QA is a technology business located in country A. It sells Artificial Intelligence (AI) devices that answer questions in consumers' homes. These devices are called 'smart speakers'. Consumers can link the device to other smart technology in their homes, such as fridges and televisions. Consumers can ask the device questions and it uses an internet connection to find the answers. QA started trading in 2016 and demand for its first device QA1 was high. However, after the pandemic in 2021, the owners decided to sell the business. It was bought by QA's senior management team. Timeline of QA 2021 Senior management team purchases QA for $20m. It becomes a private limited company owned by eight senior managers. They are now the directors of the company. Shares are equally split. Directors complete a SWOT analysis of the business (see Appendix 1). 2022 The Marketing Director uses the Ansoff matrix as a basis for recommending that QA focuses on product development. QA starts to develop QA2, a next generation smart speaker with many unique functions. 2023 Sales of QA1 enter the decline stage of product life cycle. QA makes a loss for the first time. The directors use a decision tree to help them decide whether to launch the QA2 early in 2024 or wait until the planned launch in 2025 after more testing (see Appendix 2). The decision is made to launch the QA2 early. 2024 QA2 is launched. Sales are higher than expected as QA is the first to market with a next generation device. This leads to excess demand. QA is unable to increase supply. QA launch a website to show inventory availability at retail outlets. Complaints from customers about QA2. Research is commissioned about QA2 (see Appendix 3). QA makes a public apology. Developing a marketing strategy Sales of QA1 are in decline and there is poor public perception of QA as a business. The QA2 device has now been more fully tested and improved and needs to be relaunched. The business needs to develop a coordinated marketing strategy to achieve success with QA2. Appendix 1: Extract from SWOT analysis of QA in 2021 Strengths • High sales of QA1 • QA has 6% market share of the global smart speaker market • QA outsources all production to manufacturers in low income countries Opportunities • Good public perception of QA as a technology business • Smart speaker market in country A is expected to grow by 17% by 2025 Weaknesses • QA only has one product on the market • QA does not own any manufacturing facilities Threats • Market research suggests QA1 is in the maturity stage of product life cycle • There is a growing concern about the data security of smart speakers Appendix 2: Decision tree for the launch of QA2 Launch QA2 early (2024): Initial cost: $18m Success (40% probability): 60m Failure (60% probability): (10m) Launch QA2 (2025): Initial cost: $17.4m Success (65% probability): $30m Failure (35% probability): ($6m) Appendix 3: Market research of QA2 in 2024 • 18% of QA2 devices were returned as faulty. All customers were given a free replacement product. • 72% of customers thought the price was too high. • An estimated 4m potential customers were unable to purchase a QA2 due to a lack of supply. • 65% of customers would not recommend the QA2 to someone else. The main concerns were: ◦ poor product quality ◦ high price ◦ did not meet expectations given in promotional materials.

Mark scheme

Marking points from the official mark scheme (May/June 2025). The mark or mark type is shown in brackets.

1. [K1] (AO1 Knowledge) SWOT analysis – strengths, weaknesses, opportunities and threats. Allows a business to identify the influences within the business and those which it may be able to take advantage of / be wary of in the future.
2. [K1] (AO1 Knowledge) Ansoff matrix – cross references existing and new markets against existing and new product to help decide whether a business should use market development, market penetration, product development or diversification.
3. [K1] (AO1 Knowledge) Product life cycle – model to plan the movement of a product through the stages of sales; launch, growth, maturity, saturation, decline.
4. [K1] (AO1 Knowledge) Decision tree – allows a quantitative understanding of the risks and expected values from a decision. These are calculated using probabilities and expected outcomes from making decisions.
5. [K1] (AO1 Knowledge) Blue ocean strategy / red ocean strategy
6. [K1] (AO1 Knowledge) Scenario planning
7. [K1] (AO1 Knowledge) PEST analysis
8. [K1] (AO1 Knowledge) Porter's five forces
9. [K1] (AO1 Knowledge) Core competence framework
10. [K1] (AO1 Knowledge) Force field analysis
11. [K1] (AO1 Knowledge) Boston matrix (BCG)
12. [K1] (AO1 Knowledge) Investment appraisal (payback, ARR, IRR and NPV).
13. [K1] (AO1 Knowledge) Identified an approach, so only K.
14. [K1] (AO1 Knowledge) (for identification) and [DEV1] (for a point of explanation).
15. [K1] (AO1 Knowledge) (for identification) and [DEV2] (for two points of explanation).
16. [APP1] (AO2 Application) Technology business located in country A
17. [APP1] (AO2 Application) Sells AI smart speakers
18. [APP1] (AO2 Application) 2016 first device (QA1) had high demand
19. [APP1] (AO2 Application) Management buyout (for $20m) in 2012
20. [APP1] (AO2 Application) 2021 QA becomes a private limited company
21. [APP1] (AO2 Application) Use of Appendix 1 – SWOT analysis
22. [APP1] (AO2 Application) Eight senior managers become directors with equal share split
23. [APP1] (AO2 Application) 2022 - Ansoff matrix used – product development (new product, existing market)
24. [APP1] (AO2 Application) 2022 - QA2 in development – next generation speaker with improved functionality
25. [APP1] (AO2 Application) 2023 - sales of QA1 enter decline stage of product life cycle
26. [APP1] (AO2 Application) 2023 – QA makes a loss for the first time
27. [APP1] (AO2 Application) Use of Appendix 2 – Decision tree
28. [APP1] (AO2 Application) 2023 - decision made to launch the QA2 early
29. [APP1] (AO2 Application) 2024 - launch of QA2 (early)
30. [APP1] (AO2 Application) 2024 - sales higher than expected
31. [APP1] (AO2 Application) 2024 - excess demand, QA unable to increase supply
32. [APP1] (AO2 Application) 2024 - QA launch a website to show inventory availability at retail outlets
33. [APP1] (AO2 Application) 2024 - complaints from customers about QA2
34. [APP1] (AO2 Application) 2024 - QA makes a public apology about the QA2.
35. [L3 Analysis] (AO3 Analysis) SWOT analysis – allows a business to use its strengths to overcome its threats and use its opportunities to overcome its weaknesses.
36. [L3 Analysis] (AO3 Analysis) SWOT analysis – Provides an overview of QA and provides the directors with data to enable effective formulation of a business strategy.
37. [L3 Analysis] (AO3 Analysis) SWOT analysis – only provides a picture of where the business is right now. Data about the threats and opportunities is only as good as the research it is based upon.
38. [L3 Analysis] (AO3 Analysis) SWOT analysis – In particular, this is a new technology market and QA is a relatively small player in the market – the actions of competitors are unlikely to be covered by a SWOT analysis.
39. [L3 Analysis] (AO3 Analysis) Ansoff matrix – useful tool for helping decision makers in a business see the risk involved in different business strategies.
40. [L3 Analysis] (AO3 Analysis) Ansoff matrix – A choice of product development (new product to existing market) allows the business to see a medium level risk.
41. [L3 Analysis] (AO3 Analysis) Ansoff matrix – However, this is a simplistic tool and unlikely to be much use in such a dynamic and technologically changing market.
42. [L3 Analysis] (AO3 Analysis) Ansoff matrix – With product development being crucial in the technology market, will the Ansoff matrix provide enough data to enable to directors to decide upon a new strategy?
43. [L3 Analysis] (AO3 Analysis) Product life cycle – useful to plot the likely movement of products through stages of sales, which allows QA to predict what might be likely to happen to sales of QA1 as time progresses.
44. [L3 Analysis] (AO3 Analysis) Product life cycle – Particularly useful when combined with other analytical tools such as the Boston matrix to analyse the product portfolio of QA.
45. [L3 Analysis] (AO3 Analysis) Product life cycle – However, it is not certain that every product will follow the same pattern of sales, especially in the technology market where there is not an established pattern of sales.
46. [L3 Analysis] (AO3 Analysis) Product life cycle – There are also large competitors (Google, Apple) who dominate this market, and their actions are likely to have massive effects on the lifecycle of the product.
47. [L3 Analysis] (AO3 Analysis) Decision tree – Allows a quantitative understanding of the outcomes and risks of a decision.
48. [L3 Analysis] (AO3 Analysis) Decision tree – Allows the directors to make an objective decision based on the data without subjective issues getting in the way.
49. [L3 Analysis] (AO3 Analysis) Decision tree – However, the data can only be as good as the research upon which it is based.
50. [L3 Analysis] (AO3 Analysis) Decision tree – Especially in this market where outcomes are unlikely to be known and probabilities may rely on subjective issues which cannot be known in advance.
51. [L3 Evaluation] (AO4 Evaluation) A judgement over the usefulness of each approach that QA used to develop strategy between 2021 and 2024.
52. [L3 Evaluation] (AO4 Evaluation) A judgement over the most useful approach that QA used to develop strategy between 2021 and 2024.
53. [L3 Evaluation] (AO4 Evaluation) A judgement over the other approaches QA could have used to develop strategy between 2021 and 2024.
54. [L3 Evaluation] (AO4 Evaluation) What the judgement(s) may depend upon; the actions of competitors, the external environment (social, legal, economic, political, technological), the objectives of stakeholders etc.
55. [L3 Evaluation] (AO4 Evaluation) Weighing up of the relative usefulness of each approach used in the development of strategy between 2021 and 2024.
56. [L3 Evaluation] (AO4 Evaluation) SWOT analysis is the most useful approach because it shows internal data, like QA having a 6% market share and external factors, unlike other approaches, like PEST, which only shows external factors.
57. [L3 Evaluation] (AO4 Evaluation) The usefulness of the approaches depends on the accuracy of the data because if QA makes decisions based on inaccurate data, they might make the wrong decision, leading to failure. This might be particularly bad since the decision tree only has a $0.6m difference, which if it is wrong, could have meant QA launched the QA2 at the wrong time.