Q-commerce

KVI & basket value analysis

KVI and basket value analysis in Q-commerce tracks key value items and average basket spend across platforms to guide pricing and promotions. Prices, regional variations, and promotions keep shifting, so many clients only know roughly what competitors charge. We replace that rough idea with matched KVI and basket prices, delivery fees and promotions included.

Enterprise scraping and matching from 5,000+ sources.

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

Gaps in KVI data lead to overpricing and needless discounts

DIY KVI and basket value analysis drains resources and suffers from outdated data and inconsistent accuracy. Without specialized expertise in enterprise-grade data scraping, advanced matching, and proxy management, internal efforts cost more than they deliver.

Basket values known only roughly

Q-commerce companies struggle to compare key value items and basket values across competitors. Shifting prices, regional variations, and fluctuating promotions skew the picture and cost revenue. Clients say they end up "knowing roughly" what competitors do, without the granular, verifiable data a precise strategy needs.

Overpriced KVIs and needless discounts

Unreliable KVI and basket value data leads to weak pricing, ineffective promotions, and a significant loss of market share. Companies overprice critical items or discount unnecessarily, which hurts the bottom line and weakens their position in the Q-commerce market.

Geo-blocking and proxy detection

KVI prices, basket values, and associated fees change constantly across multiple Q-commerce platforms. Sophisticated anti-bot measures, geo-blocking, frequent website and app structure changes, and proxy detection make it very hard for non-specialist teams to capture the data at the required scale and frequency.

Engineering focus lost to upkeep

In-house KVI and basket value infrastructure, with its scraping, matching, and quality assurance, consumes vast internal resources. Skilled engineering and data science teams leave core product development and strategic initiatives, operational costs rise, and innovation and growth slow.

Our solutions

Verified KVI and basket value data

KVI and basket data covers delivery fees and promotions as well as item prices, so we scrape all three, match them, check them manually, and run automated QA. The data you receive is ready for benchmarking.

Full baskets with delivery fees included

Verified data covers competitor KVIs and full basket values, including variables like delivery fees and promotions. Our process delivers the accuracy and granularity that precise competitive benchmarking needs and turns "rough ideas" into actionable intelligence.

Promotions priced on accurate KVI data

Timely, accurate KVI and basket data supports responsive pricing strategies and highly effective promotions. Revenue improves, your market position gets stronger, and growth holds up in the competitive Q-commerce environment.

KVI collection under a 99.9% uptime SLA

Our technical infrastructure and expertise are built for the hardest data extraction obstacles. We get past anti-bot systems, manage complex proxy networks, and adapt to platform changes, so KVI and basket data collection stays reliable and high-frequency, backed by a 99.9% uptime SLA.

Scraping and matching pipelines off your plate

Your in-house teams no longer build and maintain complex scraping and matching pipelines. Valuable engineering and data science talent goes back to core product innovation and strategic initiatives, where it drives greater business value.

Other use cases

Q-commerce use cases beyond KVI analysis

The high-volume scraping, sophisticated matching, and infrastructure management behind KVI and basket value analysis also power the Q-commerce use cases below, delivered end to end for enterprise clients.

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Plan your KVI and basket data feed

Bring your KVI and basket value questions to a dedicated Q-commerce data specialist who knows this analysis in depth. Share your goals, and we will shape a data strategy around them.

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