Zigi Increases User Retention by 40% Using MoEngage's Automated Flows | MoEngage
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Zigi Increases User Retention by 40% Using MoEngage’s Automated Flows

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40%

Increase in User Retention

Zigi App
About Zigi

Zigi is a digital financial services app launched by Banco Industrial, Guatemala’s largest bank. It was created to modernize the traditional sector, which has low digital penetration, offering young people their first banking experience.

Zigi’s mission is to eliminate the use of physical money in everyday life and promote digital financial inclusion. The platform replaces complex processes with a simple experience, such as direct interaction via WhatsApp, making resource management more accessible and integrated into the user’s routine.

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What Zigi was looking for


Smart Segmentation: accurately identify different user groups to initiate relevant conversations. It was crucial to understand each person's behavior within the app in order to personalize communication.

Data Unification: consolidate all user-generated information into a single, centralized view.

Security and Compliance: As a financial institution, Zigi handles a huge amount of sensitive data. It was imperative to ensure that any solution adopted met the most stringent security and privacy standards.

What Zigi was looking for

The main points were the positive impact on customer retention, the ease of use for creating campaigns and flows, and comprehensive data management, features that allow Zigi to eliminate manual work and personalize the experience directly for each type of customer.

Francisco Amaya
Francisco Amaya

Marketing Own Media

Previously, we manually associated customer IDs with coupons. Now, MoEngage completely automates this process and even sends alerts about coupons that are about to expire or are low on stock, allowing us to manage our campaigns much more efficiently.

Francisco Amaya
Francisco Amaya

Marketing Own Media

How was the implementation process?


Francisco Amaya described choosing the ideal platform as the most challenging step. However, once the contract was signed, onboarding and training were quick and easy.

The main technical challenge during implementation was ensuring secure data sharing between Zigi's database and the MoEngage platform—an obstacle that was successfully overcome by MoEngage's technical team.

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Flows and Churn Prevention


Before MoEngage, Zigi lacked personalization tools, which prevented it from creating specific actions for each user.

With the implementation of MoEngage, the company was able to create intelligent churn prevention flows that dynamically adapt to customer behavior. This personalization based on each user's individual actions was key to significantly improving customer retention rates.

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Implementation of NPS


Zigi implemented NPS (Net Promoter Score) surveys through MoEngage. The platform proved to be much more agile for the UX team to create surveys and access data, compared to the previous SDK solution.

According to Kathy Santizo, UX Lead at Zigi, this change has significantly improved feedback collection, allowing the company to gather real-time insights immediately after a customer uses a product.

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Products Used

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Customer Insights & Analytics

Create omnichannel, personalized experiences using AI-powered insights and analytics.

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Customer Journey Orchestration

Create unique, seamless experiences at every stage of your customer’s journey.

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Omnichannel Flows

Create connected experiences at every stage of customer journey across channels using Omnichannel Flows.

Results

Os principais pontos foram o impacto positivo na retenção de clientes, a facilidade de uso para criar campanhas e fluxos e os recursos abrangentes de gerenciamento de dados que permitiram à Zigi eliminar a análise manual de dados e a dependência de sua equipe de dados.

  • Transaction abandonment flow: recorded a 38% decrease compared to the control group.
  • Retention rate: grew 40% in the first quarter of 2025 compared to the second quarter of 2024.
  • Churn prevention flow: achieved a remarkable 34% increase in efficiency against user attrition compared to the control group, using segmentation based on the RF model.
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