Happy Eats

TL;DR

Food ordering is a high-frequency, low-patience workflow. Users want to decide fast, avoid surprises, and trust what they’re buying.

I redesigned the core funnel to:

• Increase decision confidence (ratings, ETA, fees, dietary info)
• Reduce checkout anxiety (transparent totals, fewer surprises)
• Improve conversion by removing friction and clarifying primary actions

This case study uses assumptions where exact numbers were not available for publication.

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1) Context

Users

• Hungry, time-constrained users browsing on mobile
• Returning users with habitual orders
• New users who need trust cues to order

Job-to-be-done

“Help me find something I’ll like, understand the total cost and ETA, and checkout quickly.”

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2) The problem

Observed issues (funnel)

• Browse: weak signals early (rating/ETA/fees not prominent enough)
• Menu: hard to compare options and build a “good” cart confidently
• Cart: modifications and add-ons added friction
• Checkout: surprises (fees, delivery, minimums) increased drop-off

Root causes

• Information hierarchy didn’t match decision-making
• Too many secondary elements competing with primary CTAs
• Lack of trust cues at key decision points (cart/checkout)

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3) Goals & success metrics

Goals

• Improve discovery and “decision velocity”
• Increase add-to-cart and checkout completion
• Reduce drop-offs caused by fees/payment confusion

Success metrics (assumptions)

• +10% add-to-cart rate
• -15% checkout drop-off
• +5% repeat orders from improved trust

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4) Research & insights

What I did (PM workflow)

• Funnel review and stage-level drop-off analysis [assumption]
• Quick qual: usability tests on menu and checkout [assumption]
• Competitive scan of ordering apps for best patterns [assumption]

What we learned

1. Users decide with 4 signals: rating, ETA, price, and dietary fit.
2. Users abandon when totals change late (fees + delivery surprises).
3. Modifications are necessary, but they must be low-friction and reversible.

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5) The solution

5.1 Browse & discovery

• Stronger “decision signals” above the fold:
  • rating, delivery time window, fees, dietary tags
• Cleaner list cards with a single primary action

5.2 Menu (decision support)

• Clearer grouping (popular items, combos, add-ons)
• “Compare-friendly” pattern for similar dishes [assumption]
• Inline add/remove controls to reduce context switching

5.3 Cart (confidence + control)

• Clear totals breakdown (items + taxes + delivery + fees)
• Fast edit for items and preferences
• Trust copy for substitutions and refunds [assumption]

5.4 Checkout (remove surprises)

• Transparent totals early
• Address and payment flows optimized for mobile
• Confirmation screen with clear next steps (tracking, support)

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6) Delivery plan

• Defined MVP: top 3 drop-off points + top 2 decision moments
• Produced a UI component checklist (card, price row, CTA, fees module)
• Planned experiment rollouts:
  • Pricing/fees placement test
  • Menu layout test (grouping vs list) [assumption]

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7) Results (directional / assumptions)

• Add-to-cart increased by ~8–15% due to clearer decision signals
• Checkout drop-off reduced by ~10–20% due to fee transparency and clearer payment steps
• Improved perceived trust and fewer support requests about “unexpected fees” [assumption]

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8) Learnings

• Clarity beats novelty in high-frequency commerce flows.
• Surfacing the right information early is the fastest conversion lever.
• Users don’t mind fees—they mind surprises.