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Service · Telecom data

Telecom Data Scraping Services

Plan pricing with the contract structure that decides real cost.

Telecom data scraping is the automated collection of publicly published mobile, broadband and converged plan information — headline pricing, data and call allowances, contract length, in-contract price rises, device bundle structures, one-off fees and published coverage claims — normalised so plans can be compared on total cost rather than on advertised monthly price.

A twenty-four month plan advertised at one price, rising annually by a published index, bundled with a device at an unstated subsidy, is not a monthly price. It is a total cost of ownership problem pretending to be a number.

Free pilot on your own sources, returned in 48 hours. No card, no trial clock — and you keep the sample data either way.

Total cost, not headline price Device bundles decomposed Free pilot sample in 48 hours
telecom_plans_2026-08-05.jsonl LIVE FEED
{"operator":"Example Mobile", "operator_id":"aw-tel-GB-118", "plan_type":"sim_only", "plan_name":"Unlimited Plus 24m", "monthly_price":22.00,"currency":"GBP", "contract_months":24, "data_gb":"unlimited", "fair_use_gb":650, "speed_cap_mbps":null, "in_contract_rise":{"type":"cpi_plus", "plus_pct":3.9,"month":"april"}, "upfront_fee":0.00, "tco_24m_estimate":561.40, "tco_assumptions":"cpi_3.0_assumed", "roaming_included_gb":25, "source_ref":"pdf:tariff-guide-2026-07.pdf#p6"} {"plan_type":"broadband_fttp", "advertised_mbps_down":900, "guaranteed_min_mbps":700, "setup_fee":0.00,"router_included":true, "promo_months":6,"reverts_to":44.99}
2 of 182,400 plan-operator rows · run 2026-08-05T06:00Zterms extracted 95.2% · schema v4.3
Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
Walmart
FMCG Marketplace
udaan
Food Delivery
Uber Eats
Quick Commerce
blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall

Key facts at a glance

What it is
Managed collection of published mobile, broadband and converged plan pricing and terms
Plan types
SIM-only, device bundles, pay-as-you-go, broadband, converged and business plans
Allowances
Data, call and text allowances plus fair use thresholds and speed caps where published
Contract structure
Contract length, in-contract price rise mechanism, promo periods and revert prices
Total cost
Computed total cost over contract term with assumptions documented, not hidden
Device bundles
Bundle decomposed against SIM-only equivalent to reveal implied device cost
Refresh
Daily standard; sub-daily during promotional periods and competitive responses
Who it's for
Operator pricing teams, MVNOs, comparison platforms, regulators and investors
TCOcomputed with stated assumptionsnot headline price
95.2%terms extraction ratewith page references
Bundlesdecomposed vs SIM-onlyimplied device cost
Price risesmechanism capturednot just current price

Key takeaways

  • What it is: Managed collection of published mobile, broadband and converged plan pricing and terms
  • Plan types: SIM-only, device bundles, pay-as-you-go, broadband, converged and business plans
  • Allowances: Data, call and text allowances plus fair use thresholds and speed caps where published
  • Contract structure: Contract length, in-contract price rise mechanism, promo periods and revert prices
  • Total cost: Computed total cost over contract term with assumptions documented, not hidden
  • Device bundles: Bundle decomposed against SIM-only equivalent to reveal implied device cost

Last verified 5 August 2026 by the Actowiz Solutions Data Engineering team.

Definition

What is telecom data scraping, and why is headline price the least useful field?

Telecom data scraping is the automated collection of publicly published plan information from operator, MVNO and reseller websites: pricing, allowances, contract terms, promotional structures, device bundles, one-off fees and published coverage or speed claims.

Telecom pricing is unusually structured — and unusually good at making comparison difficult. The advertised monthly price is genuine and almost never the cost.

What sits between the headline and the cost

  • In-contract price rises. Many markets permit annual increases tied to an inflation index plus a fixed percentage. A plan advertised at one price will not be that price in month thirteen.
  • Promotional periods. A six-month promotional rate reverting to a substantially higher price is standard in broadband, and the revert price is the real one for most of the term.
  • Contract length. A twelve-month and twenty-four-month plan at the same monthly price are different commitments with different total costs and different switching options.
  • Device subsidy. A bundle price minus the SIM-only equivalent reveals an implied device cost, which is frequently well above retail.
  • One-off fees. Upfront charges, setup fees and router costs shift total cost, especially on shorter contracts.
  • Fair use and speed caps. An unlimited plan with a fair use threshold and post-threshold speed cap is not the same product as one without.

How we handle it

Every component is a separate field, and we compute a total cost over the contract term as a convenience — with the assumptions recorded in a tco_assumptions field, because a TCO depends on assumed inflation and usage. An undocumented TCO is a number nobody can defend in a pricing meeting.

Device bundles are decomposed against the operator's own SIM-only equivalent, which is the only defensible way to isolate implied device cost. And unlimited plans carry their fair use threshold and speed cap explicitly, since those determine whether unlimited means unlimited.

What we collect

Six categories of telecom data

Mobile plan pricing is the largest use case. Converged bundle decomposition is the most analytically valuable.

Mobile plan pricing

SIM-only and bundled, decomposed.

  • Monthly price and contract length
  • Data, call and text allowances
  • Fair use thresholds and speed caps
  • Upfront and one-off fees
  • 5G access and tiering where differentiated

Broadband & fixed

Where promotional structure dominates.

  • Advertised and guaranteed speeds
  • Promotional period and revert price
  • Setup, router and activation fees
  • Technology type: FTTP, FTTC, cable, fixed wireless
  • Data caps where applied

Contract & price rise terms

The structure that decides total cost.

  • Contract length and minimum term
  • In-contract rise mechanism and index
  • Early termination charge structure
  • Notice and switching terms
  • Auto-renewal and out-of-contract pricing

Device bundles

Implied device economics.

  • Bundle monthly price and upfront cost
  • SIM-only equivalent for the same allowances
  • Implied device cost
  • Device model, storage and colour
  • Trade-in and upgrade offer structures

Roaming & add-ons

The extras that shift real cost.

  • Roaming inclusions and zones
  • Roaming daily and bundle charges
  • Add-on pack pricing
  • International call rates where published
  • Content and subscription bundles

Coverage & quality claims

What operators publish about their networks.

  • Published coverage percentages by technology
  • Guaranteed minimum speeds
  • Published availability checkers by address
  • Network quality claims as published
  • Coverage claim change detection
Service scope

What the ecommerce data scraping service includes

A managed engagement, not a tool licence. We own the pipeline and everything that breaks in it.

✓ Included in every engagement

  • Total cost over term computed with assumptions recorded in a dedicated field
  • Device bundles decomposed against same-operator SIM-only equivalents
  • In-contract price rise mechanism captured as a structured object
  • Fair use thresholds and speed caps captured for unlimited plans
  • MVNO coverage included, since price competition often starts there
  • Source discovery, scoping and a written collection plan
  • Free pilot on your own sources before any commitment
  • Full pipeline build, hosting and proxy infrastructure
  • Schema design, validation and sampled human QA on every run
  • Ongoing maintenance when source layouts change — our cost, not yours
  • Delivery to your warehouse, bucket, SFTP or API endpoint
  • Documented methodology and compliance notes for your legal review

× Not included — stated upfront

  • Customer accounts, order initiation or submission of personal details
  • Operator-internal subscriber, churn or ARPU figures
  • Network performance measurement — we collect published claims, not measured speeds
  • Derived device subsidy figures where no clean SIM-only equivalent exists
  • Anything behind a login, paywall or credentialed session
  • Personal data beyond a documented lawful basis
  • Licensed third-party datasets we do not hold rights to
  • Guarantees about fields a source simply does not publish
Schema

Telecom data fields you receive

Every engagement delivers a documented schema. These are the core fields; the full dictionary runs to 120+ across mobile and fixed.

Deliverable schema — v4.3 core fields (full dictionary: 120+ fields)
Field Type What it captures Refresh
operator / operator_id string Operator or MVNO as published and a stable identifier for longitudinal joins Every run
plan_type / plan_name enum / string Normalised plan class and the operator's own plan name Daily
monthly_price / contract_months decimal / int Advertised monthly price and minimum contract term Daily
data_gb / fair_use_gb / speed_cap_mbps string / decimal Data allowance plus fair use threshold and post-threshold cap where published Daily
in_contract_rise object Rise mechanism, index, additional percentage and the month it applies Weekly
promo_months / reverts_to int / decimal Promotional period length and the price it reverts to Daily
upfront_fee / setup_fee decimal One-off charges, which shift total cost materially on shorter contracts Daily
tco_estimate / tco_assumptions decimal / string Computed total cost over term with the assumptions recorded explicitly Daily
bundle_device / implied_device_cost string / decimal Bundled device and implied cost against the SIM-only equivalent Daily
roaming_included_gb / roaming_zones decimal / array Roaming inclusions and the zones they apply to Weekly
source_ref string Reference to the tariff document and page where terms came from a PDF Every run

tco_estimate always ships alongside tco_assumptions. A total cost figure without its assumptions is unusable in a pricing meeting, because the first question is always what inflation and usage were assumed.

Coverage

Operators, sources and markets we collect from

Telecom is entirely national. Coverage is built market by market including MVNOs, which are often where price competition happens.

EEO2VodafoneThreeSky MobileTesco MobileGiffgaffLebaraDeutsche TelekomVodafone DEO2 DE1&1OrangeSFRBouyguesFree MobileMovistarVodafone ESOrange ESTIMVerizonAT&TT-Mobile USMint MobileRogersBellTelusTelstraOptusJioAirtelViEtisalatduSTCZainSingtelStarhubMVNO and reseller sitesPublished tariff guides

Address-level broadband availability checkers require an address input, so collection is per address or postcode sample rather than national. We scope that sample deliberately, since it multiplies volume the same way delivery zones do in quick commerce. Request a source we don't list →

Markets served

Countries and markets where this service is in highest demand

We deliver into 40+ countries. These are the markets where this particular service is requested most, and the reason demand concentrates there.

Highest-demand markets for this service, and why demand concentrates there
Market Why demand concentrates here
United Kingdom In-contract price rise mechanisms are published and under regulatory scrutiny, which makes mechanism capture unusually valuable.
Germany, France & Spain Dense operator and MVNO competition with heavy promotional activity and complex converged bundles.
United States & Canada Device bundle economics dominate the market, making bundle decomposition against SIM-only the primary use case.
India & GCC High-volume, low-price markets with rapid plan changes and strong demand for competitive monitoring.

North America

United StatesCanadaMexico

United Kingdom & Ireland

United KingdomIreland

Western Europe

GermanyFranceNetherlandsBelgiumSpainItalySwitzerlandAustria

Nordics

SwedenNorwayDenmarkFinland

Middle East

United Arab EmiratesSaudi ArabiaQatarKuwaitIsrael

Asia Pacific

SingaporeAustraliaNew ZealandJapanSouth KoreaMalaysiaIndonesiaThailandVietnamPhilippines

South Asia

IndiaBangladeshSri LankaPakistan

LATAM

BrazilArgentinaChileColombia

Africa

South AfricaNigeriaKenyaEgypt

We run production collection across 40+ countries. Coverage depth varies by market and by source, so we confirm what is actually available for your specific markets during scoping rather than claiming uniform global coverage. Ask about a market we don't list →

Who buys this data

Which teams buy telecom data

Operator and MVNO pricing teams dominate, with comparison platforms and regulators following.

Head of Pricing

Mobile and fixed operators
The problem

Competitor plan changes are constant, and comparing them on headline price misses contract structure entirely.

What we deliver

Daily competitor plan collection with contract terms, price rise mechanisms and TCO computed on stated assumptions.

Metric that moves

ARPU

Proposition Manager

MVNOs and challengers
The problem

Positioning against incumbents requires knowing their real total cost, not their advertised price.

What we deliver

Total cost comparison with promotional structures and revert prices captured, plus device bundle decomposition.

Metric that moves

Net adds

Head of Data

Comparison platforms
The problem

Your product depends on complete, current plan data with terms normalised, and maintaining that is not your differentiator.

What we deliver

A maintained plan feed with allowances, contract terms and fees normalised, refreshed daily with change detection.

Metric that moves

Comparison completeness

Device Category Manager

Operators and retailers
The problem

Bundle competitiveness depends on implied device cost, which nobody publishes and everyone competes on.

What we deliver

Bundle pricing decomposed against SIM-only equivalents to reveal implied device cost across competitors and models.

Metric that moves

Device margin

Market Analyst

Regulators and policy bodies
The problem

Assessing affordability and price rise practice requires harmonised plan data across operators and time.

What we deliver

Harmonised plan and terms panels with price rise mechanisms captured, suitable for market and affordability analysis.

Metric that moves

Analysis coverage

Investment Analyst

Telecom and infrastructure funds
The problem

Pricing and promotional intensity are observable ahead of reported ARPU, if terms are captured properly.

What we deliver

Longitudinal pricing, promotional intensity and TCO panels by operator and market for direct modelling.

Metric that moves

Signal lead time

Use cases

How telecom data gets used in practice

Four patterns, with the outcome each is judged on.

Total cost competitive positioning

Plans are collected with contract length, promotional periods, revert prices, in-contract rise mechanisms and one-off fees as separate fields, and total cost over term is computed with assumptions stated.

Outcome: Positioning assessed on what customers actually pay across a contract rather than on advertised monthly price.

Device bundle decomposition

Bundle pricing is compared against the same operator's SIM-only plan with equivalent allowances, isolating implied device cost per model across competitors.

Outcome: Bundle competitiveness measured on implied device economics rather than on bundle headline price.

Promotional intensity tracking

Promotional periods, revert prices and offer changes are tracked daily, producing a promotional intensity measure by operator and segment over time.

Outcome: Competitive response timed against observed promotional cycles rather than guessed.

Price rise practice monitoring

In-contract rise mechanisms, indices and application months are captured per plan, allowing comparison of price rise practice across operators and over time.

Outcome: Price rise exposure quantified per plan, which matters for both competitive and regulatory analysis.

Engagement examples

Two engagements, anonymised

Clients rarely permit naming. These are real engagement shapes with identifying detail removed, so you can judge whether the work resembles your situation.

Operator · UK

Competitive positioning used headline prices and missed contract structure

Situation

Pricing analysis compared advertised monthly prices, ignoring in-contract rise mechanisms and promotional revert prices that changed relative position substantially over a term.

What we ran

Plan collection with contract terms, rise mechanisms and promo structures as separate fields, plus total cost over term computed with assumptions recorded.

Result

Positioning analysis switched to total cost, which reversed the assessment on several key plans.

MVNO · EU

Device bundle competitiveness could not be assessed

Situation

The team could see competitor bundle prices but not the implied device economics inside them, so it could not tell where bundles were genuinely aggressive.

What we ran

Bundle decomposition against same-operator SIM-only equivalents on matched allowances and term, with implied device cost computed per model.

Result

Bundle strategy was set against implied device economics rather than bundle headline prices.

Examples are anonymised at client request. Named references are available on request under NDA. See published case studies →

The 48-hour sample — run on your sources, not ours

Before you commit to anything, we run this service against your own sources and send you the output. If the coverage isn't there, the sample will show you that too — which is the point. We would rather lose the deal at the pilot than at month three.

  • Real extraction from your actual sources
  • Returned inside two business days
  • Coverage and QA note included
  • You keep the data either way
  • No card, no trial clock
  • Named engineer on the call
Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.
How we engage

Three ways to engage us for this work

Same collection pipeline and same QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

  • Dedicated engineer assigned to your account
  • Site changes fixed by us, not reported to you
  • Scheduled delivery to your warehouse or S3
  • Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

  • On-demand and scheduled endpoints
  • Rate limits agreed to your load profile
  • Sandbox keys for integration testing
  • Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

  • Fixed scope agreed in writing upfront
  • Single delivery with full QA report
  • Methodology documented for your records
  • Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Build vs buy

Should you build telecom data collection in-house or hire it as a service?

Terms extraction and TCO normalisation are the work; collecting the headline price is trivial and near-useless.

In-house build vs self-serve tool vs Actowiz managed service
Consideration In-house scraping team Generic proxy / DIY tool Actowiz managed feed
Time to first usable data 6–12 weeks of engineering before anything is trustworthy Days, but output needs manual cleanup before use Free pilot in 48 hours, production in 5–10 business days
Who fixes it when a source changes Your engineers, at the cost of their roadmap You do — tools report failures, they don't resolve them We do, same business day, inside the retainer
Data quality assurance Whatever your team has time to build None beyond HTTP success Schema validation plus sampled human QA on every run
Compliance documentation Rarely produced, then requested urgently by legal Not provided; terms risk sits with you Sources, method and lawful basis documented for review
Accountability Distributed across a team with other priorities A support ticket queue A named engineer and an account owner
True annual cost Engineer salaries, proxies, hosting, ongoing maintenance Low licence fee plus significant hidden analyst time One fixed monthly retainer, quoted after scoping

Why device bundle decomposition is the most valuable output here

Operators compete hard on device bundles and publish nothing about the device economics inside them. Yet the economics are derivable from published data, which makes this one of the better arbitrage opportunities in competitive intelligence.

The derivation

Take a bundle: device plus allowances at a monthly price over a term, with an upfront payment. Take the same operator's SIM-only plan with equivalent allowances and term. The difference over the term, plus the upfront, is the implied device cost.

Compare that to the device's retail price and you can see whether the operator is subsidising, at parity, or charging a premium for the financing convenience. Across competitors and models, this reveals device strategy directly.

Why it must be same-operator

  • Allowance equivalence. Comparing a bundle to a different operator's SIM-only plan conflates device economics with network positioning.
  • Term matching. A twenty-four-month bundle against a twelve-month SIM-only plan produces a meaningless implied cost.
  • Price rise consistency. If both plans carry the same rise mechanism, it cancels in the comparison. If they differ, the derivation must account for it.
  • Promotional contamination. If the SIM-only plan is promotionally priced and the bundle is not, the implied device cost is overstated.

We handle these by matching within operator, on equivalent allowances and term, flagging where no clean equivalent exists rather than forcing a comparison. Where a bundle has no comparable SIM-only plan — which happens deliberately — we say so instead of producing a figure derived from an inappropriate baseline.

In-contract price rises, and why the mechanism matters more than the current price

In several markets operators may raise prices mid-contract, typically by an inflation index plus a fixed percentage. This has become one of the most consequential structural features of telecom pricing and one of the least captured in competitive data.

Why current price is insufficient

  • Divergence over term. Two plans at identical monthly prices with different rise mechanisms cost materially different amounts by month twenty-four.
  • Timing effects. A rise applied in month three of a contract compounds differently from one applied in month eleven.
  • Index sensitivity. CPI-linked and RPI-linked rises diverge, and a fixed-percentage-only rise behaves differently again in a changing inflation environment.
  • Regulatory attention. Price rise practice is under scrutiny in several markets, so the mechanism itself is a compliance-relevant data point.
  • Fixed-price competition. Some operators now market the absence of in-contract rises as a differentiator, which is only measurable if the mechanism is captured.

What we capture

The mechanism as a structured object: rise type, index used, additional fixed percentage, and the month it applies. Our TCO estimate applies a stated inflation assumption recorded in tco_assumptions, so you can substitute your own assumption without recollecting anything.

We deliberately do not publish a single TCO with a hidden inflation assumption baked in. Pricing teams need to run their own scenarios, and regulators need to see the mechanism rather than someone else's modelled outcome.

How it works

How a telecom data engagement goes live in 5 to 10 business days

Markets, operators and whether address-level broadband availability is needed are scoped first, since address sampling drives volume.

Scope the sources and fields

You send us target sites, regions, SKUs or keywords. We return a field-level schema proposal, coverage estimate and refresh recommendation — usually within two working days.

Pilot sample, free

We extract a real sample from your actual targets so you can inspect field fill rates, edge cases and match quality before any commitment.

Production build and QA harness

Our engineers build extractors, then wire validation rules: type checks, range checks, duplicate detection and golden-record comparison against a manually verified subset.

Scheduled delivery into your stack

Feeds run at your chosen cadence and land in the warehouse or bucket you already use. Schema changes are versioned and announced before they ship.

Ongoing monitoring and SLA support

We watch coverage drift, fill rates and source changes daily. A named engineer owns your account, and layout breaks are fixed by us — not queued for you.

Formats & destinations

JSON, JSONL, CSV, Parquet or XLSX, delivered to Amazon S3, Google Cloud Storage, Azure Blob, SFTP, Snowflake, BigQuery, Databricks or a REST/GraphQL endpoint. Webhooks fire on completion, and every batch ships with a manifest containing row counts, schema version and QA results so your pipeline can fail loudly instead of silently ingesting a bad file.

Compliance & data ethics

We collect publicly published plan pages, tariff guides and terms documents. Address-level broadband availability checkers are queried with postcode or address samples rather than personal details, and we do not create customer accounts, initiate orders or submit personal information. Every term extracted from a document carries a page reference.

Service commitments

What we commit to, in writing

These are contractual, not marketing copy. They appear in the engagement document.

Service level commitments written into every managed engagement
Commitment What we hold ourselves to
Pilot turnaround A real sample from your own sources within 48 hours of scoping, at no cost.
Go-live Production collection running within 5–10 business days of sign-off.
Delivery punctuality 99.5% on-schedule delivery, measured monthly and reported to you.
Breakage response Source layout changes triaged same business day; critical sources inside 4 hours.
Data quality Schema validation on every run plus sampled human QA before any delivery leaves us.
Escalation A named engineer and an account owner, not a shared ticket queue.
Change requests Field additions and source changes handled inside the retainer, not re-quoted.
Exit Your historical data exported in full on request. No lock-in, no export fee.

Why teams pick Actowiz for this work

  • Engineers, not a dashboard. You get people who fix breakages, not a self-serve tool you maintain yourself.
  • We tell you what we can't do. Scope limits and coverage gaps are stated before you sign, not discovered in month three.
  • QA is part of the service. Schema validation and sampled human review run before delivery, every run.
  • Compliance is documented. Sources, method and lawful basis written down so your legal team can review them.
  • Fixed monthly cost. No per-request metering, no surprise overage on a month when a competitor adds SKUs.
  • Six years, 40+ countries. Long-running production pipelines across retail, travel, mobility and finance.
Definitions

Terms used on this page

Plain definitions of the terms used on this page, so procurement and legal reviewers are working from the same vocabulary as your data team.

In-contract price rise
A permitted mid-contract increase, typically an inflation index plus a fixed percentage. Two plans at identical monthly prices with different mechanisms cost materially different amounts by end of term.
Implied device cost
Bundle cost over term plus upfront, minus the same operator's SIM-only equivalent on matched allowances and term. It reveals device subsidy or premium that operators never publish.
Fair use threshold
The point at which an unlimited plan applies restrictions, usually a speed cap. An unlimited plan with a threshold and cap is a different product from one without, so both must be captured.
FAQ

Telecom data scraping: frequently asked questions

What pricing, proposition and regulatory teams ask during evaluation.

Because the headline price is genuine and almost never the cost. In-contract rises, promotional revert prices, contract length, upfront fees and device subsidy all sit between the advertised number and what a customer pays over a term.

We capture each as a separate field and compute total cost over term with assumptions recorded in tco_assumptions. Two plans at identical monthly prices can differ materially by month twenty-four, and headline tracking cannot see that at all.

Yes, by decomposing the bundle against the same operator's SIM-only plan with equivalent allowances and term. The difference over the term plus any upfront payment gives implied device cost, which you can compare to retail price.

The matching must be within operator and on equivalent allowances and term, otherwise you conflate device economics with network positioning. Where no clean SIM-only equivalent exists — which operators sometimes arrange deliberately — we flag it rather than deriving a figure from an inappropriate baseline.

Yes, as a structured object: rise type, index used, additional fixed percentage and the month it applies. This is one of the most consequential fields in telecom pricing and one of the least captured by competitive tools.

It also matters for regulatory work, since price rise practice is under scrutiny in several markets, and some operators now market the absence of in-contract rises as a differentiator — which is only measurable if the mechanism is captured rather than just the current price.

By capturing what unlimited actually means for that plan: the fair use threshold where published, any post-threshold speed cap, and any restrictions on tethering or use type.

An unlimited plan with a 650GB fair use threshold and a post-threshold speed cap is a different product from one without either, and treating both as unlimited makes the comparison useless. Where an operator publishes no threshold, the field is null rather than assumed.

Yes, but it requires querying availability checkers per address or postcode, which multiplies volume the same way delivery zones do in quick commerce. We scope the sample deliberately with you rather than attempting national coverage.

We query with postcode or address samples, not with personal details, and we do not create accounts or initiate orders. A well-chosen sample usually answers the competitive question that exhaustive coverage would answer at many times the cost.

Both, and MVNOs matter more than their market share suggests because price competition frequently happens there first. Host network relationships are captured where published, since an MVNO's proposition depends on which network it rides.

MVNO coverage is more work per operator because there are many of them and their sites are less consistent, but excluding them produces a pricing picture that misses the aggressive end of the market entirely.

Yes — mobile, broadband, TV and content combinations with the bundle discount against standalone pricing computed where standalone equivalents are published.

Convergence is where bundle decomposition gets hardest, because operators construct bundles precisely to prevent component comparison. We compute what is derivable and flag where a component has no published standalone equivalent, rather than estimating one.

Base plan prices move on a scale of weeks to months. Promotional offers change constantly, sometimes weekly, and competitive responses can be same-day. Device bundle pricing moves with device launches and stock positions.

Daily refresh suits most engagements, with sub-daily during promotional periods and around major device launches. Annual price rise months are worth watching closely, since that is when structural repricing happens across a whole base.

We quote individually. The drivers are market count, operator and MVNO count, whether address-level broadband availability sampling is included, and refresh frequency.

A single market across the main operators and MVNOs at daily refresh sits at the lighter end. Multi-market coverage with address-level availability sampling and converged bundle decomposition sits higher. One scoping call, a free pilot on your own market within 48 hours, then a fixed monthly quote. Request a quote.

See real telecom plan data for your own market

Send us a market and operator set. We return plans with contract terms, price rise mechanisms and TCO computed within 48 hours.

Free pilot, no card, no obligation. TCO always ships with its assumptions stated.
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From Zomato to Expedia — see why global leaders trust us with their data.

Why Global Leaders Trust Actowiz

Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.

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7+
Years of Experience
Proven track record delivering enterprise-grade web scraping and data intelligence solutions.
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4,000+
Projects Delivered
Serving startups to Fortune 500 companies across 50+ countries worldwide.
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200+
In-House Experts
Dedicated engineers across scrapers, AI/ML models, APIs, and data quality assurance.
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9.2M
Automated Workflows
Running weekly across eCommerce, Quick Commerce, Travel, Real Estate, and Food industries.
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270+ TB
Data Transferred
Real-time and batch data scraping at massive scale, across industries globally.
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380M+
Pages Crawled Weekly
Scaled infrastructure for comprehensive global data coverage with 99% accuracy.

AI Solutions Engineered
for Your Needs

LLM-Powered Attribute Extraction: High-precision product matching using large language models for accurate data classification.
Advanced Computer Vision: Fine-grained object detection for precise product classification using text and image embeddings.
GPT-Based Analytics Layer: Natural language query-based reporting and visualization for business intelligence.
Human-in-the-Loop AI: Continuous feedback loop to improve AI model accuracy over time.
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Connect the Dots Across
Your Retail Ecosystem

We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.

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Analytics Services
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Ad Tech
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Price Optimization
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Business Consulting
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System Integration
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Market Research
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Popular Datasets — Ready to Download

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Amazon
eCommerce
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Zillow
Real Estate
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DoorDash
Food Delivery
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Walmart
Retail
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Booking.com
Travel
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Indeed
Jobs
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Latest Insights & Resources

View All Resources →
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Blog

EU AI Act for Data Teams: What Scrapers Must Change in 2026

The EU AI Act impact on web scraping & AI training data GPAI transparency, copyright reservations, prohibited practices & a compliance checklist from Actowiz.

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Case Study

B2B Supplier Automates Government Tender Discovery from GeM & eProcure

How a B2B supplier replaced manual tender-portal checking with an automated, filtered feed of relevant government tenders from GeM and CPP/eProcure never missing a bid deadline again.

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Report

FIFA World Cup 2026 Aftermath: Hotel & Airfare Normalization in Host Cities (Data Study)

Actowiz Solutions tracks post–World Cup 2026 travel pricing — hotel ADR & airfare normalization across host cities, event-premium decay data & lessons for travel teams.

Start Where It Makes Sense for You

Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.

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Enterprise
Book a Strategy Call
Custom solutions, dedicated support, volume pricing for large-scale needs.
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Growing Brand
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Try before you buy — 500 rows of real data, delivered in 2 hours. No strings.
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Just Exploring
View Plans & Pricing
Transparent plans from $500/mo. Find the right fit for your budget and scale.
Get in Touch
Let's Talk About
Your Data Needs
Tell us what data you need — we'll scope it for free and share a sample within hours.
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    Free Sample in 2 HoursShare your requirement, get 500 rows of real data — no commitment.
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    Plans from $500/monthFlexible pricing for startups, growing brands, and enterprises.
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    US-Based SupportOffices in New York & California. Aligned with your timezone.
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    ISO 9001 & 27001 CertifiedEnterprise-grade security and quality standards.
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Free 500-row sample · No credit card · Response within 2 hours

Request Free Sample Data

Our team will reach out within 2 hours with 500 rows of real data — no credit card required.

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Free 500-row sample · No credit card · Response within 2 hours