GoMechanic Boosts Orders by 138% with Action-based Push Notifications
Technology Category
- Analytics & Modeling - Predictive Analytics
- Sensors - Autonomous Driving Sensors
Applicable Industries
- Automotive
- E-Commerce
Applicable Functions
- Procurement
- Sales & Marketing
Use Cases
- Smart Parking
- Vehicle-to-Infrastructure
About The Customer
GoMechanic is a car servicing solutions provider that offers a wide assortment of scheduled car services at affordable prices. The company operates in 29+ cities across India and offers over 200 services across 12 different categories through its custom app, the GoMechanic App. The app has over 1 million downloads on the Google Play Store and an average rating of 4.7+ stars. GoMechanic aims to provide a complete car solution ecosystem to its users, working in several different verticals such as spares and accessories, denting and painting, wheel, alloy, and rim care, and many others. The company also recently launched On-Board Diagnostics (OBD) services on the GoMechanic App.
The Challenge
GoMechanic, a car servicing solutions provider with a strong presence in 29+ cities across India, was facing a challenge with customer retention and conversion. The company observed that customers often conducted extensive research before booking a car service, leading to a longer conversion window than usual for online businesses. This resulted in a higher chance of drop-offs, with users adding services to their cart and then forgetting about them. GoMechanic needed a way to subtly yet compellingly bring users back to their app and incentivize them to complete their purchases.
The Solution
GoMechanic turned to the MoEngage platform to send persuasive push notifications to users, notifying them of personalized offers. The company's primary strategy was to incentivize purchases and prime customers for conversion. Custom offers were always displayed in a fixed corner of the app, prompting the next step for the customer to get the service. GoMechanic implemented two flows to focus on multiple campaigns based on their analysis of customer preferences and needs. The first campaign involved creating behavior workflows that triggered communication based on actions and events, such as cart abandonment and cross-selling products. The second campaign focused on using Predictive Analytics to send notifications, creating a predictive model that analyzed customers’ historical data and predicted the next potential car service requirements. GoMechanic also created a system collecting and storing 80 data points for every car repaired with the company, predicting future wear and tear, and triggering personalized notifications based on those data points.
Operational Impact
Quantitative Benefit
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