Sales Forecasting Notes

Sales Forecasts

Purpose of Sales Forecasts

  • Helps a business to plan effectively.
  • Determines how much stock is needed.
  • Informs the HR plan, indicating how many employees are required.
  • Schedules the quantity and type of production.
  • Informs cash inflows in cash flow forecasts.
  • Reduces uncertainty in business operations.
  • It's more difficult for startup businesses to forecast sales compared to already established business because they have no sales data.

What a Business Might Want to Predict

  • Future sales of products.
  • The effect of promotions on sales.
  • Potential changes to the size of the market in the future.
  • How sales change at different times of the year (e.g., seasonal products).

Data Sources for Sales Forecasting

  • Market research.
  • Information provided by managers.
  • Historical data within the business.

Extrapolating Data Trends to Predict Future Sales

  • Time series analysis: Assumes past figures are a reliable indicator of future trends.
    • Okay if trading conditions are stable or predicting short-term trends.
    • Useful to know overall trends and seasonal/random fluctuations.

Consumer Trends

  • Seasonal variations: Examples include hotels and ice cream, which see increased sales during specific seasons.
  • Long term trends: Shift from traditional TV consumption to online streaming services.
  • Fashion: Affects clothes, shoes and music

Economic Factors

  • Increasing GDP:
    • Leads to increased consumer confidence and more spending.
    • Consumers buy more luxury goods and fewer inferior goods.
  • Decreasing GDP: Results in less purchasing.
  • Interest rates:
    • Decreasing interest rates encourage more borrowing and spending, especially on luxuries like new cars and houses.
  • Inflation:
    • Too high inflation leads to less spending due to uncertainty in prices.
  • Unemployment:
    • More unemployment means less earning and spending.
  • Exchange rates:
    • Appreciating exchange rates benefit exporters.

Competitor Actions

  • Short-term promotions by competitors can affect sales.

Stability and Forecasting

  • Stable consumer trends and business environment make it useful to extrapolate and predict future sales.
  • Changing economy, trends, and new competitors make sales forecasting difficult.

Sales Calculations

  • Example: Mr. Landry’s Pizzas Ltd.
    • 2019 sales revenue: 400,000400,000
    • 2020 sales revenue: 750,000750,000
    • Percentage change in sales revenue from 2019 to 2020:
      750,000−400,000400,000×100=87.5%\frac{750,000 - 400,000}{400,000} \times 100 = 87.5\%

Time Series Analysis

  • Assumes that past figures are a reliable indicator of future trends, especially in stable trading conditions or for short-term predictions.
  • Useful for identifying:
    • Overall trend.
    • Seasonal fluctuations.
    • Cyclical fluctuations.
    • Random fluctuations.

Factors Impacting Sales Forecasting

  • Consumer Trends:

    • Seasonal variations (e.g., electricity consumption, hotels, ice cream, Christmas trees).
      • Holiday seasons and summertime affect sales.
    • Long-term trends (e.g., move from terrestrial TV to online subscriptions, 'Work from home' impact on coffee shops, online/app-based banking & finance).
    • Fashion (e.g., clothing, shoes, music tastes).

  • Economic Factors:

    • Economic growth (GDP): Increasing GDP leads to higher consumer confidence and spending, with more purchases of luxury goods and fewer inferior goods.

  • Negative GDP (recession) might cause decreased purchasing and sales.

    • Interest rates: Decreasing interest rates encourage borrowing and spending, increasing purchases of new cars, houses, or expensive holidays.
    • Rising interest rates might cause the opposite pattern.
    • Inflation: High inflation leads to less spending due to price uncertainty.
    • Low levels of inflation are generally acceptable.
      • Hyperinflation can be detrimental.
    • Unemployment: Increasing unemployment reduces earning and spending.
    • Exchange rates: Example: If the £ strengthened against the ฿ (Thai Baht):
      • A Thai rice exporter may see increased purchases from the UK due to cheaper prices.
      • A UK-based holiday company selling holidays in Thailand may experience cheaper hotel sales and more sales overall.
      • A UK chocolate manufacturer selling in Thailand may see decreased sales due to more expensive products.

  • Actions of Competitors: Sales patterns are affected by competitors' actions, such as short-term promotions or new businesses entering the market.

Conditions for Effective Sales Forecasting

  • Extrapolating data to predict future sales is useful if consumer trends and the business environment are stable.
  • Sales forecasting becomes more difficult if the economy is changing, fashions are evolving, and new competitors are entering the market.

Evaluation Points

  • Sales forecasting can use simple time series analysis or more sophisticated modeling and market research data.
  • Some industries have easily predictable trends, while others, like consumer technology, are less predictable due to rapid evolution and new competitors.
  • Improving economic conditions won’t affect all businesses equally; luxury supermarkets may see increased sales, while budget supermarkets may see decreased sales as consumers upgrade their purchases.
  • Simply extrapolating data may not be a good indication of future sales, and a wide range of other factors should be considered.

Difficulties in Sales Forecasting

  • Volatile consumer tastes and preferences.
  • Range of data available; sometimes more data makes analysis difficult, especially for smaller businesses.
  • Subjective expert opinion from those making the forecasts.