Benefits & Limitations of Sales Forecasting (DP IB Business Management): Revision Note

An introduction to sales forecasting

• Sales forecasts predict future revenues based on past figures, including

  • The volume and value of sales

  • The size of the market

  • Sales as a result of promotional activity

  • Sales as a result of cyclical factors

• Sales forecasts are an important tool to support planning and can improve the validity of cash flow forecasts
  

• Businesses use sales forecasts to determine resource requirements

A typical sales forecasting process

• In order to construct effective sales forecasts, businesses use a range of techniques, including

  • Market research

    • Can include primary and secondary research sources

    • May rely on test marketing to understand customer reactions

    • Sample size needs to be sufficient to provide high data confidence

  • Extrapolation

    • Using historical data to identify and extend trends to predict future sales

    • Typically uses a line of best fit to make predictions

    • Requires strong correlations between data sets such as spending on promotional activity and sales revenue

  • Time series analysis

    • Identifying underlying trends from past sales figures recorded at regular intervals

    • Must take into account seasonal, cyclical and random variations
       

Factors that require sales forecasts to be adjusted

• Developing accurate sales forecasts is a skill and requires an understanding of several factors which can influence the reliability of the forecast

Consumer trends

• Seasonal variations

  • Demand for certain products fluctuates during specific times of the year

  • Events like religious festivals, holidays and annual occasions influence what consumers buy

  • Example: Sales of basic homewares rise each September when students begin university

• Fashion

  • Trends driven by celebrities can cause short-term sales spikes

  • Their influence is often sudden and unpredictable

  • Example: In September 2021, Boohoo’s sales surged by over 400% after Megan Fox wore one of their dresses at a high-profile event

• Long-term trends

  • Consumer preferences and values evolve over time

  • Increasing demand for eco-friendly products is shaping sales forecasts

  • Example: In late 2022, Ford raised its electric vehicle sales forecast by nearly 70% to meet growing demand

Changing economic conditions

• Economic growth

  • When the economy grows, rising consumer incomes lead to higher-than-expected sales

  • During a slowdown or recession, consumers spend less, and sales may fall below forecasts

• Inflation

  • Rising prices reduce consumers’ spending power

  • Businesses may lower sales forecasts during high inflation and raise them when inflation falls

• Unemployment

  • Unemployment often rises in a recession and reduces consumer spending

  • Sales of non-essential or luxury items may fall as consumers prioritise essentials

• Interest rates

  • Higher interest rates make borrowing more expensive for consumers

  • Businesses selling products often bought on credit (e.g. homes, cars) may lower forecasts

  • Example: Property sales in 2023 are expected to drop to £1.01 billion from £1.27 billion in 2022, prompting estate agents to adjust forecasts

• Exchange rates

  • A weaker pound makes UK exports cheaper for overseas buyers

  • Businesses that export or serve tourists may raise forecasts in response to expected demand

  • Example: Visit Britain predicts 14% more tourists will visit the UK in 2023 compared to 2022

 Changing competitive conditions

• Actions of competitors

  • Sales forecasts must take into account both short-term competitor actions like sales promotions and long-term strategies such as product changes or expansion

  • Competitor behaviour is hard to predict, making past data less reliable for future forecasting

  • Example: Marks and Spencer announced plans to open 20 new stores in 2023, partly in response to rival closures like Debenhams

Evaluating sales forecasts

• Sales forecasting usually involves the use of past data to predict the future

• In the short-term, sales forecasts are likely to reflect the recent past

• Longer-term sales forecasting is often more problematic as several factors affect its reliability

Why sales forecasting is difficult

• Effective sales forecasting requires skill, time and the accurate use of timely data

  • Smaller businesses in particular may lack the experience or specialised personnel to construct, analyse and interpret sales forecasts

    • It is difficult to avoid experience bias (e.g. opinions of the future based on experiences in the past) 

    • Businesses may face problems in constructing sales forecasts that ignore the priorities of key stakeholders

• The future seldom repeats the occurrences of the past

  • Sales forecasts will rarely reflect the full range of external influences that can affect future inflows, such as fashions, trends and the actions of competitors 

• Too much data blurs the analysis

  • Internal data, such as previous sales figures, will be a key source of information when constructing forecasts

  • Selecting the most appropriate external data is extremely challenging and requires careful evaluation

Advantages of sales forecasting