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January 14, 2020
Best Practices for Improving Sales Forecasting Accuracy
Whether you’ve been trained on sales forecasting or are trying to better understand the best approach for your organization, accurately predicting sales forecasts is not always a clear-cut process.
Most organizations don’t even have a repeatable, scalable process for forecasting sales, which is a little scary since so many critical choices from hiring to investment decisions are based on sales forecasts.
Just listen to Yamini Rangan, Chief Customer Officer at Dropbox:
If you’re a sales manager scrambling to get accurate numbers to executive leadership, sales operations tasked with rolling up the forecast, an individual sales team member whose manager keeps getting on your case, or an executive making those big decisions, this piece is for you.
But before we get into how to improve, we really should know what is sales forecasting accuracy?
What Is Sales Forecast Accuracy?
According to Hubspot, “a sales forecast predicts what a salesperson, team, or company will sell weekly, monthly, quarterly, or annually.” We see it as much more than that. It’s not just the practice of calling a number and trying to hit it. It requires careful inspection and execution throughout the quarter.
“The forecasting process is so much more than just calling a number. It represents the entire operating rhythm of the whole company,” says Kevin Knieriem, Chief Revenue Officer at Clari. “Hitting your number at the end of the quarter doesn’t just happen. It requires careful inspection and execution throughout the quarter.”
Sales forecasting accuracy is essentially how close you come to actually hitting your forecast.
But why does that matter?
How accurate you are on your forecast determines how much room a company has to confidently invest in growth or cut spending in a timely manner if needed. The better your sales forecasting accuracy, the more likely a business is able to operate smoothly and be agile in reacting to the changing dynamics of the market.
Here’s Carl Eschenbach, Partner at Sequoia Capital and former President of VMware, where he grew revenue from $30 million to $8 billion:
According to Eschenbach, “It takes many, many years to gain credibility for your forecasts and your ability to deliver the number. You can lose it all in 90 days with a single miss”, which is why improving and maintaining sales forecasting accuracy is critical for building enduring businesses.
How Do You Improve Sales Forecasting Accuracy?
You can start improving sales forecasting accuracy by following a few best practices, including:
2. Understand the types of sales forecasting reporting your organization uses today
3. Remove the guessing game from sales forecast prediction techniques
It’s important to start by understanding where your flaws are, how you can sharpen the tools you have available today and identify new resources to help you make forecasting more efficient and accurate for any segment of your business.
Identify Common Mistakes with Accurate Sales Forecasting
We believe the sales forecast is the single most important business process in an organization. That’s a lofty statement, but when you realize the impact inaccurate sales forecasts can have on your business, you realize how important accurate sales forecasts really are and why it’s critical to identify mistakes early on.
While a sales forecast is always a moving target, most organizations typically under-forecast or over-forecast; both of which lead to problems.
Under-forecasting. When an organization under forecasts, meaning they over-deliver (which might seem like a good problem to have), the organization isn’t able to make decisions with enough lead time to think things through, especially around hiring, marketing or R&D. They miss out on opportunities to cast a wider net for growth, while their competitors move in.
Over-Forecasting. If an organization over-forecasts, they under-deliver and the entire business can be in jeopardy. This could lead to devastating long-term consequences, such as lay-offs, which are never good for morale or the future growth of the business.
This is why accurate sales forecasting is so critical. You want to ensure that you’re making decisions based on real, achievable numbers so you can grow the business with confidence.
Inaccurate sales forecasts typically stem from a few key problems:
- No Training. Sales organizations and individuals aren’t trained on how to accurately forecast sales so they go by their gut. Even for the most tenured reps this is a shaky foundation to build a predictable revenue machine.
- Time-consuming Processes. If you’re still having every rep fill out spreadsheets for forecasting, you’re wasting time and risk having outdated data. Deals evolve in real time. When you pull deal data into a spreadsheet it becomes disconnected from what’s actually happening in the real world.
- Accidental Inaccuracies. Relying on spreadsheets is prone to human error and can also lead to small mistakes that can throw everything off. Multiple versions, mistyped formulas, and accidental deletions are just a few of the missteps that can throw a forecast into disarray. This requires painful heavy lifting by the back office to roll up the forecast into one cohesive and congruent document.
- Subjective Rep Behavior. Reps are either afraid to put deals in Closed Lost because they might fear getting scolded or they might hesitate putting a deal in Commit so they can enjoy end-of-quarter heroics. Both scenarios will throw off your forecast accuracy. Not enough deal inspection throughout the quarter means managers have no real visibility into the true state of the sales pipeline and are unable to determine what’s real and what’s not.
- No Clear Sales Process. If reps don’t know when to move someone to the next phase of your sales process (or what the exact parameters that define each stage), your sales forecasts will be dependent on being able to decipher each rep’s call, instead of a predictable sales cycle.
- Not Looking at Historical Data. Historical deal data is the best indicator of how current and future deals will behave. How much activity and engagement was there between your rep and the prospect? How quickly did the deal move through each stage? If you don’t have visibility into the past, you can’t make accurate predictions for the future.
- Overly-Optimistic About CRM Capabilities. Existing CRM tools, like Salesforce, provide a system of record to manage account and transaction information. While the CRM is an essential system of record for business, it wasn’t designed to manage the modern revenue process. Generating revenue has changed dramatically since CRM systems were introduced over 20 years ago.
Now that we know the typical mistakes in sales forecasting, let’s talk about how to accurately predict a sales forecast.
Understanding the 6 Types of Sales Forecasting Reporting
Organizations typically fall into 6 types of sales forecasting reporting depending on the maturity of their business, including:
- Verbal Reports
- CRM Reports
- CRM + BI + Spreadsheets (again)
- Automated Reporting
- Predictive Forecasting
Verbal Reports. A very early stage startup or small business is going to likely rely on verbal reports between a handful of employees. Heck, they might not even have a CRM yet. Verbal reports might involve weekly calls to discuss each deal, an inconsistent tracking method between each person, and a lot of built-up institutional knowledge between the sales manager and the rep that doesn’t always get communicated or transferred to the greater team.
Spreadsheets. Once an organization has a bigger sales team and is beginning to really look at sales forecasting, they typically move to spreadsheets. This is where most organizations get stuck — because it’s the status quo. But…it’s tedious and prone to inaccuracies as we detailed above. It also limits how much data can be used for accurate sales forecasts because everything has to be manually maintained and updated. Beyond these points, using spreadsheets doesn’t scale as your sales organization grows (imagine trying to corral data from hundreds or thousands of reps into a single spreadsheet), use real-time data since they rely on reports exported from the systems used to collect data in the first place, nor promote collaboration between team members and management.
Most of all, this process takes away time from the entire sales organization — time they could be using to sell.
CRM Reports. Cloud-based CRMs (Customer Relationship Management systems) are an upgrade from this because they’re a shared database accessible across your entire sales team that can aggregate some of these numbers into forecasting reports. However, CRMs are only as good as the data that exists in it and that requires every team member to update it on a regular basis. If data in the CRM isn’t accurate when you pull your report, then your insights will be off base. This can often lead to last minute requests from managers to reps to input their sales activity data and deal information at the 11th hour before a forecast call — and lots of grumpy reps.
CRM + BI + Spreadsheets (Again). At this point, companies on the path to becoming more sophisticated realize CRM is essential for doing business, but it’s not enough. CRM can’t alert you that you’re running short on pipeline for next quarter. Or show you that the deal you're chasing is a dud. Or predict where you'll finish the quarter by week 3 so you can turn it around. Or spot renewal risk or upside opportunities across your current customer base. Or automate front line tasks for sales, or back office work for operations. You get the picture.
Instead, they resort to managing revenue with a disconnected arrangement of spreadsheets and reports from business intelligence systems. This model is not scalable and impossible to standardize, requiring heavy lifting from sales ops to validate, standardize and consolidate the myriad reports into a single document.
Automated Reporting. This is the catalyst behind automated reporting. Tools powered by AI harvest much of the activity data so reps don’t have to add it manually and the forecast is automatically rolled up and in real-time. It not only saves time for reps, but ensures data quality is optimal so managers and reps can trust the numbers they see and understand the health of a deal at a glance.
When it’s time to forecast, the rep can confidently make their call, and the manager can confirm or apply judgement to call a number for his team or territory. An automated forecast would then roll up that call in real-time. No more spreadsheets. No more lengthy forecast calls. No more pulling reports from CRM into BI and Excel. Automated reporting saves time for the operations team in the back office by minimizing operational spreadsheets and custom ad-hoc report building.
One thing this level of forecasting is missing is the ability to look at past data to predict where you’ll land based on historical trends.
Predictive Forecasting. This is where predictive reporting comes into play. Predictive reporting uses AI and machine learning to analyze historical data to accurately predict future sales. The most advanced systems use time series databases that can record data at scale any time it’s updated, tracking change through time, then applying AI and machine learning processes to derive insights.
While AI and predictive analytics can’t solve all of your forecasting problems, it’s a highly sophisticated data point that can guide your team in the right direction. Imagine having a system that can analyze your deal data and predict where you’ll land at the end of the quarter, or even next quarter.
Removing the Guessing Game From Predicting Your Sales Forecast
Two typical sales forecasting methods or techniques in sales forecasting include qualitative and quantitative. The method organizations follow is usually dictated by their maturity in the marketplace. However, as we’ll explain, one method helps organizations take the gamble out of predicting your sales forecast.
Many early stage companies and startups will use qualitative sales forecasts in place of real numbers that don’t yet exist. Qualitative sales forecasting is usually done by analyzing the realistic number of customers you expect in a certain time period who will pay a certain price. For example, an early stage event marketing software startup might look at how many event organizers there are who don’t yet have a similar solution. From there, they will assess how many of those organizations they think they could convert while attaching a somewhat random price tag (as product/market fit has yet to be established). The forecasting technique is a guessing game for the most part.
If you only have one sales team member, or don’t even have a sales team yet, it’s going to be difficult to close hundreds of enterprise level deals, let alone predict your sales number. With qualitative forecasts it can be hard to take into account all of the factors involved in a sale, such as what is the reasonable amount of sales your team can actually close.
Here is an example of a qualitative sales forecast:
Surely, there has to be a more efficient solution for older, more established businesses to accurately forecast sales than just using a tedious spreadsheet. Or better yet, how can any organization looking to grow faster forecast sales better?
A quantitative sales forecast uses the existing data in your CRM or other tools to reflect a potential forecast. However, there are a few catches here. If your CRM is full of dirty data or your reps don’t follow the established sales process, your quantitative forecast can be just as much of a guessing game as the qualitative forecast.
Think of sales forecasting as flying a 747 jet: Autopilot works well when things are fairly smooth (in this analogy, this refers to quantitative forecasting), but you still want someone in the seat who can come in if something unexpected happens. Teams need a mixture of quantitative analysis layered with human judgement.
To get an accurate, high-quality quantitative sales forecast we recommend:
- Identify Your Sales Forecasting Method of Choice: Consider the variety of sales forecasting methods, tools and techniques to choose from and figure out what will work best for your business model.
- Establishing a Clear Sales Process: If you don’t have a sales process, there is no way you can accurately predict your forecast, making the development of a clear process and procedures to follow critical.
- Ensuring Team Follows Process: Your 1:1s with your reps should cover sales pipeline inspection and coaching, with a 4-point deal inspection identifying gaps in process compliance and ensuring rigor in understanding definitions (i.e “commit” means the same thing for every sales rep)
- Incentivizing Accuracy: Do you offer spiffs to reps that meet their quotas consistently each quarter? Are you using scoreboards or other public displays to gamify accuracy? These help to bring the competitive nature out of your reps, while promoting accuracy.
- Augment Your Team With New Technology: Use tools that can help you easily flag areas that need updating in your forecasts, whether on an individual rep basis or across all reps and deals
Today, the last point is maybe the most critical. In many organizations, individual reps put together a spreadsheet with their individual sales forecast. The sales manager then spends a week pouring over these until they’re satisfied and roll them up into an executive level report. This arduous, time-consuming process is potentially full of inaccuracies.
Beyond that, it’s a time-consuming, non-automated process. Spreadsheets also don't include real-time indicators showing what has changed (i.e. slipped deals, increase/decrease in deal size) and they don’t enable a modern sales team that is always on the go. Your business is constantly changing and new deals are constantly coming in.
Not only that, this process does not give visibility to the entire revenue operations team, which includes sales, marketing and customer success. Without such visibility, it is impossible to align goals and collaborate towards the number.
Enhancing Accuracy in Sales Forecasting by Modernizing Your Process
After you’ve identified where you might be making mistakes, the types of reporting you might be using today, and the techniques you have in place for sales forecasting predictions, let’s talk about how you can bring your forecasting accuracy into the 21st century.
First, a look at what has changed:
- The B2B buyer's journey has become more sophisticated, nuanced, non-linear and unpredictable, making ongoing engagement and activity insights critical to success.
- New revenue models based on subscription and consumption require different measurement and management approaches, with metrics like recurring revenue, churn, upsell and net dollar retention now key success indicators.
- And, the pace of change continues to accelerate, with businesses constantly changing new growth initiatives and strategies. Every new program requires new analytical frameworks and metrics.
Modern B2B teams are tasked with what can seem like an insurmountable challenge, and the truth is, the forecast isn’t just the responsibility of sales anymore. Those new revenue models mean marketing and customer success also have a hand to play.
Once again, Mr. Eschenbach and Ms. Rangan:
Here at Clari, we typically come within 2% of our sales forecasts and are happy to share what we think is critical to modernize sales forecasting accuracy:
Triangulate a Reality Check. Understand what history is telling you, what your team is telling you and what you’re being asked to deliver. These steps often happen in the opposite direction. You look at your quota, then you ask your reps are what they’re willing to commit, and then, based on previous quarters and what you know about your reps’ performance, you might do some historical analysis in your head. This step is where AI/ML is changing the game.
Inspect current and out-quarter pipeline. With insights from the previous step in hand, it’s time to look for risk in the current quarter and opportunities to accelerate deals that are slated for end of quarter or for out quarter. This requires a deep dive into each deal’s sales data and customer engagement activity. Are prospects responsive? Are the right files and attachments being sent back and forth? What is the reality of the deal? If your reps are diligent about adding data to CRM, you’re in luck. If not, consider automating sales data capture so you have visibility into what’s actually happening.
Conduct data-driven 1:1 coaching sessions. Armed with data around deals at risk, you can head into your 1:1s with a data-driven POV so you can coach instead of interrogate. Let’s face it, most coaching sessions end up being a line-by-line Q&A on the status of every deal. Instead of using that valuable time asking for data points on every deal, the previous step allows you to focus on the deals that need your attention most so you can strategize with your rep, instead of interrogate them. “Your 1:1s should be 90% coaching around risk mitigation, sales pipeline acceleration, and deal execution, and 10% reporting the news,” says Anthony Cessario, AVP of Sales at Clari. “Unfortunately, almost all 1:1s are on the exact opposite side of that metric.”
Bring your revenue operations teams together. One of the benefits of getting a clear view of your out-quarter deals and forecasting out-quarter pipeline is that this forward-looking visibility gives the entire revenue team much more confidence in the pipeline going into the quarter. In this weekly meeting, sales, marketing and customer success all play a part in ensuring you hit your forecast. Here’s a look at what our forecast call looks like.
How does Clari’s Revenue Operations platform improve sales forecasting accuracy?
If you have the right technology, a lot of the hard parts of accurately reporting sales forecasts is gone. Clari’s Revenue Operations platform improves sales forecasting accuracy for each of the above steps:
- Clari’s Time Series Data Hub, which historically tracks and time-stamps every single field in the CRM every 15 minutes, automatically analyzes millions of data points all the way down to the individual sales rep, looking back at years of data to predict the likelihood of each deal closing that quarter. It also predicts how much pipeline will likely be needed to hit the number you’re being asked to hit based on historical conversion rates so you can prepare and plan for out-quarter opportunities.
- Automatically harvest sales activity and customer engagement data, saving your reps from manual and time consuming data entry into CRM
- With data entry covered, you have clean and complete opportunity data so you can inspect deals before heading to your 1:1 strategy sessions. If there is risk in the quarter, they can spot it early and pull in out-quarter deals to cover. Clari even takes into consideration each individual rep’s unique behavior (sandbaggers, happy ears and everything in between) and uses that to drive our predictive models. This also helps sales managers understand how to better coach reps and spend time identifying deals at risk to take action.
- Because Clari lives in the cloud, it can be accessed from anywhere and by anyone, serving as a single source of truth for the entire revenue operations team.
In addition, part of Clari's value is it helps enforce your sales process, standardizing stages and creating compliance across your team. All these things help filter out the noisy deals that don't belong in your forecast, spot risky deals that might need additional attention, and make it easier for the entire revenue team to have a shared goal in mind (which is to forecast accurately and meet or exceed goals).
Improve Your Sales Forecasting Accuracy Process Today
We’ve talked about a lot in this article, including:
- Why accurate sales forecasting is critical and how you can identify mistakes
- Determining where you fall within the spectrum of sales forecasting reporting
- How to remove common causes of inaccuracies in predicting your sales forecast
- Identifying key steps to modernizing accuracy in sales forecasting
If you walk away with anything from this article, we hope it’s that you have the power to improve your sales forecasting accuracy well within 5% (although we’ve seen our customers come within 1%). By taking the steps to identify where your roadblocks are, getting your team on board with consistent processes and procedures, and using tools that help you streamline forecasting, you’ll be able to help advance your revenue operations.
Whether you’re on an accelerated growth path or are in the advanced stages of forecasting and are looking for a way to introduce predictive forecasting, get in touch with Clari today.
Rev Up Your Forecast with AI
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