Sakshi News App: Telugu Cinema Feature Test

Solution

In the context of the Sakshi News App A/B test for the "Daily Telugu Cinema Updates" feature, low statistical power (e.g., 30%) means that the experiment had a low probability of detecting a true effect if one actually existed.

• Definition of Statistical Power: Power is the probability of correctly rejecting a false null hypothesis. In this case, the null hypothesis (H₀) would be that the new cinema feature has no positive impact on user retention compared to the control group (no feature). The alternative hypothesis (H₁) would be that the feature does have a positive impact on user retention.
• Meaning of 30% Power: A 30% statistical power signifies that if the "Daily Telugu Cinema Updates" feature genuinely improved user retention by a specific, predetermined amount (the Minimum Detectable Effect or MDE the Sakshi team at their Madhapur office considered meaningful), the A/B test conducted during the Prabhas movie release week had only a 30% chance of producing a statistically significant result that would lead the team to correctly conclude the feature was effective.
• Implication of Low Power (Type II Error): Conversely, this means there was a 70% chance (Beta = 1 - Power = 1 - 0.30 = 0.70) of committing a Type II error (false negative). A Type II error occurs when the test fails to reject the null hypothesis even though it is false (i.e., the feature is effective, but the test doesn't detect it).

Specific Probability Estimated by 30% Power:

The 30% power was an estimate of the following probability:

P(Test correctly concludes the cinema feature improves retention | The cinema feature truly does improve retention by at least the MDE)

In relation to detecting genuine differences in retention between regular news readers and those interested in Telugu film industry updates, the 30% power implies that if the new feature specifically caused a meaningful increase in retention among its target audience (those interested in Telugu cinema updates, who might otherwise churn or engage less), the Sakshi News App's test was poorly equipped to discover this. The test had a high likelihood of overlooking a real, positive impact on this specific segment of their 2 million Telugu users across Telangana and Andhra Pradesh.

Solution

Making a decision based on an underpowered A/B test, like the one Sakshi News App conducted for its "Daily Telugu Cinema Updates" feature, can have several negative consequences for their business and competitive standing:

• 1. Missed Opportunity (High Risk of False Negative):
  • The primary consequence is the high probability (70% in this case) of a Type II error. Sakshi News App may have discarded a feature that genuinely improves user retention, especially among the segment interested in Telugu cinema. This means lost potential for increased user engagement, longer session times, higher ad impressions, and ultimately, better monetization for their 2 million Telugu users.
• 2. Wasted Resources:
  • Time and resources were invested by the product and engineering teams in Hyderabad to develop and test the "Daily Telugu Cinema Updates" feature. If a valuable feature is incorrectly rejected, these resources are effectively wasted.
• 3. Incorrect Strategic Insights:
  • The team might incorrectly conclude that features related to Telugu cinema are not effective for retention. This could stifle future innovation or lead to misinformed product strategy, deterring them from exploring similar potentially successful features.
• 4. Competitive Disadvantage:
  • During major regional events like Sankranti film releases, major Prabhas movie releases, or Tollywood award seasons, user interest in cinema news peaks across Telangana and Andhra Pradesh.
  • If competitors like Eenadu and TV9 offer engaging cinema update features, and Sakshi News App does not (due to wrongly discarding their feature), Sakshi could lose users to these competitors. Users seeking comprehensive cinema news might switch to apps that better cater to this interest.
  • This can lead to a loss of market share and make it harder to attract and retain users who value timely and engaging entertainment content alongside general news.
• 5. Reduced User Satisfaction and Loyalty:
  • A segment of users (those interested in Telugu film industry updates) might be disappointed by the lack of such a feature, especially if they were part of the test group that saw and liked it. This could lead to lower satisfaction and loyalty among this user base.
• 6. Inefficient Learning and Iteration:
  • A/B testing is a learning process. An underpowered test provides unreliable feedback, hindering the team's ability to learn effectively from experiments and iterate on product features. The Madhapur office review revealed this flaw, but the initial decision was already made.

In essence, deciding based on an underpowered test means Sakshi News App might be leaving value on the table and allowing competitors to gain an edge, particularly in capturing the significant audience segment passionate about Telugu cinema during peak interest periods.

Solution

Several factors influence statistical power. For the Sakshi News App's test of the "Daily Telugu Cinema Updates" feature, the low power likely stemmed from one or more of these:

Factors Contributing to Low Power:

• 1. Insufficient Sample Size: The most common reason. If the number of users in each group (with the feature vs. without) was too small relative to the effect size and variance, the test wouldn't have enough data to reliably detect a true difference. Even with 2 million total users, the A/B test might have been run on a small fraction.
• 2. Small Effect Size (MDE): The actual impact of the "Daily Telugu Cinema Updates" feature on user retention might have been modest. Smaller true effects require larger sample sizes to detect with adequate power.
• 3. High Variance in the Metric: User retention can be a noisy metric. Many factors influence why a user stays or leaves an app. High variability makes it harder to distinguish the signal (effect of the feature) from the noise.
• 4. Chosen Significance Level (Alpha): While typically 0.05, if a more stringent alpha was used (unlikely if power was an issue), it would reduce power for a given sample size.
• 5. Measurement Error or Imprecise Metric Definition: If "retention" was not defined or measured consistently and accurately.

How the Team Could Have Increased Power:

• 1. Increase Sample Size: Allocate more users from their 2 million Telugu user base to the A/B test. This is often the most direct way to boost power.
• 2. Increase Experiment Duration: Running the test for a longer period collects more data points per user and can average out short-term fluctuations, effectively increasing the sample of user-time periods.
• 3. Increase the Minimum Detectable Effect (MDE): This is more about product strategy – designing a feature with a more substantial expected impact. However, for a given feature, you can decide to only care about detecting larger effects, which requires less power (but you might miss smaller, still valuable, effects).
• 4. Reduce Variance in the Metric:
  • Targeting/Segmentation: Test the feature on a more homogeneous subgroup of users who are more likely to be interested (e.g., users who frequently read entertainment news). Their response might be less varied.
  • Use more stable or sensitive metrics: For example, instead of binary retention (retained/not retained), use a continuous measure like "days active in next X days" or use pre-experiment data to control for baseline user activity (e.g., using CUPED - Controlled-experiment Using Pre-Experiment Data).
• 5. Increase Alpha (Significance Level): For example, from 0.05 to 0.10. This increases power but also increases the Type I error rate (false positives). Generally not recommended unless the cost of a false negative is much higher than a false positive.

Running Test During Regular Period vs. Blockbuster Film Release:

• Blockbuster Film Release Week (e.g., Prabhas movie):
  • Potential Effect on Power: Could decrease power. User engagement and retention might be artificially inflated across both groups due to the general excitement. This high baseline and potentially increased variance can make the specific, incremental effect of the "Daily Telugu Cinema Updates" feature harder to detect (smaller relative effect size against a noisy background). While the absolute number of interactions with cinema content might be high, the difference attributable to the feature might be masked.
  • Relevance: Tests the feature under peak load/interest, which might be a specific use case the Sakshi team in Hyderabad wants to understand.
• Regular Period:
  • Potential Effect on Power: Could increase power (for a given absolute effect size). The baseline user retention might be lower and less volatile. Any lift provided by the feature might stand out more clearly against this quieter background, leading to a larger relative effect size or lower variance.
  • Relevance: Better reflects the feature's day-to-day impact on user retention, which is important for long-term strategy.

Tradeoffs in Increasing Power for Specific Telugu Audience Segments:

Targeting specific segments like rural users in Rayalaseema versus tech-savvy users in Hyderabad's IT corridor for the "Daily Telugu Cinema Updates" feature involves these tradeoffs:

• 1. Relevance and Effect Size:
  • Pro: The feature's impact (effect size) might be significantly different across these segments. Cinema updates could be highly engaging for tech-savvy Hyderabad users with good data access but less so for rural Rayalaseema users if their primary interests or access patterns differ. Testing on a highly interested segment can yield a larger observed effect size, thus increasing power for that segment.
  • Con: A feature highly effective in one segment might show no effect in another.
• 2. Sample Size within Segments:
  • Pro: If a segment is more homogeneous in its response, variance might be lower, improving power.
  • Con: Achieving adequate sample size within each specific segment can be challenging and may require a much larger overall pool of test participants or longer test durations. Sakshi News App has bureaus across all districts, but the user density and characteristics vary.
• 3. Implementation Complexity and Cost:
  • Pro: Insights can lead to personalized feature rollouts.
  • Con: Segmented A/B testing is more complex to set up, track, and analyze. Identifying and reliably targeting users in "rural Rayalaseema" vs. "Hyderabad IT corridor" needs robust segmentation logic.
• 4. Generalizability:
  • Pro: Provides specific insights for targeted strategies.
  • Con: Findings from one segment (e.g., Hyderabad IT corridor) may not be generalizable to other segments or the entire Telugu user base of 2 million. A decision to launch a feature based on success in one segment needs careful consideration for others.

The Sakshi News App team at their Madhapur office must carefully plan their A/B tests, considering these factors to ensure they have sufficient power to make informed product decisions, especially given their large user base across diverse regions of Telangana and Andhra Pradesh.