Question 1

Do I need my data stack set up before using Pavo?

Accepted Answer

No. You can start with one streaming goal, such as improving content discovery or repeat engagement. Connect the relevant sources so Pavo can investigate audience behavior, recommendation logic and past experiments. Your team reviews the findings and approves a plan before agents build and test changes. You can add more sources and goals as you learn, with each project retaining evidence about your audiences, content and the changes your team tested against the agreed business goals.

Question 2

How easy is it to set up Pavo?

Accepted Answer

Start with a streaming goal, connect sources, review Pavo’s plan, then approve your first improvement project.

Question 3

Does Pavo replace our streaming, marketing, or testing tools?

Accepted Answer

Your team keeps its streaming, messaging and experimentation tools. Pavo connects warehouses, code and experiment history to understand your platform and prepare improvements for review. You can use Snowflake, Databricks, GitHub, Eppo, Statsig or BigQuery as relevant sources. Your existing systems continue serving content, running campaigns and measuring the live experiments you approve.

Question 4

How much data do we need to make Pavo work?

Accepted Answer

The amount depends on your goal, audience size and the effect you want to detect. Check whether your logs capture relevant exposures and outcomes, then agree how much evidence the experiment needs across audience groups and time.

Question 5

We already track listening, viewing and subscriber behaviour in our connected tools. Do we need to rebuild our data setup for Pavo?

Accepted Answer

Start with your existing sources. Pavo can use warehouse data and uploaded exports to investigate listening, viewing and subscriptions. Check access, event definitions and coverage.

Question 6

How do we connect data with Pavo?

Accepted Answer

Connect your warehouse, recommendation code and experiment history, or start with uploaded files. You grant scoped, read-only access to the sources the approved project needs. Pavo reads that context to plan the work. Add listening, viewing or subscription data as your goals and evidence requirements develop.

Question 7

How do we check that a change is ready for a live audience test?

Accepted Answer

Start with the evidence. Pavo evaluates candidates offline against your agreed metric, audience cohorts and guardrails, so your team can review alternatives before using live traffic. Check recommendation relevance, downstream engagement and audience impact alongside implementation risks. Your team then approves a controlled experiment to measure live results. An offline gain supports further testing; your live experiment measures whether the change improves audience outcomes within the agreed limits.

Question 8

Does Pavo need fixed audience segments?

Accepted Answer

No. Pavo can investigate which audience signals matter, using your existing data and evaluating differences between user groups.

Question 9

Does Pavo focus only on more plays?

Accepted Answer

You choose the outcome. Pavo can compare listening, viewing, satisfaction, retention and audience value. Your team defines success and guardrails, and interprets engagement signals for each audience and content format.

Question 10

How does Pavo self-improve?

Accepted Answer

Pavo keeps accepted findings, evidence, and decisions about your platform. Each new project starts with that knowledge, so agents can build on what your team has learned instead of repeating earlier work each time.

Question 11

Is Pavo another A/B testing platform?

Accepted Answer

Pavo helps your team prepare better experiments: understanding the platform, finding opportunities, building candidates and evaluating them offline. Agents explore approaches in parallel and prepare changes for review. Your existing experimentation tools measure live impact through controlled tests. Pavo retains the findings and decisions, so your next project can use what you learned about recommendations, messages, audience behavior and the changes worth testing.

Question 12

We already have a great data science team. Why would we use Pavo?

Accepted Answer

Pavo gives your team more capacity to explore. Agents handle analysis, candidate development and evaluation in parallel, while your scientists set goals and review evidence. Your team guides the methods and decides which changes deserve a live experiment.

Question 13

We do not have a data science team. Can we still benefit from Pavo?

Accepted Answer

Yes. Through an enterprise engagement, a Pavo applied scientist can help your team define goals, run experiments and review results. You can build your own capability as you work alongside them.

Question 14

If Pavo runs our experiments, will we lose control?

Accepted Answer

You stay in control. Your team approves the plan and makes decisions at review checkpoints. You can redirect agents, add work or change direction before approving external or production actions.

Question 15

Can we still release content and run campaigns when Pavo runs?

Accepted Answer

Yes. Your team keeps using its existing tools to release content, run campaigns and manage the platform. Share changes that affect Pavo’s current question, such as a new release or pricing change, so agents can update the plan and interpret experiment results.

Question 16

Can Pavo build and test improvements, or does it only recommend changes?

Accepted Answer

Yes. Pavo’s agents can build candidate models, develop code and evaluate approaches offline, then prepare PRs for review. Your team checks the evidence and approves changes before production.

Question 17

Can Pavo help us improve discovery, repeat engagement and audience value as well as user acquisition?

Accepted Answer

Yes. Pavo can investigate acquisition quality, content discovery, queue recommendations, return engagement, subscriber retention and plan offers. Start with a goal, agree audience outcomes and guardrails, then evaluate candidates before your team approves a controlled live experiment.

Question 18

Why should we trust Pavo’s results and technology?

Accepted Answer

Your team can inspect the work. Pavo records the sources, code, evaluations and decisions behind a result, including its limits. Review the evidence at checkpoints and redirect work that needs improvement. Predictions and offline gains help select candidates; controlled live experiments measure audience impact. Your team decides which findings to accept and which changes are ready to ship to your streaming platform.

Continuously improve discovery, engagement, retention and audience value with agentic experimentation.

Picked for tonight

Turn audience signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what drives lasting audience value

Customer spotlight

Keep improvingwith the tools you already use

From audience insight to validated change, Pavo runs the improvement loop.

Understand and plan

Build and test

Review

Learn and repeat

Deploy with confidence,on your terms

Your Questions,Answered

Continuously improve discovery, engagement, retention and audience value with agentic experimentation.Continuously improvediscoveryengagementretentionaudience value withagentic experimentation.

Turn audience signals intosmarter growth experiments

Understand

Run parallel experiments

Self-improve

Test what drives lasting audience value

Acquire valuable users

Improve content discovery

Personalize each session

Bring audiences back

Retain more subscribers

Grow audience value

Customer spotlight

Keep improvingwith the tools you already use

Snowflake

Databricks

GitHub

Eppo

Statsig

Google BigQuery