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Enhancing Sales Forecasting with Linear Regression Analysis

This document outlines a framework for developing a tool that enables Ferris's marketing department to accurately forecast sales based on product attributes. Key pitfalls in traditional sales prediction methods are discussed, including misconceptions about using sales history. By focusing on relative measurements such as market share, price, and product attributes, we can better predict demand and respond to competitor behaviors. The process involves continuous updates and learning from errors, emphasizing that while forecasting is a science, it requires an artistic approach to adapt to dynamic market conditions.

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Enhancing Sales Forecasting with Linear Regression Analysis

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  1. Ferris Forecasting May 9, 2007

  2. Goal Build a tool that will allow our marketing department to accurately forecast sales based on our products’ attributes

  3. Linear Regression – Take 1 • 2 Fundamental Mistakes • Sales vs. Potential Market Share • Using sales history to predict demand provides a skewed view, as it includes the affects of stockouts by the competition • Using Potential Market Share removes this issue • ‘Absolute’ vs. ‘Relative’ Attribute Measurements • We originally used our absolute price (e.g. $35.00), MTBF (17500), etc. • It is not our absolute price that is important, it is our price relative to our competitors in the market segment

  4. Linear Regression • Minitab helps us understand most important attributes affecting potential market share: • Expected Market Share (Segment Demand / # of Products inSegment) • Relative Price (Our Price minus Expected Average Price) • Relative MTBF (Our MTBF minus Expected Average MTBF) • Relative Awareness (Our Awareness minus Expected Average Awareness) • Though other factors are at play, statistics show that these are the attributes that have the bulk of the effect on demand

  5. Linear Regression

  6. Forecasting Tool Known – From Simulation Assumptions – Must Predict Competitor Behavior Known – From our Product Attributes Results – From Regression & Assumptions Error – How good were our assumptions ? Underestimated competitor’s price cuts

  7. Process • Update Regression after each round • Update coefficients based on total rounds to date • Look for new factors that are beginning to affect demand • If the current factors converge for all products in the segment, other factors will more heavily impact demand • Learn from error • Did we make a bad assumption on competitor’s price / MTBF / etc. ?

  8. Key Learnings • Forecasting is ‘Art’ as well as ‘Science’ … can never perfectly predict competitive behavior • Must evaluate your product vs. competition (market conditions) • Process was simplified for us due to our primary involvement in a single market segment

  9. Questions? David Domnisch (302) 999-3240 Shannon Koerber (302) 695-1598 Kristen Falcone (302) 992-2195 Bill Potts (302) 992-2164 For more information, contact any member of our Ferris Family:

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