Q-commerce

Share of shelf & search positioning

Share of shelf and search positioning data tracks how visible your brand is on Q-commerce platforms, from app listings to in-app search results, where customers decide quickly. Positions shift across numerous platforms and competing offers, and in-house data often gives only a fragmented view. Our data shows where your products land in those results, next to your competitors'.

Enterprise scraping and matching from 5,000+ sources.

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  • Uber Eats
  • Getir
  • Eventim
  • Booking.com
  • Audible
  • Skyscanner
Common problems

Shelf and search positions shift across numerous platforms

In-house tracking of Q-commerce share of shelf and search positioning runs into heavy technical hurdles, and the data ends up inaccurate and costly to produce. Dedicated specialists in enterprise-grade web scraping, advanced data matching, and proxy management deliver data quality and reliability.

Conflicting placement and ranking data

Clients often receive disparate, inconsistent data on where their products or competitors' products appear on Q-commerce platforms. They describe this as 'a fragmented view of the market,' which makes it impossible to judge true share of shelf or search ranking with confidence and keeps strategies reactive.

Promotions planned without ranking data

Without accurate data on competitive shelf and search positioning, Q-commerce brands often make weak pricing or promotional decisions. Revenue drops, market share erodes, and they stay exposed to competitors who use precise data for aggressive market capture.

Page and API changes deter scrapers

Q-commerce platforms use sophisticated anti-bot technologies and CAPTCHAs and frequently change their page structures or APIs to deter automated data collection. Getting past these barriers at scale takes constant, resource-intensive maintenance and specialized proxy solutions, which pulls a non-specialist team away from core business innovation.

Delayed data from an in-house team

Keeping an in-house data acquisition team for share of shelf and search positioning drains valuable engineering and data science resources. Operational costs balloon, data arrives late, and core teams lose time for strategic product development and the deeper analysis that drives growth.

Our solutions

Shelf and search placement data for Q-commerce

Share of shelf and search positioning data is scraped from app listings and search results, matched across platforms, checked manually, and verified by automated QA before it lands in your systems.

One source for shelf and search placements

Curated, consistent data on competitive shelf and search placements gives you a single source of truth for Q-commerce market intelligence and the confidence to make data-backed decisions.

Positioning that wins shelf share

Visibility into competitor moves helps Q-commerce clients plan smarter pricing, stronger promotions, and product positioning that captures greater market share and boosts profitability.

Placement data on a 99.9% uptime SLA

Sophisticated anti-bot measures, proxy networks, and platform changes are routine work for our infrastructure and expertise. Data keeps flowing from even the most challenging Q-commerce environments, backed by a 99.9% uptime SLA.

Predictable data input, faster insights

The data acquisition burden moves to our managed service, which turns a resource-intensive chore into a predictable, high-value input. Your expert teams can move faster on innovation and strategic initiatives.

Other use cases

More Q-commerce data use cases

Our end-to-end managed services for Q-commerce, with sophisticated data scraping, precise matching, and infrastructure, support share of shelf tracking and the use cases below for enterprise clients.

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Measure your Q-commerce share of shelf

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