Wants & Needs

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Overview

Wants & Needs is a mobile app designed to reduce post-purchase regret by inserting a structured pause before impulse purchases. Users complete a short decision flow and receive one of three outcomes: Buy, Hold, or Pass. Choosing Pass increases a visible “Money Saved” counter. The app is live on iOS and Android as an MVP.

Role

Problem definition, UX wireframing, decision logic design, MVP scoping, and roadmap direction. Partnered with an engineer who implemented the technical build and final UI execution.

Problem

Modern commerce is optimized for conversion. Social media platforms amplify targeted ads within emotionally charged environments, often during moments of boredom, stress, excitement, or insecurity. These triggers intersect with frictionless checkout systems, increasing the likelihood of reactive purchases.

For individuals prone to impulse behavior, the issue is not financial literacy. It is decision fatigue. Users may resist dozens of ads successfully, but under repeated exposure and cognitive load, eventually make purchases misaligned with long-term goals.

Existing budgeting tools operate post hoc. They track what has already happened. They do not intervene at the moment of cognitive vulnerability.

Impulse purchasing often leads to:

  • Post-purchase regret

  • Accumulated discretionary spending (often thousands annually)

  • Emotional guilt cycles

  • Reduced confidence in financial discipline

Wants & Needs was designed to intervene at the exact inflection point between desire and transaction.

Target User

Digitally native consumers (primarily 22–35) who:

  • Spend significant time on social media

  • Have discretionary income but struggle with impulse control

  • Experience recurring post-purchase regret

  • Do not want full budgeting systems

These users are not financially irresponsible — they are decision-fatigued.

Jobs to Be Done

  • Pause before overspending

  • Walk away confidently

  • Defer desire without loss

  • See visible progress when resisting temptation

This is a micro-decision tool, not a budgeting replacement.

Product Walkthrough

Product Decisions & Roadmap

Guided Question Decision Matrix & Outcomes

The decision matrix was informed by regret pattern analysis and mindful spending frameworks. The goal is to interrupt emotional momentum within 1–2 minutes.

Three outcomes:

  • Pass – reinforced restraint

  • Hold – deferred decision to reduce loss aversion, preserves desire without urgency bias.

  • Buy – intentional affirmation

Monetization Philosophy

Monetization must align with user trust and show real outsized value for users looking to spend more mindfully. We currently run a freemium, subscription-based monetization model. With a free tier, users can log a limited number of products per month with not additional cost. We believe that our app can save users hundreds, if not thousands, of dollars per year and we intentionally show our value proposition through our onboarding process.

Future updated monetization models can include:

  • Deeper AI-driven behavioral insights

  • Optional cashback, referral integrations, and lowest possible price options for intentional “Buy” decisions

The product avoids models that incentivize increased spending.

MVP Tradeoffs

The hardest product decision was choosing not to overbuild.

We intentionally did not:

  • Add bank integrations

  • Over-polish micro-interactions

  • Add personalization layers

Instead, we validated the core loop: Structured pause → intentional decision → reinforcement.

Early Learnings

Although still early-stage, qualitative observations surfaced several insights:

  • “Hold” is often the preferred option. Users want to preserve desire without immediate loss.

  • The Money Saved counter creates disproportionate satisfaction relative to its simplicity.

  • Requiring users to remember to open the app is a major friction point.

  • Repeated use of the full matrix can feel cognitively heavy, motivating the Quick Decision roadmap feature.

These insights directly informed the phased roadmap below.

Current Limitations

  • Reliance on user memory to open the app

  • Cognitive load from repeated full decision matrix

  • No personalization

  • No environmental intervention (e.g., checkout detection)