Anna can help consumer complaints teams identify complaints patterns, explore complaint causes and save time on reporting. You can read more here on our website.
This article will cover:
- Example insights from this use case
- Recommended data structure for this use case
What sort of insights can Anna help me uncover?
We've outlined some example questions which Anna can help answer through a combination of her proactive insights, Search, and What caused this? analysis:
Identify complaint patterns
- Time to closure by state
- Complaints by company
- Time to closure by complaint issue
- Complaints for Fraud or scams for last year
Explore complaint causes
- Compensation by issue and outcome
- Complaints by product
- Complaints by company and issue
- Average compensation for aggressive debt collection methods last 3 years
Save time on reporting
- Time to closure by team and responsible manager
- Compensation by issue and product team
- Complaints by company by issue
How do I structure my data?
- Anna requires structured, transactional data, with at least 1 measure (e.g. Compensation) and 5 segments (e.g. Complaint submission method). In addition, we recommend at least 7 months of data (at monthly or daily granularity) so you can take full advantage of Anna's Unexpected Changes feature.
- Anna can connect to databases and .csv files.
Example data structure
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Please refer to this article for more information about data structure.
Here are some of the typical segments we find in revenue data. A segment is a qualitative value, like names or categories:
- Complaint cause attributes: Product, sub product, issue, company complaint issued against, etc.
- Case attributes: Time to closure, compensation paid, company response to complaint, etc.
- Other attributes: State, city, complaint submission method, etc.
A measure is a quantitative, numeric value. Some of the typical measures include time to closure, compensation paid, etc.
If you have any questions, please contact us at email@example.com.