The overview section allows you to discover different angles and views of your collected data.
This is mainly the tool to really dig deeper into the data once tagging is done, but also to apply differences from variables collected through the participant profile. E.g. What differences are there between men and women, between people who have used the service for the first time vs. ones who have already used it several times, etc.
Overview alpha provides you with multi-dimensional filtering options to unveil meaningful insights. For example, here are some questions you can answer by using this feature.
How do customers feel when making use of specific parts of the offering?
In order to solve this question, filter data by tags:
You see that the tag station is mostly related to negative experiences, whereas ticket is used for touchpoints that were assigned more positive or neutral emotional values.
What issues affect customers most?
You can sort the touchpoints by number of participants affected (P), number of touchpoints affected (T), or average emotion (A):
In this case you see that orientation does not seem to be a big issue - only one participant was affected by it. The tag “ticket” has been mentioned 27 times by 11 participants and therefore seems to be of more importance to customers.
Do gender groups differ in their experience of my service?
In order to learn about group differences, you can for example group by gender and split by emotion:
In this example you can see that the distribution of touchpoints between the gender groups is quite similar. All groups report less negative touchpoints than positive touchpoints, and also the number of neutral touchpoints is quite balanced.
If you want to learn more about the causes of the experiences, you can do so by the mouse-over function and get more detail on each single touchpoint.