Guided Entry System

Overview

This case study walks through a full product cycle on smallcase: a UX teardown of existing flows, reverse-engineering what makes its core features work, user segmentation, interviews and survey validation, a SWOT analysis, and finally a prioritized, wireframed solution aimed at one problem.

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About smallcase

smallcase exists to introduce a new generation of investors to the Indian equity markets using technology. It monetizes through transaction fees on investments and SIPs, premium subscription plans, and commissions via broker partnerships for trades executed through its platform.

For users, smallcase simplifies investing by reducing cognitive effort and making portfolio decisions easier and more guided. For the business, this translates into higher transaction frequency and subscription adoption, driving retention and revenue.

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Product Teardown: Flows & Frictions

Before identifying a problem to solve, I mapped three core money-movement flows in the existing product end-to-end - what works, and where users hit friction.

Invest More

Flow: Existing smallcase → Invest more → Make Payment → Order placed.

Add Funds

Flow: Profile → Account management → See available funds → Add Funds (CTA) → Redirect to broker.

Rebalance

Flow: smallcase → Rebalance → Redirected → Modify or approve → Order placed.

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Feature Reverse Engineering

I reverse-engineered three existing smallcase features to understand the user problem each was designed to solve, and why it works for both sides of the marketplace.

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User Segmentation

I segmented smallcase's user base into six groups to identify where the sharpest, most addressable problem lived.

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Initial Problem Hypotheses

From the teardown and segmentation, I drafted three early problem hypotheses, prioritized by gut confidence ahead of user validation.

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User Research

Interviews

I interviewed 3 early-career professionals (~20–25 minutes each), spanning SIP investors, a stock investor, and a crypto-first investor, to validate the P2 hypothesis.

Pain signals from real experiences:

• "I rely on my brother for most investment decisions."
• "I want to invest and forget about it."
• "I don't know where to start with SIPs."
• "There's too much information online."

Survey Validation

To check whether these patterns held beyond 3 interviews, I ran a short survey with 13 responses, mostly SIP users, after the interviews.

Together, this confirmed the interview patterns: investors prefer low-effort approaches, decision confidence is moderate to low, and confusion plus choice overload slow down action. Guidance and simplicity are strongly desired.

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SWOT Analysis

Built from the interviews, survey, product teardown, and comparative observation of platforms like mutual fund apps, broker platforms, and SIP-first solutions. Since most participants weren't existing smallcase users, this reflects external user behaviour and market positioning rather than internal usage data.

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Problem Statement & Goal

Resulting behaviour: users delay starting investments, depend on trusted people for decisions, and avoid trying new platforms without clear direction. Interviews showed users paused investing entirely when guidance was unavailable.

This hesitation directly reduces Activation as users discover investment options but don't take the first investment action due to decision uncertainty.

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Personas

Priya Gupta - Passive SIP Investor (family-managed)

Mid-20s, UX Designer. Decision style: trust-based, but curious to learn.

Goals: Grow money safely over the long term, avoid wrong decisions, learn investing gradually without spending too much time.

Behaviours: SIP investments managed by an uncle/family member; rarely decides independently; checks portfolio occasionally (mainly total value/profit); shows interest in learning but doesn't actively research.

Frustrations: Too many investment options feel overwhelming; research feels time-consuming and confusing; low confidence choosing investments independently.

"I just want to invest and forget, but I also want to understand what's happening."

Deep Patel - Early-Stage Investor (started but paused)

Early-20s, UX Designer and Developer. Decision style: relies on trusted family guidance.

Goals: Invest early and grow money over time, in a way that feels safe and guided, without needing constant involvement.

Behaviours: Started investing after his first salary; followed his brother's guidance for stock selection; stopped investing once that guidance became less available; watches YouTube content but rarely acts on it independently.

Frustrations: Fear of losing money on wrong decisions; feels investing requires constant research and market tracking; unsure if current investments will grow meaningfully; feels stuck between continuing, switching, or withdrawing.

"I stopped investing because I didn't feel confident making decisions on my own once my brother got busy."

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Problem Prioritization

Considered problems from interviews, survey, and the SWOT: confusion about where to invest, low confidence in decisions, dependency on trusted people for guidance, and research feeling heavy and time-consuming. All four contribute to hesitation in starting investments.

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Solution Brainstorming

Ten candidate solutions were generated against the P0 statement and categorized by build effort.

RICE Prioritization

The Guided Entry System scored highest on RICE and became the P0 solution.

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P0 Solution: The Guided Entry System

The Guided Entry System introduces a structured onboarding flow that collects user goals, risk appetite, investment capacity, and involvement preference. Based on this, users receive a curated roadmap of best-fit smallcases along with contextual explanations.

This directly reduces:

• Choice overload
• Decision anxiety
• Reliance on external guidance

Entry Trigger

Location: shown on first sign-in, or as a Home banner - "Invest with direction." CTA: "Build my roadmap."

This ensures new users get guidance immediately, while existing passive users can re-enter decision support at any time.

Flow Summary (5 Steps)

A short onboarding flow captures the user's investment goal, time horizon, risk appetite, investment capacity, and involvement preference to build an investor profile.

1. Goal selection: e.g. building long-term wealth, saving for a future goal, planning for retirement, or just exploring.
2. Time horizon: within 1 year, 1–3 years, 3–7 years, or 7+ years.
3. Risk tolerance: framed behaviorally around a 20% portfolio drop scenario (exit immediately, feel anxious but wait, stay calm and hold, or buy more on the dip).
4. Monthly capacity: under ₹2,000, ₹2,000–₹5,000, ₹5,000–₹15,000, or ₹15,000+.
5. Involvement preference: set it and forget it, occasional check-ins, or actively involved.

Recommendation Layer

Each recommended smallcase surfaces:

• A fit explanation across goal match, risk match, and horizon match
• A simplified summary
• Minimum investment required
• A performance snapshot

Based on user inputs, the system generates a curated shortlist of relevant smallcases along with contextual explanations that help users understand why each recommendation is a good fit.

How This Solves the P0

Problem: users delay investing due to confusion and too many options.

This solution narrows choices down to 3–8 contextual picks, explains the reasoning behind each, and makes the decision feel structured rather than overwhelming.

Expected behavioral shift: from browsing endlessly, to selecting from a guided shortlist.

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Success Metrics

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Pitfalls & Mitigations

Key assumptions behind the Guided Entry System, mapped against their failure modes and mitigation plans.

Detection signals tracked per risk: quiz start and completion rate, investment rate from recommendations, 30-day churn, and completion-time anomalies.