Self-Service Product Analytics
Get Instant Product Insights for Everyone.
Formula.
You don't need to know SQL or any technical jargon. Just construct your query using natural language thanks to our wizard-like user interface.
Segmentation.
Slice-and-dice the metrics data into different groups and identify the issues through comparison.
Schedule.
Schedule repeated analysis execution automatically and receive an email report whenever anomalies are detected.
Query.
Choose your measures and apply some filters; then make a formula and segment (slice-and-dice) the users with breakdowns or cohorts. That's all it takes to construct your queries!
The best part? Everybody else will be able to easily understand the query results, since all the information is presented in plain English.
Funnel.
Analyze the conversion rate between each step in a user funnel. For example, see how many users dropped out of your registration or checkout flows--and why. You can also segment (slice-and-dice) the data by cohorts and other breakdowns.
You’ll also see the conversion trend over time and get alerts automatically.
Retention.
Understand how different user cohorts retain over time and monitor this critical measurement for your app.
Select a starting event (for example "Install"), followed by a recurring event (like “Listen”), and let the chart show the valuable retention data for each cohort of users (like by "Install Date").
Path.
Show how your users move from a starting event to an ending event using a Sankey diagram, and employ the powerful Path feature to understand users' journeys and identify potential design issues.
Compare among different user groups, add or remove steps, or reverse the direction of the path.
Effect.
Show the impact of a trigger event on an affected event by measuring the changes in event count, frequency or percentage of active users.
This impact analysis tool is very handy to answer questions like, “Did the new feature lead to better X, Y or Z?”
Prediction.
Prediction shows how one action event can “predict” the probability which users will move from a source cohort to a target cohort.
This statistics-heavy correlation analysis feature masterfully identifies key user actions in the app.
Breakdown.
Breakdown usually means examining data by user properties or attributes (for example: gender, age, country, device, or app version). In technical terms, this is sometimes called ”dimension.”
Select a breakdown to segment the data by all the groups of a certain property within any formula.
Cohort.
Cohort generally means a group of users who match certain criteria. For example, “new users installed on August 12, 2019,” or "users who sang more than five songs every day".
Kubit makes it easy and straightforward to define cohorts by populating filters and conditions. Cohorts can be used to segment the metrics as breakdowns within all Formulas.
Event Sequence.
Kubit tracks the exact sequence number whenever an event is triggered for a certain user, such as “first listen,” “third song,” or “fifth launch.”
Use event sequences as filter (like “Launch Sequence = 1" means "first ever launch”) to view new active user data, or measure frequency (like "Sing Frequency > 5 every day" means “active singer cohort”) to define users who sing more than five times a day.
Dashboard.
Unlike the read-only dashboards in traditional business intelligence reporting tools, every user can manage Kubit's dashboard in the browser--and easily change layout and view options on the fly.
Every chart's definition is easily understandable, with the ability to get a deeper analysis using the Formula feature.
KPI.
Define critical metrics as Key Performance Indicators (KPIs) to support anomaly detection monitoring and automated diagnostics.
If there’s an issue with your KPI, Kubit makes it easy and efficient to complete the troubleshooting process and find root causes through AI-powered diagnostics.
Inspect.
Inspect individual user behavior in the app by looking at their events and profile. This sampling step is an important approach for troubleshooting and validating analyses.
Jupyter Integration.
The more technically savvy user can launch any Kubit chart and import the raw data into a Jupyter notebook in just one click. Then simply start conducting in-depth analysis using statistics methods.
Statistics.
You won’t need to create yet another spreadsheet--many of the basic statistics are provided within all the charts. Data points include min/max/average, moving average, standard deviation, Pearson Correlation Coefficient, and Z-Score.
