Left in a safe place is not the same completion as handed to the customer, and the difference is where the claims come from.
ScrapeIt builds and runs the pipeline as a managed service. You tell us the lanes and whether you need tracking, ParcelShops or both; we build the collection with delivery outcomes typed, normalise into your cross carrier model and hand back CSV, JSON, Excel or a push into your warehouse.
We honour the crawl rules the site publishes and pace requests. Carrier tracking surfaces are easy to overload and hard to get back once access is withdrawn.
We collect no recipient names, no address detail beyond the postcode and no delivery photographs. Bring the use case to your own counsel before the project starts.
Tracking output carries the tracking number, the raw status text as published, the normalised status, the event timestamp with time zone, the event location and the depot or courier round identifier where given.
Delivery outcome is a first class field with the four completions kept apart: handed to customer, left with neighbour, left in safe place, collected from ParcelShop. Each carries the detail the carrier publishes, such as the safe place description, without the evidence image itself.
We do not collect the delivery photograph. A doorstep photo frequently shows a front door, a house number and sometimes a person, which makes it personal data about the recipient and occasionally about a passer by. The outcome and its description are what the analysis needs; the image is a liability with no analytical value, and we say so rather than collecting it because it is there.
Shipment fields cover origin and destination postcode, service level, promised delivery window where published, actual delivery, attempt count and exception reason.
ParcelShop records carry the shop identifier, name, full address, postcode, coordinates where published, opening hours structured by day, services offered and any size or weight limits stated.
The ParcelShop estate needs a genuinely frequent refresh by the standards of location data. These are independent businesses rather than owned branches, and churn is material. Monthly with change records is the minimum we would recommend for anything customer facing.
Evidence handling deserves repeating because clients do ask for it. We collect the outcome and its description and not the photograph. If a brief insists on images we will explain the exposure: doorstep photos are personal data about the recipient, often about the property, and occasionally about somebody who never consented to anything. That is not a dataset we will assemble.
Multi carrier design matters here more than usual because the outcome taxonomy is the part that differs most between carriers. A common model has to be rich enough to hold safe place and neighbour completions, and if it is designed around a postal carrier first it will not be. We define it before the second carrier is added.
Historical backfill is limited by retention, as on any carrier tracking surface. The dataset is built going forward.
Evri is one of the largest parcel networks in the United Kingdom, built on a courier model rather than a traditional postal one. Its delivery patterns reflect that: a large share of parcels complete at a ParcelShop, with a neighbour, or in a safe place chosen at the door.
Those outcomes are the distinguishing feature of this carrier's data and the part generic tooling handles worst. Delivered is not one state here. Handed to the customer, left with a named neighbour, left in a safe place and collected from a ParcelShop are four different completions with four different risk profiles, and the last two are where claims and disputes concentrate.
The tracking surface is public and responds, and crawl rules are published. The ParcelShop estate is large and its locations, hours and services are the second dataset, powering out of home options in UK checkouts where their share is high and rising.
Delivery evidence, typically a photograph, is presented on the tracking page for safe place deliveries. That is worth thinking about carefully before collecting, and we do.
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A retailer looking at a carrier report sees a delivery rate and is reassured. The rate hides the thing that actually costs money, which is that some of those completions will turn into claims.
Safe place and neighbour deliveries are the ones that do. The parcel is recorded as delivered, the customer says it never arrived, and the dispute goes to the retailer rather than the carrier. Tracking the outcome mix by lane, by depot and over time gives a retailer the one number that predicts claim volume before the claims arrive.
That analysis is impossible if the four completions are flattened into delivered, which is exactly what generic multi carrier tooling does. Keeping them apart costs nothing at collection time and cannot be recovered afterwards.
The second reason is the out of home share. A large and growing proportion of parcels complete at a ParcelShop, and for a retailer that changes both cost and customer experience. Measuring the share by area, and its trend, informs whether to promote the option at checkout.
The third is the estate itself. ParcelShops are independent businesses, so they open, close and change hours far more often than a postal branch network, and a locator built on a stale export starts sending customers to closed shops within weeks.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, watches it as status wording and the ParcelShop estate change, and repairs it before your locator starts sending customers to closed shops.
You see a sample first, in your format, on your own lanes, with the four completion types separated so you can see your safe place share before committing.
Because they carry different risk. Handed to the customer, left with a neighbour, left in a safe place and collected from a ParcelShop are four completions, and the middle two are where claims concentrate: the parcel is recorded as delivered, the customer says it never arrived, and the dispute lands with the retailer. Flattened into delivered, that signal is gone and cannot be recovered.
No. A doorstep photo shows a front door, a house number and sometimes a person, which makes it personal data about the recipient and occasionally about a passer by. The outcome and its description are what the analysis needs; the image is a liability with no analytical value, and we decline briefs that ask for it.
Far more often than a postal branch network, because ParcelShops are independent businesses. Churn is material, and a locator built on a stale export starts sending customers to closed shops within weeks. Monthly refresh with change records is the minimum for anything customer facing.
Yes, and the outcome taxonomy is the part that needs designing first. A common status model built around a postal carrier will not be rich enough to hold safe place and neighbour completions, so it has to be defined before the second carrier is added rather than retrofitted.
Not far. Carrier tracking surfaces retain limited history, so this dataset is built going forward rather than backfilled. Delivery is CSV, Excel, JSON, JSONLines or XML over FTP, SFTP, Amazon S3, Google Cloud Storage, Dropbox, Google Drive or email, or written straight into your database.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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