Quantitative Data Analysis (Discover phase)

Some activities in the Discover phase (especially to answer open questions about your current business model, the behaviour of your customers and users) as well as some experiments of the Validate phase require the collection and analysis of large amounts of data with the help of descriptive or inferential statistics. Data sources could be

Any quantitative data analysis is usually based on basic key statistics. Choose the ones that are appropriate for your data and help you get answers to open questions or to validate your hypotheses. Before starting the analysis, you probably have to prepare and edit your raw data in a spreadsheet (e.g. check for completeness and remove errors). 

Key Elements

Statistics

Description

Frequencies

Count of the number of times a particular value is found

Percentages

Set of values as a percentage of the whole

Mean

Numerical average of the values

Median

Middle attribute in the ranked distribution

Mode

Most frequent value

Range

Distance between highest and lowest value

Variance

Variability of the distribution

Standard deviation

Amount of variability: a high standard deviation means data are more dispersed

Correlation

Describes the relationship (strong, weak, statistically significant) between two variables

ANOVA

Determines whether the difference of two samples is significant

Regression

Determines whether one variable is a predictor for another one

Usage Scenarios

Example Tools

Tips

Funnily enough, we've observed that most people have no interest in validating their business models. Tangible prototypes and feedback from a small number of customers makes them more happy than quantitative statistics that actually reduce their risks. Making things concrete can make people feel scared.