Decluttering Amazon's Offers for Products below 1500 Rs for Non-Prime users

Product Overview

Amazon.in is India's largest e-commerce platform, catering to a highly price-sensitive market where "Offers" (Bank Discounts, Cashback, No Cost EMI) are primary drivers of purchase decisions. The Product Detail Page (PDP) is the critical "moment of truth" where a user evaluates value.

Currently, the "Bank & Partner Offers" section aggregates all potential promotions associated with a category. While this maximizes the number of offers shown, it often fails to filter for Cashback relative to the specific item in the cart or the user's eligibility.

This case study analyzes how the current logic displaying high-value but ineligible offers first creates cognitive friction and proposes a context-aware sorting mechanism to improve trust and speed-to-checkout.

The Problem

Visual Evidence Analysis

Why This Matters

• Cognitive Load: Users visited a second page just to do a math : "If Price (899) < Threshold (1500) & Not a Prime Member, then Offer is Null." This should be system-handled.
• Banner Blindness: When users repeatedly click "Cashback Offers" only to find they don't qualify, they stop checking the section entirely, missing out on genuine savings (like the ₹10 offer with AmazonPay).
• Trust Erosion: Showing a "₹399 Cashback" banner on an ₹899 item feels deceptive, even if the T&C explains the mismatch.
• Conversion Friction: The disappointment of "losing" a discount pauses the checkout flow. The user might leave to find a cheaper product to meet the threshold or abandon the cart.
• Missed Upsell Opportunity: The current UI says "Min order ₹1500" as a restriction. It should frame it as an opportunity: "Add ₹601 more to unlock ₹399 cashback."

Current vs. Ideal State

Current State: Offers are likely sorted by Offer Value (High to Low). An invalid ₹399 offer ranks higher than a valid ₹10 offer.

Ideal State: Offers are sorted by Applicability. Valid offers appear first. Invalid offers appear second, clearly marked as "Upsell Opportunities."

Root Cause Analysis

The sorting logic likely stems from these technical/business factors:

• Category-Level Fetching: The API retrieves offers linked to "Electronics > Headphones" without cross-referencing the specific SKU price (₹899) in real-time.
• Marketing Bias: There is a legacy assumption that showing larger numbers (₹399) leads to higher CTR than small numbers (₹10), ignoring the post-click disappointment.
• Static Ranking: The ranking algorithm does not ingest User Context (Prime vs. Non-Prime) or Cart Context (Current Item Price) effectively.

User Personas & Pain Points

Persona	Priya (The Deal Hunter)	Rahul (The Quick Shopper)
Behavior	Calculates every rupee. Will switch payment methods for ₹50 savings.	Wants to buy in 3 clicks. Does not read T&C.
Pain Point	"I wasted 2 minutes reading an offer for Prime members only to realize I can't use it. Why show it to me?"	"Just tell me the final price. Don't make me click a menu to find out there's no discount."
Goal	Maximize savings transparency.	Speed and clarity.

User Journey: The "Double Lock" Friction

Let's analyze the journey of buying the Wireless Earbuds (₹899) referenced in the screenshots.

Pain Points Analysis

Pain Point	Impact	Frequency	Why It Happens
Irrelevant offers shown first	High cognitive load, wasted time, false expectations	Every product page visit (estimated 70-80% of offers shown are not applicable)	Offers sorted by value, not applicability. No filtering by product price or user eligibility.
Misleading offer count	Creates expectation of savings that don't exist	Every product with offers badge	Counter includes all offers in database, not just applicable ones for this product/user.
Membership-locked offers without context	Non-Prime users feel excluded or pressured	40-50% of offers require Prime	System doesn't pre-filter based on user's Prime status or show clear upgrade path.
Complex eligibility criteria buried in fine print	Users miss important conditions, leads to checkout surprises	Almost all offers have conditions	Legal/compliance requirement to show full terms, but no smart summarization.
Actually applicable offers hidden below irrelevant ones	Users miss real savings opportunities	Common when multiple offers exist	Sorting algorithm prioritizes offer value over user relevance.

User Expectations

Prioritization Framework (RICE)

Solution	Reach	Impact	Confidence	Effort	RICE Score
Smart Filtering: Show Only Applicable Offers	10/10	9/10	9/10	5/10	162
Reorder: Applicable Offers First	10/10	7/10	9/10	2/10	315
Add "Applies to this product" badge	10/10	6/10	8/10	1/10	480
Fix offer count to show only applicable	10/10	8/10	9/10	3/10	240

RICE Scoring Explanation

Smart Filtering: Show Only Applicable Offers (Score: 162)

• Reach (10/10): Affects every user viewing product pages with offers. Amazon has millions of daily product page views.
• Impact (9/10): Dramatically reduces user frustration, improves trust, increases conversion by removing friction. Directly addresses root cause.
• Confidence (9/10): High confidence this will work. Industry standard (Flipkart, Myntra already do this). User research strongly supports it.
• Effort (5/10): Moderate engineering effort. Requires building filtering logic based on product price, user Prime status, payment method, and minimum purchase thresholds. Backend changes to offers engine needed.

Reorder: Applicable Offers First (Score: 315)

• Reach (10/10): Universal impact across all product pages.
• Impact (7/10): Helps users find relevant offers faster but doesn't remove irrelevant ones completely. Partial solution.
• Confidence (9/10): Simple logic change, low risk.
• Effort (2/10): Low effort. Just changing sorting algorithm from "offer value" to "applicability score". Can be done in 1-2 sprints.

Add "Applies to this product" Badge (Score: 480)

• Reach (10/10): Every user sees badges on offers.
• Impact (6/10): Improves clarity but users still need to see and process irrelevant offers. Better than nothing.
• Confidence (8/10): Visual indicators work, but doesn't solve root problem of showing irrelevant content.
• Effort (1/10): Very low effort. Frontend-only change to add badge based on existing filtering logic.

Fix Offer Count (Score: 240)

• Reach (10/10): Affects everyone who looks at offer count before clicking.
• Impact (8/10): Removes false expectations. Sets honest expectation of what user will find.
• Confidence (9/10): Straightforward logic change with clear benefit.
• Effort (3/10): Low-moderate effort. Change counter logic to filter by applicability before counting.

Why Contextual Sorting Wins?

• Low Effort: It doesn't require redesigning the page widgets (Solution 3) or changing complex business logic to "hide" offers which might be an upsell opportunity (Solution 2).
• High Impact: It solves the immediate frustration by ensuring the first thing the user sees is something they can actually use.
• Upsell Preservation: By moving the ₹1500 offer to the bottom (instead of hiding it), we still allow the "Add more to cart to unlock" behavior, but we don't block the primary purchase flow.

Proposed Solution: The Smart and Honest Selling

We move from a static list to a State-Based Logic Engine. The UI changes dynamically based on who the user is (Prime Status) and what they are buying (Cart Value).

1. The "Hero" Slot: Elevating the ₹10 Offer

Currently, the API likely returns a list of offers linked to the Category. We need to inject Product Price and User Context into the ranking logic.

2. The UI Change (Frontend)

Redesign the Offer List Sheet to clearly distinguish between "Ready to Use" and "Needs Action".

Before vs After Comparison

Aspect	Current (Before)	Proposed (After)
Offers Shown	All offers in Amazon database	Rearranged applicable offers product + user
Offer Count	"2 cashback offers" (includes non-applicable)	"1 cashback offer available" (honest count)
Sorting	By offer value (₹399 first, ₹10 second)	By applicability (₹10 first, ₹399/Prime offers later)
Messaging	"Get ₹10 cashback when you shop for Rs. 500..."	"You'll get ₹10 cashback with Amazon Pay Balance"
Visual Hierarchy	All offers look equally important	Applicable offers highlighted, All platform Offers collapsed
User Time	45-60 seconds reading all offers	10-15 seconds seeing applicable offer instantly

Success Metrics (KPIs)

Metric	Current Baseline (Assumed)	Target After Change	How to Measure
Offer Click-Through Rate	12-15%	20-25%	% of users who click "See all offers" on product page
Offer Redemption Rate	3-5%	10-15%	% of users who view offers and actually use one at checkout
Time Spent on Offers Page	45-60 seconds	15-20 seconds	Average session time from click to return to product page
User Satisfaction (Offers Section)	2.8/5 stars	4.2/5 stars	Post-purchase survey: "How helpful were the offers shown?"
Support Tickets (Offer-related)	Estimated 50K/month	Under 20K/month	Tickets mentioning "cashback not received", "offer not working"

Business Impact Metrics

Impact Area	Expected Change
Conversion Rate	+2-3% improvement from reduced friction and clearer value proposition
Amazon Pay Adoption	+15-20% increase as payment-specific offers become more visible
Prime Subscriptions	Potential uplift from clearer Prime-exclusive offer messaging (when implemented correctly)
Customer Trust Score	Improvement in NPS related to "honest pricing" and "clear communication"

Risks & Mitigation

Risk	Likelihood	Mitigation Strategy
Reduced visibility of high-value (upsell) coupons	Medium	If we push the ₹399 offer to the bottom, fewer people might see it. Mitigation: Use a "Card" visual for these offers to draw attention to the upsell potential.
Latency in fetching offers	Low	Calculating eligibility for every SKU in real-time might add ms to load time. Mitigation: Compute this asynchronously or cache common price-bracket results.

Conclusion

The current "One Size Fits All" approach to Amazon's offer section creates a paradox. In trying to show all value, we show no immediate value to the user. For a product priced at ₹899, showing a ₹1500 threshold offer that is also locked behind Prime is a UX anti-pattern. It breeds distrust and increases cognitive load.

By implementing Context-Aware Sorting, we respect the user's current intent. We validate their purchase choice by showing applicable offers first. Simultaneously, we transform the "ineligible" offers into a clearer, gamified upsell mechanism. This small logic change drives trust, speed, and potentially higher basket sizes.