1. Pick the right Experiment

To Increase the subscription rate from the wave of users created from the IPL 2024 run - free to paid conversion needs to increase. Not just looking at the IPL project as an income generation tool only from the AVoD model (Advertising Video On Demand) standpoint but also pivoting into the SVoD model (Subscription Video On Demand) standpoint. This starts from Subscription page discovery to then free to paid conversion.

2. Overview

FACT 1: JioCinema saw 11.3 crore viewers log on to the platform on day 1 of the IPL 2024, a 51% increase in viewers compared to the previous edition. The platform, which garnered 449 million viewers during the IPL 2023 season, has set an internal target of 650 million viewers for the IPL 2024.
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FACT 2: Further, Jio till Jan 2024 offered only annual subscriptions for JioCinema Premium @ Rs.999/year. However, from Feb 2024 the company has now changed to a monthly subscription model @ Rs 99 per month.
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There is a clear intent to capitalize on the high discovery/app download rate during the IPL season to convert Free users to paid ones especially given the highly luring nature of the new short & cheap subscription/monetization strategy.

Problem with the approach: It depends on the users exploring the content library of JioCinema but users might only be interested in sports content or not have time apart from the match duration.

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3. Hypothesis

If additional features in the JioCinema app for IPL 2024 including MultiCam, Stats and 360 VR are branded as pro features and hidden behind the paywall of subscription, then it can lead to an increase in free to paid users rates as inquisitive sports fans might want to pay for these features and subscribe to the platform instead of subscribing due to exploration of other content library in the application.

4. Goal

• Primary Goal: To increase the landing rate on the Subscription Plans discovery page i.e the Subscription Plan discovery rate by an absolute 20 to 40 %
  
• Secondary Goal: To achieve a change in free to paid conversion rate/subscription rate of 0.5 to 2 % points given the industry standards for it currently lies on low single digits i.e. between 0-5 %.

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5. Success Metric

Success Metric: (Primary metric to judge the success of the metric)
- Worst Case Scenario: The Subscription Plan discovery rate increases only by an absolute 20% and free to paid conversion rate only increases by 0.5% or lesser

- Best Case Scenario: The subscription Plan discovery rate increases by an absolute 40% or higher and free to paid conversion rate increases by 1% or higher

Health Metric: (Secondary metric to judge if the experiment itself is causing issues)

The churn rate needs to be checked to measure free users logging out of the platform during experimentation vs originally for the duration of the IPL games. It should not drop considerably.

6. Experimentation Design

a. What are you testing?

IPL Streaming with additional features - Current scenario - without changes (Test A)
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Highlighting these features with a pro indicator for paid users behind a paywall (Test B)
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Having a fixed amount of trials before the paywall triggers with the Pro features indicator (Test C)

b. Variation Design

I. Test - A: (Original Scenario)

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Current CTA for Subscription :

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II. Test - B: Highlighting features -MultiCam, Stats and 360 VR with a pro indicator

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III. Test - C: Highlighting features -MultiCam, Stats and 360 VR with a pro indicator but with 3 free trials per week

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c. Audience & Sample Size

Assuming the following:
- Subscription Plan discovery rate is 20%

- the Minimum Detectable Effect in absolute change being 20%
- Statistical significance of 95 %

The Sample size per variation at least needs to be 1,100 ( According to: Optimizely )

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d. Implementation & A/A Test

With two randomized samples of size 1,100 one needs to check with Variation Design Test - A (Original Scenario) if the subscription Plan discovery rate and free-to-paid conversion rate remain similar for both samples if the same Test is run.

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7. Post Experiment

a. Experiment Result

To be filled

A geography-level analysis can help to understand better if the experiment is more successful in specific areas like tier 1 or tier 2 cities. This could be achieved using a Rate and Mix impact study.

b. Release Decision

To be filled based on what the results of the experiment were

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c. Learnings

To be filled

d. Next Steps

To be filled on findings from the experiments on other types of experiments that could be conducted.

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8. Stakeholder Management

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