Sales forecasting examples

Sales forecasting examples are a valuable way to get a specific answer to the question, “what is a sales forecast?” There can be lots of abstract language and speculation around what a sales forecast really is, not to mention that each organization has historically done sales forecasting in a unique way.

Read more below.

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Sales forecasting examples

Sales forecasting examples: Dos and don’ts

We’ve compiled this list of sales forecasting examples, as well as what can be learned from them, to help modernize your approach to sales forecasting. In an accelerating world where customer expectations continue to grow, learning from these examples can help sellers move beyond the distraction of achieving sales forecasting accuracy to focus on the true goal — increasing revenue.

What should be included in a sales forecast?

A sales forecast should include data, analytics, and insights that help sales teams achieve their goals. This definition is a little different than the historical conception of a sales forecast. In the past, many sales leaders have seen the sales forecast as a tool for maintaining control and enforcing accountability. When the accuracy of the sales forecast is the dominant concern, salespeople are pressured to achieve the forecast by any means necessary and punished when they fall short of projections.

But what if the sales forecast worked for sales teams, not against them? An adaptive forecast that is powered by machine learning and artificial intelligence can be generated on a daily basis. This forecast should include information that helps each sales professional reduce friction and inspire them to look forward to opportunities coming tomorrow.

What should a sales forecast include? The modern answer:

  • Insight into the prospect’s historic actions, like how long it takes them to make a buying decision.
  • Real-time information on fluctuations in the market and specific buyer and company information that can inform deals and decision-making on the buyer side.
  • Prompts for effective follow-up, like who else in a professional’s network could help them get a meeting or close a sale.
  • Prioritization of the highest-value activities to help everyone plan their workday.
  • Automated CRM updates that relieve sales professionals of mundane tasks and keeps everyone in sync on the progress of deals.
  • Real-time updates on the pipeline and growing profits, as well as available inventory and cost of goods and/or services.

But as we said, historically, sales forecasts have worked very differently than this example. Here is an example of what is usually included in a more traditional sales forecast:

What should a sales forecast include? Here’s the traditional answer:

  • Historic sales data from the selling side, like past deals, customers, and length of deal cycles.
  • Number of products or new services that are expected or assumed to be sold in the timeframe of the forecast.
  • Estimated cost of providing those goods or services to calculate profit margin.
  • High-level overview of marketing or business initiatives that will affect the sales team.

This example makes it clear that traditional sales forecasts are often based at least in part on assumptions, and when they need to be updated, a lot of conversation and manual rework might be required. That’s why it’s so exciting that technology like artificial intelligence is starting to disrupt the sales industry across market sectors and verticals.

What are the steps involved in sales forecasting?

Sticking with the concept of a traditional sales forecast, the number of steps allegedly involved varies from source to source. Some experts will tell you there should be four steps, while others break it down into five, seven, or more. Plus, there’s the question of how often the steps need to be repeated. Some businesses still work from quarterly or monthly forecasts, while others have responded to the faster-paced digital economy by creating weekly sales forecasts. In some cases, sales professionals must spend hours of each day updating the CRM and other less-automated digital tools to achieve as much accuracy as possible.

What are the steps to preparing a sales forecast? Here’s the standard answer:

  1. Define forecast goals, like new business, recurring business, or volume of inventory that will be sold if the goal is achieved.
  2. Agree on the average sales cycle by discussing sales team experiences and opinions about how long the average deal takes to close.
  3. Get buy-in — not just from the sales team, but other departments like finance, HR, and marketing that will make their business decisions based on the assumptions in the forecast.
  4. Agree on a sales process and shared definitions by creating a unified sales process that each salesperson will be required to adhere to in order to protect the accuracy of the forecast. This includes shared definitions of the different prospect stages and when each lead should be manually moved along to the next stage in the CRM.
  5. Review Historic Data such as similar quarters from years past and the individual performance of each team member. This helps you determine if highly-productive past quarters might be skewing numbers to an unrealistic assumption.
  6. Consider Seasonality and Big Events like holidays, conferences, or pending legislation that might affect your sales and the general market.
  7. Make a Final Assessment of the forecast, including the number of assumptions that contributed, how well those assumptions were examined, and where there might be unknowns or potential errors.

No matter how you organize them or divide them up, these traditional steps to creating a sales forecast present a variety of blind spots, margin for error, and opportunities for even the most accurate sales forecast to have a negative impact on company culture.

For instance, why must part of the sales forecast include dictating to sales talent the exact methods by which they must do their work? Including too many elements of process in a sales forecast is part of the controlling and at time punitive approach we mentioned earlier. When the only concern is preserving the numbers that have been projected, the well-being and respect of salespeople can fall by the wayside.

Secondly, what happens when the big events impacting your market or sales can’t be predicted when the forecast is made? Even when events break in the news, like the COVID-19 pandemic, civil unrest, or unexpected legislation, the market impact of those announcements won’t be immediately clear or even possible for the team to predict.

To that end, relying on only historic data and the opinions of employees to provide the main foundation of the forecast isn’t an accurate approach. Historic data can’t predict the future. Just because the first week of April was sunny for the last two years, doesn’t mean this year will not be full of rain, no matter how much we might believe there will be sun.

And while we’re on a roll, why limit the forecast to just one or two stated goals in the market? What if the goal could just be—growing revenue—and salespeople could be better-equipped to pursue that business across audiences or product and service lines, using their network and unique talents?

Let’s review the steps of creating a sales forecast that will work for your team, helping you improve revenue without obsessing about the accuracy of projections from the past:

What are the steps to preparing a sales forecast?

Here’s the modern answer: 1. Add critical technical layers first by figuring out how to pool your information. This means CRM data, market research, community intelligence, and data from non-CRM users like your legal team or vendors. Aggregate everything that should be analyzed in one place. 2. Remove bias, time, and human error through applied machine learning that takes account of all this and other data to provide the sales forecast. 3. Refer to daily updates and projections that incorporate the impact of dynamic market data like current events, automated updates to the CRM, new members of the professional network, and more. 4. Support each individual as needed by using the daily progress in the forecast to identify and reinforce top performers while simultaneously coaching those who need more support.

Using the sales forecast generated by these steps means releasing past perceptions that the accuracy of the forecast is the goal. When the forecast changes every day, this allows the goal to be growing revenue.

Actionable sales forecast examples

Here are some examples of traditional approaches to sales forecasting and what can be learned from each one:

Sales Forecast Example 1:

How do you forecast sales in a marketing plan?

A B2B software company is working on a quarterly marketing plan, which includes a sales forecast projecting how new marketing initiatives will lead to growth. To complete this sales forecast, the marketing team has done research and defined two target audience segments. Based on the historic data in their city, they have projected how these audiences will grow not just in the next quarter, but over the next year.

Then, the marketing team does more research to figure out the average amount each business in their target audiences might be willing to spend on software. Further analysis of audience attitudes toward their company helps them “reality check” some of their expectations and assumptions. Lastly, the marketing team turns to its own historic data to see how well their campaigns have performed in the past.

What to learn: This sales forecasting process is subject to a lot of assumptions, and the people responsible for completing it are spending weeks doing research and fact-finding to confirm or counteract those assumptions. Additionally, there are many unknowns about the next quarter that could render this forecast completely inaccurate.

Sales Forecast Example 2:

How to forecast sales for a new product

A toy company’s approach to forecasting holiday season sales for its new product begins with analyzing the cost per unit to produce the toy. This includes not just manufacturing costs, but marketing, shipping, and display costs. The cost to produce each toy is subtracted from the average unit price to define exactly how much profit will be earned from each unit sold. In a separate calculation the company conducts market research, focus groups, and looks at their sales from past holiday seasons to project how many toys they can expect to sell. Multiplying the profit per unit by the projected sales allows them to forecast revenue from the new product.

What to Learn: This sales forecasting process might not account for discounts like Black Friday or other promotions that marketing is working on with retailers. Also, simply basing the projected sales of the toys on historic data and what customers share in focus groups or on social media might leave out important market factors the company is not aware of.

Sales Forecast Example 3:

How to Calculate Sales Forecast in Excel

The new sales manager of a commercial HVAC company wants to create a year-long sales forecast in Excel using the company’s historic data. They look at various sales forecasting examples in Excel they find online and see there is a data analysis tab in the menu bar as well as a built-in feature that will leverage the company’s historical time-based data into a forecast sheet in Excel. These basic projections of how sales will grow over the next quarter allows them to communicate with purchasing and the technical team about inventory and new hires.

What to Learn: This sales forecast example using Excel is based solely on historic data, and therefore only historic inventory. If a new product or improved part comes on the market in the next six months, the company may not be able to adapt in a timely fashion because they have already ordered based on these projections.

Marketers and sales leaders who still want to take a more traditional approach to sales forecasting can benefit from templates like those in this sales forecast example pdf. However, all these approaches still suffer from putting the sales team in a reactive versus proactive position. When the department is working toward maintaining the accuracy of a sales forecast, they are constantly required to react to outside factors that put them at risk of not achieving the forecast goals.

With a sales forecast powered by artificial intelligence and machine learning, the predictions and projections can serve the team as a source of proactive inspiration and suggestions for next steps, not a looming threat that gets bigger with every day that passes.

Set a sales forecast example with Collective[i]

Have you ever thought of sales forecasting as an example of something that could set your business apart from the competition? If everyone else in your industry is still working from historic data and requiring their sales teams to spend hours a week managing the CRM, automating and improving these processes for yourself will have many benefits. The mundane, repetitive tasks necessary for manual sales forecasting can be removed from every individuals’ to-do list, leaving them more available for high-priority activity like, well, selling.

We developed the Collective[i] solution as a resource for end-to-end digital transformation in sales. Collective[i] goes beyond the sales forecast to provide a full portrait of the sales climate. This includes not just a sales forecast that is updated daily, but also recommendations about networking, and opportunity odds of each deal closing based on how that company or individual has done business in the past. And our Intelligent WriteBackTM feature automates the manual processes of filling in your CRM, leading to average productivity gains of 15-20%.

Our platform puts the human element back at the center of sales by removing repetition and shifting focus away from hypothetical projections. Instead, real-time facts, figures, and market insights give everyone in your organization the information they need about the sales team’s performance, allowing all departments to make better decisions based on data.

Stop managing to sales forecast numbers and obsessing over maintaining accuracy. Start waking up every day excited to see the newest forecast and the opportunity it represents. Start exploring Collective{i].

Work together, win together

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