With over two decades of leadership experience, Arnab Mishra is a seasoned software executive and CEO of Xactly Corporation.
Sales forecasting—estimating the future sales of products or services based on historical data, market trends and other relevant factors—is important for any organization. According to Aberdeen, 97% of companies implementing best-in-class forecasting processes achieve their quotes, while only 55% achieved the same without.
Efficient and effective sales forecasting not only helps teams identify which products are in high demand and should be prioritized in marketing efforts but can also provide invaluable insights into potential challenges and opportunities across both product lines and the workforce. As businesses navigate increasingly complex economic pressures, the demand for more reliable and sophisticated sales forecasting methods has only grown in importance. By enabling your company to anticipate shifts and act decisively, accurate sales forecasting can give you a competitive edge, allowing you to strategize proactively and stay ahead of the curve.
The Challenges Of Precise Sales Forecasting
Today, consistently producing accurate sales forecasts poses one of the biggest hurdles confronting the sales industry. According to research from Gartner in 2023, 67% of sales operations leaders believed that creating accurate sales forecasts was harder than it had been in 2020, only three years earlier.
Inaccurate forecasts can develop a range of issues that can echo throughout the organization, impacting budgets, revenue opportunities, strategy, planning and overall business performance. According to my company’s research, while industry leaders recognize the importance of accuracy, 4 in 5 sales and finance teams report at least one missed sales forecast per quarter. The ramifications of missed forecasts can be substantial, potentially leading to challenges such as organizational restructuring or adjustments to benefits or incentive plans.
For comprehensive forecasting analysis, surfacing the correct data can offer value. However, our research found that the most common roadblock to accurate sales forecasting is reporting systems that can’t access historical CRM or performance data, followed by a lack of certainty about the source of pipeline data. These disparities highlight a pressing need for change in how forecasting data is navigated and interpreted.
So, what can leaders do to introduce change and overcome the sales forecasting hurdle? Let’s take a look at two strategies I have found to be effective.
Encouraging Cross-Functional Collaboration
In my experience, cross-functional collaboration is very important to improving internal workflows and forecast accuracy. Unfortunately, many sales teams struggle with processes not being collaborative enough, with internal roadblocks, or with departments being too siloed. To overcome these barriers and ensure consistency, start by ensuring your sales team has clearly established guidelines and procedures for creating and calculating reports from data.
I’ve found that the best practices for achieving optimal results within cross-functional teams involve several key strategies:
1. Clearly define roles within the forecasting process. For example, the sales team could own the opportunity pipeline data, the marketing team could contribute lead generation metrics, and the finance team could provide historical revenue data. Also, consider designating a “forecast champion” who can facilitate communication and ensure accountability across departments.
2. Create a shared vision and establish open communication channels. This can help foster stronger understanding and collaboration. Regular cross-functional meetings provide a forum for sharing, and using collaborative software platforms can further enhance transparency and streamline communication.
3. Pull from varied experiences across the business for a holistic view. For instance, product teams might offer insights into upcoming releases that could impact sales, while customer success teams can provide valuable feedback on customer trends and potential churn.
In my experience, implementing these tactics can help businesses foster a collaborative environment and ultimately improve the accuracy and reliability of their sales forecasts.
Implementing AI-Powered Tools
Investing in advanced data analysis and collaboration tools can also help businesses streamline their sales processes. I have found that AI-driven sales forecasting can reduce the need for time-consuming data analysis by automating the process with speed and precision. Rather than replacing your sales professionals, however, it’s important to consider how AI can enhance their capabilities, empowering them to overcome the complexities of sales forecasting and driving better results.
For example, organizations can utilize AI to enable more personalized customer interactions. Another powerful application of AI is in automating routine tasks, such as scheduling meetings, sending follow-up emails and even generating initial drafts of proposals. This can free up your sales professionals to focus on building relationships, understanding customer needs and closing deals.
Here are a few strategies you can use to unlock AI’s full potential for your sales team:
1. Invest in change management and build AI literacy within your workforce. Although everyone is talking about AI, for most people, these concepts can seem abstract or even worrisome. Part of effective change management is clearly communicating the benefits of the AI tools and how they can help improve your employees’ work.
I recommend building AI awareness through companywide literacy programs, with a focus on teaching your teams about AI’s application in sales. Include guidance on how they can interpret AI-generated insights and incorporate them into sales strategies.
2. Identify the best areas of application. Conduct a thorough assessment of current sales processes to identify areas where AI can have the most significant impact. This might involve analyzing bottlenecks, identifying repetitive tasks or pinpointing opportunities for improved data analysis.
3. Invest in AI tools and platforms that align with your business requirements and goals. This typically requires careful evaluation of different potential solutions. Consider factors like the AI platform’s integration capabilities, ease of use and cost.
4. Establish clear metrics to track the impact of AI on sales performance. Reviewing and analyzing metrics such as lead conversion rates, deal closure rates and overall revenue growth can help you assess an AI tool’s effectiveness and make adjustments.
As economic dynamics continue to evolve throughout this year, I believe precise sales forecasting will be an important tool for organizations aiming to remain competitive and resilient. Consider how you can embrace strategies like cross-functional collaboration and AI-powered tools to address challenges such as data silos, inaccurate reporting and inefficient workflows so that your sales team can forecast with confidence.
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