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 firstname.lastname@example.org.