Sales forecasting is one of the biggest challenges every business leader has to deal with. Month after month, sales teams come up with revenue forecasts that management teams have to accept while knowing there is at the most a 46% accuracy of meeting targets. This is a forecast that is less accurate than flipping a coin. Optimism is great, but for CEOs trying to set up expenses that are dependent on revenue, this is always a cause for concern. More and more businesses are turning toward predictive modeling specifically for revenue forecasting.
Predictive modeling reveals outcomes based on historical data, helps companies understand the complexities of the present, and combines these insights to predict what the future can be with greater accuracy. The value this brings can be better understood with a deeper look into traditional vs. predictive estimations.
The Traditional Approach to Revenue Forecasting
Until now, revenue forecasting relied heavily on human bias and spreadsheet models that lack sophistication and currency compared to technological advances available today. Traditionally, the sales approach to predicting revenue has consisted of these practices.
- Use the first quarter of the year to set monthly estimates for the rest of the year.
Cons: This does not account for seasonal fluctuations, product changes and unrelated factors like labor fluctuations.
- Use the previous year’s outcomes as the basis for setting new estimates. This approach can have greater accuracy as it factors in seasonal fluctuations and company-specific trends.
Cons: Outside influences are not considered, like new competitors, market fluctuations, staff changes, mergers and supply problems.
- Rely completely on the sales department’s predictions.
Cons: There is inherent human bias and error at work here since around half of the deals predicted by the sales team never close.
How Predictive Modeling Works for Sales
Predictive analytics can seem difficult to grasp since it brings together multiple sources and technologies, making sense of the overwhelming data available today. The components it pulls together include historical sales data, data mining, artificial intelligence and machine learning. It is a powerful data-driven approach that provides a holistic view to your key business influencers. Let’s look more closely at the details of how predictive modeling works for effective results.
1. Improves Sales Closing
Forecasting algorithms use machine learning on combined data points, such as internal customer data and win/loss ratios along with external data sets like customer revenue (in B2B sales), executive changes and social media activity. Predictive modeling reveals patterns from these large volumes of data at a speed that is not humanly possible. The relationships that are spotted in the data are then used to score each deal in the pipeline. The effectiveness of this can be seen from the 82% accuracy rate deal by deal compared to 46% by other methods.
2. Fine-tuning Pricing
A McKinsey article reported that 75% of a company’s revenue comes from its standard products, but estimated that 30% of the pricing decisions that a company makes does not deliver the best price. At any fixed optimal price point, there will always be customers who would pay more and customers who might buy if it was at a lower but still profitable price. An effective way to deal with this issue is to create customer segments to determine the best price point.
How customer predictions are used to generate action-based responses varies from business to business. Businesses could make a trade-off between customer segments that have a greater likelihood of making a purchase against a group that might be twice as large but with a slightly lesser probability. One thing to keep in mind is that predictive models need to be regularly re-trained so that they continue to deliver accurately in the promotion optimization system.
3. Better Customer Responsiveness
Businesses are always built around customers. Like the sales team, customer service is another agency that has high customer interaction, especially in the B2C space. These employees/representatives need to have access to the best tools for better upselling and cross-selling opportunities. Predictive analytic tools can pull out data from social media platforms to examine customer engagement, customize communication, and provide personalized recommendations. Brand giants like L’Oreal have been using this approach to engage users in creating brand awareness. This is an invaluable source to provide touch points to improve customer relationships.
Predictive modeling analyzes multiple sources of information for revenue forecasting that is more efficient, accurate and less biased than a sales team. With predictive analytics taking care of the forecasting, the sales representatives will have more time to focus on what they do best. If you are interested in more information or want to see how predictive modeling can improve your business, contact the team at SevenTablets today. Our analytics platform Sertics optimizes forecasting and predictive analysis, empowering better business decisions.
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VK studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India and earned a Master’s Degree in computer science at George Mason University.
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