Benefits and limitations of quantitative data

Quantitative data is numerical information. It answers questions such as how many customers buy, how often they buy and how much they spend. For example, a bakery might survey 150 shoppers about a new breakfast deal: 96 say they would buy it once a week, and, separately, 78 say they would spend £4 or more. These are quantitative results.

Benefits

• Quick and easy to analyse: totals, averages and percentages can be worked out, so you can see the main result without reading lots of comments.
• Large samples and wide coverage: a business can ask many people the same questions and collect a lot of information in a short time.
• Patterns, trends and comparisons: summarising the numbers in tables, bar charts or line graphs makes the main findings easier to understand. The bakery could compare how many younger and older customers would buy the deal, or see whether interest changes over several weeks.
• Easy to share: charts and summary figures communicate results clearly to a manager, investor or business partner.

Limitations

• No reasons: numbers do not explain why 54 shoppers said they would not buy it once a week or how they feel about the price. They give limited insight into customers' opinions, thoughts and feelings.
• Sampling bias: if the bakery only surveys shoppers who already buy breakfast there, the sample is not representative of the wider market. A large sample can still be biased if it is chosen badly.
• Outdated quickly: tastes, prices and competitors change, so a survey from several months ago may no longer reflect demand.
• May need further research: the bakery might need interviews or a small discussion group to find out why customers like or dislike the deal before changing its menu.