Overview

A major reason for poor product conversion was incomplete product information. Customers often reached purchase decisions without enough images, videos, FAQs, feature bullets, descriptions, or upsell context. LQS created a clear product-quality benchmark that catalog teams could use every day.

Executive Summary

Listing Quality Score turned subjective catalog quality into a measurable product-quality system. By automatically scoring every product against images, videos, descriptions, feature bullets, FAQs, and upsell recommendations, the catalog team could prioritize the listings most likely to hurt customer confidence and conversion.

Problem Statement

The catalog team had no measurable way to determine listing quality, so product quality was inconsistent, improvement work was difficult to prioritize, and conversion rate remained lower than expected.

Existing Workflow

Before LQS, catalog improvements were largely manual and judgment-driven. Teams reviewed products reactively, checked missing information by hand, and had no single score that explained whether a listing was ready for high-quality customer discovery.

Business Context

Product listing quality directly influenced customer trust and conversion. A scoring system gave business and catalog teams a repeatable way to improve the listings most likely to affect revenue.

Architecture

Built as a scoring pipeline over product catalog data, where configurable quality parameters evaluate each product and produce a visible rating badge. Scores are persisted for tracking, surfaced in dashboards, and designed to support future parameters without hardcoding every rule.

Solution

Designed and built an automated quality-scoring system where every product receives a rating from A+ to D based on content completeness. The catalog team adopted LQS as a primary KPI and continuously improved product listings until they reached A or A+.

Business Impact

Increased product conversion rate from 0.70 to 0.85 by improving listing completeness, customer confidence, and purchase decision quality.

Overview

Before every sale event, category managers negotiated discounted prices with vendors. After negotiations, catalog teams manually prepared pricing sheets and uploaded them late at night before the sale, where validation failures often delayed launches.

Executive Summary

Event Master moved sale preparation from late-night manual uploads to a scheduled, validated, auditable workflow. It let category and catalog teams prepare campaigns weeks in advance, run validations early, fix errors before launch, and publish sales automatically at the scheduled time.

Problem Statement

Sale launches were delayed by manual uploads, repeated validation failures, late-night operations, and revenue-impacting campaign activation delays.

Existing Workflow

Teams waited until the sale day to upload pricing sheets. If validation errors appeared, catalog and operations teams had to fix them immediately under launch pressure, often delaying sale activation by 4-5 hours.

Business Context

Sale events are time-sensitive revenue moments. A delayed campaign means customers do not see intended pricing on time, business teams lose launch confidence, and operational teams spend energy on avoidable manual recovery.

Architecture

Designed as an event lifecycle platform with scheduled events, bulk price ingestion, pre-launch validation, retryable processing, audit logs, and automated publishing at the configured launch time.

Solution

Built Event Master so teams could create events weeks in advance, upload pricing immediately after vendor confirmation, run validations before launch, fix issues early, schedule the event, and let the platform publish validated pricing automatically.

Business Impact

Reduced sale launch time from 4-5 hours to under 30 minutes, removed repetitive manual operations, reduced launch failures, and improved customer experience during sales.