Sales forecasting: different types of products in the market

Existent products characteristic

Products that have been in the market for a significant period

Sales forecasting methods: historical data extrapolation

Use past sales data to predict future performance ( trends, seasonal patterns)

Sales forecasting method: salespeople input

Gather insights from the sales team. Leverage their knowledge about customer preferences, market conditions and upcoming sales opportunities

Advantage: reliable data

Established products provide a wealth of historical data

Advantage: predictable demand

Consistent sales patterns

Challenge: market saturation

Changes in the market conditions or increased competition

Challenge: product lifecycle

Must consider the stage of product lifecycle ( growth, maturity, decline)

New products in company characteristics

Products new to the company but existing in other markets

Sales forecasting methods: market extrapolation

Use data from other markets where the product is already available

sales forecasting methods: corrections and adjustments

Make adjustments based on specific company and market factors

Advantages: benchmarking

Availability of data from other markets provides a starting point for forecasting

Advantages: market insights

Insights from other markets can inform marketing and sales stratégies

Challenges: market difference

Variations in market dynamics can lead to inaccurate forecasts if not properly adjusted

Challenges: adoption rates

Predicting how quickly the new product will be adopted in the new market

Totally new products: characteristics

Products that are completely new and have no direct market history

Sales forecasting methods: intuitive method

Use expert judgment and intuition to predict demand from experts

Sales forecasting methods: acceptance test

Conduct tests with potential clients to garage interest and potential demand

Advantages: innovation potential

New products can capture new market segments and drive growth

Advantages: direct feedback

Acceptance tests provide direct feedback from potential customers

Challenges: lack of data

Absence of historical data makes accurate forecasting difficult

Challenges: market uncertainty

Unpredictable customer response and market conditions can affect demand