Forecasting Methods for Product Managers

Elements of Forecasting and Its Applications in Product Management

Image by Mirko Grisendi from Pixabay

Categories of Forecasting

  • Short-term forecasting: to take immediate actions, running experiments, quick improvements/fixes, delivering, or purchasing
  • Medium-term forecasting: for warehouse, inventory, and material supply planning
  • Long-term forecasting: strategic product planning, pivoting directions, maximizing returns, etc.

Forecasting Methods

  • Qualitative Methods
  • Time-series Methods
  • Causal Methods
  • Indirect Methods

Qualitative Methods

1. Delphi Technique

2. Jury of Experts

3. Sales-force Composite

4. Consumer Surveys

  • Complete Enumeration Method: This method works well when the consumer population is concentrated in one place. In this method, the interviewer will contact almost all potential product users to get probable individual demand (PD) and aggregates them to estimate the total probable demand (TPD). See below for a simple equation.
TPD = PD1 + PD2 + PD3 + PD4 + …… + PDn
  • Sample Survey Method: When the target population under study is large, this method can be helpful. The total probable demand is estimated by surveying a sample size of consumers and using the below equation.
TPD = (CR * TCP * AEC) / CS
  • CR is the Number of Reporting Consumers, TCP is the Total Consumer Population, AEC is the Average Expected Consumption from reporting consumers, and CS is the Number of Consumers Surveyed.
  • End-use Method: This method helps to estimate the demand for inputs by consuming industries. The forecaster usually builds a schedule of probable aggregate future demand for inputs by consuming industries keeping the desirable norms of product consumption fixed. The buyer takes the burden of demand forecasting. This method works well for industries that supply goods in bulk to industries.

Time-series Methods

1. Naive

2. Moving Averages

Image Courtesy: Fidelity

3. Exponential Smoothing

Image Courtesy: Fidelity

4. Trend Projections

Image Courtesy: Fidelity

Causal Methods

1. Simple Regression Methods

  • Variation in product sales in correlation with advertising expenditure, pricing, regional economy, etc
  • Sales of baby products depend on the number of births
  • Sales of home appliances based on new home constructions
  • Increase in vehicle traffic on roads leading to several jobs or an increase in income
Image Courtesy: Lorraine Li

2. Multiple Regression Methods

Indirect Methods

1. Analogy

  • F(t) is the probability of adoption at time t
  • f(t) is the rate at which adoption is changing with respect to t
  • N(t) is the number of adopters at time t
  • m is the total number of consumers who will eventually adopt
  • p is the coefficient of innovation
  • q is the coefficient of imitation

2. Input-Output

3. Top-down/Bottom-Up

Closing

References

  1. Product Forecasting — Wikipedia
  2. "How to Choose the Right Forecasting Technique" by John C. Chambers
  3. "Forecasting Best Practices For Product Managers" by Kate Kurzawska

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