# Files for your first read

> A quick guide to the data step of First Read onboarding: what to upload, which column to pick, and four sample datasets to download.

{/* Source for the steps and rules below: the First Read onboarding code as of 2026-10-27 (DataStage,
    FileProfileCard and SignalAssessment; accepted file types; column scoring; files read in the
    browser before upload). Labels in bold are the words on screen. */}

First Read builds a report, dashboard or deck from a file you upload while you sign up. It is looser than a [Data Sources upload](/data/upload-a-file): there is no column mapping and your columns can have any names. You only pick which column holds the text people wrote. No file of your own yet? [Download a sample dataset](#sample-datasets-to-download) and use that.

## The data step, in five moves

1. **Add your file.** Drop it on the box or select **Browse**. CSV, JSON, JSONL or Parquet; for an Excel file, save it as CSV first. You can add several files, each with a different name.
2. **Check what was read.** Each file opens as a card with its size, rows and columns. The file is read on your computer: nothing is uploaded until you select **Build my First Read** on the last step.
3. **Pick the text column.** Under **Which column holds what customers wrote?**, choose the column of free text. Use the table below if you're not sure which one. That file is marked **Main**.
4. **Read the signal score.** The **Signal assessment** on the right scores the column out of 100, from **No signal yet** to **Strong signal**, and shows whether it found text, timestamps and a customer segment. If the score is low, try a longer text column or a file with fuller answers.
5. **Select Next.** Any other files you added travel with the main one as context.

## Which column to pick

| Your data | One row is | Pick as the text column | Columns worth keeping |
|---|---|---|---|
| Reviews | One review | The review body, not the title | Rating, date, store or location, source |
| Survey answers | One response | The open-ended answer, such as "Why did you give that score?". If there are several, pick the one with the longest answers | Score (NPS, CSAT), date, plan or segment |
| Support tickets and email | One ticket, or one message | The customer's message, or the whole thread | Channel, date, plan, status, outcome |
| Chat and chatbot conversations | One conversation, or one message | The transcript or message text | Channel, date, whether it was resolved |
| Call transcripts | One call, or one speaker turn | The transcript text | Agent, date, call reason |
| Social posts and community threads | One post | The post text | Platform, date, topic |

When a conversation takes several rows (one per message), keep a column that groups them, such as a conversation or ticket id, and one that says who wrote each message.

## Lay it out like this

- **One row per thing someone wrote:** one review, one survey response, one ticket or chat. A whole thread can sit in one cell, or take one row per message as above.
- **Longer text gives a better read.** Setup scores the text column on how much was written and how many rows have any. Full sentences score well; a column that averages a couple of sentences or more scores strongest.
- **A date column, if you have one,** so the first read can show what changed over time.
- **Columns that say who or where:** a rating or score, a plan, a location, a channel. They let the first read split and compare what people said.
- **Leave out rows with no text** rather than filling them with placeholders such as `N/A`.

## Sample datasets to download

Pick one, download it and upload it as it is. Each dataset has [Dimensions](/dimensions) already filled in as extra columns (sentiment, topics, signals such as churn), so you can also open it to see what enrichment adds to text.


In the two support datasets a conversation spans several rows: `sessionId` groups them and `incoming` is `true` for what the customer wrote.

## After your first read

The first read works from your file as it is. To get [Dimensions](/dimensions) on the same data, add it under **Data Sources**: see [Upload a file](/data/upload-a-file). Your project's **Overview** tab walks through the steps.