OxfordAQA A-Level Business: Analysing future sales (9725)

Written for the OxfordAQA International AS and A-level Business (9725) specification, Version 1.1, for International AS exams May/June 2027 onwards and International A-level exams May/June 2028 onwards. Section 3.3.5 Analysing future sales closes the 3.3 Business analysis block, and every outcome on this page is International A-level only. Anyone still sitting the older 9625 specification should work from that document instead. Every business here is invented.

Companion pages: condensed revision notes and original practice questions. For the whole course, use the 9725 hub and the printable 9725 checklist; to find weak areas, try the free diagnostics.

What this section covers

Spec area (3.3.5 Forecasting sales)	What you must be able to do	Stage
Three period moving average	Calculate three period moving averages and use them to forecast sales	International A-level only
Extrapolation	Judge the value of projecting a past trend forward	International A-level only
Qualitative forecasting	Weigh the advantages and disadvantages of judgement-based forecasts	International A-level only
Value of sales forecasting	Explain why a forecast is worth producing	International A-level only
Difficulties of sales forecasting	Explain why forecasts are often wrong	International A-level only
Impact on decision-making	Analyse how forecasts shape decisions across the business	International A-level only

Where it is tested. Unit 3: Business analysis covers section 3.3. That exam lasts 1 hour 45 minutes, carries 80 marks and is worth 30% of the International A-level. It is built around two case studies, and according to the specification each one opens with a calculation worth 4 marks, so a moving average forecast is a natural candidate. Forecasting also reappears in section 3.4.6, where it is listed as one way to manage risk.

1. What a sales forecast is

A sales forecast is an estimate of the sales volume or sales revenue a business expects over a future period. It can be built in two ways:

• Quantitative: uses numbers, usually past sales, and looks for a pattern. The moving average method and extrapolation are both quantitative.
• Qualitative: uses judgement and opinion, such as the views of experienced managers, sales staff or customers.

Many businesses combine the two, adjusting a number-based forecast using the judgement of people who know the market.

2. The three period moving average

Raw sales figures jump around from month to month. A moving average smooths out these short-term ups and downs so the underlying trend is easier to see.

Method in steps

1. Add the first three periods of sales.
2. Divide by 3. Write the answer against the middle period of the three.
3. Drop the oldest period, add the next one, and repeat until you run out of data.
4. Look at how the moving averages change. That change is the trend.

Worked example: Corvasse Scooters

Corvasse Scooters sells electric scooters. Its unit sales for January to August were:

Month	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug
Units sold	410	446	431	470	488	467	512	527

Step 1 and 2: the first average. (410 + 446 + 431) ÷ 3 = 1,287 ÷ 3 = 429, placed against February (the middle of Jan, Feb, Mar).

Step 3: move along one month each time.

Feb: (410 + 446 + 431) ÷ 3 = 1,287 ÷ 3 = 429
Mar: (446 + 431 + 470) ÷ 3 = 1,347 ÷ 3 = 449
Apr: (431 + 470 + 488) ÷ 3 = 1,389 ÷ 3 = 463
May: (470 + 488 + 467) ÷ 3 = 1,425 ÷ 3 = 475
Jun: (488 + 467 + 512) ÷ 3 = 1,467 ÷ 3 = 489
Jul: (467 + 512 + 527) ÷ 3 = 1,506 ÷ 3 = 502

Eight months of data give only six averages: the first and last months are never the middle of a group of three.

Step 4: read the trend. The raw data dips in March and June, but the moving averages rise every month. The trend is clearly upward.

Using the moving averages to forecast

To forecast, you extend the trend forward. Find the average change per period in the moving averages:

Total change   = 502 − 429 = 73 units
Number of gaps = 6 averages − 1 = 5
Average change = 73 ÷ 5 = 14.6 units per month

The latest trend value (502) sits against July. September is two months after July, so:

September forecast = 502 + (2 × 14.6) = 531.2 ≈ 531 units
October forecast   = 502 + (3 × 14.6) = 545.8 ≈ 546 units

The key step that is often missed: count forward from the month the last moving average is placed against, not from the last month of actual data.

You can also plot the moving averages against their middle months, draw a line of best fit and extend it.

Why not just use 502? A simpler approach treats the most recent moving average as next month’s forecast. That works when sales are flat. When there is a clear trend it lags behind: here it would forecast 502 for September, about 29 units below the trend-based figure.

3. The value of extrapolation

Extrapolation means projecting a past trend into the future, on the assumption that the pattern will continue. The Corvasse forecast above is an extrapolation.

Why it is useful

• It is quick and cheap: the business already holds its past sales data.
• It is objective. Two managers using the same data reach the same answer, so the forecast is less open to bias or wishful thinking.
• It works well in stable markets where tastes, competition and the economy change slowly, such as basic groceries or household utilities.
• It gives a baseline that managers can adjust.

Its limits

• It assumes the future will look like the past. A new rival, a recession or a change in fashion breaks the pattern, and the trend line cannot see it coming.
• Reliability falls the further ahead you project. A forecast for next month is far safer than one for three years ahead.
• It needs a reasonable run of past data, so it is of little help for a new product or a new market.

Judgement: extrapolation is most valuable for short-term forecasts in stable conditions and least valuable when the business or its market is changing quickly.

4. Qualitative sales forecasting

Qualitative forecasting relies on informed judgement rather than past figures. Common forms include:

• Expert opinion: asking people with deep knowledge of the market, such as industry consultants or senior managers.
• The Delphi technique: a panel of experts answers anonymously in several rounds, seeing a summary of the others’ views each time, until the forecasts move closer together.
• Sales force composite: each salesperson estimates sales in their own area and the estimates are added up.
• Hunch or intuition: an owner’s feel for the market, built from experience.

Advantages	Disadvantages
Works where there is no past data, e.g. a new product or market	Subjective: open to bias, optimism or self-interest
Can pick up changes the numbers cannot, such as a rival’s plans or a shift in taste	Hard to check or repeat; two panels may reach very different answers
Draws on staff who are close to customers	Expert panels can be slow and costly to run
Flexible: can be updated quickly when news breaks	Sales staff may forecast low so that their targets are easier to beat

Worked scenario: Hollinbrae Paints

Hollinbrae Paints is launching a range of low-odour paints for children’s bedrooms. It has no sales history for the range, so a moving average is impossible. It asks its twelve area sales managers for estimates and runs a small Delphi panel of interior designers.

• Advantage in this case: the sales managers know which retailers are asking for low-odour products, information that no past data holds.
• Disadvantage in this case: the managers are paid a bonus for beating targets, so they may give low estimates. The designers may be enthusiastic about a product that ordinary buyers see as a luxury.
• Judgement: for a new range, qualitative forecasting is the only realistic option. Hollinbrae should treat the result as a range, not a single figure, and replace it with actual sales data as soon as the first months are in.

5. The value of sales forecasting

A forecast is valuable because almost every plan in the business starts from expected sales.

• It lets managers plan ahead rather than react, reducing the risk of being caught short.
• It is the starting point for budgets and cash flow forecasts.
• It helps the business co-ordinate its functions so that marketing, operations, HR and finance work to the same numbers.
• It supports applications for finance: lenders and investors expect to see expected sales.
• Comparing forecast with actual gives early warning of change.

Its value depends on accuracy, and on managers treating it as a guide, not a promise.

6. Difficulties of sales forecasting

• External change: shifts in the economy, interest rates, exchange rates or consumer tastes. These are the factors in section 3.3.4 (external environment study guide).
• Competitor actions: a new entrant or a rival’s price cut can change sales overnight.
• Random events: extreme weather, supply disruption or a viral social media post.
• New products and markets: no past data to work from.
• Time horizon: the further ahead, the wider the margin of error.
• Bias in qualitative estimates, and poor-quality or incomplete data in quantitative ones.

You can measure how wrong a forecast was. If Corvasse’s actual September sales were 509 against a forecast of 531:

Forecast error = (509 − 531) ÷ 531 × 100 = −4.1% (1 d.p.)

The negative sign shows that sales came in below the forecast.

7. The impact of sales forecasting on decision-making

A forecast feeds directly into decisions in every functional area.

• Operations: how much capacity to have, how much inventory to hold, and when to order from suppliers.
• Human resources: how many staff to recruit, train or redeploy, and whether to offer overtime or temporary contracts.
• Finance: budgets, cash flow forecasts and whether external finance is needed.
• Marketing: whether to boost promotion if sales look weak, or adjust prices if demand looks strong.

Worked example: Corvasse’s capacity decision

Corvasse can assemble 540 scooters a month. The September forecast of 531 is just inside capacity, but October’s 546 is about 6 units above it, and the trend keeps rising by roughly 15 units a month.

• Operations may need overtime in October or to bring forward deliveries from its supplier.
• HR may recruit temporary assembly staff before the autumn.
• Finance must fund the extra stock and wages before the sales revenue arrives.
• Strategic question: if the trend continues, should Corvasse invest in a larger workshop? That is a high-cost, hard-to-reverse decision, so it should not rest on six moving averages alone. Managers should test the forecast against market research and consider what could break the trend.

Common errors

• Placing a three period moving average against the third month instead of the middle one.
• Counting forward from the last month of actual data instead of the month of the last moving average.
• Dividing the total change by the number of averages instead of the number of gaps between them.
• Treating a forecast as certain. Every forecast is an estimate.
• Listing difficulties without linking them to the business in the case.
• Calling qualitative forecasting “unreliable” with no explanation of why or when.

For the AS skills that support this topic, see correlation and market research in 3.1 Business and markets, budgets and cash flow in AS finance, and workforce planning in AS human resources. Section 3.3 opens with 3.3.1 on missions and SWOT. For exam technique, read the 9725 exam preparation guide.

Next: shrink this page into the revision notes, try the practice questions, and spot wider gaps with the free diagnostics.

Official syllabus

OxfordAQA International AS and A-level Business (9725) specification, Version 1.1, for International AS exams May/June 2027 onwards and International A-level exams May/June 2028 onwards. Published by OxfordAQA.