How to Enhance Sales Forecasting with Decision Theory

Sales Forecasting Made Easy

As an individual who works a lot with product marketing and sales. I have always wanted ways to see if my economics background could be applied to sales forecasting and revenue projections. Besides, marketing is kind of a big deal in driving revenue!

Sales forecasting is a crucial aspect of any business's success. Accurate revenue projections and achieving sales targets are top priorities for sales teams. Surprisingly, despite the widespread use of predictive analytics, many organisations still struggle with this task. Around 2016, over 85% of companies raised their revenue forecasts, but fewer than 60% of salespeople met their annual sales quotas. So, how can we improve this process and bridge this gap?

Embracing a Data-Driven Approach

To tackle this challenge effectively, it's essential to consider various factors that influence sales forecasting. It goes beyond the surface traits of salespeople, such as extroversion and confidence. It involves abstract variables like personality, perception, and decision-making. Incorporating qualitative data and expert intuition can potentially revolutionise the way we forecast sales.

Understanding the Forecasting Process

Most organisations use a standard process to forecast future revenue, typically within a 90-day window. This process involves assessing factors like the sales cycle stage, opportunity value, probability of sales occurring, and the expected close date. While CRM systems and sales forecasting tools have improved accuracy, there's room for further refinement.

Key Parameters for a Base Rate Forecasting Model

For a robust forecasting model, you need to consider several key parameters:

• Monthly and quarterly run rate, which is recurring revenue from existing customers.
• New business opportunities in the sales pipeline, including estimated value and close date.
• Estimated closing rates at each stage of the sales cycle.
• Historical sales closing ratios and velocity.
• A forecast adjustment process to account for won, closed, or delayed business.

Additionally, long-term forecasts (one to five years) should consider macroeconomic factors, household disposable income, cyclical business trends, product lifecycle, supply chain disruptions, and historical sales data.

The Role of Probabilistic Modeling

Applying Deming's PDCA (Plan, Do, Check, Act) process can continually improve sales forecasting. It involves assigning probabilities to various stages of the sales cycle and individual team members based on their forecasting accuracy. This iterative approach helps in building a realistic forecast and adjusting it based on actual results and not just thump-sucking them!

Accounting for Personality Traits and Decision-Making

Approximately 80% of salespeople fall into the extroverted "Driver" or "Motivator" category. These personalities often exhibit overconfidence and low attention to detail. Recognizing these traits is essential to avoid unrealistic forecasting. Common decision-making errors, such as relying on "best-case" scenarios or forecasting overly optimistic sales cycles, can also lead to inaccuracies.

Validation and Continuous Improvement

Regularly validating your forecasting model against actual results is crucial. This involves monthly meetings to review sales results, forecasting assumptions, and necessary adjustments. Analysing past forecasts versus actual sales results can identify performance issues and help refine the model over time.

Embrace a Probabilistic Model

Instead of relying solely on expert intuition, embrace a consistent probabilistic model. This approach reduces bias and inconsistency and aligns more closely with data analytics, ultimately leading to more accurate forecasts.

Incorporating Dark Data

We live in a? data-driven world, and leveraging both structured and unstructured data sets can enhance forecasting accuracy. New big data software platforms make it easier to incorporate "dark data." However, regardless of data sources, a consistent process validated against actual results remains paramount and I also go with a benchmark approach to this and sort of compare actual vs. variance.

As a wrap, leveraging data for applying decision theory economics to sales forecasting can significantly improve accuracy. By understanding personality traits, and decision-making errors, and embracing a probabilistic model, organisations can optimize their forecasting process and achieve more reliable results. Accurate forecasting is not just about numbers; it's about making informed decisions that drive business success!