Why Most Sales Forecasts Are Inaccurate
Here is a great tool to help CEOs ensure your sales team is forecasting revenues correctly.
Nothing upsets the board more than an inaccurate forecast. Projecting $10M in revenues for next quarter and landing a $7M result is painful. The challenge is knowing whether or not your forecast is real.
Most CEOs I talk to feel their pipeline forecasts are about 50% accurate. They head into board meetings with a coin flip’s chance of hitting the number.
What causes this? Sales processes are almost always designed inward out. A group of sales managers and reps get together and determine how they think they should sell.. They cobble together activities that occur during a sale. “Let’s see, we:
- Determine they have budget (Phase 1)
- Identify need (Phase 2)
- Submit a proposal (Phase 3)
- Negotiate pricing (Phase 4)
- Submit contracts (Phase 5)
- Close the deal” (Phase 6)
Sales assigns arbitrary metrics to the various stages of the process. For instance, phase 1 gets a 10% chance of closing. Phase 2 is 25%, and so on.
Why This Results in Inaccurate Forecasts
It doesn’t work. The above exercise is a useful one for developing a hypothesis. Most organizations stop here without ever validating these things as true.
The reps race to get proposals on the table as fast as possible. Your funnel looks like an upside down pear. Fat at the top and middle. Skinny on the bottom. Every now and then, a deal drips out of the bottom and someone wins a sale.
This is a sign your sales team doesn’t know how your customers buy. They are focused on the macro decisions your customers make. They are focused inward out. They ignore all the micro-decisions a buyer makes when they go through a purchase process. They are not outward in.
What is A Micro-Decision?
A micro-decision is a series of questions a buyer asks himself along the journey. For example, let’s say you are a Director of IT and your servers crash at a branch office. An “event” has occurred. The macro view suggests the Director is going to look for a solution to the crash. While this is true, it fails to capture what the buyer is really thinking:
- What happened?
- Is this going to happen again?
- Is it impacting my customers?
- Is my neck on the line for this?
- How quickly can this be fixed?
If the buyer’s cannot answer these questions, your chances of winning are low. Your rep is focused on a server crash as a compelling event. Meanwhile, the buyer has decided it’s a one-time event and isn’t a real issue. The deal is dead, but sits in your forecast while the rep tries to push a rope.
Validating your customers’ buying process is the solution. You need to research past, present and future customers.
The past are your existing accounts. They know a lot about you. You’ve helped define the solution to their problem with your product.
The present are opportunities. These are people currently evaluating whether or not you can solve their problem. Their view is slightly less tainted.
The future are prospects. These folks have little to no knowledge you exist. Their view of the market problems and how to solve is pure. Go heavy here. These conversations bring the greatest insight.
How Can You Do This?
Here are the broad strokes of how to complete this:
- Start with a generic buying process (see image)
- Gather an expert panel to create the micro-decision map
- Interview the past, present and future to validate/invalidate their micro-decisions
- Implement the map to measure the buyer’s journey properly
- Forecast accurately
It takes months of hardcore research to do this properly. Your long-time sales reps will think they know what the buyer is thinking. You will be surprised by what you find.
Companies who nail this have forecasts that exceed 75% accuracy.
Here is a tool to prevent you from getting slaughtered in your next board meeting. It is called The Sales Forecast BS Detector. It allows a CEO to determine how big of a problem forecasting is. If The Sales Forecast BS Detector reveals a problem, consider implementing the outward in process above. If the tool suggests the forecast is accurate, don’t worry about forecasting. It is good enough.
Author: Ryan Tognazzini
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