“The global potential market for copying machines is 5,000, at most.” IBM to Xerox in 1959.
After launching the first copier in 1959, Xerox garnered revenues of over $500 million in five years’ time. It is hard to overstate the importance of an accurate forecast. IBM’s legendary statement to Xerox in the fifties is a case in point. Sales teams worldwide have relied on market forecasts for a better understanding of the organization’s go-to-market efforts. Although recent world events make forecasting a herculean task for all involved, accurate forecasts are not only a means of generating higher revenue, but they also lend credibility to the forecasting organization/solution in the market.
Sales forecasting data finds value across the enterprise. While the sales function leverages it for revenue generation from new accounts and to maximize cross-sell and up-sell opportunities from key accounts, other functions also find forecasts effective. Planning of production cycles, setting financial budgets, material purchases, territory planning, channel partner strategies, and many other key enterprise activities rely on accurate forecasts. Let us look at forecasting from the viewpoint of the sales leadership and how a dynamic forecasting tool can support that agenda.
Chief sales and revenue officers bear the primary responsibility of enhancing enterprise profitability and growth. The organization looks up to them to provide direction on when, how and with what to enter new markets, as they strategize on ways and means to achieve corporate objectives. An accurate sales forecast empowers sales leaders in strategic decision-making as they have precise insight into sales numbers vs. market performance.
Simple, predictive and iterative – Three must haves for forecasting success
With increasing complexities of business and technology ecosystems, simplicity is evolving as a key mantra for enterprise leaders. Sales leaders are more than ever on the lookout for forecasting solutions that can enable them to access market and sales insights intuitively without much technical intervention. Tools that don’t require technology expertise in terms of configuration, deployment or operation are highly favored for their ability to give access to insights and allow easy conversion of customer data, while bypassing tedious data preparation processes. Understanding key data drivers simplifies strategic decision making.
Modern forecasting tools that leverage machine learning capabilities have the ability to Such tools enable sales decision makers to evaluate diverse scenarios, using huge volume of data, and drive future outcomes with high accuracy. The sales teams can be made more accountable for sales closures, and sales leaders can better identify potential risks and over-commits. As the volume of data increases with continuous usage, the accuracy of future forecasts improves over time.
Many organizations also look at developing a forecasting model that can be customized to their unique requirement. In such a scenario, an iterative, detailed and expertise-driven approach can be taken with the right tool. Running automatic analysis on huge volumes of data can uncover unseen insights. The sales leadership can then choose the best model for their needs, tie forecasts to territories, quotas or incentives, and analyze trends over time, regions, teams, or products. The iterative nature of the forecast ensures continuous learning as the forecast capability evolves over time to deliver further enhanced insights.
A success story
A top global software and cloud computing company was facing several sales constraints – unoptimized territory planning, high cost of sales operations and random budgets that were not aligned to revenue targets. Implementing a dynamic sales forecasting solution, the software giant was able to bring down sales coverage across 1,000 territories by 40 days, with granular insights into sales regions and sub-regions. Dynamic scenario planning enabled faster closures on sales quotas.
Sales forecasting enables the creation of a realistic picture of what to expect in the immediate future, not having an optimum model for the same can prove to be costly for businesses. However, with the surplus of tools available in the market it is not easy to select the one most relevant for the business and its unique requirements. Data driven sales forecasting tools can deliver better outcomes, empowering the sales team to provide accurate deal commits and closures.