The score a policyholder gives after a claim depends on very different things than the score at purchase. Survey Coder Pro codes each moment with its own codebook and the same criteria every wave.
Customers deal with their insurer at only a few moments, and each one weighs differently on their score.
A claims comment mixes the repair shop, the adjuster, the agent and the insurer: you need to know who each complaint is about.
Non-renewal reasons (price, a bad claims experience, another broker's offer) need to be separated to decide what to do.
Sales, claims and renewal each have their own vocabulary, and one codebook for everything ends up too generic.
Auto, health, home and life are measured with similar surveys but have very different problems.
You generate a codebook per moment (sales, claims, renewal) or load the one the insurer already uses. The AI codes each comment with all the codes that apply and your team reviews only the doubtful answers.
Nets that separate the parties in a claim: insurer, repair shop or provider, adjuster, agent or broker.
The same codebook is reused wave after wave, so trends by moment are comparable.
Codes as columns per respondent to cross with line of business, sales channel or claim type.
With the .sav, a tabulation book with line of business or channel in the banner and significance testing.
Applications for insurance companies
Analyze claims process feedback identifying friction points.
Renewal and non-renewal reasons with decision drivers.
Contracting experience and coverage understanding.
Differentiated analysis: auto, life, health, home, commercial.
App, web portal and digital self-service feedback.
Broker and insurance agent evaluation.
Transactional survey of auto policyholders who closed a claim in the last month. Here is part of the dataset after coding.
Open-ended question
Thinking about your claim, why did you give us that score?
| Response | Codes |
|---|---|
| “The body shop took three weeks longer than they told me and nobody kept me updated.” | Repair shop delayNo follow-up |
| “The adjuster came quickly and was very clear, I appreciated that.” | Fast adjuster (positive)Clear explanation (positive) |
| “I had to pay a deductible I didn't know existed.” | Unexpected deductibleDidn't understand coverage |
| “I had to email the same documents three times.” | Repeated paperwork |
| “The tow truck took almost two hours to reach me on the highway.” | Roadside assistance delay |
| “My agent was on top of everything, I don't know what I would have done without him.” | Agent support (positive) |
| “They gave me a rental car but only for five days and the repair took fifteen.” | Insufficient rental carRepair shop delay |
| Net / code | % of respondents who answered |
|---|---|
| NET Timing | 44% |
| Repair shop delay | 28% |
| Roadside assistance delay | 10% |
| No follow-up | 15% |
| NET Coverage and payment | 27% |
| Unexpected deductible | 16% |
| Didn't understand coverage | 12% |
| Insufficient rental car | 6% |
| NET Paperwork | 13% |
| Repeated paperwork | 13% |
| NET Positives | 31% |
| Fast adjuster (positive) | 12% |
| Agent support (positive) | 14% |
| Clear explanation (positive) | 9% |
Multi-code question: one response can carry several codes, so percentages add up to more than 100%. A net counts each respondent once, even if they have several codes from that group.
Example built for this page: this is not client data and the percentages are illustrative.
Usually not. Each moment has different reasons, so you work with one codebook per moment. Shared nets (timing, clarity, service) can use the same names to compare across moments.
With separate codes per party in the codebook. If the comment doesn't make it clear who it is about, the AI flags it as doubtful for your team to review.
Yes. It is an open-end like any other: you generate or load the codebook (price, claims experience, competitor offer, change in needs) and reuse it every wave.
Yes. The usual setup is line of business in the tabulation banner and, if the reasons are very different, specific codes per line inside the codebook.
Upload a dataset with an open-ended question and see the codebook the AI proposes. The free trial includes 250 responses.