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How to do sales forecasting the right way for your business

The world always has high regard for people who have the esoteric skill of being able to predict the future. In the business world, luck favors sales professionals who can prophesize the yearly sales numbers.

A sales forecast is an expression of expected sales revenue for a certain time period (a quarter or year). A sales forecast is as data-led as it can get. When it comes to your sales forecast, the process by which you create it determines its accuracy.

Without a well-structured, consistent, and transparent process, the right data doesn’t enter your system and it reflects on what your forecast looks like. Therefore, it’s important to automate data entry as much as possible so that your salespeople still have time to sell, sales managers have time to coach, and the leaders have visibility into the overall pipeline to make reliable forecasts.

In this post, we will show you how to do an effective sales forecast in an efficient manner.

We have gleaned from the insights of the experts who do sales forecasting day in and day out as part of their everyday job. We will also label what not to do in your sales forecasting to get close-to-accurate results.

With that being said, let’s first understand a few basic things about sales forecasting in the B2B world.

We at Avoma often come across a lot of companies that run their business without doing any sales forecasting. It feels like going on a road trip in an unknown country without carrying a map-you’re going to get lost along the way more than you expect.

Yes, none of us can predict the future 100%. Sales forecasting is more or less a calculated guess-you can’t predict the market conditions, seasonality’s impact, and other forces that will throw cold water on your plan. But you are still much better off having a plan so that you can course correct along the way and still reach your destination.

Having a sales forecast is important because it stands at the intersection of analyzing past trends, identifying existing patterns, and projecting the future. The world of business is always riddled with uncertainty. Being able to bring predictability into your sales goals and processes gives you some level of visibility to control future outcomes.

In other words, if you can envision a plan for the future and adjust your sail in the present-you can more or less reach your destination. The opposite is also true: without forecasting, your business will float aimlessly in the wild waters like a rudderless ship.

Good sales forecasting helps you identify the loopholes in your sales processes and adjust your strategies accordingly so that you can meet the revenue targets necessary to grow your business.

Forecasting, as we practice today, has come a long way from bottom-up forecasting i.e. weighting your forecast by deal-stages to the current multi-forecasting process which includes deal engagement and detailed deal-by-deal forecasting for an ultra-precise measure of which deals will close and which will not.

Over the years, the forecasting process has become more in-depth, analytical, and complex. For instance, modern revenue intelligence platforms like Avoma give you a quick real-time overview of your pipeline, and the deals at each stage so that you can run better pipeline reviews and forecasting meetings.

The forecasting methods in sales mirror the principle of the “garbage in, garbage out” concept of computer science. No matter what technique or how sophisticated a software you use for sales forecasting-if you aren’t entering accurate data into your forecasting process, you will get inaccurate results.

In addition, several other internal (mostly strategic and tactical) and external (mostly financial factors impact the accuracy of a sales forecast.

You don’t have to go too far to understand this-just recall what the COVID-19 pandemic did to the world in 2020 and you have your answer. Many other “black swans” like COVID can throw your forecasting off its balance.

Examples include regional wars, political unrest, the crypto crash, mass layoffs, the Great Resignation, funding divestments, etc. You have no control whatsoever over these external market conditions. And the worst part-you can’t factor these trends into your forecasting plan as a margin for error because you don’t know how big of a dent it’s going to put in the economy.

Your forecasting numbers can drastically deviate when you make changes to your product, service, or pricing strategy. Prospects may or may not take the changes favorably, depending on the context of the changes you have rolled out.

In general, people are resistant to change because change leads to uncertainty, and people like predictability-at least in business. Sometimes, these changes can be enforced by external factors, such as changes in regulatory requirements or compliance standards (e.g. GDPR).

This applies mostly to startups or businesses that are testing new markets. It’s way easier to make sales forecasts when you have access to a ton of historical sales data. If you don’t have a data point to reference, you can’t just make up a number and set a forecasting target off of that. That never works.

When you are launching a new product lacking past sales data-it means you have to bank a lot on secondary market data or your salesforce’s intuition.

Like we alluded to earlier, garbage data almost always generates garbage outputs. If your sales team isn’t committed to investing in sourcing high-quality lead data or updating context-rich data in the CRM, they will inadvertently contribute to inaccurate predictions.

And no amount of software, or its high-tech automation capability, can fix the problem of inaccurate data.

Bill Gates once said:

Sales forecasting is one such area where the quality of data that you feed to your forecasting engine will compound and generate matching results.

Inaccurate data is mostly a problem born out of manual data entry into CRM. When your sales reps update the CRM with half-hearted information, the result is going to look equally weak. And it’s not entirely their fault-nobody likes spending hours in the CRM enthusiastically keying in customer details.

A much better alternative is to identify tools that can automate the process while retaining the context of sales conversations. Take Avoma, for instance. Avoma doesn’t just automatically record, transcribe, and analyze your sales conversations-it also takes notes for you automatically and syncs it into your CRM to avoid manual errors and save you time.

You can connect and configure your CRM with Avoma by logging into your Avoma account, clicking on Settings, and clicking on CRM.

Seasonal trends such as summer slumps, fiscal year closing, holidays, and year-end slowdowns have as much impact on B2B sales as they do on B2C retail businesses. Of course, these factors vary and depend heavily on your industry, location, and the type of audience you are selling to-but seasonality does come into play in B2B sales.

Most forecasting methods fail to consider the impact of seasonality on sales, skewing the chances of accurate forecasting in the wrong direction. In general, SaaS businesses have three seasonal trends to look out for:

Now that we looked at the challenges in getting the sales forecast right, let’s now look at the right KPIs that need to be measured to get an accurate forecast.

Before getting into how to do an effective sales forecast, one needs to have clarity on what KPIs to measure. Without the right metrics and processes in place, no amount of tools will be helpful.

Here are 3 key metrics that can be a great starting point:

While you might have an annual forecast at the beginning of your financial year, it makes more sense to keep revising your forecast from the lens of quarterly performance and results. It helps you pause and check if your annual goal is still achievable and what are the gaps that need to be filled towards that pursuit.

Pipeline coverage is another key KPI for your forecast. It means having a target pipeline value to achieve your forecasted sales numbers. Depending on the deal cycle and close rate of your organization, pipeline coverage can be anywhere from 3–5x your forecasted monthly and quarterly sales revenue.

You need to have clarity on what are your current open deals, which deals are committed for the month, what’s the best-case scenario, and more. If you do a good job in maintaining your CRM hygiene, platforms like Avoma help you further in identifying the potential risks for each deal based on the account engagement. It helps you ensure you don’t let deals slip through the cracks.

With that, let’s now look at how to do sales forecasting effectively.

Sales forecasting is certainly a guessing game-but your job is to make educated guesses. No matter what, there are always going to be erroneous calculations in your forecasting-which is why you have to have some margin for error.

The sales forecasting tips that we are going to share with you below will help you minimize the guesswork and get you closer to the actual forecasting goals.

Pricing is one of the most important factors that impact B2B companies in their sales strategy and forecasting. It’s also a tricky thing to plan—most pricing strategies are driven by the market, i.e. the value that customers perceive about your product. People won’t pay more than $5 for a sandwich at 7-Eleven, but will readily fork out $30 per month for a software license. It’s the difference in perception of value that customers get out of each of those items.

If you have a product with the wrong price, it can hurt your sales and growth. On the other hand, your business always has some leeway to adjust pricing to improve profit or market share. In order to make your forecasting effective, you need to have a clear-cut pricing model that the market favors.

Look at the closest rivals in your niche and figure out their pricing model to figure out what the market will pay for. The price point can vary a lot depending on your industry or the type of product you are selling.

In B2B SaaS, the differences in product prices are generally marginal-making your pricing model even more critical. However, the pricing differences can vary a lot if you are in the business of selling professional services like consulting, marketing, coaching, or implementation.

In our case, there are a lot of products in the conversation intelligence market that charge a lot more than Avoma. There also are several new products that charge a lot less than we do.

We arrived at our pricing decision based on our analysis of what’s fair and flexible in the market and the value that we are confident our customers will get. We didn’t price ourselves lesser because we weren’t worth as much, but because it was a price point that we were sure we could get our customers to say “yes” to for the value that they were going to get out of our product.

It’s a strategy that has paid off because it helped us get our name out there fairly quickly, earn trust in the market, and earn happy testimonials from our customers. We were able to get people to pay attention to our offerings because we were priced right. Over the years, the credibility that we built through our product has helped us raise our prices and introduce higher-tier subscription plans.

The point is—you have to figure out what your options are, how you want to position yourself, and how much value you will be able to offer to your customers. So do some pricing research first so that you can go in with the right price.

The worst thing that you can do to your sales forecasting is to “hope for the best.” Optimism is good, but it should be backed by data. Like our VP of Sales Nate Hymas says, “Hope isn’t a sales strategy.”

Instead, base your forecasting on data. Figure out how many hours each of your reps can dedicate per week—and how many deals can they close each week. More often than not, sales is a numbers game. The output controls the outcomes. The number of focused hours your team can put into sales largely determines the number of deals you can close-it’s as simple as that.

Building a predictable revenue always boils down to creating repeatable systems, experimenting with them, and executing them in a controlled business environment. The more predictable your processes are—the better your chances to achieve revenue predictability. Greater control over these processes ensures a high likelihood of success.

If your sales reps can’t dedicate enough time each week to sales—or you don’t have the right resources dedicated to sales—you will not have enough leads coming through your pipeline. In such cases, your sales forecasting number is just going to be a ridiculous pie in the sky. Therefore, make sure you pick a sales to estimate that you can practically achieve and back it up with the necessary time and resources it deserves.

If you are in B2B SaaS, you probably have more than one product/subscription plan. At Avoma, for instance, we have three-tiered plans for revenue teams-Premium, Business, and Enterprise-with features that help our customers solve different things and are available for different price points.

If we were to make a sales forecasting plan for Avoma, we would have to set a goal around how many seats of each of those plans are we planning/going to sell to achieve our forecasting numbers.

Ideally for Avoma, we will be at a great place if we can sell to at least one enterprise account a month, 20–30 Business plans (priced at $85/user/month), and more than 30 Premium plans ($65/user/month). Of course, these are random examples-but you get the drift.

The point here is that you need to look at each of your product units (i.e. seats) and set a unit volume goal for each.

Now is the time to get down to the brass tacks of actual forecasting. Take your pricing for each of your products and multiply it by your unit goals. E.g., if we want to sell at least 30 Business plans ($85/user/month) each month, we will have to multiply:

$85 * 30 = $42,550.

Similarly, if we wanted to sell 35 Premium seats, that would mean:

$65 * 35 = $2,275

Same thing with the Enterprise plan. Once you are done with these calculations, add all of those numbers together. That will give you the sales goals for the entire year. You can take this a step further to make your forecasting goals more granular at a weekly level. Here’s the formula to get there:

Yearly sales goal / Number of weeks you can sell actively = Weekly sales goal

Caveat: A year has 52 weeks, but you will need to adjust it to ~49 because there will always be periods where your sales numbers are going to dip (e.g., holidays, downtime, Q1 slump). Of course, the number of weeks might look different from one business to another.

The idea is to figure out the ideal number of weeks you can sell actively-it looks different for every business depending on the nature of their product, their market, customer type, etc. We pegged ~49 weeks because that’s the number of weeks where our sales team is in full swing.

Calculating your yearly, monthly, or weekly sales goal will help you allocate the right quota to your sales reps for the corresponding timeline. That means the SDRs will know the ballpark estimates of the number of prospects they have to reach out to every week, month, or quarter. This also helps you allocate the right budget for sourcing inbound leads through SEO, social media, events, or other marketing channels.

And if you are wondering how to incorporate the revenue coming in via recurring revenue/existing customers, we will get to that in just a bit.

If you have an online sales funnel (e.g., an ebook, webinar, ROI calculator) or you have been running paid ads on social media, you need to factor the leads and revenue coming from those sources into your forecasting.

You specifically need to look at three things across your online lead gen channels:

Finding answers to these questions is critical to make close-to-accurate projections around your online sales. As a rule of thumb, be conservative in your estimates because external market factors will influence your prediction a lot-like we discussed earlier in the post.

In the context of online sales, these factors mostly range from ads/keyword bidding, SEO, fluctuation in website traffic, to platform-specific algorithm changes.

Recurring revenue is the bedrock of SaaS businesses-your forecasting won’t be complete and accurate until you incorporate that data into your projections. Ask yourself a few questions such as:

Like many things in businesses, the answers to these questions will once again vary based on what you sell and who you sell to. For us at Avoma, most customers sign up with us for a yearly subscription plan after they see the value in our product during the trial or demo stage. That makes it easy for us to calculate the current average recurring rate. For us, it stands at a healthy 80% right now.

Keep in mind that regardless of the renewals and average revenue rate, the unit value (i.e. seat) of your product might go up or down—depending on the customer requirement. You can’t estimate this number until it happens, but you can always factor it in as an anomaly in your forecasting.

Take all of these into consideration to come up with a goal for your recurring revenue in order to make near-perfect sales forecasts.

To make sure you arrive at a healthy forecasting accuracy, you also need to take into account the costs of resources you might be pouring into your sales process. These costs could include—but are not limited to:

These are some of the major expenses that most B2B organizations spend in their sales processes without fail. If you want to notch it up and arrive at a more granular calculation, you can derive a specific number by figuring out your cost of sales. This essentially means adding up all your sales- and marketing-related expenses and dividing it by the total number of reps in your team.

In point #4, we discussed one way to come up with yearly and monthly sales projections. There is one more way to do that—this time, by incorporating the recurring sales numbers into the forecast.

All you have to do is add up your new customer acquisitions, add up your online sales total, and add the estimated total recurring revenue. That will give you the total sales for the entire year. Now divide the yearly sales figure by 12 (# of months in a year) to get your monthly sales projections.

This is the most straightforward method to do sales forecasting, especially in SaaS, because you have a lot of contract renewals. Like we discussed earlier, arriving at this calculation will also help you assign specific sales quotas around how much you need to sell each month to achieve the yearly sales target.

Roughly put, the sales forecasting will look like a graph that grows month over month because of the new customer acquisitions plus the subscription renewals that stack up on each other every month.

To keep the projections realistic, we have kept room for error because some customers are going to churn away no matter how good your product or services are.

Now that you have a fair idea of how to map a sales forecasting plan for your business, you need to put it into actual practice. Every technique, process, or method becomes alive when the rubber meets the road—when you walk the talk. Action leads to information, oftentimes new. The more you apply the sales forecasting techniques that we have shared here, the better you get and more patterns start to emerge which will make the projections more efficient.

Without trying, every sales forecasting tool or technique is just another idea in your head. You can have dozens of those sitting idly in the back of your head, but they won’t help you until you put them into action.

Use sales forecasting as a tool to make informed business decisions, to see if you are on track to achieve your sales targets, and to course correct your processes if they aren’t leading you to desired outcomes. If something doesn’t add up, that’s not the end of the world. Take a step back, reproject your forecasting exercise or re-budget your sales activities.

All the best!

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