Guide
Mar 15, 2021
While data transformation may be on the radar, there are steps that can be taken today to both improve accuracy as well as pave the way for greater forecast accuracy in the future. Learn how in this blog.
Growth is good! It’s what you’ve been aiming for in your business. It means more customers, more revenue and more resources - and it establishes you, the sales leader, as a critical member of the leadership team.
However, with growth comes greater responsibility. The larger your company becomes, the more critical careful planning becomes. Future investment in people and resources hinges on the number you call; you need to provide better financial guidance to investors to build the business, inventory needs to be stocked, and quotas need to be allocated more accurately as compensation spend is becoming erratic. You have some data, but how do you leverage it? What can you do now to make a difference? And what can you do when your business is just at the start of a digital transformation process to improve forecast accuracy and at the same time prepare for the digital evolution of your business?
How To Increase Sales Accuracy
There are some immediate steps that you can take, even before a full data transformation, that will make an impact on the accuracy of your sales forecasts. A little work on the back end will yield huge results in accuracy, and the data is usually already at hand.
Many pre-data transformation companies have built some type of pipeline analysis. Pipeline analysis involves looking at your sales funnel to understand the deals that are active, but this usually gives up to a six-month view of deals (at most). With a little analysis of historical close rates by product/customer/territory type, you can assign probabilities to each sale in the pipeline to derive a baseline. However, you need to fill out the longer-term projections to supplement the shorter-term pipeline forecast. Most likely, some historical trending is needed. Is your business seasonal? Do your Q4 sales usually increase by a certain percentage? The key ingredient in forecast accuracy is the leading indicator. A leading indicator is a data trend that can be leveraged to predict the future outcome for sales as a whole.
One recent pre-transformation client we worked with went through an exercise of analyzing historical data to identify leading indicators that could improve the accuracy of forecasts (note: that this is what an AI system would do). They found that two territories – one urban and one rural – tended to lead order volumes by six months. These two territory growth rates could then be used to more accurately gauge where the other territories would be trending much later.
Another client we worked with was not able to find leading indicators in their data to increase forecast accuracy, so they created them. This software company needed to allocate implementation service resources based on demand and it took time to staff-up and train. With this in mind, the company allowed prospects to make “reservations” on future implementation resources with a pricing discount and no upfront obligation. While only 78 percent of those that made reservations eventually converted to revenue, it was still the most accurate leading indicator that the company had ever had.
While leading indicators are often unique for each industry and company (depending on the availability of data), the lack of availability is often a self-inflicted wound. While you may not have great leading indicators today for your forecast, why aren’t you now collecting the data that may be needed for a more accurate forecast next quarter or next year? One client we worked with in the software security industry recently enlisted sales representatives’ help in capturing data now that would serve in future forecasts. Reps were tasked with logging individual conversion rates, even though they had never done this before. The company anticipated that after logging conversion rates over a number of periods and combining this information with pipeline they would increase forecast accuracy.
While data transformation may be on the radar, there are steps that can be taken today to both improve accuracy as well as pave the way for greater forecast accuracy in the future. Since most forecasts revolve around the identification of leading indicators most, companies focus on either identifying them or creating them. By doing this work now you will increase forecast accuracy in the short term as well as in the long term.
