Full Product Capture + Recurring Price, Ratings & Review Feeds for a Consumer Brand Across Amazon, Flipkart & Brand Site

Introduction

Industry: Consumer Electronics & Accessories (D2C Brand)

Region: India

Sources covered: Brand website, Amazon.in, Flipkart

Services used: One-Time Product Data Capture, Recurring Price & Ratings Feed, Daily Reviews Feed with Images

The Client

A fast-growing Indian consumer brand selling 600+ SKUs through its own website, Amazon, and Flipkart. The brand's catalog, pricing, and reputation lived in three places at once — and the three never quite agreed.

The Challenge

The brand's e-commerce and product teams faced three distinct data problems with three different rhythms:

• A catalog they couldn't see whole. Product attributes, specifications, descriptions, and images had drifted across the three channels over years of listing edits, marketplace requirements, and seller-side changes. There was no single source of truth showing what each channel actually displayed today — making content audits, compliance checks, and rebranding work guess-driven.
• Prices and ratings that move fast. Marketplace prices shift with deals, coupons, and third-party sellers; ratings move daily. The team needed a high-frequency feed to catch price errors, unauthorized discounting, and MAP violations within hours, not weeks.
• Reviews as an unmined asset. Hundreds of reviews — including customer-uploaded review images — landed weekly across channels. Product and CX teams wanted them daily, structured, with images, to detect quality issues early and feed voice-of-customer analysis.

A useful simplification: the client already maintained product URLs for every SKU on all three sources, so cross-source product matching wasn't needed — the engagement could focus entirely on capture depth, feed frequency, and delivery quality.

The Solution

Actowiz Solutions structured the engagement exactly as the requirement naturally split — one foundation capture plus two recurring feeds on different clocks.

1. One-time full product data capture.

A complete extraction of all 600+ SKUs across all three sources from the client's URL list: every attribute and specification table, full descriptions and bullet content, A+/rich content presence, complete image galleries (not just primary images), variant structures, and category placement. Delivered as a structured dataset that became the brand's first true cross-channel catalog snapshot — and the baseline for change detection.

2. High-frequency price & ratings feed.

Every 4 hours, all SKUs across all sources: selling price, MRP/list price, discount, active deal/coupon badges, buy-box seller (on marketplaces), stock status, rating average, and rating count. Webhook alerts fire on price drops below the brand's MAP thresholds and on buy-box loss to third-party sellers.

3. Daily reviews feed with images.

Once daily: all new reviews per SKU per source — rating, title, body, verified-purchase flag, reviewer name as displayed, review date, helpful votes, and all customer-uploaded review images downloaded and delivered alongside structured records. Deltas only; no re-delivery of old reviews.

4. Change detection against baseline.

Because the one-time capture established a baseline, the recurring pipeline also flags content drift — a changed description, a swapped image, a removed specification — closing the loop the client originally wanted from a "product updates" mechanism.

5. Delivery.

JSON via REST API for the price/ratings feed (for the team's internal dashboard), daily CSV + image bundle to S3 for reviews, and the full capture as a one-time structured export.

The Results

• The first complete cross-channel catalog audit in company history, surfacing 140+ SKUs with inconsistent specifications and 60+ listings missing gallery images on at least one channel — fixed within the first month.
• MAP/price-error detection time dropped from days to under 4 hours, with 30+ third-party underpricing incidents flagged and acted on in the first quarter.
• Buy-box loss alerts recovered an estimated ₹[X] lakh in monthly revenue previously leaking to third-party sellers, per the client's internal attribution.
• Daily structured reviews (with images) cut quality-issue detection lag from ~3 weeks to 2–3 days — one packaging defect was caught from review images and corrected mid-production-run.
• The CX team built a voice-of-customer dashboard on the review feed covering 100% of new reviews across channels, replacing a manual sampling process that read <10%.

"Placeholder for client quote — e.g., 'One snapshot, two feeds, three channels — it mapped exactly onto how our teams actually work.'" — Head of E-Commerce, Client

Why It Worked

• Different data, different clocks. Catalog content, prices, and reviews change at different speeds; forcing them into one feed wastes money or misses changes. Splitting them matched cost to need.
• The baseline unlocks change detection. A one-time capture isn't just an audit — it's the reference that makes every future change visible.
• Review images are data too. Structured text plus downloaded images turned reviews from sentiment trivia into a quality-control sensor.

FAQs

Can Actowiz capture complete image galleries, not just primary images?

Yes — full gallery capture including variant images and customer-uploaded review images, delivered as files alongside structured records.

How frequent can the pricing feed be?

From daily down to hourly depending on SKU count and sources; 2–6 hour cadences are typical for brand price monitoring.

Do recurring feeds re-send old data?

No — feeds are delta-based, delivering only new and changed records, with change-type flags against the baseline capture.

Can you include barcodes, categories, and subcategories in custom datasets?

Yes — custom datasets are built to your field specification, including barcode/EAN, category/subcategory, and any attribute publicly displayed on the source.