Key takeaways
- Asynchronous CSV export of analyzed conversations is now available directly from the conversations list, with the exact same filters you use on screen (period, teams, models, attributes, questions)
- A format ready to plug into your data stack: one row per (conversation, question) pair, the full conversation context, all your custom attributes as columns, UTF-8 BOM encoding and
;separator natively read by Excel, Power BI, Tableau, Looker and your Python / R scripts - Up to 50,000 conversations per export, asynchronous processing: launch the export and keep working while it is generated
- Notification in the menu bell as soon as the file is ready, download link valid for 7 days
Why a CSV export of conversations in Raisetalk?
A CSV export is not just a technical feature — it is a shift in posture toward conversational data. Here is what it concretely enables for a Raisetalk customer.
Reclaiming your data
Your conversations analyzed by Raisetalk can now flow freely to your CRM, your data warehouse, your BI. Conversational data joins the rest of your data assets, in the tools where your other indicators already live. Your customers speak to you in Raisetalk; their analyses speak to your stack.
Crossing and enrichment
Once in CSV, conversational data crosses with what you already measure elsewhere: NPS, churn, customer revenue, ticket volumes, commercial segments, e-commerce baskets. This is where the voice of the customer becomes a real business lever and not just an internal dashboard. To go further on qualitative exploration as a complement, see our article on querying customer calls in natural language.
Analytical freedom
Your quality, ops and marketing teams can build their own ad hoc analyses, without depending on a vendor or a new ticket. An Excel pivot, a Power BI join, a Python notebook: the data is there, in a format your tools have always known how to read. Autonomy without intermediary.
Auditability and compliance
For regulated sectors (banking, insurance, healthcare, financial services), being able to extract and archive the analyses of conversations is a strong compliance argument. The export becomes a building block of your audit trail: who was evaluated, on which questions, with which scores, on which date, in which language. Everything is traceable and reusable outside the application.
Industrialization
CSV is the gateway to automation: feeding an analytical pipeline, training an internal model, generating an automated weekly report, triggering a Slack alert when a score drops below a threshold. Anything that consumes tabular data can consume your Raisetalk analyses. To automate further via API and generate a client in the language of your choice, see our article on OpenAPI documentation.
What does the exported file contain?
The file follows a standardized format, directly readable by every tabular tool on the market.
| Column block | Content |
|---|---|
| Conversation context | raisetalk_id, creation date, creator, concerned user, interaction score, duration, language, evaluation model |
| Custom attributes | One column per attribute defined on your instance, label in the user's language. Multi-values are joined by the | character |
| Evaluated question | Category, question identifier, label, score, max score, explanation, citation |
Format details:
- One row per (conversation, question) pair: the entire conversation context is repeated on each row to ease pivots and joins.
- UTF-8 encoding with BOM: Excel, Numbers, Google Sheets and almost every BI tool open the file with no encoding fiddling.
;separator: compatible with Excel-FR by default, configurable on the tool side for other environments.- ISO date format in the time zone of the user who launched the export.
- Conversations with no answers (analysis in progress, no model applied): one row with empty question columns, so the conversation is never lost in the export.
How do you launch an export?
Triggering happens from the conversations list, in just a few seconds.
| Step | What happens |
|---|---|
| 1. Filter | You apply your usual filters on the conversations list: period, teams, agents, models, attributes, questions. The export reflects exactly what you see on screen |
| 2. Click Export | Processing starts asynchronously, you keep working in Raisetalk. The interface is never blocked |
| 3. Notification | A red dot appears on the menu bell as soon as the file is ready, with the number of available exports. A click opens the list of your exports |
| 4. Download | One click on the ready entry, the CSV lands on your machine. The export remains visible in grey in the bell as a memo; you can dismiss it with one click when you no longer need it |
If you work with several Raisetalk tabs open in parallel, they synchronize automatically: the bell shows the same list regardless of the tab you are on. Once retrieved, the file stays on your machine, and the server does not keep it beyond the download: your local copy is the source of truth.
How does it plug into your data stack?
The format is designed to minimize friction, whatever the destination tool is.
- Excel and Google Sheets: direct opening, encoding correctly detected thanks to the BOM. Pivot, chart and pivot table in a few minutes.
- Power BI, Tableau, Looker: direct import, source manually refreshable on each new export. Automatic push is on the roadmap.
- n8n, Make, Zapier: file retrieval via the secure link, triggering a workflow (CRM update, Slack alert when a threshold is crossed, sync with a shared spreadsheet).
- Custom scripts:
pandas.read_csv(path, sep=';', encoding='utf-8-sig')on the Python side, trivial equivalents in every common language. To automate further without going through the file, see our OpenAPI article which details how to generate an SDK in the language of your choice.
Your conversations, freed and ready to connect to your entire data ecosystem. The same data that powers your evaluation grids now feeds your cross-analyses, your executive reports and your internal pipelines, in the tools where the rest of your data assets already live.
Three concrete use cases
The CSV export does not serve the same need depending on your role. Here are three representative scenarios.
| Role | Typical question | With the CSV export |
|---|---|---|
| Quality Monitoring lead | "How do I track the monthly evolution of scores by team over 6 months?" | Monthly export, Excel pivot by team and agent, consolidated visual for your quality reviews |
| Voice of the Customer lead | "How do I cross detected drivers with CRM segments?" | Filtered export, Power BI join on the agent identifier or the concerned customer, transversal Voice of the Customer dashboard |
| Customer relations director | "How do I compare our internal centers and our BPO partner this quarter?" | Export by center, shared Tableau dashboard for executive committee, cross-analysis with CSAT indicators and contact volumes |
To frame the upstream Quality Monitoring that feeds these analyses, see our Quality Monitoring guide for call centers. To automate inbound call capture, see our article on the Aircall connector.
Scope of V1: CSV format, cap of 50,000 conversations per export, download link valid for 7 days. For larger volumes, split by period or by team. Excel and Parquet formats, scheduled exports and automatic push to your cloud buckets are among the planned evolutions.
What's next?
Several building blocks are under consideration in the continuation of this export:
- Automatic push to your storage buckets (GCS, S3) without manual intervention.
- Recurring scheduled exports: receive a weekly or monthly file without re-launching manually.
- Excel and Parquet formats for more advanced BI or data engineering use cases.
- Triggering via direct API call to embed the export in your own workflows.
The overall goal: that conversational data flows without intervention in your ecosystem, from the first call to the executive dashboard.
Take action
- Book a demo or open a discussion: www.raisetalk.com/en/contact
- Close the loop with automatic call capture: the Aircall connector
- Frame upstream Quality Monitoring: our Quality Monitoring guide
- Automate via API rather than via file: the OpenAPI documentation
The voice of your customers becomes a piece of data in your stack on the same level as your financial or commercial indicators. It flows, it crosses, it enriches. And that is where conversational analysis becomes a true enterprise-grade lever.