By: Don Gray

What really causes a bad forecast? What is the impact to the business when you have a poor or unreliable forecast? I have found that the financial impact can be quite severe when forecasts are not reliable.

I’d like to share an example of the impact an unreliable forecast had on one of my clients.



The Situation

The client is a large manufacturer in the wireless industry. They manufacture their equipment offshore and also warehouse a large portion of their inventory offshore.

The Problem

Because of unreliable sales forecasts, they’ve had to maintain a large inventory presence onshore in addition to offshore. Their poor forecasting not only forced them to warehouse millions of dollars of inventory onshore, but also negatively impacted supply chain management, causing them to over-manufacture “just in case”.

“What does 75% closed really mean?”

I was reviewing the forecasting process with one of the client’s sales managers and noticed that every stage of their pipeline forecast had a percentage associated with it. This is a fairly common practice, but here is where I ran into the real root cause of their forecasting challenge: I selected a stage that was marked 75% and asked, “What does 75% closed really mean?”

He couldn’t tell me.

There was no clear definition for the 75% stage, nor for any of the other stages as well. Without a clear definition of each stage in the pipeline, how could they have a reliable forecast? How can they tell if an opportunity was truly advancing? How many of you have seen a similar situation?

Resolution Approach

So what to do? We began by stepping back and looking at their entire sales process model in their CRM system. While they had defined some characteristics of each stage, the definitions were not very clear and left a lot of room for interpretation.

To fix this, we reexamined how they were selling their solutions, how the customer was buying the solutions, and came up with key buying milestones that we could turn into measurable criteria to advance an opportunity through the pipeline. Next, we linked the definition of each stage of the sales process to a stage in the customer’s buying process. We made a clear definition of the criteria necessary for advancing an opportunity to the next stage. With advancement criteria clearly defined, we were ready to improve forecasting accuracy.

Business Outcome

We assessed the existing pipeline and found ways to more effectively track all opportunities. We applied our new criteria to all of the opportunities in the pipeline. As you might expect, there was a lot of movement – most of it to the earlier stages of the sales process. Once the projections were adjusted to match the criteria, the forecast became much more reliable.

With more reliable monthly and quarterly forecasts, communications with the supply chain management organization improved. Within one quarter, they were able to reduce the level of on-hand inventory required in the U.S. (onshore) by approximately 15%, saving millions of dollars in inventory and manufacturing costs.

I wish these stories were few and far between, but they actually are not. I would like to hear from those who have experienced something similar and what steps they took to improve their forecasting. You can find me @Grasan2001 or comment below.

About Don Gray

Don Gray is the Founder and President of Sales Engineering Group, a sales performance consultancy. Don has worked with a variety of small, medium, and large B2B organizations to help them develop their distinct value messaging, identify how customers buy their solutions, and skill their sales teams to drive predictable sales results. You can find him leading the monthly Sales & Marketing Shared Interest Groups.