Visualization has become the new buzz word. First there was 'analysis' (came to the fore back when we first started doing pivot tables), then we had 'insights' (because it sounds cooler and technology companies wanted to be thought leaders). These concepts have morphed into machine learning (because it all seems too complex for mere mortals) and or visualization.
Visualization is used for two very different purposes:
- exploration: looking for that insight that changes how we market
- explanation: illustrating what we want to communicate after we've found it
Exploration visualization needs to focus on patterns and relationships - the first image uses the sample data sets accompanying "Doing Data Science" and is produced in R which allows for a train of thought to be followed quickly. It shows the distribution of click thru rate by age of consumer. We already know that CTR is low (big bump to the left.) What is important to takeaway is that the relationship between click thru rate and age category is consistent, i.e. the lines pretty much stack on top of each other. This leads to another question: do we need to rethink the idea that age-based cohorts are different.
In contrast explanation needs to look like Consumer Barometer where a lot of design time went into creating an interactive display of how/where people buy stuff. Like any dashboard, it provides a series of predefined navigation paths. In this case the user can traverse along geographic, demographic, product dimensions. This specific image captures variance in online research by product category (color) and penetration (size) for 40+ year old consumers in Hong Kong. Instantly we see that "they are more active in computer/electronic categories than any other." This is where we partner with the creative or UX teams to highlight the differences and communicate key points.