Revision and practice on using correlation to predict demand and spot patterns in buyer behaviour.
Key vocabulary: Positive Correlation, Negative Correlation, Strong/Weak Correlation
Correlation looks at the strength of a relationship between two variables. Marketing data may help a business identify a correlation between different factors and demand for a product — for example, comparing sales against price.
Positive correlation: as the independent variable increases in value, so does the dependent variable (e.g. as temperature rises, ice cream sales rise).
Negative correlation: as the independent variable increases in value, the dependent variable falls (e.g. as price rises, sales fall).
No correlation: there is no discernible relationship between the two variables.
A line of best fit is drawn through plotted data points to indicate the strength of a correlation.
If data suggests a strong correlation, the relationship might be used to help make marketing predictions.
A business can use a strong, reliable correlation to help forecast demand and inform marketing decisions — for example, spotting that sales rise with advertising spend, or predicting when to stock a seasonal product. But correlation is not the same as causation — just because two variables move together doesn't prove one causes the other. A third, unseen factor might be driving both, and relationships that held in the past may not continue to hold in future, so correlation should always be treated as one input into a decision, not a guarantee.
In late June 2026, as average UK temperatures hit around 26°C during a heatwave, ice cream value sales rocketed by nearly 88% in a single week compared to the equivalent period, with volume sales up over 93%. Retailers reported a similar pattern across other summer categories — Tesco's paddling pool sales multiplied fourteen-fold and inflatable sprinkler sales rose by over 1,500% during the same warm spell. This is about as clear a positive correlation as retail data gets: as the "independent variable" (temperature) rises, the "dependent variable" (sales of warm-weather products) rises sharply alongside it.
Source: verified via search, September 2026 — The Grocer and Wholesale Manager coverage of the June 2026 UK heatwave's impact on retail sales.
Retail analysts studying weather-linked sales data are careful to note that a correlation between two variables (like temperature and ice cream sales) doesn't by itself prove one directly causes the other in every case — other factors, such as school holidays falling at the same time as a heatwave, or a promotional campaign timed for summer, could also be contributing to the same sales spike. This is exactly why AQA Business expects students to treat a strong correlation as useful evidence for a marketing decision, not as absolute proof on its own.
Source: this reflects standard statistical reasoning (correlation vs causation) referenced across UK retail-weather sales reporting, rather than a single specific study.
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As the independent variable increases, the dependent variable falls.
How close the data points sit to the line of best fit.
Two variables moving together doesn't prove one causes the other — a third factor might be driving both, or it could be coincidence.
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"Strong correlation" doesn't mean "certain" — it means the historical data fits the pattern closely. A strong correlation can still break down if external conditions change (a competitor enters the market, the economy shifts, or consumer tastes move on), so forecasts based on correlation should always be treated as a starting point, not a guarantee.
Top-band evaluation on correlation questions usually raises the causation issue explicitly — e.g. "although ice cream sales correlate strongly with temperature, this doesn't prove temperature alone causes the sales rise; a heatwave might also coincide with school holidays, boosting footfall for other reasons."
Before using this: AI tools can get facts or mark scheme details wrong, and quality varies by tool. Always check anything factual against your notes or ask your teacher.
Describe a short business scenario with two variables that might be correlated (e.g. advertising spend and sales). Ask me to say whether I'd expect positive, negative or no correlation, and to explain one limitation of relying on that correlation for a marketing decision. Then mark my answer.