You're a Meta PM supporting Instagram stories and shopping. Your data scientists are telling you that shopping is cannibalizing stories. What would you do next, if anything?

Introduction

The trade-off we're examining is between Instagram stories and shopping, with data suggesting that shopping is cannibalizing stories. This scenario presents a complex product challenge that requires careful analysis of user behavior, business goals, and platform dynamics. I'll approach this by first clarifying the situation, then diving into a detailed analysis of the trade-off, its potential impacts, and finally, recommending a course of action.

Step 1

Clarifying Questions (3 minutes)

To fully understand the situation, I'd like to ask a few key questions:

• What specific metrics indicate that shopping is cannibalizing stories?

• Why it matters: This helps quantify the issue and identify which aspects of stories are being affected.
• Hypothetical answer: Time spent on stories has decreased by 15% since the introduction of shopping features.
• Impact: This would guide our focus on engagement metrics in our analysis.

• Are there particular user segments where this cannibalization is more pronounced?

• Why it matters: Identifies if this is a widespread issue or specific to certain user groups.
• Hypothetical answer: The effect is strongest among users aged 18-24 in urban areas.
• Impact: This would inform our targeting strategy for any interventions or experiments.

• What's the current revenue split between stories and shopping?

• Why it matters: Helps understand the business impact of this cannibalization.
• Hypothetical answer: Shopping currently generates 30% more revenue than stories advertising.
• Impact: This would influence how we weigh engagement versus monetization in our decision-making.

• Have we seen any changes in creator behavior or content production?

• Why it matters: Creators are key stakeholders in the Instagram ecosystem.
• Hypothetical answer: There's been a 10% decrease in story creation from top creators.
• Impact: This would highlight the need to consider creator incentives in our solution.

• What's the strategic priority for Instagram right now - user growth, engagement, or monetization?

• Why it matters: Aligns our approach with broader company goals.
• Hypothetical answer: The current focus is on maintaining user engagement while growing monetization.
• Impact: This would guide our balance between preserving stories' engagement and leveraging shopping's revenue potential.

Step 2

Trade-off Type Identification (1 minute)

This scenario falls under the trade-off sub-type (c): different products on the same surface. Stories and shopping are distinct features competing for user attention and engagement within the Instagram platform.

Identifying this trade-off type informs our strategic approach by highlighting the need to balance two potentially complementary but currently competing features. It impacts our analysis by focusing on how these features interact within the user experience, their respective value propositions, and how we might optimize their coexistence rather than choosing one over the other.

Step 3

Product Understanding (5 minutes)

Instagram stories and shopping are both core features of the Instagram platform, each serving distinct but potentially overlapping user needs.

Stories:

• Ephemeral content that disappears after 24 hours
• Allows users to share moments from their day
• Includes features like filters, stickers, and interactive elements
• Serves as a key engagement driver for both regular users and creators

Shopping:

• Allows users to discover and purchase products directly within the app
• Includes features like product tags, collections, and checkout
• Provides monetization opportunities for businesses and creators
• Aims to create a seamless shopping experience within the social context

Key stakeholders include:

1. Users: Seeking entertainment, connection, and now, shopping convenience
2. Creators: Looking to engage their audience and monetize their content
3. Businesses: Aiming to reach customers and drive sales
4. Instagram/Meta: Balancing user engagement with revenue generation

The value proposition of Instagram lies in its ability to connect people through visual content while increasingly serving as a platform for commerce. This aligns with Meta's mission to bring the world closer together, now extending into economic opportunities.

The user flow typically involves users scrolling through their feed, watching stories, and potentially discovering products they can purchase. Creators produce content that can either be purely engaging (stories) or commercial (shopping), while businesses leverage both features to reach and convert customers.

Step 4

Trade-off Agreement and Hypothesis (5 minutes)

The trade-off we're considering is between maintaining high engagement with stories versus growing the shopping feature for increased monetization. Our hypothesis is that as users spend more time exploring and using shopping features, they're allocating less time to consuming and creating stories.

This trade-off is being considered because:

1. Stories have been a key driver of user engagement and platform stickiness
2. Shopping represents a significant revenue opportunity and aligns with the platform's evolution towards e-commerce

Potential impacts:

Considering different user types:

• Regular users might find the platform less engaging if stories content decreases
• Creators might struggle to maintain audience connection if story views drop
• Businesses could see improved ROI through direct sales, but potentially reduced brand engagement

If we were to fully lean into shopping at the expense of stories for an extended period:

• Extreme positive outcome: Instagram becomes the go-to platform for social commerce, significantly increasing revenue
• Extreme negative outcome: User engagement plummets as the app loses its social appeal, leading to a decline in active users and eventual platform value

Step 5

Key Metrics Identification (4 minutes)

North Star Metric: Daily Active Users (DAU) This metric aligns with the higher-level goal of maintaining a strong, engaged user base while allowing for the growth of new features like shopping.

Supporting metrics:

1. Time spent on stories vs. shopping

   • Importance: Directly measures the trade-off in user attention
   • Stakeholder relation: Affects user engagement and creator content strategy

2. Story creation rate

   • Importance: Indicates creator engagement and content supply
   • Stakeholder relation: Critical for creators and overall platform health

3. Shopping conversion rate

   • Importance: Measures the effectiveness of the shopping feature
   • Stakeholder relation: Key for businesses and Instagram's revenue

4. Revenue per user

   • Importance: Tracks monetization progress
   • Stakeholder relation: Critical for Instagram's business model

5. User satisfaction score

   • Importance: Ensures changes don't negatively impact user experience
   • Stakeholder relation: Affects all stakeholders by indicating platform health

6. Creator retention rate

   • Importance: Measures platform attractiveness for content producers
   • Stakeholder relation: Critical for maintaining a vibrant content ecosystem

7. Shopping feature adoption rate

   • Importance: Indicates user interest and potential for shopping growth
   • Stakeholder relation: Important for businesses and Instagram's strategic direction

Step 6

Experiment Design (3 minutes)

I propose an A/B/C test to validate our hypotheses about the stories-shopping trade-off:

Experiment Hypothesis: Adjusting the visibility and integration of shopping features will impact user engagement with stories without significantly affecting overall shopping revenue.

Control Group (A): Current implementation Treatment Group B: Reduced shopping visibility in stories Treatment Group C: Enhanced integration of stories and shopping features

Target Audience: 5% of our user base, stratified to represent key demographics and usage patterns Duration: 4 weeks to account for novelty effects and establish stable behavior patterns

Key considerations:

• Randomization: Use a hash function on user IDs to ensure consistent group assignment
• Sample size: Calculated to detect a 5% change in our primary metrics with 95% confidence
• Novelty effect mitigation: Analyze data in week-over-week cohorts to identify and account for initial behavior changes

Guardrail metrics:

• Overall app usage time (to ensure we're not driving users away)
• Creator posting frequency (to monitor content supply)
• Revenue per user (to watch for unexpected monetization impacts)

Step 7

Data Analysis Plan (3 minutes)

To evaluate the experiment results, we'll analyze:

1. Primary metrics: Time spent on stories, shopping conversion rate, and revenue per user
2. Secondary metrics: Story creation rate, shopping feature adoption rate, and user satisfaction scores

We'll interpret outcomes by:

• Comparing each treatment group to the control and to each other
• Looking at the delta in our metrics and assessing statistical significance

If metrics move in opposite directions, we'll need to weigh the trade-offs. For example, if we see increased story engagement but decreased shopping revenue, we'll need to calculate the long-term value of engagement versus the short-term revenue impact.

Specific analyses:

• Segment analysis: Break down results by user age, geography, and historical engagement levels
• Cohort analysis: Track how behavior changes over time for users exposed to each variant
• Correlation study: Examine the relationship between story engagement and shopping activity at the user level

We'll pay special attention to any anomalies, such as unexpected increases in both stories and shopping engagement, as these could reveal new opportunities for feature synergy.

Step 8

Decision Framework (4 minutes)

Decision tree approach:

Red flags that would prevent shipping:

• Significant decline in overall user satisfaction
• Drop in creator retention rate
• Unexpected negative impact on other key features (e.g., feed engagement)

For mixed results:

• Prioritize based on current strategic goals (e.g., engagement vs. monetization)
• Consider the long-term implications of favoring one metric over another
• Consult with cross-functional teams to understand broader impacts

If guardrail metrics are hit but target metrics aren't:

• Don't ship, but analyze the cause of the guardrail metric movement
• Consider if the guardrail metric improvement justifies further exploration of the change

For inconclusive results:

• Extend the experiment duration if the sample size was insufficient
• Conduct qualitative research to gain deeper insights into user behavior
• Iterate on the experiment design to test more specific hypotheses

Step 9

Recommendation and Next Steps (3 minutes)

Based on our analysis, my recommendation would depend on the experiment results. However, assuming we see a positive impact from the enhanced integration of stories and shopping (Treatment C), I would recommend:

1. Gradually roll out the enhanced integration feature to all users
2. Conduct follow-up experiments to optimize the balance between stories and shopping visibility
3. Initiate a user research study to gather qualitative feedback on the new integration
4. Develop a creator education program to help them leverage the integrated features effectively
5. Monitor long-term trends in user engagement and shopping behavior to ensure sustained positive impact

Implications to consider:

• Related features: Assess how this change might affect feed engagement and explore opportunities for similar integrations
• Broader ecosystem: Consider how this might influence our relationships with creators and businesses
• Long-term strategy: Align this move with Instagram's vision of becoming a comprehensive platform for social interaction, content creation, and commerce

To ensure cross-functional alignment:

• Present findings and recommendations to product, engineering, and business leadership
• Collaborate with the design team to refine the user experience based on experiment insights
• Work with the data science team to establish ongoing monitoring of key metrics
• Engage with the partnerships team to communicate changes to key creators and businesses

Expand Your Perspective

• How might TikTok's success with in-app commerce inform our approach to integrating shopping and stories? For example, TikTok's seamless integration of shoppable videos into their main feed.

• Considering the rise of augmented reality in social media, how could AR features be leveraged to create a more immersive shopping experience within stories?

• What alternative approaches could we explore to balance engagement and monetization, such as a separate tab for shopping or a stories-first shopping experience?

Related Topics

• Product Strategy: Balancing user engagement with monetization in social media platforms

• Technical Architecture: Integrating e-commerce capabilities into a content-driven social platform

• User Experience: Designing seamless transitions between content consumption and shopping activities

• Creator Ecosystems: Developing tools and incentives for creators to produce both engaging and commercially viable content

• A/B Testing at Scale: Implementing and analyzing complex experiments in large-scale social media applications