No one expects the meteorologist to be right 100% of the time, but people still check the forecast. In weather and in business, it’s better to make an educated guess than charge forward blindly.
That’s why sales forecasting, and CRM forecasting in particular, is so important to so many businesses. Companies invest significant time and money to better understand their sales cycle and predict future sales.
In this article, we’ll explain what CRM forecasting is exactly and why it’s so important. From there, we’ll cover how forecasting works and some best practices to get more accurate sales forecasts.
CRM forecasting is when you use your customer relationship management system and the data within it to predict future sales outcomes. Sales forecasting generally means predicting how much product or service your company will sell in a given period of time. For example, many companies forecast their sales for the next year. They then use these estimates to inform other business decisions.
Here’s a concrete example using paper-and-pen math (that a CRM would do automatically for you):
Let's say we have Business A, which has four salespeople. We want to calculate the average monthly sales figure for each salesperson and then use that data to determine the average sales figure for the company as a whole.
To do this, we'd take each salesperson's total sales for the month and add them up.
Let's say Salesperson 1 made $80,511.00 in sales for a year. We'd then divide that by 12 to get their average sales for a month. That means Salesperson 1 had a monthly average of $6,709.25.
If we do that for all four salespeople, let's say they all had these monthly averages:
Once we have those figures, we'd add them all together and divide by 4 (for the four salespeople) to get the average monthly sales figure for each salesperson of $6832.96.
This figure represents the average amount of sales made by each of the company's salespeople per month. Using this data, the company can gain insights into its sales performance and make strategic decisions to improve its overall sales figures.
When a business sells low-friction, high-volume items, sales forecasting can be as simple as an extrapolation of data from the past. For example, if you sell coffee mugs and sold 10,000 units last year, it’s not a huge jump to think you’ll sell that many this year.
Businesses that are complex enough to require a CRM, however, often sell products or services at a higher price point and have a longer sales cycle. Because these businesses need to account for different deal sizes, different buying cycles, and other variables, they use account data in their CRM to inform their forecasting. For instance, if a SaaS company has been nurturing relationships with several large potential customers, they may forecast that their revenue will drastically increase next year when the deals close.
Whether you use simple extrapolation or more sophisticated CRM forecasting, the drawback is that what's happened in the past might not happen in the future. Businesses can’t just assume that market conditions and customer tastes will stay the same.
Although sales forecasting and CRM forecasting are a company’s best guess of future sales, they’ll never be 100% accurate.
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With so many factors at play, why do businesses spend time and money on sales forecasting? To put it simply, it’s still better than flying blind. For many businesses, sales forecasting is an important factor in production plans. If they’re expecting big demand, they’ll need to create a big supply.
For digital products, like software as a service, sales forecasting can still help the company plan for the number of customer service agents they’ll need or even whether they need to upgrade their servers. Forecasting is also closely tied to goal-setting. An accurate forecast gives companies somewhere to start when setting quotas for lead generation, sales meeting, and closed revenue.
Sales forecasting (and revenue forecasting) can also be very important for startups looking for funding or other small businesses seeking loans. When you have forecasts based on reliable data, investors may feel more comfortable pitching in.
Sales forecasting also helps companies project their cash flow, which informs decisions around hiring and other investments. If a business is forecasting big sales in the near future, they may be willing to accelerate hiring or invest in developing a new product.
As we mentioned, CRM forecasting allows organizations to set expectations for a quarter or year. Sales managers and their teams can set reasonable goals and quotas. While sales forecasts and sales goals aren't the same, they do inform one another.
Forecasting also helps businesses plan for significant growth. For SaaS companies and other startups, it's not uncommon to undergo periods of extreme growth (often called scaling). CRM forecasting helps businesses project the trajectory of their growth and plan for how to achieve forecasts. For example, if you’re expecting deals to double six months from now, you'll need to start hiring sales reps as soon as possible so that they are trained and ramped by then.
Another important benefit of CRM forecasting is that it forces you to continually reassess your sales process. Sales forecasting asks individual sales reps and your sales team as a whole to make estimations on the likelihood of winning deals, along with other key metrics, like how long it takes to close a deal. Then, each month or quarter, you can go back to those estimations and see how accurate they were.
Let’s say, for example, a sales rep expects to close a big deal by the end of the month. If the process stalls, causing the rep to not meet their quota, it creates a learning opportunity. The sales team can regroup and ask why the deal didn’t close.
Was it within the sales rep’s control? Did outside factors cause the delay? How can this experience change the sales team’s approach to deals like this? Even when they go astray, sales forecasting can lead to process improvements.
CRM forecasting can be a complex calculation and there are many different ways of approaching it. Most methods begin with historical data. This can include metrics such as average deal size, average time to close, new customers acquired, retention, and more.
To this historical data, the business adds information about any upcoming changes. If prices are being increased, for example, that has to be taken into account. Or if you have twice as many sales reps as you did this time last year, that'll affect forecasting.
Here are some of the most common methods for CRM forecasting.
With intuitive forecasting, you rely on the opinions and expectations of your sales representatives. Typically, you'd ask your reps how many deals they expect to close and how much revenue they'll bring in. Because it's based on these subjective answers, intuitive forecasting is one of the least precise methods. If you don’t have much historical sales data, this is at least somewhere to start.
Many businesses that use CRMs to track account data have divided the buying journey into different stages. The opportunity stage method uses which stage a potential customer is in to determine the likelihood that they will convert.
For example, you might expect 10% of accounts in the top of the sales funnel to convert, 30% in the middle of the funnel to convert, and 70% in the bottom of the funnel to convert. You would use these probabilities to forecast future sales.
Like opportunity stage forecasting, pipeline forecasting draws on what you know about the potential customers you are selling to (those that are “in the pipeline”). It incorporates likelihood to close based on opportunity stage, but it also adds other information. Pipeline forecasting often considers potential deal sizes, industry trends, and future pipeline accounts to whom you haven’t yet started selling.
As the name suggests, this method uses the average length of your sales cycle to make predictions about future revenue. For SaaS products, the sales cycle can be anywhere from a few weeks to more than a year. Businesses use historical data about when accounts first became leads and when they became paying customers to determine the average sales cycle length. They then evaluate how long the leads they’ve been working with have been in the cycle to forecast sales.
Multivariable analysis is perhaps the most sophisticated approach to sales forecasting. As its name suggests, it incorporates multiple variables and data points and uses predictive analytics to estimate future sales. Along with previous metrics like length of sales cycle and deal size, multivariable analysis might use more sophisticated sales data, such as individual sales rep performance or customer personas.
Some CRMs have built-in forecasting tools where companies can set different standards for closing likelihood and other stages.
Whatever approach you take to predicting sales, these tips can help you achieve more accurate forecasting.
Your CRM forecasts will only be as accurate as the data in your CRM. There’s even an industry phrase for the results of bad data: garbage in, garbage out. This is one of many reasons that companies need to set and communicate clear standards about documentation.
For example, if a major account you expected to close has been ignoring communications for your sales rep, that change should be noted in the CRM. When forecasting, the company can then adjust their calculations based on how much less likely this deal is to close.
It’s especially important for your sales teams to record deal size and any discussions on bespoke payment plans. If you typically charge annually, but a deal has negotiated quarterly payments, that will affect each quarter's forecast. TigerLRM’s extreme ease of use allows sales teams to quickly and easily keep updated account records.
Whenever you try to forecast sales, you have to keep in mind all the outside factors that could affect your business or product in particular. A change in culture, economics, or society can have a huge impact on business sales.
The biggest recent example was the COVID-19 pandemic. While some industries had an unexpected drop in interest, other businesses couldn’t keep up with demand. When the COVID-19 pandemic hit in 2020, Disneyland’s level of interest and demand dipped suddenly in a way they couldn't have forecasted.
But global events aren’t the only type that can affect your sales forecasts. New technologies or campaigns and products from competitors can also upset your forecasts.
When forecasting, always take a moment to ask what upcoming events (singular or seasonal) might affect your business. And when something comes up, revisit your forecasts. Recognizing how outside events could affect your sales allows you to respond and adapt to challenges.
As we covered previously, there are many different methods and models of CRM forecasting. At first, you can make your best guess of which will work for your business, but as you collect more data, it’s worth considering other approaches.
For example, you may use the length of the sales cycle to create sales forecasts at first. You may later analyze the data and find that you can get more accurate forecasts from an opportunity stage forecasting method.
As your business grows and evolves, the forecasting methods you used before may become less relevant. If you are diligent about collecting and documenting sales activities, each month will provide you with new data that can be incorporated into your forecasts.
Because there are so many factors and moving parts, the information you use for a sales forecast can change significantly within just a few months or even a few weeks. That’s why it’s so important for businesses to continually revisit and revise their forecasts.
While it’s common for most businesses to use CRM data to forecast for the next year, many also return to their estimates on a quarterly basis. They use all the new information they’ve gained over the quarter to make better predictions about the future. Effective sales teams are constantly monitoring their progress in comparison to their goals and forecasts.
CRM forecasting is clearly not an exact science, but its benefits to current and future business decisions shouldn’t be ignored. It can help you set goals, make hiring plans, and even attract funding.
Businesses should approach CRM forecasting as an ongoing project, incorporating new information as it becomes available. In TigerLRM, the speech-to-text feature, mobile app, and email integrations make it easy to compile all your account information in one place.
To see how TigerLRM gives small and mid-sized businesses a powerful CRM with sales enablement, schedule a demo.
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