Prevent Online Shopify App

- CASE OF STUDY -

MPS is developing Prevent Online Fraud Shopify App which helps to calculate the fraud risks percentage in Shopify orders

What is Prevent Online Shopify App

MPS is developing Prevent Online Fraud Shopify App which helps to calculate the fraud risks percentage in Shopify orders. Depending on the risk level, the order is approved, needs an approval from the customer or cancelled. Embedded Shopify application integrates Prevent Online Fraud Shopify App functionality to Shopify stores via API, allows customer to setup risk level they want, displays reports and statistics in graphs. 

Prevent Online Fraud Shopify App service is a server app which prevents Card-Not-Present fraud, analyzing: 

  • Geolocation
  • Address Validation
  • Email and its tenure
  • IP address
  • Device tracking
  • Transaction history
  • Many other factors

The Project

Business situation

During the first discussion of the project requirements, the task of our team was to integrate a ready-made server Prevent Online Fraud Shopify App into the Shopify platform.

A great advantage of working with the client is his professional technical background and the existence of well-prepared and structured documentation for the Prevent Online Fraud Shopify App API.

Despite the lack of ready-made technical requirements, the client presented a demo version of the product to our team which was attempted to develop earlier.

Since the demo version couldn’t be integrated into the Shopify platform so that we couldn’t understand its operating principles, our primary task was to compile specifications.

The design for the application was taken from the web demo version.

During the development process, Prevent Online Fraud Shopify App service bugs were identified, and therefore the timeline and project budget, as well as the amount of our work, were increased.

In the process of refinement the Prevent Online Fraud Shopify App service, another errors were identified and corrected, and also improvements were made that significantly improved the service.

After fixing the Prevent Online Fraud Shopify App service, we were able to continue working on its integration with Shopify. We continue to support this project.

Challenges
  • Our estimate was decreased because the Client decided to move management of the project to the third party company, but we still had to perform managing.
  • Deadlines have been shifted due to found Prevent Online Fraud Shopify App side issues and defects and adding of the new features.  
  • Delay which was caused by the client’s occupation, so we had to wait for his feedback to continue the work.
  • Client isn’t a “technical person”. Therefore, it took more than usual to describe some aspects of the development, QA process and Shopify platform restrictions.
  • Due to the fact that the client disappeared from time to time and was not technically skilled, so we had to refresh the project information many times.
Work process

As we got fully described requirements and the app should go live when it’s absolutely ready - we have decided to use waterfall model. We used JIRA for project management and Confluence for collaboration on the project.
First, the Fixed Price pricing model was chosen for the working process. But when issues on Prevent Online Fraud Shopify App service’ side were found, it was changed to Time and Material one.

The Solution

Before proposing solution to the Client we analyzed the project needs from features and technology perspectives, wrote requirement specification and estimated an approximate cost. After that, we sent a proposal that completely satisfied the Client's needs. Next, we carried out a whole complex of work on project management, business analysis for new features, frontend, backend development and testing. 

Now we:

  • manage and test Prevent Online Fraud Shopify App service side issues, develop new features and perform regression tests
  • work closely with our Client to prepare a Beta version of the application and publish it to Shopify App Store
  • develop a work scheme with customers during the testing of the Beta version

We changed our team model during the project: 

First - Ruby developer was added when we faced Prevent Online Fraud Shopify App service side issues
Second  - One more QA engineer was added when the Client asked us to accelerate publishing a Beta version of the app

So now our team model is:

  • Delivery manager
  • Project Manager/Business analyst 
  • Shopify Developer
  • Ruby Developer
  • 2 QA Engineers
  • DevOps engineer
Team

Technologies

  • PHP
  • MySQL
  • Linux
  • Apache/Nginx
  • Shopify API
  • HTML
  • CSS
  • Prevent Online Fraud Shopify App API
  • Prevent Online Fraud Shopify App Merchant Broker API

Goals Achieved

ResultsResults
Results
Results
  • Shopify application development
  • Prevent Online Fraud Shopify App service side deploy to new server
  • Twilio account setup
  • SMTP server setup
  • Prevent Online Fraud Shopify App service side issues bug fixing and improving

Prevent Online Shopify App Features

Order processing

6 graphs display different types of statistics:

  • Authenticator Statistics by Day 
  • Defender Statistics by Day
  • Fraud Volume
  • Fraud Dollars
  • Negative Database
  • Negative Queries
Email notification
  • When the app is installed, it sends customers instructions how to setup the store
  • Order is cancelled by Varsgen application
  • Order is approved by Varsgen application
  • Trial period is completed 
  •  User's transactions are over
  • Subscription isn’t paid
  • A customer removes the application
Messaging

Set SMS/Email messages for 3 types of order risk level:

  • Text for SMS/Email Authentication - for orders in yellow risk level
  • Text for SMS/Email Reminder Authentication - for orders in yellow risk level
  • Text for SMS/Email Approved - for approved orders in yellow risk level
  • Text for SMS/Email Rejected - for rejected orders in yellow risk level

Text for SMS/Email Blocked - for blocked orders in yellow risk level

Settings
To allow a customer to set up percentage of each type of fraud risk level: Green Threshold Yellow Threshold Red Threshold
Dashboard
6 graphs display different types of statistics: Authenticator Statistics by Day - displays a number of orders with calculated yellow risk level, a number of different buyer/Prevent Online Fraud Shopify App responses Defender Statistics by Day Fraud Volume Fraud Dollars Negative Database Negative Queries
Billing
Monthly trial period - calculates a number of orders per month and suggest next plans: Mini Plan Small Plan Medium Plan Customer plan
Authenticator report
The report displays buyer response and all orders which risk level was calculated as yellow
Defender report
The report displays a list of all orders sent to the application
Negative Queries report
The report displays a list of all orders sent to the application and cancelled by Prevent Online Fraud Shopify App because of red risk level, invalid address or match with Negative Database value

Integration Scheme

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