Use cases

What teams do with our data.

Twelve situations we are asked to solve again and again. Each one shows the problem, the data we collect and what your team receives, with links to the solution and industry pages that go deeper.

Pricing and revenue

Festive and seasonal sale tracking

See how competitors price, discount and stock during the sale periods that define your year.

The situation

Ramadan and Eid in the Gulf, Diwali in India, 11.11 in Southeast Asia and Black Friday in the West each move prices, bundles and stock within days. Teams that check manually see the pattern only after the event is over.

What we collect

Listed and sale pricesDiscount depth and mechanicsBundles and gift-with-purchase offersStock status by cityBanner and homepage placements

What you get

A before, during and after view of every tracked product, refreshed several times a day during the sale window, with a summary your team can act on while the event is still running.

Typical cadence: Hourly during sale windows, daily otherwise
Markets: GCC, India, Southeast Asia, Europe, Americas

Go deeper:Promotion tracking E-commerce and retail Quick commerce

Pricing and revenue

Cross-border price comparison

Compare the same product across countries on a like-for-like basis, after currency, tax and pack size.

The situation

Regional teams need to know whether a product is priced consistently in Dubai, Riyadh, Mumbai and Singapore. Raw prices mislead: VAT rules, pack sizes and exchange rates all differ.

What we collect

Local-currency pricesTax-inclusive and exclusive pricesPack size and unitSeller and fulfilment typeDaily exchange rates

What you get

A normalised price index by country and product, showing where you are over- or under-priced against local competitors and against your own other markets.

Typical cadence: Daily or weekly
Markets: All five regions

Go deeper:Cross-border price parity Multi-currency benchmarking Electronics and appliances

Pricing and revenue

Hotel and airfare rate parity

Check that your rates are consistent across booking channels, and see how competitors price the same dates.

The situation

Rates for the same room or route can differ across online travel agencies, metasearch and direct channels. Undercutting erodes direct bookings and breaks rate agreements.

What we collect

Room or fare rates by dateCancellation and meal termsChannel and sellerAvailabilityCompetitor rates for the same dates

What you get

A parity report by property, route and channel, with flagged undercuts and a view of competitor pricing for the dates that matter to you.

Typical cadence: Daily, or several times a day around peak dates
Markets: All five regions

Go deeper:Travel rates and fares data Travel and hospitality

Marketing and customer experience

Review analysis in Arabic, Hindi and English

Understand what customers say about you and your competitors, in the languages they actually write in.

The situation

In the Gulf and India, many reviews mix Arabic or Hindi with English, or use transliteration. Tools built for English alone miss or misread a large share of the feedback.

What we collect

Star ratingsReview text as writtenLanguage and scriptReview date and productSeller or branch

What you get

A structured review dataset with original text preserved, themes grouped across languages, and trends by product, branch or competitor over time.

Typical cadence: Daily or weekly
Markets: GCC, India, and any multilingual market

Go deeper:Reviews and sentiment analysis Food delivery and restaurants Arabic text in data pipelines

Sales and growth

B2B lead lists from public sources

Build targeted company lists from public directories, registries and industry listings.

The situation

Sales teams entering a new market or segment spend weeks assembling prospect lists by hand, and bought lists are often out of date or poorly matched to the target profile.

What we collect

Company name and categoryLocation and branch countWebsite and public business contact detailsRegistration and licence data where publishedSize and activity signals

What you get

A de-duplicated company list matched to your ideal customer profile, refreshed on your schedule. We collect business information, not personal data about individuals.

Typical cadence: One-off or monthly refresh
Markets: All five regions

Go deeper:Company and lead data Manufacturing Logistics and freight

Sales and growth

Government tender and procurement monitoring

Catch relevant public tenders as soon as they are published, across many portals and formats.

The situation

Tenders are spread across national, regional and agency portals, often as PDFs, in several languages. Missing a notice by a few days can mean missing the bid window.

What we collect

Tender title and referenceIssuing authorityCategory and value where publishedDeadlinesDocuments and amendments

What you get

A daily feed of new and amended tenders filtered to your categories and regions, with key fields extracted from PDFs and links to the source notice.

Typical cadence: Daily
Markets: GCC, India, Europe, Americas

Go deeper:Government and public data Document and PDF extraction

Strategy and investment

Market entry research

Size up a new country before you commit: competitors, assortment, prices and channels.

The situation

Leadership wants to know whether a product line will work in a new market. Desk research is slow and often relies on reports that are months old.

What we collect

Competitor assortment and brandsPrice ranges by categoryChannel and marketplace presenceDelivery promises and feesReview volume as a demand signal

What you get

A market snapshot built from live public data, showing who sells what, at what price and through which channels, with the gaps where you could compete.

Typical cadence: One-off study, with an optional quarterly refresh
Markets: All five regions

Go deeper:Assortment gap analysis Global coverage

Strategy and investment

Store and branch expansion planning

Map where competitors and complementary businesses are, to choose where to open next.

The situation

Retailers, restaurant groups and clinics planning new locations need an accurate map of the competitive landscape, and public listings change constantly.

What we collect

Store and branch locationsCategory and brandOpening hoursRatings and review countsDelivery coverage areas

What you get

A geocoded dataset of competitor and complementary locations by city and district, ready for your mapping or planning tools.

Typical cadence: Monthly or quarterly
Markets: All five regions

Go deeper:Location intelligence Location and POI data Grocery and FMCG

Strategy and investment

Property price and rental yield tracking

Track asking prices and rents by area to spot where yields are moving.

The situation

Investors, lenders and developers need current price and rent levels by district. Official figures are published with a lag and at a coarse level.

What we collect

Asking price and rentSize and configurationLocation to district levelListing age and changesOff-plan launches and payment plans

What you get

Area-level price and rent series with de-duplicated listings, so you can compare yields and see momentum by district.

Typical cadence: Weekly
Markets: GCC, India, Singapore, UK, Americas

Go deeper:Real estate listings data Real estate and proptech Finance and investment

People and HR

Salary and hiring benchmarking

See what employers are hiring for, and what they advertise to pay, by role and city.

The situation

HR and talent teams set pay ranges and plan hiring with surveys that are expensive and quickly dated, especially for fast-moving technology roles.

What we collect

Job titles and seniorityAdvertised salary rangesSkills requestedLocation and work arrangementEmployer and industry

What you get

A role-by-city view of advertised pay and demand, normalised to a common basis, to support offers, pay reviews and workforce planning.

Typical cadence: Monthly
Markets: All five regions

Go deeper:Jobs and salary data Jobs and recruitment

Data and AI

Training data for AI models

Source large, clean and well-documented datasets for training or evaluating AI models.

The situation

Data science teams need domain-specific text, product or listing data at scale, with duplicates removed and a clear record of where each item came from.

What we collect

Text and structured records from permitted public sourcesSource and collection dateLanguage and category labelsQuality and duplicate flags

What you get

A de-duplicated, documented dataset in the format your pipeline needs, with provenance for every record and filtering agreed during scoping.

Typical cadence: One-off or recurring
Markets: All five regions

Go deeper:AI training data Responsible web data collection

Data and AI

Enriching your own catalogue or CRM

Fill gaps in your product catalogue or customer database with accurate public web data.

The situation

Internal records often lack attributes, images, categories or up-to-date company details. Filling them by hand does not scale.

What we collect

Product attributes and specificationsCategories and identifiersCompany details and locationsMatching keys to your records

What you get

Your records returned with missing fields filled and matched with a confidence score, ready to load back into your systems.

Typical cadence: One-off, then incremental
Markets: All five regions

Go deeper:Catalogue and assortment data Company and lead data

Have a different use case in mind? Most projects start as a question we have not seen in exactly that form. Tell us yours →

What teams do with our data.

Festive and seasonal sale tracking

The situation

What we collect

What you get

Cross-border price comparison

The situation

What we collect

What you get

Hotel and airfare rate parity

The situation

What we collect

What you get

Review analysis in Arabic, Hindi and English

The situation

What we collect

What you get

B2B lead lists from public sources

The situation

What we collect

What you get

Government tender and procurement monitoring

The situation

What we collect

What you get

Market entry research

The situation

What we collect

What you get

Store and branch expansion planning

The situation

What we collect

What you get

Property price and rental yield tracking

The situation

What we collect

What you get

Salary and hiring benchmarking

The situation

What we collect

What you get

Training data for AI models

The situation

What we collect

What you get

Enriching your own catalogue or CRM

The situation

What we collect

What you get

See your own data before you commit.