As I wrote about in a previous post, there are parts of the forecast you can trust and there are parts that require your energy and scrutiny. It’s virtually impossible to get everyone to trust the entire forecast, but you can get your company to trust parts of your forecast. How can you segment your forecast, so that you can spend your energy on the right parts?
Wouldn’t it be nice if you had a simple pie chart telling you where to focus?
Imagine that you had a simple pie chart that told you which parts of your forecast were trustworthy, which ones were medium risk, and which ones were completely untrustworthy? You would count on the green ones, focus your energy on the yellow ones and put the red ones in upside.
Today, we’re announcing Right90 Trust Analytics™, which provides exactly this capability. You can watch our Right90 Trust Analytics demo here as well.
Use historical performance to evaluate trustworthiness
By measuring the historical performance of the forecast in your organization, you can determine which segments of the forecast you can trust. For example, if a particular forecaster has historically been 95% accurate when forecasting in a specific region, then you are more likely to trust that person’s forecast for that particular region. In fact, there are four key elements of historical performance on which you can gauge trust in the forecast.
To that end, Right90 Trust Analytics features the ABCs of Trust:
- Accuracy: A percentage measurement of actual shipments versus the forecast (as of a specific time period prior to actual shipment).
- Bias: A percentage measurement, of the positive or negative variance between actual shipment and forecast. For example, a bias of -20% would mean the forecast was 20% below actual shipments, whereas a bias of 35% would mean the forecast was 35% above actual shipments.
- Completeness: A percentage measurement of how many forecast items have been updated in a specified time period. For example, if 80% of all accounts’ forecast have been updated in the past 2 weeks, that represents 80% completeness of the account based forecast.
- Consistency: A positive or negative measurement of forecast variance over time. For example, a sales rep with bias of 20% in Q1 and bias of -25% in Q2 is less consistent than a sales rep with bias of 30% in both Q1 and Q2.
By combining all of these historical measures into a single measure called the Right90 Trust Factor, you can segment your forecast into elements that can be highly trusted (for example, a trust factor between 8 – 10), or have moderate trust risk (4– 7) and those that are highly risky (1 – 3).
Now that you have a single number on which you can gauge trust in forecast, you can focus your energy on the areas of your forecast on which you can have an impact. If there is a customer or set of products that is subject to a particularly risky forecast, spend your time to ensure that order will come in or so that operations builds the units required to meet the customer demand and win that business.
Real value ($)
Enterprises weigh each of the ABCs to determine the relative importance in quantifying trust. The system automatically computes and presents the trust factor based on the weighting. Users can drill down into the data and make sales performance management, decisions, plan inventory and reduce risk.
By aggregating and segmenting the Trust Factor across the entire forecast, the patented technology of Right90 highlights the specific areas of risk. An executive can drill into the risk areas, judge the forecast, take necessary action, and create a trusted actionable sales forecast. Global 1,000 companies are utilizing a trusted, actionable forecast to make the key business decisions in time to immediately impact the business.
Many companies don’t realize it, but there is gold in the forecast. The hard part isn’t mining the gold – most sales executives can do the right thing if they know where to focus their energy. The hard part is knowing where to look for that gold. With the Right90 Trust Factor, you have that capability. In the final post of this 3 part series, I will describe companies that have you used a trusted forecast to increase revenues and forecast accuracy while decreasing inventory levels.
Tags: Best Practices, change analytics, revenue performance management, sales forecasting, trust