3.3.1 Quantitative sales forecasting

Question 1

Correlation:

Answer 1

• relationship between variable and how strong
  e. g. positive, negative, none (none = close to 0)
• stronger = closer to line of best fit (regression line)
• scatter diagram
• a statistical technique used to establish strength of relationship between 2 variables

Question 2

dependant variable

Answer 2

y-axis

-being influenced

Question 3

independant varibale

Answer 3

x-axis

-one causing other to change

Question 4

Line of best fit/regression line

Answer 4

• forecast sales & identify factors influencing demand

- strong correlation = relationship used to make marketing predictions/decisions

Question 5

positive correlation

Answer 5

• direct relationship
• close to 1
• as one increases so does other

e.g. sales & advertising or income & sales

Question 6

negative correlation

Answer 6

• inverse relationship
• close to -1
• as one variable increases other decreases

e.g. price and demand or as interest rate rises there is a fall in demand for new house

Question 7

4 Pros of using correlation to forecast sales:

Answer 7

1-predict sales & demand factor
2-link = influenced for benefit of business
3-simple technique & useful for tactical thinking
4-appears regularly = chance correlation exists

Question 8

Cons of using correlation to forecast sales:

Answer 8

• uncertainty = wrong extrapolation
• past wont repeat (dynamic)
• doesn’t show cause and effect
• coincidental links (doesn’t casual links)
• shows link (hard to distinguish between cause and effect)
• need to find casual link by looking at other factors (treated with caution = impossible to isolate factors)

Question 9

Extrapolation

Answer 9

• extending line of best fit (dotted line)
• predict future levels such as sales
• analysing trends in past data

Question 10

3 Affects to extrapolation

Answer 10

1-new competitor
2-increase price
3-external factors

Question 11

Pros of extrapolation to forecast sales:

Answer 11

• quick and easy to implement
• accurate = based on past sales trends (static)
• quantitative target to predict future sales

Question 12

Cons of extrapolation to forecast sales:

Answer 12

• doesn’t account for external factors
• assumes past repeats itself - may not be likely
• not useful for some markets (dynamic) (fast moving consumer goods)
• not statistically valid
• ignoring significant outliers

Question 13

Moving averages

Answer 13

• time series analysis
• statistical calculation of an underlying trend in data

3 period: (add 3 together divide by 3)

Question 14

why is a moving average useful:

Answer 14

• useful when dealing with erratic/personal data
• average of multiple time periods
• minimises effect go extreme value as an average taken
• used to emphasise direction of a trend & reduce ‘noise’ that can confuse interpretation

Question 15

Analysing markets

Answer 15

• looks at several periods at a time & averages out the data
• helps iron out all peaks/troughs in demand = gives more accurate figure of whether sales have risen/fallen in market over time

Question 16

variation

Answer 16

sales in a specific time period - moving average sales

Question 17

how is a sales forecast useful

Answer 17

1. create cash flow forecast = data can be used to predict receipts and repayments
2. create revenue budgets = predict budgeted revenue and cost data
3. create operations plan = used for production/operations plan as data can be used to predict scale of production = stock requirements and likely C.U
4. create a HR plan = data can be used to predict staffing levels required

Question 18

benefits and drawbacks of using extrapolation

Answer 18

benefits:
- simple method of forecasting
- not much data required
- quick and cheap

drawbacks:
- unreliable if significant fluctuation in historical data
- assumes past trend will continue to future (unlikely in many competitive business environment)
- ignores qualitative factors e.g. changes in trends/fashion

Question 19

key factors affecting sales forecasts:

Answer 19

1. consumer trends
   - demand in many markets changes as tastes/fashion change
   - affects overall market demand and market share of existing competitors
2. economic variable
   - sensitive to change in variable (exchange rates, interest rates, taxation)
   - overall strength of economy GDP growth important
3. competitor actions
   - hard to predict
   - often significant reason why sales forecasts are over-optimistic

Question 20

situations where sales forecasts are likely to be inaccurate

Answer 20

• new business (start-up = difficult to forecast)
• market subjective to disruption from technological change
• demand is sensitive to change in price/income (elasticity)
• product is fashion item
• significant changes in marker share(new market entrants)
• management demonstrated poor sales forecasting ability in past

Question 21

what makes quantitative techniques more effective in forecasting sales

Answer 21

1. business mature (lots of past data = identify trends)
2. industry mature (rapid change = less likely)
3. stable external environment (economy, technology,competition and legislation less likely to change)