Is Your CRM System Decreasing Forecast Accuracy?

Executive Summary

  • The impact of CRM on forecasting.
  • The use of forecasting as a relative term when it comes to sales input.


CRM is an interesting software category. Before SaaS, and Salesforce, on-premises CRM had one of the highest failure rates of any application. Siebel was, of course, the market leader (Siebel was acquired by Oracle in 2005, and as is standard is now an inconsequential application), Then other companies like SAP and Oracle developed CRM applications. These applications failed in implementation at high rates. Now, most of the CRM market is delivered by SaaS, and it is the only category of enterprise software that is distributed this way. 


Some have argued that CRM is a natural fit for SaaS delivery because it is a more simple application, and requires little in the way of computation or configuration.

CRM (customer relationship management) is one of the fastest growing categories of enterprise software. Forbes predicts that in 2015 the sales of CRM will surpass that of ERP. This will be the first time that ERP will be in second place in the enterprise market since the mid-1980s. CRM continues to be a popular application. But the results of a review of CRM implementations in companies can often be quite uninspiring. CRM has shed much of its image as a widow maker for IT. But when I sit with marketing and sales personnel as they show us their sales data, I see continuing data quality issues. These sessions end with a statement from the marketing or sales director we are sitting with saying something like

“Much of this data is out of date.”

The number one problem with CRM is getting quality sales data, and this continues even with all of the advancements in CRM technologies. All manner of data quality issues has lead to software vendors that have products that clean CRM systems such as Talend and Data Cleaner (although the focus of these applications is more on the low-level quality issues such as duplicate records). 

Many companies are happy with how quickly they can get a CRM system operational, but a detailed review of the data quality is almost always disappointing. This is a greatly deemphasized topic by both consulting companies – particularly the major consulting companies as well as the CRM software vendors as if clients knew the real average quality of sales information in CRM systems, it would put a serious damper on CRM software sales.

CRM and Forecasting

CRM started as a contact management system and has since grown in many directions. CRM often has so many different attributes that it can be difficult to say definitively what CRM is.

  • CRM was never developed for forecasting.
  • IT is often used for forecasting The forecasting functionality is quite limited.

Many CRM software vendors propose that their systems are the key to improving the accuracy of the sales forecast. This is a complaint on the part of the sales forecasting vendor Right90. That many times they (Right90) will be told by prospects that sales forecasting is taken care of because they have a CRM system.

  • A major problem is that CRM is not designed to track the sales at a product-location combination.
  • The unit of measure of CRM applications tends to be sales and the focus is on the customer.

If one looks at typical sales funnel report in a CRM system. This report declares how the sales decline all the way from prospecting to won deals.

This is a view from BaseCRM. It moves from prospects all the way down to won contracts.  Notice how aggregated this report is. Furthermore, the only part of this report that would be of interest to generating the final forecast would be the number of sales won. All the information before this is exclusively for Sales.

Here is Salesforce, where a sales forecasting capability exists. However, it is very simple functionality. It provides the ability to perform manual overrides-changes and which provides visibility to sales management. This is entirely a judgment-based forecasting “application.” 

Forecasting is a Relative Term

When sales forecasting is performed within CRM systems, it is carried out at only the highest level or aggregation, which is the forecast per customer or per a particular dollar quantity. This type of forecasting does not do very much for operations. For instance, let us take the example of a consulting company.

A consulting company offers some services, which means using consultants with particular skills, and capabilities mean that the consultants are differentiated from one another and are not interchangeable. A client that wants a database administrator can’t be placated by offering them a web developer, because that is what the consulting company happens to have in “inventory.”

The CRM forecast that a particular customer will sell or a particular dollar amount will be sold does not help the manager of the consultants because the director of consultants needs to know what specific skills are required for a project.

  1. Making the Forecast: To the salesperson a forecast, which matches his or her quota, is accurate. So if a salesperson has a quota of $500,000 in a quarter, and sells $525,000 that is a very accurate forecast, according to the salesperson and the salesperson’s manager.
  2. Operational Details: The salesperson may have forecasted one customer at a high probability of being closed – which would have lead to a project that required a database administrator and a client with a small probability of being closed – which would have resulted in a project that required a web developer. (The nature of forecasting is the more detailed the level of the forecast, the lower the forecast accuracy.)
  3. Operational Outcome: If the low probability prospect did not come through, but the high probability opportunity did not come through, and the consulting manager used the forecast to hire resources, the fact that the salesperson forecasted dollars accurately does not help in the consulting manager. This is because they hired a database administrator when they now need a web developer.

This distinction between how sales measures forecast accuracy and how operations measure forecast accuracy applies throughout all different business types. This is why being able to correlate the sales forecast to activities is so important.


The high-level data that is tracked by sales in the CRM system is a forecast regarding how sales see the world. But it does not reflect a forecast the way that operations sees the world. For a sales forecast to be valuable, it must be converted into a higher level of detail by operations. CRM systems use the term forecast to represent a forecast at a level of aggregation that is not considered a forecast by operations. One can produce a sales forecast in a forecasting application, and this is done all the time. When the term forecast is used in CRM, it should be placed in quotation marks to differentiate it from other types of forecasts.

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Brightwork Forecast Explorer for Monetized Error Calculation

Improving Your Forecast Error Management

How Functional is the forecast error measurement in your company? Does it help you focus on what products to improve the forecast? What if the forecast accuracy can be improved, by the product is an inexpensive item? We take a new approach in forecast error management. The Brightwork Explorer calculates no MAPE, but instead a monetized forecast error improvement from one forecast to another. We calculate that value for every product location combination and they can be any two forecasts you feed the system:

  • The first forecast may be the constant or the naive forecast.
  • The first forecast can be statistical forecast and the second the statistical + judgment forecast.

It’s up to you.

The Brightwork Forecast Explorer is free to use in the beginning. See by clicking the image below:


Sales Forecasting Book

Sales and Stat-1

Sales and Statistical Forecasting Combined: Mixing Approaches for Improved Forecast Accuracy

The Problems with Combining Forecasts

In most companies, the statistical and sales forecast are poorly integrated, and in fact, most companies do not know how to combine them. Strange questions are often asked such as “does the final forecast match the sales forecast?” without appropriate consideration to the accuracy of each input.

Effectively combining statistical and sales forecasting requires determining which input to the forecast have the most “right” to be represented – which comes down to those that best improve forecast accuracy.

Is Everyone Focused on Forecast Accuracy?

Statistical forecasts and sales forecasts come from different parts of the company, parts that have very different incentives. Forecast accuracy is not always on the top of the agenda for all parties involved in forecasting.

By reading this book you will:

  • See the common misunderstandings that undermine being able to combine these different forecast types.
  • Learn how to effectively measure the accuracy of the various inputs to the forecast.
  • Learn how the concept of Forecast Value Add plays into the method of combining the two forecast types.
  • Learn how to effectively run competitions between the best-fit statistical forecast, homegrown statistical models, the sales forecast, the consensus forecast, and how to find the winning approach per forecasted item.
  • Learn how CRM supports (or does not support) the sales forecasting process.
  • Learn the importance of the quality of statistical forecast in improving the creation and use of the sales forecast.
  • Gain an understanding of both the business and the software perspective on how to combine statistical and sales forecasting.


  • Chapter 1: Introduction
  • Chapter 2 Where Demand Planning Fits within the Supply Chain Planning Footprint
  • Chapter 3: The Common Problems with Statistical Forecasting
  • Chapter 4: Introduction to Best Fit Forecasting
  • Chapter 5: Comparing Best Fit to Home Grown Statistical Forecasting Methods
  • Chapter 6: Sales Forecasting
  • Chapter 7: Sales Forecasting and CRM
  • Chapter 8: Conclusion

Software Ratings: Demand Planning

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