Spotting a business opportunity Study Guide

A market map, also known as a perceptual map, is a visual representation that plots competing products or businesses onto two dimensions chosen by the business, typically illustrating customer perceptions of key product characteristics. By visually charting these characteristics (e.g., price vs. quality, or luxury vs. basic features), a business can effectively identify gaps in the market where consumer needs might not be fully met by existing offerings. This can highlight potential positioning opportunities for new products or services. Furthermore, market maps provide a clear overview of the competitive landscape, showing how direct and indirect competitors are positioned relative to each other, helping a business refine its strategy to differentiate itself and appeal to a specific target market.

Market research involves gathering information about consumers and markets. This data can be broadly categorised into two main types: qualitative and quantitative. Quantitative data deals with numbers and statistics; it can be measured, counted, and expressed numerically. Examples include sales figures, market share percentages, and customer demographics like age or income. It helps businesses understand 'what' is happening. On the other hand, qualitative data is non-numerical and descriptive. It focuses on understanding underlying reasons, opinions, and motivations. This type of data is often collected through methods like focus groups, interviews, and observations, asking 'why' and 'how' questions. Examples include customer feedback on product design or opinions about brand perception. The usefulness and reliability of both types of data depend on various factors, including the research methodology and the source. Social media platforms have emerged as a significant source for both qualitative (e.g., sentiment analysis from comments) and quantitative (e.g., follower counts, engagement rates) market research due to their vast user base and real-time information flow, but businesses must be mindful of potential biases and accuracy issues.