App Scraping & Delivery Process - Mobile App Scraping
Undertaking Apps scraping at a substantial scale is a challenging feat. However, our battle-tested approach streamlines data extraction to the utmost. Delve into the comprehensive step-by-step breakdown of our process at Mobile App Scraping. Give us a try – we're here to impress!
Initial Interaction
Upon your contact, we'll initiate an email exchange to gain insights into your challenge.
Feasibility Analysis
We'll conduct a feasibility assessment and develop a tailored solution after acquiring project specifics.
Pricing and Payment
Pricing is determined by site complexity and data volume. Once agreed, an invoice will be shared for credit card, wire transfer, or PayPal settlement. Our "Data or Money-Back" pledge stands firm.
Communication Channel
Post payment, a dedicated support portal account is established using Freshdesk, connecting you with our data mining engineers and success manager.
Setup and Data Sample
Upon portal access, our data mining team configures scrapers on our platform and furnishes a sample data set for your review.
Sample Approval
Sample issues are resolved by the data mining team, often involving format or parsing adjustments. Approval usually takes a couple of iterations.
Complete Data Extraction
Our distributed extraction platform conducts a complete scrape after sample approval, funneling data to our Quality Assurance tool.
Quality Assurance
Our dual-stage Q&A process integrates machine learning and human verification to rectify faulty data. Verified data undergoes random checks for added quality assurance.
Data Delivery
Successfully vetted data is shared via platforms like Amazon S3, Dropbox, FTP, or custom APIs for effortless access.
Support and Maintenance
Subscription includes complimentary scraper maintenance. For recurring data needs, automated schedules can be set up.
Customer Success Meeting
Collaborating closely, we leverage our data handling prowess to guide you toward success, advising on optimal software development strategies for data-intensive applications.
