How AJIO and Myntra Product Comparison API Solves Pricing and Assortment Gaps for Fashion Brands

Introduction

Fashion brands can solve pricing and assortment visibility gaps by systematically comparing product, price, availability, discount, and category information across major online fashion marketplaces. The AJIO and Myntra Product Comparison API approach gives retailers a structured way to compare competing products, identify pricing differences, monitor assortment changes, and strengthen marketplace intelligence without relying on repetitive manual checks.

This requirement is becoming more important as India's digital fashion ecosystem expands. Reliance Industries reported that AJIO expanded its catalogue to 2.7 million-plus options, up 35% year over year, in its FY2025-26 reporting, while AJIO Rush expanded to more than 300 PIN codes across six major cities. Myntra's current fashion catalogue page also displays millions of listed items across thousands of brands and categories, demonstrating the scale that fashion businesses may need to monitor.

For fashion manufacturers, D2C brands, retailers, marketplaces, and agencies, this scale creates a practical problem: how can teams determine whether their prices are competitive, whether comparable products are available, and whether their assortment is sufficiently visible? Structured marketplace comparison data can answer these questions while creating a consistent foundation for pricing intelligence, product benchmarking, assortment planning, and competitive research.

What Makes Cross-Marketplace Product Comparison Important?

Online fashion marketplaces change continuously. New products are launched, prices are adjusted, discounts appear and disappear, products go out of stock, and brands expand or reduce their assortment. A retailer comparing marketplaces manually may therefore make decisions using incomplete or outdated information.

Ecommerce Data Scraping can help businesses collect accessible product information from different marketplace environments and convert it into standardized records. Once the information is normalized, brands can compare similar products based on attributes such as brand, product title, SKU, category, price, MRP, discount, ratings, availability, color, size, and other accessible fields.

This creates a more practical comparison framework.

The objective is not simply to collect more information. The objective is to convert marketplace information into comparable business metrics.

For example, a fashion brand may discover that one of its products is priced 8% higher on one marketplace than another. A category manager may identify that a competing brand has 30% more listed SKUs in a particular segment. A merchandising team may find that certain sizes or colors are consistently unavailable. These insights can directly influence pricing, inventory, promotions, and assortment decisions.

How Can Brands Improve Competitive Pricing Visibility?

Brands can use an AJIO and Myntra Price Monitoring API workflow to observe price movements and compare product-level pricing across both marketplaces. The focus should not be limited to the current selling price. A useful pricing dataset should also retain MRP, discount percentage, pack or variant information, product identifiers, and collection timestamps wherever available.

This allows retailers to distinguish regular pricing from promotional pricing. For example, a product priced at ₹1,499 after a 25% discount has a different competitive position from a similar product consistently priced at ₹1,499 with no promotional activity.

Price normalization is especially important in fashion because comparable products can have different base prices, discounts, and variants. A structured dataset can calculate metrics such as price difference, discount gap, average category price, median price, and price index.

Reliance has reported that AJIO's average bill value increased 23% and average selling price increased 17% in its latest investor communication, while the catalogue reached approximately three million options. These figures illustrate why price and assortment monitoring need to operate together rather than as isolated activities.

For brands, a lower price does not automatically mean stronger competitiveness. Product quality, brand positioning, ratings, discounts, availability, and assortment breadth can influence customer choice. Pricing intelligence should therefore be analyzed alongside product and marketplace context.

How Can Availability Data Improve Inventory and Assortment Decisions?

Product availability is one of the most important signals in digital fashion retail. A product may exist in a catalogue but still be unavailable in important locations, sizes, or colors. This creates a difference between nominal assortment and customer-accessible assortment.

An AJIO and Myntra Product Availability API workflow can help retailers monitor accessible availability information and identify changes over time. When availability is captured regularly, businesses can analyze which products remain consistently visible, which products frequently disappear, and which categories experience availability gaps.

For example, a brand may have 500 products listed on both platforms but discover that only 410 are consistently available on one marketplace. This difference can influence marketplace revenue, campaign performance, and customer experience.

Availability data becomes even more useful when paired with pricing information. A competitor with a lower price but frequent stockouts may not represent the same competitive threat as a consistently available competitor.

Reliance has highlighted the scale of AJIO's assortment expansion and its efforts to improve customer experience through faster delivery services. Its 2025 reporting noted that AJIO added 1.9 million new customers and expanded its portfolio to 2.4 million options, representing 44% year-over-year growth. Such expansion makes marketplace-level assortment and availability monitoring increasingly valuable for competing brands.

What Product Attributes Should Fashion Brands Compare?

An AJIO and Myntra product data scraping API can be structured around the exact fields required by the retailer. Rather than collecting every possible attribute, brands should first define the fields that directly support their commercial decisions.

Core fields can include product name, brand, SKU or product identifier, category, subcategory, MRP, selling price, discount, availability, rating, number of reviews, product URL, images, colors, sizes, material information, and other publicly accessible attributes where applicable.

The resulting data can be standardized into a common schema so that products from both platforms can be compared more effectively.

Product matching is one of the most important technical components. The same or similar fashion item may have slightly different titles across marketplaces. A reliable comparison system therefore needs matching rules based on combinations of brand, SKU, product name, attributes, and other identifiers where available.

The goal is to avoid treating two different products as identical simply because their titles appear similar. Strong normalization and validation processes can improve the accuracy of cross-marketplace comparisons.

How Can Brands Build a Reliable Product Comparison Dataset?

Brands can use AJIO and Myntra product data extraction to create a centralized dataset that supports pricing, assortment, competitor, and product intelligence. The comparison system can collect information on a recurring schedule and maintain historical snapshots for trend analysis.

The historical layer is particularly important. A one-time dataset can show the current state of a marketplace, but recurring collection can reveal how the marketplace changes.

For instance, if a competitor's assortment increases from 500 products to 700 products over six months, that expansion may indicate a stronger investment in the category. If its average discount increases during the same period, the brand may also be pursuing an aggressive promotional strategy.

This creates a broader intelligence framework:

A comparison dataset can also support dashboard development. Category managers can monitor price movements, product launches, assortment changes, and availability trends through visual reports instead of repeatedly checking marketplace pages.

How Did Digital Fashion Competition Evolve from 2020 to 2026?

Between 2020 and 2026, Indian fashion retail became increasingly digital, data-driven, and assortment-intensive. The pandemic accelerated online shopping adoption, while subsequent years brought stronger marketplace competition, faster fashion cycles, social-media-led discovery, and greater customer expectations around price and availability. Reliance's own reporting shows the scale of AJIO's expansion: its catalogue moved from more than 500,000 options in 2020 to 2.4 million options by early 2025 and approximately 2.7 million-plus options during FY2025-26 reporting. Reliance also reported that AJIO had grown seven times in five years in its latest investor communication. Meanwhile, Myntra's current catalogue page shows millions of listed items spread across hundreds of categories and tens of thousands of brands. This progression means competitive monitoring can no longer depend on occasional manual checks. By 2026, fashion brands need recurring datasets that capture product, pricing, availability, and assortment changes at scale. Historical comparison also helps businesses separate temporary sale events from long-term competitive shifts.

How Can Brands Compare Marketplace Assortments More Accurately?

Brands can Extract product comparison data from AJIO and Myntra to benchmark their assortment against competitors and identify gaps in category coverage. The analysis can focus on product counts, price bands, brand participation, product variants, discounts, and availability.

A useful assortment comparison does not simply ask which platform has more products. It asks whether each platform has the right products for the target customer. For example, a premium fashion brand may prefer a smaller assortment with higher average prices, while a value-focused brand may prioritize wider coverage across price bands.

Assortment analysis can therefore be segmented by:

This type of comparison can help brands identify white spaces. Suppose a category has strong demand but relatively few products in a specific price range. That may create an opportunity for a new product launch.

Likewise, if competitors consistently offer a broader selection in a strategic category, the brand can investigate whether it needs more SKUs, new variants, or improved marketplace onboarding.

How Can Product Intelligence Support Fashion Strategy?

Ajio Data Scraping can support a broader competitive intelligence workflow when product, price, availability, and assortment information are collected consistently. The objective should be to transform raw marketplace observations into metrics that merchandising, pricing, marketing, and strategy teams can act upon.

For example, a retailer could create a marketplace scorecard containing average selling price, discount depth, assortment count, availability rate, average rating, review volume, and competitor presence. These metrics can then be tracked weekly or monthly.

A practical comparison framework might look like this:

These numbers are illustrative rather than reported marketplace figures.

The strategic advantage comes from combining these metrics. A retailer may find that one marketplace has more products but lower availability, while another has fewer products but stronger price positioning. Such findings can influence channel-specific assortment and promotional decisions.

How Can Actowiz Solutions Help?

Actowiz Solutions helps fashion brands and retailers build scalable marketplace intelligence workflows based on their specific business requirements. Myntra Data Scraping can be incorporated into a broader marketplace monitoring strategy alongside product, pricing, availability, assortment, and competitive data from relevant sources.

The process starts by defining the client's objectives. A brand focused on pricing intelligence may prioritize SKU, MRP, selling price, discount, and historical price fields. A merchandising team may require category, brand, size, color, availability, and assortment information. A competitive intelligence team may need all of these dimensions combined.

Actowiz Solutions can then design a structured collection workflow with normalization and validation processes. Product records can be organized into consistent schemas, duplicate records can be identified, and historical snapshots can be retained for trend analysis.

The resulting datasets can be delivered in formats suitable for databases, dashboards, analytics environments, or internal reporting systems. Recurring collection schedules can also be configured according to business requirements.

The approach is scalable. Brands can start with a small set of categories and competitors and expand the monitoring scope as their intelligence requirements grow. Additional marketplaces, categories, locations, products, and attributes can be incorporated into the data architecture.

The most important advantage is that business teams receive structured information rather than having to manually browse thousands of product pages. This allows pricing teams to focus on strategy, merchandising teams to focus on assortment, and leadership teams to focus on market opportunities.

Conclusion

Pricing & Product Data Scraping can give fashion brands a structured foundation for monitoring prices, products, assortment, availability, and competitive movements across digital marketplaces. As online fashion catalogues become larger and more dynamic, manual comparison becomes increasingly difficult to maintain.

AJIO's catalogue expansion to millions of options and Myntra's current multi-million-item catalogue demonstrate the scale of digital fashion information that brands may need to evaluate. A recurring comparison framework can help businesses identify price gaps, assortment opportunities, availability problems, promotional changes, and competitor expansion.

For businesses looking to operationalize this intelligence, a Web scraping API can provide a scalable technical layer for recurring data collection, while Custom Datasets can align the output with specific product, pricing, and competitive requirements. An instant data scraper can also support rapid data collection requirements where applicable and permitted.

The strongest strategy is not simply to collect more marketplace data. It is to collect the right fields, normalize them consistently, maintain historical records, and convert the information into actionable commercial metrics.

Talk to Actowiz Solutions today to build a scalable marketplace intelligence solution for smarter pricing, stronger assortment planning, and better competitive decisions!

You can also reach us for all your mobile app scraping, data collection, web scraping , and instant data scraper service requirements!