How a new sales methodology accelerated indirect sales
The national accounts team at a global insurer asked Korn Ferry for help recapturing market share, resulting in a 52% rise in win rate.
Does sales forecasting accuracy seem elusive and difficult to predict under the best of circumstances? If so, you’re in good company. Fewer than 25% of sales organizations have a sales forecasting accuracy of 75% or greater. And inaccurate sales forecasting causes plenty of trouble when it comes to predicting sales performance and meeting revenue goals.
There’s a significant payoff to getting sales forecasting right. We’ve found that organizations with a dynamic (formal, structured and continuously improved) review process increase their win rates of forecasted deals by 17% versus those that take a less formal approach.
In our Second Annual Sales Operations and Technology Report, we identified the four greatest challenges that sales organizations face in forecast accuracy. Read on to learn about these sales forecasting challenges and the sales operations best practices that will help you address them.
This classic challenge stems from sellers who rely on gut feelings about an opportunity’s possibility rather than objective data. However, gut feelings can often be wrong - that’s why more than 40% of sales operations leaders identified seller subjectivity as their greatest challenge to forecast accuracy.
While a seller’s instinct is an asset, it isn’t a fully accurate source of truth when it comes to sales forecasting. Sellers are often too subjective on deals that are close-but-not-quite-closed opportunities, particularly as they struggle with a lack of pipeline and increasingly limited time to sell.
What sales leaders can do: Invest in coaching, processes and technology — and bring them together. Sellers learn how to objectively evaluate an opportunity when their managers coach them to evaluate opportunities based on their prior experience, buyer roles, and decision-making process using accurate and current data.
CRMs are prevalent in sales organizations. But many sellers see entering data into their CRM as an administrative task that takes away from selling. As a result, CRM data is often poor.
Low-quality data causes more than short-term pain for current deals. It causes long-term sales forecasting challenges because it prevents sales organizations from having historic information to feed predictive patterns. Only a little more than 25% of sales operations leaders indicated that they had a sufficient set of tools to supplement CRM functionality.
What sales leaders can do: Employ a data strategy. By investing in a documented plan to manage and use data as a sales asset, you emphasize data quality and process and system integration. Then demonstrate to sellers why keeping their data up to date matters. First, make sure sellers’ data is accurate. And find a tool to connect that data to show how past performance can apply to current opportunities.
Sales teams don’t lack for investment in technology. On average, sales organizations routinely use more than 10 sales technology tools and plan to add four more in the next year.
The challenge in sales tech and sales forecasting comes more from a lack of planning and integration. Nearly 30% of survey participants indicated that their sales technology stack integrated closely with all of its applications, including CRM. Roughly the same percentage believed that their sales tech stack seamlessly supported a seller’s daily routine.
What sales leaders can do: Integrating technology tools to work together seamlessly requires time, effort, and resources. This is a case where you need to go slow to go fast. By bringing together the right resources and following a structured plan for implementing sales tools — along with data strategy and an enablement plan — sales organizations can make sure that their CRM and other critical tools play nicely together and lead to better performance.
Sales managers report spending twice as much time on forecasting and internal reporting as they do on coaching salespeople. Yet more than 30% of our study respondents indicated that sales management rigor is one of the major challenges to sales forecasting.
This is where the challenges we’ve laid out so far all culminate into a bigger issue.
Seller subjectivity leads to poor data quality. Inaccurate data makes predicting patterns impossible. And in many organizations, technology tools aren’t integrated. That’s a difficult base upon which to build an accurate forecast and a successful, rigorous sales process to underpin it.
But sales managers can also take the lead in modeling sales operations best practices to show sellers a better, more consistent approach. They should implement a formal cadence for forecast review. Additionally, they should invest in a data strategy and be more strategic with their sales technology.
What sales leaders can do: Work with sales managers to determine where they struggle to put rigor into the sales forecasting process. Help them define a cadence for forecast reviews — not just between leaders and sales managers, but between managers and sellers. Define expectations and develop a plan to improve data quality. Finally, invest in enablement, giving them structure around the process, the technology they need to use, and the schedule they need to follow. This structure will make reviews a repeatable behavior rather than an initiative.
Accurate sales forecasting is a science as well as an art. But it doesn’t have to be complicated.
By meeting these four major challenges head-on, you’ll equip your frontline sellers, managers, and the operations team that supports them with the tools they need to learn how to develop sales forecasts.
Get in touch for help coming up with a formal sales forecasting process that works for your organization.