These prompts are meant to be open-ended, as in there is no one perfect answer. Instead, we've built them to feel as close to the "real-world" as possible. So, there's a lot of digging you have to do.
For instance, there isn't a data dictionary that tells you exactly how the Customer Support datasets connect together. Trust is, this is definitely commonplace in the workplace. There's also likely some clean-up you'll have to do in the data.
After you've finished a prompt below, feel free to post your answer and work (e.g. Google Sheet, SQL Queries, and Looker reports) in a Community Post within the Immersion topic! This way other San Francisco Data School members can see your approach and/or provide peer-to-peer coaching.
Alright, on to the prompts:
1. Who is the highest performing Customer Support teammate?
Things to think about:
- How are you defining "performance"?
- Is there any data validity issues that are concerning?
2. How are CSAT results trending over time?
Things to think about:
- What is CSAT, and how is it measured?
- Why is it important to measure this in the first place?
3. Are ticket close rates getting faster or slower?
Things to think about:
- How are you going to define "close rate"?
- At what time interval do you visualize this data (e.g. daily, weekly, monthly, etc.)
4. What insights can you provide about the customers who are opening support tickets?
Things to think about:
- Which segments do you think are important to analyze, and why?
5. What's the biggest recommendation you could make to the team after analyzing this data?
Things to think about:
- What's one metric you could build that surfaces the best status for how things are going?
- How would you present your findings to the team?