Campaign Summary

Grab Vietnam, a ride-hailing company, created personalized user profiles using an AI-supported customer relationship management (CRM) system.





Dynamic Wang/Unsplash


Strategy

Objective

As the market leader in Vietnam's ride-hailing industry, Grab aimed for more precision in marketing to its prospects by incorporating Grab's signature AI-supported CRM system and the vast data signals available, which included:

• Differences in point-of-interest (POI) commuting, for example commercial building goers, grocery shoppers, hospital visitation, etc.
• Differences in reasons for use of transport, like price, promotion, reliability, etc.
• Distinct riding behaviors, like at night or during the day
• Frequency of usage, like daily commuters or occasional riders

Grab Vietnam had two objectives for its campaign: Effectively increase the engagement among the "persona" profiles and generate growth in transport usage among the "persona" profiles.

Context

At Grab, data and machine learning are used daily. But this was the first time that Grab Vietnam leveraged millions of POI data insights and models in combination with riding motivation signals, usage frequency, and riding behaviors to accurately "profile" several defined characteristics of users. The brand called these "Riding Personas."

Examples of Riding Personas are foodies, shoppers, office workers, and students. Using Riding Personas and Grab's AI-powered CRM automation system, the brand could deliver tailor-made messages and offerings to its audience, making marketing to specific user profiles significantly more relevant and efficient.

Target Audience

Grab's target audience for the campaign was users who expect their daily commute experience tailor-made for their personal needs.

Grab understood that different Riding Personas could also include a variety of people expecting different experiences, such as:

• A young person who wants a ride to the bar with friends in the evening
• A young mom who shops at the local store on the weekend
• Office workers who try to get home in the evening after a long working day
• Students attending early classes on weekdays

Creative and Media Strategy

Grab leveraged different platforms to capture different real-time signals. For example, the brand used:

• Grab's own app to track Riding Personas (POI references, usage frequency, riding motivations, and behaviors)
• Meta for demographic and engagement behaviors
• Google for keyword searches
• YouTube for information browsing
• TikTok for content consumption

Execution/Use of Media

The Riding Persona campaign served a three-pronged purpose:

1. Personalized User Experience. Grab tailored offers to fulfill its users' specific needs to augment user stickiness and boost retention.
2. Focused Marketing Initiatives. Detailed profiles enabled Grab to design more targeted campaigns that closely matched the unique needs and preferences of each group.
3. Resource Optimization. Valuable insights derived from these profiles allowed Grab to allocate its resources, such as promotions and communications, more effectively and efficiently.

Harnessing millions of data insights related to the POIs, riding motivations, frequency patterns, and riding behaviors, Grab crafted a segmentation model powered by machine learning algorithms.
Initially, the brand labeled users based on their distinctive behaviors, utilizing machine learning to understand each user-segment's characteristics, which were derived from the initial labeling process. Subsequently, the identified characteristics aided in segmenting other users within Grab's database who shared similar patterns.

Grab delved deeper into each passenger segment, dissecting their specific behaviors and insights. This comprehensive analysis fueled the creation of distinct sets of Riding Personas.

With Riding Personas as Grab's cornerstone, it crafted tailor-made riding offerings and relevant messages. Grab incorporated real-time intent signals from various platforms such as Google's keyword searches, YouTube's informational browsing, TikTok's content engagement, and signals from Grab's own app to deliver personalized promotional offers and messages at diverse touchpoints.

Business Impact

Grab's previous promotional distribution models only provided a holistic view of its users. To efficiently stimulate demand within the constraints of its resources, Grab aimed to tailor its messages, promotions, and offerings to align with their specific use cases.

The campaign resulted in an increase in engagement among Riding Persona profiles. The in-app push notification open rate improved by 0.07 points, the book-through rate improved by 0.7 points (translating to millions of rides), and the gross merchandise value increased 21 points.

The Riding Persona solution transformed market dynamics by personalizing user experiences and targeting offers to specific needs. By utilizing detailed user profiles and real-time intent signals, the campaign optimized marketing efforts and resource allocation, significantly enhancing user engagement and retention.