Offline User Behavior Analytics
for Millions of Events

Inspect exported event CSVs locally with DataOlllo. Filter event names, compare records, and group existing user or session identifiers to prepare a summary for your product analysis.

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From Raw Events to Clear Insights

Start with an export containing an event name, timestamp, and user or session ID. Filter events such as page_view and click_buy, then group and count the records. Funnel order, retention windows, and session boundaries need explicit definitions; a grouped event count alone does not establish those metrics.

RAW EVENT LOGS
6,430,291 rows of clickstream data.
timestamp
user_id
event_name
2026-04-01T10:00:12Z
usr_992aX
page_view
2026-04-01T10:05:44Z
usr_992aX
checkout_started
2026-04-01T11:00:05Z
usr_104bY
page_view
↓
SUMMARIZED
Grouped by User with Conversion Metrics.
user_id
total_events
converted
usr_992aX
14
YES
usr_104bY
1
NO

Built for Product & Growth Teams

Inspect Event Exports

Check field types and missing identifiers in a preview before processing a large export. Follow the large CSV filtering guide to narrow your event data.

Group Existing Identifiers

Use an existing session ID or user ID to summarize events. If the export has no session ID, define and validate session boundaries before interpreting results as sessions.

Local Analysis

Standard CSV operations run on your machine. Review optional online AI data flows and your organization's policies before using identifiable event data. See the private CSV analysis guide.

Understand your users without the hassle

Product Managers, Marketers, and UX Researchers who need answers from raw event data but don't want to rely on data scientists or complex SQL queries.

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