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Business AS AQA

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  1. 1-1-the-nature-and-purpose-of-business as
    3 主题
  2. 1-2-forms-of-business as
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  3. 1-3-the-external-environment as
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  4. 2-1-management-and-leadership as
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  5. 2-2-management-decision-making as
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  6. 2-3-the-role-and-importance-of-stakeholders as
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  7. 3-1-marketing-objectives as
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  8. 3-2-understanding-markets-and-customers as
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  9. 3-3-making-marketing-decisions as
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  10. 3-4-the-marketing-mix as
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  11. 4-1-operational-objectives as
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  12. 4-2-operational-performance as
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  13. 4-3-efficiency-and-productivity as
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  14. 4-4-quality as
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  15. 4-5-inventory-and-supply-chain-management as
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  16. 5-1-financial-objectives as
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  17. 5-2-financial-performance as
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  18. 5-3-sources-of-finance as
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  19. 5-4-cash-flow-and-profit as
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  20. 6-1-human-resource-objectives as
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  21. 6-2-human-resource-performance as
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  22. 6-3-organisational-design as
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  23. 6-4-human-resource-planning as
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  24. 6-5-motivation as
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  25. 6-6-improving-employer-employee-relations as
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Exam code:7131

Making decisions using data

  • Scientific decision making involves using data to make rational, logic-based decisions

    • This method lowers risk, but it isn’t perfect — good data costs money, and numbers never tell the whole story

What the scientific approach involves

  1. Set the objective

    • E.g. increase sales of gym accessories to under‑25s by 5 % by November 2025

  2. Collect data

    • A range of internal and external sources can provide useful insights

    • Internal data, such as:

      • Sales and loyalty card records

      • Production logs

      • Finance systems

      • Website analytics

    • External data, such as:

      • Government statistics

      • Industry reports

      • Social media trends

      • Customer reviews

  3. Analyse data, and select an option

    • Use statistics, A/B tests or forecasts to determine the best options, then make a choice based on the strongest evidence

  4. Implement the decision

    • Put in place the resources required, such as finance, staff, equipment and premises

  5. Review and learn

    • Compare outcomes with the original goal; make necessary changes to keep improving

Stages in the decision-making process

Flowchart of decision-making process in a circular sequence: set objectives, gather information, choose an option, implement decision and review decision.
The stages in the decision-making process are setting objectives, gathering information, choosing an option, implementing the decision and reviewing the decision

Scientific decision-making at Tesco

Stage

Explanation

Example

Setting objectives

  • Define a clear, measurable target

  • Tesco’s leadership aimed for 4 % like‑for‑like UK sales growth in 2024/2025

Gathering information

  • Collect internal data and external facts to understand customers, rivals and costs

  • Tesco mined 23m Clubcard households’ shopping data to spot which items most influence where people shop and how often

Choosing an option

  • Compare alternatives, and pick the best to achieve the objective, considering the available resources

  • After testing several ideas, Tesco kept its “Aldi Price Match” and expanded its “Clubcard Prices”, aiming to match or beat discount rivals’ prices on more than 600 products

Implementing the decision

  • Allocate a budget, brief staff and launch and monitor the implementation

  • It rolled out bold, yellow Clubcard labels to every large store and, in late 2024, extended Aldi Price Match to its convenience stores

Reviewing the decision

  • Compare results with the objective, learn lessons and decide what to do next, making adjustments if necessary

  • In April 2025, results showed that the UK market share had risen to 28.3 %, and Tesco recorded Christmas sales growth of 4.1 %

  • This confirmed that the price strategy would continue, as it had contributed towards achieving Tesco’s objective

Benefits of scientific decision-making

  • Reduces risk

    • Decisions rest on evidence, not guesswork

    • Firms using reliable data are more likely to report better outcomes

  • Justifies investment

    • Clear numbers help win the support of a board of directors, investors or lenders, such as banks

  • Supports continuous improvement

    • Constant measurement helps a business to identify what works and what may need to be changed

Limitations of scientific decision-making

  • Cost and time

    • Gathering and analysing data is expensive

    • It may be unaffordable for smaller businesses or those with a poor cash situation

  • Data quality issues

    • Bad or biased data can lead to wrong or inappropriate decisions being made

  • Overreliance

    • Managers can ignore gut feel or ethics

    • They may miss out on opportunities that have a good chance of success because the data does not recognise their potential

  • Incomplete picture

    • Not all risks are measurable, and relying on data means that businesses can miss surprises such as rapid market change

Intuition and decision-making

  • Managers sometimes rely on gut feel, experience and pattern‑spotting rather than detailed data analysis to choose a course of action

    • Experienced managers build quick mental shortcuts from years of experience, so their gut instantly signals, “This feels right”

Situations where intuitive decision-making may work best

Situation

Reason

Example

Little time for data

  • Speed beats delay

  • Quick calls mean chances are not missed

  • Zara buyers approve new styles in hours to catch microtrends

No clear precedent

  • Past numbers cannot predict a brand‑new idea

  • Steve Jobs approved the first iPhone without any proof, trusting his instincts

Decision rests on human taste

  • The feel for design, brand or consumer mood is hard to quantify

  • Richard Branson set up Virgin Atlantic because the idea “felt fun and right”

Benefits of intuitive decision-making

  • Speed

    • Rapid action can allow a business to seize opportunities before rivals can react

  • Creativity

    • It frees managers to pursue bold ideas that data might reject

  • Uses deep expertise

    • Experienced managers base decisions on past successes, failures and patterns

Limitations of intuitive decision-making

  • Bias and overconfidence

    • Personal likes or recent events can cloud judgement

  • Hard to justify

    • Convincing investors or lenders without data to back up ideas can be difficult

  • Riskier on big bets

    • A wrong hunch can be very costly, so managers risk their personal reputations in pursuing them

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