Sales forecasting is one of the toughest parts of the job for sales leaders. You need to somehow predict revenue so you can do massively important things like allocate resources and come up with a strategy for growth. No pressure. Just like the weather, sales forecasts aren’t always totally accurate. There’s a lot that can […]
Sales forecasting is one of the toughest parts of the job for sales leaders. You need to somehow predict revenue so you can do massively important things like allocate resources and come up with a strategy for growth. No pressure.
Just like the weather, sales forecasts aren’t always totally accurate. There’s a lot that can change and go wrong along the way. In fact, less than half of sales leaders (45%) have high confidence in the accuracy of their forecasts.
But there’s a bit of light on the horizon with a secret weapon that can help you get your forecasting right: intent data. And if you nail your forecasting, it’ll set you up for a clear skies ahead.
Some basic forecasting methodologies
Before we get into the secret sauce, let’s talk first about some more familiar sales forecasting methodologies.
Opportunity stage forecasting
With opportunity stage forecasting, you can figure out the chance of a future closed-won deal based on each stage of the sales pipeline. Generally, the further along an opportunity is, the more likely it is to close in your favor.
Looking at the typical performance of opps in your pipeline at each stage, you can figure out a basic calculation. Let’s say half the opportunities that reach the quote stage tend to close in a sale. That means you can assign a 50% probability to all the opportunities currently in your pipeline at that stage. To come up with a forecast, take each opportunity in your pipeline and multiply the predicted deal size by the probability assigned to that stage. And there you have it, there’s your forecast.
Pros:
- A simple, relatively easy way to forecast
- Isn’t based on subjective opinions
Cons:
- Doesn’t differentiate based on opportunity age – fresh opps are weighted the same as stale ones
- Doesn’t take any unique characteristics of different opportunities into account
- Relies on historical data, so if your product or process has changed, your probability of closing may have too.
Length of sales cycle forecasting
The age of an opportunity might be neglected in the previous methodology, but it’s the star of the show here. The general idea for length of sales cycle forecasting is pretty simple: how long does it typically take to close a deal? Here’s how you figure it out. Add up the number of days it took to close all your deals within a certain time period and divide by the number of deals closed.
You can get more granular here too. Trade show leads might close at a different rate than referrals, which close at a different rate than inbound leads. Take a look at your opportunities based on lead type for a more specific forecast. Once you have the length of an average sales cycle, apply that to the opportunities currently sitting in your pipeline to predict how many of them will close (and when).
Pros:
- Can easily be broken down for more specific (and therefore accurate) forecasts
- Isn’t based on subjective opinions
Cons:
- Only as good as the data you have – if your reps haven’t kept up with record-keeping in the CRM, your calculations will be off.
- Doesn’t account for opportunity size
Intuitive/gut feeling forecasting
The least reliable method of forecasting is also one of the most common. Companies that are small, have super complex buying processes, or are new with no historical data will often rely on the subjective expertise of their salespeople.
It’s a pretty simple process. Salespeople are asked to estimate the likelihood of a deal closing within x amount of days (and how much that deal might be worth). That’s it! There is some value to this method. If anyone knows how to read a prospective sale, it’s the person who’s in the trenches with it every day. But they might also be a touch optimistic about a potential deal. Not to mention, there’s no way to really verify their projections.
Pros:
- Works without historical data (making it a good fit for new companies or new products)
- Takes unique characteristics of individual opportunities into account
Cons:
- Completely subjective data with little to no verification
- Want to scale up or repeat the method? Good luck.
Why it’s hard to get an accurate forecast
Any time you try to tell the future, there’s bound to be some things you get wrong. Sales forecasting isn’t going to be spot on 100% of the time, but some forecasts are definitely more accurate than others. A few challenges get in the way of creating an accurate forecast:
- Subjective data points – a seller’s instinct is often a good thing, but when it comes to forecasting, you need more than just gut feelings. The best forecasts are a combination of subjective experiences and objective data points.
- Low quality data – you need historic data to identify trends, but if your CRM data is weak, then your predictive capabilities will be too.
- Lack of integrated technology – the majority of businesses use 4-10 tools in their sales tech stack. If those pieces of technology don’t play well together, you might have issues getting the information you need for an accurate forecast.
Plus, all those basic forecasting methods we just went over? Unfortunately, they often fall victim to forecasting bias. Think about it – you’re likely to over- (or under-) forecast for a whole host of reasons. Like working with internal data that might be stuck in a disorganized, incomplete CRM. And there’s bound to be a positive bias when your whole livelihood is based on closing those deals. If you’re judged and incentivized based on how well you generate opportunities and revenue, then of course you’re going to give an optimistic forecast for your pipeline.
That’s why there’s a need for something else that can pressure-test that forecast and bring in some unbiased information.
The secret tool for clear forecasts that go beyond the basics (without bias)
There are a lot of ways you can approach getting an accurate sales forecast, and the best forecasts come from a combination of methods. You can get some great results by combining insights from average sales cycle length, probability of closing based on opportunity type, and individual sales rep performance.
And if you want your Q3 sales forecast to be even stronger, we’ve got an ace up our sleeves. If you use intent data (like Lusha’s Intent), you can get an even better picture of what’s on the horizon.
What does that look like?
Well first, let’s cover the basics of how Intent works. When companies search for a solution to a pain point, they leave a trail of bread crumbs all over the internet. Those bread crumbs are intent signals. This is a combination of searches, page visits, and more that add up to show that they’re interested in a specific topic.
On the Lusha platform, you can search Intent by topic. A company that shows high intent for a topic will have a high score, meaning that they’re super interested in your solution right now (take a look at this demo if you want to see what Intent looks like).
Using intent data as predictive analytics
Johanna Reyes, Bombora’s VP of Segment Marketing, has a few tips for using Intent as a predictive tool.
“What we actually do with our intent data is we do a historical look back on what success actually looks like. We can map that to the buyer’s journey – when things are spiking, when it’s a lull. Companies that have a three to six month buying signal if it’s a big deal size, it really helps you track the engagement level of the accounts ”
When you analyze the intent patterns of current customers and/or your ICP, you can get handy insights about their buying process. And when you understand their buying process, you’re better able to predict your future sales for the quarter. You can actually forecast your pipeline when you map out an entire buyer’s journey. Track from the first search to final sale and combine it with other information like company data. Then you can assign behaviors to each stage of the buyer’s journey and pick out trends that will predict whether a deal closes. Keep an eye on companies that fit your ICP to track how their Intent score changes throughout a buying cycle.
Tracking (and predicting) a buying cycle with Intent
Every company’s buyer journey is going to look a little different, but there’s a general pattern you can use to map it out, according to Jo:
“Normally what happens is the first surge comes from someone that’s doing the research, that has bought in on this technology or this product that’s going to help them increase their efficiency. And then the second one is normally where more decision-makers are coming in. Now they’ve heard what the research said. Now they’re doing their research and perhaps aligning it to the overall company goal. And then normally that last piece of surge happens with all of the departments that need to cross the t’s and c’s – the legal, the finance. I think it’s really important to align your marketing efforts to a historical look-back.”
And even for new companies in hypergrowth that are lacking closed-won data, you can use Intent to do predictive modeling.
With the economy being as uncertain as it is, forecasting is more important than ever. You don’t want to make assumptions about whether a prospect is actively ready to buy just because they fit the ICP. With the extra insights from Intent data, you can more accurately predict how many of your target accounts are actually likely to buy this quarter.
Key Takeaways
- Sales forecasting is important for proper allocation of resources, hiring at the right time, and avoiding potential pitfalls.
- Intent data shows a company’s interest in a topic related to your business, a.k.a. whether they might turn into a sale.
- Mapping out your buyer’s journey using intent data allows you to implement predictive analytics in your sales forecasting.
