Business Research Proposal: User Reaction to Farcaster's Micro-Tipping Feature

Recommended Framework: Jobs-to-be-Done (JTBD) + Technology Adoption Lifecycle

• Jobs-to-be-Done (JTBD): This framework posits that users "hire" a product to get a "job" done. This "job" is the real progress they are trying to make in a given circumstance. It has functional, social, and emotional dimensions. Instead of focusing on the feature itself, we will focus on understanding the "jobs" that users currently hire Farcaster's social interactions (likes, follows) for. Does automatic tipping help them do that job better, or does it get in the way?
• Technology Adoption Lifecycle: This model segments the market into categories based on their willingness to adopt new technology: Innovators, Early Adopters, Early Majority, Late Majority, and Laggards. Your user base, from "crypto natives" to "skeptical mainstream users," maps directly onto this spectrum. Each group has different motivations and requires a different strategy.

1. What are the underlying user needs? (Answered by JTBD)
2. How do these needs differ across key user groups? (Answered by the Adoption Lifecycle)

Part 1: Information Collection

1. Web Search Planning

• Search Topics:

  • user sentiment on crypto tipping features Reddit

    : To find candid, unfiltered discussions from crypto-savvy users about their experiences with tipping.

  • discussions on Farcaster creator monetization

    : To understand the current state of earning money on the platform and how users and creators perceive it.

  • barriers to mainstream crypto adoption 2025

    : To gather high-level insights on the hurdles, like trust and complexity, that prevent mainstream users from engaging with crypto services.

  • psychology of microtransactions in social media

    : To understand the established psychological effects of small, frequent financial interactions online, including potential downsides like anxiety or addiction.
• Purpose of Web Search:
  • The goal is not to find definitive answers but to get a "lay of the land." This background knowledge will help refine the questions asked in user interviews, ensuring they are relevant and resonate with the language users themselves are using. It also helps in identifying hypotheses to test (e.g., "Mainstream users are primarily concerned with security and accidental spending").

2. User Interview Planning

• Interview Subjects (Recruitment): We will recruit 12-15 users, divided into three distinct segments based on the Technology Adoption Lifecycle:
  1. The Crypto Natives (Innovators): 4-5 users who are highly active on Farcaster and deeply engaged in the broader Web3 ecosystem.
  2. The Curious Mainstream (Early Majority): 4-5 users who are active on traditional social media (e.g., X, Instagram), may have a Coinbase account, and are on Farcaster but are not power users.
  3. The Crypto Skeptics (Late Majority/Laggards): 4-5 users who are active on traditional social media but have no crypto holdings and express general disinterest or distrust in the technology.
• Interview Purpose: The purpose is to deeply understand the "job" each segment hires social media for and to capture their gut emotional reactions, perceived barriers, and potential excitement for the micro-tipping concept.
• Core Interview Questions:
  1. Context & JTBD: "Walk me through the last time you were scrolling on a social platform like Farcaster or X. What was your main goal? What were you hoping to accomplish or feel?"
     • Analysis Purpose: This uncovers their core 'Job-to-be-Done'—are they looking for connection, information, status, or entertainment?
  2. Probing Existing Motivations: "Think about a time you really loved a post or a creator's work. What did you do to show your appreciation? What were the limitations of just 'liking' it?"
     • Analysis Purpose: This identifies if the 'job' of 'showing greater appreciation' already exists and what workarounds they use, indicating a potential opening for our feature.
  3. Concept Reaction: "I'm going to describe a new feature. Imagine that on Farcaster, every 'like' you give automatically sends a small tip—say, between 2 and 10 cents—to the creator. What is your immediate, gut reaction to that?"
     • Analysis Purpose: This captures the raw, unfiltered emotional response (e.g., "exciting," "scary," "confusing," "annoying") before they rationalize it.
  4. Barrier Identification: "What would be your biggest worries or concerns about using a feature like this? What questions would immediately come to mind?"
     • Analysis Purpose: This directly harvests the barriers to adoption (e.g., 'Will I run out of money?', 'How do I set it up?', 'Is it secure?').
  5. Trigger & Viral Loop Identification: "What would need to be true for this feature to feel exciting or valuable to you? Can you imagine a scenario where you would be telling your friends about it?"
     • Analysis Purpose: This uncovers the potential emotional hooks and conditions for viral growth (e.g., 'If I could see a leaderboard of top supporters', 'If the creator gave a shout-out').

Part 2: Information Analysis

1. Synthesize JTBD for Each Segment:
   • Review the interview notes for each segment (Natives, Curious, Skeptics). For each group, complete the sentence: "When I use social media, the main 'job' I'm trying to get done is ______, so that I can feel ______."
   • Example Output (Hypothetical): For Crypto Natives, the job might be "to participate in the frontier of the decentralized web, so I can feel like a pioneer." For Skeptics, it might be "to be entertained during my downtime, so I can feel relaxed and distracted."
2. Create Segment-Specific Reaction Maps:
   • For each segment, create a simple table. In one column, list their direct quotes and paraphrased concerns from the "Barrier Identification" question. In another column, list their quotes and ideas from the "Trigger Identification" question.
   • Example Output (Hypothetical): For the Curious Mainstream segment, a "Barrier" might be "The mental overhead of tracking micro-payments." A "Trigger" might be "The ability to earn a special badge from a creator for being a top tipper."
3. Analyze the Feature's Fit with the JTBD:
   • Now, combine the first two steps. For each segment, ask: "Does the micro-tipping feature, as described, help or hinder them in doing their core job?"
   • Use this analysis to form your core strategic insight.
   • Example Output (Hypothetical): "For Natives, this feature directly enhances their job of pioneering the decentralized web. For Skeptics, it actively hinders their job of relaxing by introducing financial anxiety and complexity."
4. Develop the Final Strategic Recommendations:
   • Based on your analysis, you will now construct the final, actionable output.
   • User Segmentation: Define 2-3 user personas based on your findings (e.g., "The Web3 Patron," "The Cautious Participant," "The Content Consumer"). Describe their core JTBD and primary concerns.
   • Feature Adoption Strategy: For each persona, recommend a specific strategy.
     • Example: "For the 'Web3 Patron,' launch the feature as-is, emphasizing its on-chain nature. For the 'Cautious Participant,' the feature must be strictly opt-in, include a free starting balance of 'tips,' and offer clear, non-technical onboarding guides."
   • Viral Growth Blueprint: Consolidate all the "Triggers" you identified. Cluster them into themes (e.g., "Status & Recognition," "Gamification," "Direct Creator Interaction"). These themes are your building blocks for creating viral loops. Recommend 2-3 specific features to build based on these themes.
   • Key Metrics for Success: Propose 3-5 metrics to track that align with the JTBD of your target segments. Avoid vanity metrics.
     • Example: Instead of just "Total tips sent," use "Percentage of 'Cautious Participants' who opt-in" and "Creator revenue growth from tips" to measure if the feature is solving the right problems for the right people.