In part three of Anaplan’s five-part webinar series with the Sales Management Association, Brandon Kulik, national leader for Deloitte’s sales force effectiveness practice, joins Rowan Tonkin, Anaplan’s Head of Sales and Marketing Solutions, to talk about the importance of creating a comprehensive approach to sales forecasting. William Shakespeare once wrote “It is not in the stars to hold our destiny but in ourselves.” For many companies, accurately predicting the future in terms of competitive threats, customer trends, and their own sales forecast sometimes feels like relying on the stars. Sales leaders will readily admit that accurate and efficient sales forecasting is fundamental to making good business decisions, whether it’s prioritizing customer focus, adjusting sales coverage, or making account- and deal-based pricing decisions. Market research shows companies that adopt leading sales forecasting methodologies realize about a 20 percent improvement in forecast accuracy, helping them to achieve as much as a 7 percent year-over-year increase in deal size and an equally strong improvement in sales cycle time. In spite of these benefits, organizations struggle to move off of very manual, data-poor, unscientific processes. Why is this? We see several major challenges:
- Inaccuracy and mistrust of data. While spreadsheets and heavily manipulated source data have an advantage in that business users are familiar with them, they drive errors and inconsistent methodologies by functions or geos, as well as mistrust and rework.
- Usability. Lack of collaboration by product, sales, and finance teams is another forecast accuracy limiter. Manual or spreadsheet-based approaches inhibit the collaboration necessary in efficient processes.
- Subjectivity. This comes down to decision-making—it is knowing whether you are making the right decision or not. Companies forecasting with simple arithmetic pipeline weightings miss the nuances of the real drivers of accuracy, which may be headcount, pricing decisions, or route-to-market points of emphasis. Companies generally rely more on judgement and less on credible predictive analytics than they should.
Keys to success in sales forecastingStrong organizational coordination, automation, reliable data, and analytics-based methodologies are all critical to improving sales forecasting accuracy and efficiency.
- Collaborative. Leaders should seek rapid and easy-to-gather data, and synthesize input from a variety of sales roles, business units, and regions. The frontline teams are also of great value—they may provide you with a real pulse on the market you hadn’t considered before.
- Data-driven. Leverage predictive analytics to reduce the impact of subjectivity, which is often more backward-looking rather than forward-looking. Using common data definitions and baselines will foster alignment and save time.
- Real time. Investing in real-time capability to course-correct or reforecast allows sales leaders to quickly gain insight so they can make more informed decisions. It enables them to quickly and accurately update the forecast based on demand or market changes.
- Single-source, multiple views. Enhance visibility into rep, region, and company performance, and better align different business functions across the organization.
- Learning and improving. Use the insights provided by an improved sales forecasting process to create more refined future forecasts where accuracy improves over time against a set of accuracy goals.