How are you using your sales forecast data for enhancing the sales process?

Sales managers often head into an Excel grid without giving it much thought. They use spreadsheets for presenting their historical sales data in an insightful tabular or graphical format. Unfortunately, they forget to check that the sales forecasting process needs careful consideration.

Before building a sales forecast model, ask these key questions:

  • What is the type of market I am working in?
  • Does anything take precedence over what I do?
  • Is there data accuracy?
  • Have other models been tested?
  • Which model is the best fit for my sales process?

If you do not get concrete answers to these questions, it may lead to the selection of an inappropriate sales forecasting process with your sales management process model. This can prove disastrous for the prediction model that will provide off the mark data to you.

To help you avoid making a mess of your sales prediction model, I am putting together a small guide that will help you set up a better process.

The sales forecasting process

Before I proceed, let’s discuss something critical to your business process.

As I listed out at the start of the article, it’s critical that you understand the market in which you operate. The market you operate in heavily inclines your choice of the sales forecasting process.

1. Defining your market segment

You must define the specific areas or niche of the market.

For example, you are working in the automation sector.

  • Are you targeting a specific segment or working with every segment in the sector?
  • Is their product/service focused more on assembling in the car manufacturing sector or in the service sector for integration with SaaS tools?
  • The sector you work with is in the niche category?

Clearly define your market; doing so helps in drawing up your sales forecast in a better way.

Characteristics of the market

After defining the market, you are going to compete in, look at the characteristics of this target market. The characteristics include:

Market growth

Have you entered a market that already has established itself with a steady growth record. Make sure you avoid new, volatile markets.

Seasonality of the market

Does the market gets affected by seasonality? For instance, is there a certain time of the year where you will be dominant or busier than other players in the market? Make sure you factor-in this characteristic while choosing the forecasting model.

Sensitivity to pricing

Are you comparing yourself to the competitors? What I mean to say is do you have a parameter where you can gauge the growth against the competition?

Look for any future changes

Continuing with the fact that you are in the automation industry, self-driven cars might be available in the nearest future. And so the laws and legislatures will also take a shift. And cars will be running more on electricity than natural fuels.

  • How is this going to affect the sales output?
  • Is there a need for making costly adjustments to your product for aligning it with new regulations?
  • Are your automation techniques going to be used for the new electric car manufacturing process?

Having knowledge about these changes in advance will enable you as a business leader or sales manager to adjust the sales forecasting process.

Your 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.
  • To work with a sales forecast model, you need to know if there is any hardcore historical sales data to go with in the first place.
  • If you are new to the market, then you won’t have a historical sales data with you.
  • But if you have been in the market for sometime, now the scene changes in your favor.

Important note:

Data estimation only works in steady, stable markets where fluctuation is little to none.

2. Choosing the right forecasting model

So, you are done with your basic preparation; now it’s time to choose the sales forecasting process that will fit right into your business process.

Here are two common types of the sales forecasting process:

The quantitative sales forecasting

Quantitative sales forecasting works on the availability of historical sales data which can be used for predicting the estimated future revenue. This forecasting method works with collective mathematical equations. The 3 most popular techniques are:

Trend analysis

  • Through the study of past sales data, picking certain trends.
  • These trends are then used for predicting future fluctuations.
  • The change in the trend occurs due to seasonality, random factor analysis, and dynamic economic demand.

Exponential smoothing

  • It is considered the most accurate sales forecasting process.
  • Also, it is the most widely used sales forecasting process.
  • The process takes into account the exponential average of past sales for predicting future revenue.

Simple Moving Average

In this technique, the sales manager must estimate the sales data from a “dynamic” time period; 2–3 or even 6 months.

If you have sales data at your disposal, research the pros and cons for each of these techniques and find the one that best fits your business model.

The qualitative sales forecasting

The qualitative methods of forecasting are completely opposite to quantitative ones.

These techniques are subjective and they rely on the opinion of market experts or surveys and there is no involvement of mathematical equations.

Yet, this distinguishing characteristic does not make it any less useful.

As I mentioned earlier, as a new business in the market, you might not have historical sales data sets available with you. This is where They are going to be the best method of forecasting if you are absent of any historical sales data of which you could use

Delphi method

This method requires the help of an expert panel. You must assemble an unbiased team of market leaders, get their opinion and conduct a forecast for a set time period.

Market survey

In this method, you ask for the sales expectations of the partners and customers. Doing so, you can get a rough idea of how the market is going to grow in the targeted industry. Do thorough research into which of the techniques are applicable in your sales process. Conduct tests for determining accuracy.

3. Collection and validation of your sales data

The next step you must take for the sales forecasting process is making sure that the data available for the sales forecast is clean and accurate. Always remember this, if you push crappy data into the system, it will not provide you any useful insight.

Many times human error occurs while feeding data into the system and the sales reps face a lot of heat for this. As a manager, it is tough to be helpful to the sales reps and be in good terms with the upper management too.

This is where businesses use sales tools for easing out the manual task. Sales team members have many things to carry out apart from feeding data into the system.

They have to create their to-do list, their sales tasks; they have to make follow-up calls and send introductory emails. That’s why you need the best CRM software to help you out with the sales forecasting process.

Business owners do not have much time for getting into very technical details. As a sales manager, you can make sure that your sales reps work with a smart sales tool that helps them with automation.

So now, with minimal manual labor, your system registers data on its own, based on the workflows you set.

The data is much cleaner, and by adapting any of the above-mentioned forecasting methods and merging it with your chosen CRM system, you can create insightful reports.

What I mean to say is, why is there a need for doing manual labor when the modern system can do the heavy lifting for you. Food for thought!

A truly intelligent sales CRM for smart sales force!