Notesale: Turn your study into money

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Chapter 8: Analysing Markets and Marketing
Analysing the market
- Quantitative analysis examines statistical information in order to draw conclusions about the
market
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Reasons for market analysis
Gathering evidence for devising a new strategy
- It is essential that a business has access to any data that can help it to adopt the right strategy as
mistakes could prove to be very costly if the strategy is not planned carefully
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- A business should also be gathering data regularly so that it can monitor the current and future
success of its existing strategies
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The value of market analysis
- Helps a business to understand its existing markets
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- Sales forecasting serves a number of purposes
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- Motivate staff by presenting them with a realistic but challenging target
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- More specifically, its main use is to coordinate the activities of the business and a range of
departments:
- Allows senior management to ascertain whether a proposed new strategy is likely to be
successful
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- Helps HR in its workforce planning, so that it can identify any potential shortages or surpluses of
labour or any training needs
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This ensures
that enough funds are available to each department
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Quantitative forecasting
- Usually relies on the assumption that the past will provide an accurate prediction of future
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- However, test marketing is also used to predict sales
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- Once test marketing has taken place, the firm will use the results to make a statistical forecast of
future sales
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- Useful way of gauging the popularity of the product without incurring the huge expense of a
national launch
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- Delay in national launch may endanger chances of a profit from new launches – rapidly
dangerous markets
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- Too much effort into test marketing may mean success cannot be maintained nationally
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- Extrapolation can be carried out visually, or by calculation, although calculation is recommended
for greater accuracy
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Predicting future sales based on moving averages and seasonal variations
- Moving average are calculated by combining data over a period of time in such a way that changes
caused by fluctuations are eliminated or at least reduced
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- There are three main types of fluctuation:
- Cynical: sales of must have products fluctuate according to the trade cycle, with above average
increases in boom periods and below average rises (even falling sales) in downturns and recessions
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It is possible to measure seasonal
variations by looking at the level of sales at a particular time of a year, in comparison to the trend
figure
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- Moving averages allow an organisation to calculate a trend scientifically using a series of
calculations designed to eliminate fluctuations within a series of data
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- If fluctuation is caused by seasonal factors, the moving average should be based on a perios of

exactly one year
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- By predicting trends, firms are able to forecast future sales
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- In order to predict future sales, it is necessary to calculate:
- The trend (overall pattern of change in sales over time)
- Seasonal variations (fluctuations in sales caused by the time of the year)
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Seasons and cyclical changes are rarely repeated exactly,
and random fluctuations are very unpredictable
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- Assumes that past changes will continue into the future and thus does not take into account
changes in the business environment that will influence sales, including changes within the business
itself
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Correlation
- Can show the degree to which factors such as price, the advertising budget, or even external
factors, such as the proximity of competitors, are linked to sales of a product
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- IV= x axis
- DV- y axis
- Once the points have been plotted, a line of best fit (regression line) is added
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- The line of best fit is used to forecast
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- From this point, a line is then drawn horizontally to the y axis in order to read the forecast value
of DV
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- Before drawing any conclusions that there is a link between two sets of information, it is essential
that a causal link is discovered
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- There is a tendency to assume that a company would prefer a high correlation
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- The regression line is every bit as important as the level of correlation
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Limitations of quantitative forecasting
- Past trends do not always continue in the future
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- External influences can vary over time
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- Internal policies or actions may change
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- Forecasts become more difficult the further they project into the future
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Qualitative forecasting
Methods of qualitative forecasting
The Delphi/Oracle technique
- Relies on the firm asking individual experts for their views
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Brainstorming
- Involves all individuals involved in product/service
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Individual hunch
- A particular manager will be held responsible for product/service being considered
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Reasons for using qualitative forecasting
- When forecast concerns a new product/business so there is no previous information on which to
base predictions
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- When there is no statistical indication of future sales
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- When factors influencing sales are not easy to quantify
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Limitations of qualitative forecasting
- Experts may be knowledgeable, but it is unlikely they will understand all aspects of a market
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- Easier to persuade a senior manager if forecast based on scientific methods
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Conclusion
- New businesses and managers in small firms are more likely to use this
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- Underestimation also causes problems
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Conclusion
- Although forecasts cannot be relied on, they give direction and targets for an organisation
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- More likely to be correct when:
- Product well established and market known
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- Forecasts made and agreed by those with day-to-day contact with market
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- Test marketing or back data give the firm a clear understanding of market
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- Time saved allows comparison of strategies, improving the quality of planning
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- Allows improvement of IN and EX communications, improving efficiency and understanding of the
market
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