What is Pinterest?

Product Description and Goal:

Pinterest defines itself as a "visual discovery engine for finding ideas such as recipes, home and style inspiration, and more." Users can explore various topics, trends, and creators through search and personalized recommendations. They can discover visual inspiration by browsing images, videos, or products organized as "pins"—digital bookmarks that let users save and revisit content they find appealing. Pinterest also offers tools for refining searches, such as using the camera to find visually similar ideas or filtering results by specific categories like skin tone range or hair pattern. In addition to saving and sharing, users can shop directly from Pins, making it a platform for inspiration and "practical purchasing."

Pinterest's Mission:

Pinterest does not have a separate mission statement page aside from its "About the Company" article. Pinterest initially started as a digital pinboard and scrapbooking platform. The concept of collecting was built into its earliest iterations, which allowed users to organize and share the things they love. According to Ben Silbermann, a cofounder of Pinterest, Pinterest "at its most basic level, it's just about you...It's really about your personal interests."

However, despite having an interesting product, Pinterest struggled to find its footing in monetization during its early stages. Previously, the company ignored revenue generation for the product, meaning monetization features rolled out slowly, and it was too late to catch up to other social media monetization strategies. Only now is Pinterest focusing on providing features for advertisers, including AI and automation campaign features, personalized promotions, and deal ad modules. As you can see in the chart below, Pinterest is still struggling to break even on net income.

Pinterest's Main Issue

What Problems does Pinterest face? (Secondary Research):

As mentioned previously, Pinterest's biggest problem is the business's monetization. How do users view their business model, and does it benefit them and the company? According to a Pinterest Newsroom article, "55% of Pinners see Pinterest as a place to shop." In their footnotes, they retrieved this statistic from an in-product survey in the US, UK, Canada, Australia, France, Germany, Japan, Brazil, and Mexico. The study was conducted in Q1 2023 with an average 30-day roll-up and 3443 responses. Despite their claims, 55% of Pinners seeing Pinterest primarily as an e-commerce platform was... a suspicious statistic. In my 6 years of using Pinterest and observing Internet users' opinions, I see that the online fan base has a different perspective.

But how do I validate these views? Without having access to internal data or insight into how the study was conducted, there aren't many resources I can turn to where users are open about their opinions without being incentivized. I tried looking for a Pinterest Discord or community section, but there isn't one. Additionally, I scoured Facebook to see if they have a Pinterest group, and they do! Unfortunately, most of the posts are ads for independent bloggers. There were only a few posts about users' opinions regarding Pinterest's features. With little luck getting secondary research, I tried Pinterest's subreddit and discovered a mine of user criticism. I spent a few days saving threads and compiling them into a document, organizing the criticisms into an ordered list of problems Reddit users have with Pinterest.

Clarifying Questions for the Users:

Pinterest faces several challenges, including issues with its value proposition, feature functionality, and the impact of its chosen business model on its user base. Unfortunately, I can only use Reddit as a secondary source to evaluate the user base's perception of Pinterest. However, based on these complaints, I can start thinking about the clarifying questions I should ask when I interview actual Pinterest users.

Table

User Segments

Primary Research:

While browsing Reddit comments provides some insight into user frustrations, it lacks the depth of understanding that comes from directly interacting and talking with users. Observing complaints online doesn’t compare to walking through a day in the life of a Pinterest user or hearing firsthand about their motivations and pain points, so I decided to interview University of Washington students to dive into their experiences with Pinterest. Through these conversations, I developed the user persona of Alex Chang, a creatively inspired undergraduate.

Interview Process

To conduct these interviews, I contacted potential participants through various channels, including Discord school club servers, class servers, and announcements shared with the School of Business and one of my professors. Most responses came from Discord users, and I coordinated virtual interview sessions with them. To ensure the process was structured, I created an interview guide and brainstormed questions inspired by the book The Mom Test. While I started with a standard set of questions, I adapted them during the interviews to suit each participant, avoiding repetitive inquiries and making the conversations more personalized. Each session was recorded, so the interview felt more natural, and I could focus more on questions than on analysis.

User Persona

Sources

• Interview Guide

• Interview with Imaan [Loom Recording + Comments from Analysis]

• Interview with Malia [Loom Recording + Comments from Analysis]

• Interview with Mandy [Loom Recording + Comments from Analysis]

• Interview with Tiara [Loom Recording + Comments from Analysis]

Primary Pain Points:

While analyzing the interview transcripts and recordings, I have identified several recurring trends regarding Reddit user frustrations with Pinterest. These include issues with excessive ads, repetitive content, and limited search effectiveness. The interviewees expressed concerns about transparency in ads, distrust in shopping features, and a loss of social and creative functionality. Additionally, challenges with feed personalization, board organization, and the platform’s shift toward commercialization further reveal areas where Pinterest’s user experience could improve. Below is a breakdown of these trends.

Potential Solutions

Ad Overload

Improved Algorithm

Enhanced Search Functionality

Expansion on Social Features

Improved Board Organization

AI Content

Feature Prioritization

Rice, Kano Model, Value vs. Effort Matrix

Instead of choosing one primary framework, I used a combination of three to assess Pinterest's business challenges and user pain points. A single prioritization framework would not fully address both aspects, so a hybrid approach would be the best solution.

RICE Framework (Business-impact-driven Decisions)

RICE will help me prioritize the most significant features that will have the biggest business impacts without sacrificing user experience. Since Pinterest's primary revenue model relies on advertising, Pinterest must balance monetization with user satisfaction to prevent churn.

MoSCoW Method (Clear Priority Categorization)

The MoSCow method will help me categorize features by importance, enabling me to identify essential features (must-haves) and deprioritize less critical initiatives. This will help prevent scope creep and align development with user needs and business goals.

Value vs. Effort Matrix (Quick Wins)

The Value vs. Effort matrix will help me identify quick wins that Pinterest can implement with minimal effort while also highlighting larger projects that would require strategic resource allocation. With this matrix, I quickly categorize the high-impact features first.

RICE Table Scores

MoSCoW Framework

Impact vs. Effort Matrix

Note:
The specific impact metric I am using is "the extent to which customer engagement or satisfaction would be impacted," and I will determine effort with this criterion: High effort > 18 person-months, Low effort < 18 person-months

Final Rankings

User Needs vs. Business Strategy

Discrepancies between User Priorities and the Prioritization Framework

During my analysis, I recognized a SIGNIFICANT difference between the features that best align with user (primarily interviewee) pain points and those that ranked highest in the prioritization framework. While solutions such as the Ad Control Panel and Improved Learning Algorithm directly address users' most immediate frustrations, the prioritization framework instead ranked Advanced AI Content Filters and Board Discovery as the top solutions.

This discrepancy highlights a super important challenge in product decision-making: how do you balance what users vocalize as their most urgent needs with broader business goals, feasibility, and long-term impact?

Why is there such a big difference?

Several factors contribute to this gap between user priorities and the prioritization rankings:

Business Impact vs. User Experience Priorities

• From a user perspective, the biggest frustrations, such as ad overload, search relevancy issues, and algorithmic repetition, are immediate usability problems that degrade their experience daily. These issues affect engagement and retention, which are important for short-term user satisfaction.

• HOWEVER, from a business perspective, Pinterest’s revenue is driven by ad engagement and automation features. Solutions like AI content filtering align with Pinterest’s long-term strategic growth by reinforcing content authenticity, improving user trust, and maintaining engagement without drastically altering revenue models.

• The Ad Control Panel, for example, is a high-risk intervention. If users significantly reduce ad exposure, it could negatively impact Pinterest’s primary revenue stream. This makes it a complex problem that requires internal testing and validation, which I do not have the capacity to analyze with the data I currently have.

Feature Scope & Technical Complexity

• Features such as Visual Search Breakdown and algorithmic improvements require substantial backend development to refine machine learning models, optimize search indexing, and improve content recommendations. These resource-intensive features require extensive testing, making them harder to justify within this study's constraints.

• In contrast, Advanced AI Content Filters are a more contained, implementable solution. They provide a new customization layer without requiring deep changes to Pinterest’s core algorithms.

Long-Term Strategy vs. Short-Term Fixes

• User concerns reflect immediate usability problems, but the prioritization framework considers Pinterest’s long-term sustainability.

• AI Content Transparency and filtering align with industry trends in ethical AI use and content authenticity, helping Pinterest stay ahead of future challenges while maintaining user trust.

• In contrast, ad-related changes introduce financial uncertainty, which again, if not carefully tested and refined, could disrupt Pinterest’s ad-driven business model.

Primary Feature

Product Introduction

The AI Content Filtering feature enhances user control over AI-generated content in Pinterest’s home feed, enabling a more tailored, transparent browsing experience. In the "Tune Your Home Feed" settings, users can adjust their AI Content Balance to No AI Content, Balanced AI + Human Content, or AI-Only Content. Additionally, users can fine-tune their preferences with granular filters, enabling or disabling specific AI-generated content types, such as artwork, photography, stock images, and designs. This feature ensures that Pinterest remains a platform for authentic inspiration, allowing users to curate their experience based on personal preferences and creative needs.

Why I Chose This Feature Based on Prioritization Frameworks

To determine the most impactful feature that aligns with both user needs and Pinterest’s business goals, I evaluated the possible solutions with the rankings from the prioritization frameworks:

1. RICE: AI Content Filters scored the highest (76.49) because they can improve content discovery, trust, and engagement with minimal technical complexity.

2. With the MoSCoW method, I categorized it as a “Must-Have” because of strong user demand for AI transparency and the need for better content control.

3. Value vs. Effort Matrix: This feature is a high-value, low-effort initiative, making it a quick win that improves user experience without requiring major backend overhauls.

Why This Matters?

• Users have expressed frustration with AI-generated content cluttering their feeds. This feature strengthens Pinterest’s reputation as a trusted, user-first discovery platform.

• AI transparency is a growing industry concern, and giving users control over AI-generated content will hopefully increase trust and engagement.

• This feature enhances content personalization, aligning with Pinterest’s mission to provide relevant, user-driven inspiration.

Why I Chose Advanced AI Content Filters Over Other Top Prioritized Solutions

When evaluating which feature to prototype, I carefully considered the prioritized solutions, primarily based on interviewee concerns. While features like Improved Learning Algorithm, Board Discovery, and Ad Control Panel all address key user frustrations, I ultimately chose Advanced AI Content Filters for the following reasons:

Improved Learning Algorithm: The Algorithm Exists—It Just Needs to Improve (Hence the Name)

• The problem with Pinterest’s algorithm is that it does not effectively surface relevant content. Users experience repetitive pins, outdated interests being pushed, and an overall decline in personalized recommendations.

• This does not necessarily require a brand-new feature; instead, Pinterest needs to fine-tune and optimize its algorithm.

• Users also did not mention (and many might not even be aware of) Pinterest’s “Tune Your Home Feed” feature under profile settings. This feature allows users to hide ideas based on recent activity, input specific interests to refine recommendations, and control ideas from boards, followers, and past searches.

• Because this feature already exists, the real issue might be a marketing and awareness problem rather than a lack of functionality. Many users might not even know they can personalize their feed, meaning education and visibility improvements could significantly impact the feature without requiring a major algorithm overhaul.

Board Discovery: A Previously Removed Feature

• Board Discovery was a core feature on Pinterest. Users could find and browse boards curated by other users. Pinterest still curates boards today, specifically for brands and featured collections, but users can no longer quickly discover other users’ boards.

• Many interviewees (and Reddit discussions) expressed nostalgia for this feature, which suggests some user demand for it.

• However, I cannot access internal data to determine why this feature was removed. Pinterest may have deliberately removed board discovery due to engagement, monetization, or moderation issues.

• Since this feature was actively discontinued, reintroducing it poses unknown risks. Without insight into Pinterest’s decision-making process, it’s difficult to determine whether this feature would be viable for reimplementation.

Ad Control Panel: A BIG User Demand but a Business Risk

• Ad Overload was the #1 source of user frustration, making the Ad Control Panel one of the most impactful features to introduce.

• However, Pinterest relies on ad revenue as its primary source of monetization. Allowing users to limit ads too aggressively could significantly affect revenue, which requires internal testing and financial analysis.

• I don’t have access to Pinterest’s ad engagement data or the ability to measure the impact of reducing ad placements. Without this, I can’t confidently propose changes to the ad structure without knowing the financial consequences.

• Because this solution would require extensive A/B testing and business analysis, it is beyond the scope of this study.

Product Mock-Up

The Advanced AI Content Filters feature includes toggle controls that let users enable or disable AI-generated content at different levels.

AI Content Balance (Toggles)
Users can choose their preferred AI exposure level by toggling between:

• No AI Content → Completely removes AI-generated Pins from feed and search.

• Balanced AI + Human Content→ Shows a mix of AI-generated and human-created content.

• AI-Only Content → Prioritizes AI-generated Pins over human-created content.

• How It Works: Only one toggle can be enabled at a time. If a user selects "No AI Content," the other two options will be disabled. I also used toggles instead of a slider (even though it would make more sense) to follow Pinterest's UI design, since they seem to dislike sliders.

AI Content Type Filters (Toggles)
Users can further customize their AI filtering preferences by toggling specific AI content types ON or OFF:

• AI-Generated Artwork

• AI-Generated Photography

• AI-Generated Stock Images

• AI-Generated Designs

• How It Works: If "No AI Content" is toggled ON, these individual AI content type filters are automatically disabled. Users can also click "Turn Off All" to quickly disable all AI-generated content types.

Expected Impact & Benefits

• Increases User Trust: Filtering controls enhance transparency, preventing misleading recommendations.

• Enhances Personalization: Users can fine-tune their inspiration feeds, keeping Pinterest aligned with their creative needs.

• Reduces Frustration: Users frustrated by excessive AI-generated content can now curate their feeds to their preferences.

• Aligns with Pinterest’s Business Goals: Maintains high engagement by making content discovery more relevant while allowing Pinterest to preserve AI-powered features for users who prefer them.

Figma Mock-Up

Feature Validation

Now, to validate this feature, I would need to test several hypotheses about user behavior and content preferences.

Hypotheses:

• Users want control over AI-generated content: If users feel overwhelmed by AI-generated Pins, they will use filtering options to refine their feed.

• A toggle-based approach improves trust and transparency. Users will appreciate the ability to turn off AI-generated content to avoid misleading or irrelevant results.

• AI content filtering increases user engagement: Users who customize their AI content exposure will spend more time browsing because they see more relevant and inspiring content.

• Users will benefit from AI categorization by content type: Integrating AI content into categories (e.g., art, stock images, photography) will improve search accuracy and personalized inspiration.

Assumptions:

• Users can distinguish between AI-generated and human-created content and care about the difference.

• Too much AI content negatively impacts discovery and creativity, especially for hobbyists, designers, and artists.

• AI filtering will not significantly affect user engagement among those who do not actively adjust settings.

• Users prefer flexibility in content curation rather than an all-or-nothing approach.

How can I validate this Feature?

At the time of writing, I've been working on this case study for about 3 months, and I'm ready to move on. BUT if I were to continue this case study (e.g., part 2), I would devise a plan to validate the feature, as you would in a real PM job. To validate the effectiveness of my proposed solution, I would conduct follow-up user interviews with the same participants from my previous research and a new group. These interviews will focus on how users interpret the feature's UI and theoretical functionality rather than direct interaction with a working prototype. Examples of follow-up questions:

• Last time we spoke, you mentioned frustrations with AI content in your feed. Does this UI help address those issues? What specifically do you think would work well? Is there anything about it that doesn’t entirely solve the problem?

• You previously talked about how to adjust your feed preferences. If this AI filtering feature were available, how would you expect to use it? How often do you think you’d adjust these settings? Would you expect these settings to apply only to your feed or search results?

• You mentioned that sometimes you see AI-generated content you don’t trust or don’t want to see. Looking at these settings, do you feel like you’d have enough control over that? If not, what’s missing? Have you seen other platforms handle this better?

• When you first look at this UI, what do you immediately understand about how it works? Is there anything confusing or unclear? What would make this feature more intuitive for you?

• Last time, you mentioned that you actively seek certain types of content. How do you think this feature would impact your ability to find those? How would this feature affect your ability to find those? Would you expect this to improve your experience? Are there any issues with these filters that might prevent you from seeing what you want?

• What would it be if you could change one thing about this AI filtering UI? Why is that important to you? Have you seen similar features elsewhere that worked better?

Metrics to Measure:

To evaluate the effectiveness of AI Content Preferences, I would track the following metrics post-launch. I also described my success criteria, but, obviously, it depends on the company's internal goals, which I again don't have access to.

Conclusion & Takeaways

Over the past three months, from January to late March 2025, I poured my energy into this case study, and it turned out to be a challenging yet rewarding journey.

One of the primary lessons was understanding Pinterest’s core goal: figuring out what the company aims to achieve for its users and how it stands out in a crowded market. This insight was essential in framing the entire study. I also had my first deep dive into prioritization frameworks, exploring methods such as RICE and MoSCoW. In the past, I’d relied on more straightforward prioritization tools, so this was a refreshing change, even if building a compelling argument for why I chose a particular feature over others was one of the toughest parts of the process.

Another challenge was the lack of internal data and a team to brainstorm with. I relied heavily on secondary research and interviews to gather the necessary insights, which made success metrics largely subjective. Despite these hurdles, I’m really proud of how it turned out! I overcame my usual aversion to Figma, which led me to learn a lot about web design while creating clear, engaging data visuals.

Ultimately, this case study is close to my heart because I enjoy using Pinterest, and it’s my dream to work there! Although some solutions I envision (such as different layout options) might not be widely desired, they reflect my personal passion for the platform.