short of his or her forecast knows, it’s not just about whether a deal closes but about when it closes. Timing matters when it comes to forecasting, and while deals can sometimes encounter unforeseen roadblocks, having an intimate understanding of your stage duration can greatly improve your forecasting model’s accuracy.
Stage duration refers to the amount of time each of your deals spends in a given stage of your sales pipeline, such as qualified, quote, or close. Conducting a stage duration analysis allows sales leaders to see more than just the average time deals spend in each stage and the pipeline as a whole. It
- helps sales leaders identify the ideal time a deal should spend in each sales stage,
- highlights bottlenecks within each stage,
- calculates the win likelihood of a deal based on the amount of time spent in a particular stage compared to deals that have been won.
The example below shows the exact number of days that deals are spending in each stage.
This information will help you answer the following questions to create an optimal sales cycle:
- How long are deals staying in the Incoming stage?
- How many days do they languish in the Quote stage?
- At which stages are bottlenecks most common? (And how may your sales process need adjusting?)
- How long does it your sales reps to close deals?
- How can slower reps adopt the winning habits of reps who move through the process more quickly?
Understanding stage duration enables you to identify the types of deals that take longer to close or are more likely to get stuck in a particular pipeline stage. This information is highly valuable for building a forecasting model, knowing what deals to bank on and then nailing that forecast.
3. Measure across conversion points for accurate forecasting
More often than not, very few metrics other than revenue are factored into the sales forecast, with less than 35% of organizations taking critical measures like deal volume into consideration. And when other metrics are considered, their measurements are isolated. What good does it do your business to know how many marketing leads are accepted by sales if you can’t measure the impact this ultimately has on your bottom line?
Think of it this way: Sales revenue is the cumulative result of each key conversion point along your sales pipeline. To accurately predict sales revenue then, your forecasting model must include measurements of leads accepted, opportunities qualified, etc.
To understand how leads and opportunities flow through your sales process and pipeline, use sales metrics called process measures. They break down conversion rates stage by stage, which
- allow you to pinpoint bottlenecks and inefficiencies at various points within your sales process,
- reveal actionable insights around how to increase revenue.
A visual representation of process measures
Process measures are used to understand how leads and opportunities flow through your sales process and pipeline, giving you a more clear and accurate picture of expected revenue over time. One example of a process measure is sales cycle length, a metric that looks at how long it takes for deals to go through your pipeline. The metric shows the effectiveness of varying sales processes, so you can make adjustments if needed.
Average Sales Cycle: Total # of Days to Close Deals / # of Closed Deals
For example, let’s say it takes a total of 98 days to close 10 deals. 98 days to close deals / 10 closed deals = 9.8 days to close per deal. If you can predict how long it will take incoming deals to close, you’ll have a better idea of what your sales numbers will be at any given time.
In addition to the average sales cycle, here are eight other process measures you can use to review leads and opportunities:
With the ability to measure performance across each of these conversion points and see exactly how it affects sales revenue, businesses can create a much more accurate sales forecast.
Take the stress out of sales forecasting with a scientific forecasting model
Sales leaders willing to follow strategies like those outlined above and take a data-driven approach to their sales forecasting model will be able to make more accurate sales forecasts — without the stress that educated guesses and predictions bring.
Accurate sales forecasting improves decision-making, identifies problematic issues in advance, tracks sales reps’ performance, and ultimately helps you predict future revenue and growth. For more information around how you can begin understanding and utilizing the science of sales, download our free eBook, From Art to Science: 5 Steps to Predictable Sales Growth.