Sales Forecasting Process: A Step by Step Guide
Have you come across the old Army acronym the “The 7Ps of Planning”?
“Prior Prevention and Planning Prevents P*** Poor Performance”
The same holds for sales forecasting. Many field sales managers are guilty of charging head-on into an Excel grid without a plan. They plot their historical sales data and draw fancy, linear lines stretching far into the future.
#1 Definition of your market segment
Here you need to define the specific areas or niches of the market that your product or service serves. Let’s say, for example, you work in the automotive sector. Ask yourself if you target a specific segment of that sector.
- Is your product geared more towards the assembly of vehicles or after their distribution to the dealer?
- Are you serving a niche sector of the market? i.e., do you target high-end cars or more those available for general distribution?
Choosing to label your market as simply “automotive” leaves you at risk of undervaluing your actual market share.
If you specialize in auto parts, define your market as such. That way, when drawing up your sales forecast, it’s being done within the context of that niche, not in a much larger market like “auto parts.”
Now that you’ve defined the playing field you’ll be competing on, it’s the role of the area sales manager to look at some of its particular characteristics.
- Market growth – are you operating in an established market with a steady increase in forecast growth, or is a relatively new, openly volatile market full of unpredictability?
- Seasonality – are there certain times of the year you are likely to sell more than others? Ice cream or sun lotion distributors are more likely to be busier in the summer months than in winter. This should be factored in when choosing your sales forecasting process.
- Price sensitivity – how do you compare to your competitors? Are you undercutting the market? Or are you substantially higher than the market average? This too will affect both the volume and/or value of your product.
- Upcoming changes – sticking with the automotive industry, the introduction of self-driving vehicles will bring a whole set of new laws and legislation. So too will the switch to electric-powered cars. How will these changes affect your sales output? Will you need to make costly adjustments to your product to align with new regulations? Are your auto-parts likely to be used by electric car manufacturers? Knowing these changes in advance allows you to adjust your sales forecasting process accordingly.
Historical Sales Data
Something else you’ll want to figure out early on in your preparation is whether or not you have any hard sales data to work with. If your product is entirely new and ready to launch into the market, this isn’t possible.
However, if this isn’t your first rodeo, there should be at least some data to reference.
Now it’s important to remember that data extrapolation only works in steady, stable markets that experience little fluctuation.
If there’s a lot of disruptive, unpredictable change, you should explore an alternative forecasting method (such as exponential smoothing).
#2 Choosing the Right Model
After examining the market closely and how different forces drive it, it’s time to decide what forecasting model to use.
You will be making important business decisions based on the information it generates, so you must give this careful consideration.
There are several methods for you to choose from, but the two most prominent are top-down and bottom-up.
Each has its advantages and disadvantages. We’ll discuss them at length to give you a better insight into which to choose.
What is the Top-Down Forecasting Model?
The top-down approach allows you to make a reasonable revenue projection by assuming a market share percentage from the total addressable market or TAM.
TAM, also known as the total available market, is the overall revenue that a company can earn if it has a 100% market share for a product or a service. This metric is expressed in dollars.
It is developed based on market valuations available from reputable sources like Gartner, Forrester, or Grail Insights.
Companies use this information to determine the number of resources (effort, funding, or both) they should invest in a new business line or decide which products, customer groups, and business opportunities to focus on to maximize profit and growth.
Going back to the top-down approach, here’s how your forecast would look using this method:
Revenue = TAM ($) x Assumed Market Share (%)
To illustrate, if you run a software company and you want to expand your operation to China, and you find out that the TAM of your product for this region is $1 million, assuming a market share of 3% (or measuring it based on existing data), you get a revenue of $30,000. This would be your sales forecast using the top-down approach.
The process looks simple enough and is based on real-world market valuations. However, it comes with a caveat: it does not consider actual business deals happening in real-time that might affect the market.
Nevertheless, since it avoids data outliers that usually accompany lower-level facts and figures, it offers a bigger picture of the sales revenue. It is also helpful in identifying sales patterns.
While the top-down approach does not give you a precise projection, it allows you to quickly make a reasonable estimate, allowing you to decide if a business opportunity is worth pursuing.
This method is used by mature companies operating in various countries and those with access to decades of financial results or multiple segments.
In addition, early-stage businesses with no access to vast historical data can use it to generate sales forecasts grounded on fundamentals. The top-down method is also helpful in similar cases where markets are not well-defined.
What is the Bottom-Up Forecasting Model?
The bottom-up method makes a reasonably accurate estimate of a company’s business potential using low-level company data as a foundation and working up to revenue. As its name implies, it starts from the sales reps (bottom) instead of the managers (top).
The process kicks off with the individual members of the sales team calling up multiple potential buyers based on the business opportunities the company has in its sales pipeline. Doing this allows them to estimate the number of orders and sales volume.
Next, the company determines how much it will charge the customers for the products or services it offers. From there, they can calculate the estimated revenue simply by multiplying the potential sales volume with the average price of the commodity.
The more detailed the financial model you use, the more assumptions you can add. It can include returns, exchanges, chargebacks, or even retention and churn rates.
The managers work closely with the sales reps to inspect the pipeline, look at the projections, and examine the sales activity to produce a sales forecast. The manager also collects these numbers from across the company to create a more comprehensive projection.
Compared to the previous approach, the bottom-up method generates a more accurate sales forecast based on real-world business opportunities. Since many of your decisions and strategies moving forward will be based on your projections, using this approach might be a better way for you to go.
Aside from providing more reliable numbers, the bottom-up method also opens up more opportunities for total employee involvement as the sales reps work shoulder-to-shoulder with the managers throughout the sales forecasting process.
One thing you have to consider with this approach is that much of your success rests on how healthy your deals are. It means that your sales activity and customer engagement data should give you an accurate view of how your deals are progressing.
The information you use must be accurate, complete, and updated in real-time for this to happen. It is best to invest in tools that can automatically capture the data you need.
#3 Collection and Validation of Sales Data
The next step in the sales forecasting process is to make sure the data you’re about to use to conduct your forecast is as clean and accurate as possible.
Without it, even the most sophisticated sales forecasting process will struggle to give you any insight.
Think of the idiom garbage-in, garbage-out. Feed a system bad data, and it’s going to give you even worse insights.
The sales reps are most likely to take the blame for this.
But often the tools they are given just aren’t suitable for the job. You have to remember these guys spend most of their day traveling, meeting with clients before dashing off again for another appointment.
They don’t have time to sit down, fire up a laptop, rifle through a clunky CRM, and leave a comment. They need something more intuitive.
Mobile CRM apps are designed specifically to increase data accuracy by making life as easy as possible for the field rep. If they can enter data quickly, in real-time via an easy-to-use application, the info will be fresh, live, and reliable.
This is what you want before conducting a sales forecast.
Also, if you are using historical, extrapolated data to choose one of the suggested quantitative forecasting techniques, then there are a couple of things to watch out for.
The first is to highlight any anomalies by plotting your data into a standard excel graph as follows:
Between months 5-7, there appears to be an abnormal amount of units sold during this period compared to the rest of the data.
This could be due to several reasons: seasonality, perhaps there was an acquisition or company merger, or human error.
If you determine that this was in fact just “rogue” data and a one-off occurrence, we recommend you delete it before continuing with your forecast.
This same logic applies when deciding to use a qualitative sales forecasting method. Whether gathered from internal reports, markets surveys, or expert panels, your data will need to be checked for credibility. If other data sets are available for alternative market reports, cross-check for any apparent anomalies.
#4 Putting Theory To the Test
The next step of the sales forecasting process is to build out your model and test it. This could be through an Excel grid or a specialized software program, depending on the model you chose.
To do this, we recommend trying a Within Sample technique.
This means using a set of available data, such as your survey, market research, or sales data, to forecast a set timeframe and then compare it to the known outcomes or results.
If the out-sample forecast error (the difference between the known results and those forecast by your model) is better than the in-sample MANE (mean absolute naive error) then there’s a good chance you’re on to a workable model for your sales process.
The more data you have, the better. Data enables you to compare your output sample deviation more accurately over a more extended time.
If you’re unsure of how to calculate the MANE, it can be down as follows:
Sum of ANE (absolute naive error) divided by the number of ANE.
#5 It’s time to Validate
Don’t worry, we are heading into the penultimate stage of the sales forecasting process as we look to validate the results of our selected model.
So how do we go about this?
First, you can try adding some real-life variables to the model. For example, imagine a new car manufacturing plant opening up in your sales territory.
And as a result of the prowess and due diligence of your well-trained field sales team, you manage to win the account over your competitors.
If everything goes well, within 6 months, they’ll demand (x) amount of product, causing a severe spike in projected sales.
Add this to the assumption to your sales forecasting model and record the results. This is extremely easy to do both within Excel and most leading forecast software providers.
Now let’s say your model forecasts an increase in sales by over 400%. You must now decide whether you think that’s an overly optimistic and inflated prediction or a reasonable assumption based on the opening of the new factory.
To help your decision, you could fact check it against some previous historical sales data, maybe from the acquisition of a client of similar size or a significant increase in orders.
If you don’t have that data available, try getting it from a competitor or maybe from a completely different industry, as long as the same logic applies.
If the results don’t match, then perhaps you have to go back and tweak your model. Either the assumptions you made were incorrect or the model’s logic is flawed.
Again, you’ll have to decide what you think is the most likely explanation.
#6 Which one to choose
The final step in the sales forecasting process is choosing the model that worked for your business model.
We’re admittedly partial to the exponential smoothing method, as it usually accounts for slight unexpected changes that some of the other models can’t account for.
But honestly, this will all have depended on your testing:
- Which model consistently delivered the most accurate results?
- Which had the better out-sample forecasting error?
- Which model better accounted for A/B tested assumptions?
The importance of sales forecasting really can not be overstated. Testing all of the models on your shortlist will put you in a much better position than shots in the dark.
However, remember whichever model you end up choosing that the data you enter into it must be accurate or as close to it as possible. Inaccurate information throws any forecast off track — meaning all your hard work will have been for nothing!
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This drives revenue through intelligent recommendations for complex sales scenarios and provides the data for rich analytics that power better coaching, forecasting, and long-term customer support. Learn more about our solutions or request a live demo to see it in action.
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