Most data providers stop at the surface. They give you the username, the date, and the comment text. That is fine if you just want to count keywords or measure general sentiment. But if you are an Operations Manager or a Brand Director, that is simply not enough. You cannot fix a problem if you do not know the context.
We recently ran a data extraction project for a large bakery chain, Global Bakery Co., to show exactly what DeepWalker quality looks like. We did not just scrape the website. We went deeper into the application layer to find the data that actually matters.
The Problem with Standard Scraping
If you look at a Google Maps review on a desktop browser, you see a star rating and a comment. That is it.
But if you look at the same review through the native mobile a…
Most data providers stop at the surface. They give you the username, the date, and the comment text. That is fine if you just want to count keywords or measure general sentiment. But if you are an Operations Manager or a Brand Director, that is simply not enough. You cannot fix a problem if you do not know the context.
We recently ran a data extraction project for a large bakery chain, Global Bakery Co., to show exactly what DeepWalker quality looks like. We did not just scrape the website. We went deeper into the application layer to find the data that actually matters.
The Problem with Standard Scraping
If you look at a Google Maps review on a desktop browser, you see a star rating and a comment. That is it.
But if you look at the same review through the native mobile application, Google often displays rich attributes that are invisible on the web. These include specific sub ratings for "Food," "Service," and "Atmosphere" along with critical context like "Wait time," "Noise level," and "Price per person."
Standard scrapers miss this completely. They give you a generic 3 star review. You have no idea if the customer hated the food or just thought the music was too loud.
The DeepWalker Workflow
We build our extractors to behave like real users on real devices. This allows us to merge data sources. We combine the volume of the web view with the rich detail of the mobile view.
For the Global Bakery Co. project, we produced an Excel report that provided answers, not just text.
1. Contextual Tags
We grabbed the "Details" metadata that usually stays hidden. Now, instead of guessing why a customer was unhappy, the client can see the specific conditions.
- Was it loud? (Noise level: Loud)
- Did they wait long? (Wait time: 30+ min)
- How much did they spend? (QAR 50 to 100)
2. Sub Rating Breakdown
We extracted the individual scores. We found reviews that had a generic 3 star overall rating, but a 1 star rating for "Service" and a 5 star rating for "Food." This tells the operations team exactly where to focus their training efforts without wasting time changing the menu.
3. Sentiment & Categorization
We do not just dump raw data. Our post processing scripts automatically tag every review with a Sentiment (Positive, Neutral, Negative) and a Category (Staff, Price, Food Quality). This turns a list of text into a filterable management dashboard.
The Deliverable
We believe data should be ready to use the moment it hits your inbox. We deliver fully formatted Excel files with color coding and filters already enabled. You do not need a data scientist to clean the file. You can open it, filter by "Negative Service," and instantly see which branch manager needs a call.
Download the Sample
Actual dataset generated for Global Bakery Co.
Open it up and look at the "Details" column. That is the difference between scraping text and extracting intelligence.
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