Clip 1 - Analysing Reviews - Review Data - Review Scores by Platform
3 important questions on Clip 1 - Analysing Reviews - Review Data - Review Scores by Platform
With regards to Review Scores, these can defer by platform depending on the platform. What does this mean?
On different platforms scores can have different meanings. E.g. Uber and Vivino, they have the same scales however the distribution of ratings differs.
What do the different rating systems for the different platforms tell us?
Depending on the platform we have to adjust our rating expectations.
Here we see four possible distributions depending on the clustering in the reviews. Shortly discuss the possible clusterings and mention an example of a company to which it is applicable. List the four.
- Cluster in the high ratings area (Uber) - Likely;
- Cluster in the average ratings area (Vivino, Booking.com) - Very Likely;
- Cluster in the low ratings area (Consumerfirst) - Likely;
- No prominent clusters - Unlikely.
The question on the page originate from the summary of the following study material:
- A unique study and practice tool
- Never study anything twice again
- Get the grades you hope for
- 100% sure, 100% understanding
Remember faster, study better. Scientifically proven.
