Data Office from Scratch

The primary objective of the project is to embed a data-driven culture and approach into all aspects of the company’s development.

To achieve this objective, a clear Data Strategy for 2024–2025 was created and put into action, covering the following steps:

• Identifying business pain points and exploring data-based solutions.

• Building a Data Platform capable of supporting business needs for the next two years.

• Establishing a Data Office and specialized Centers of Excellence in Product & Marketing Analytics, Data Analytics, Data Engineering, Data Science, and AI Laboratory.

• Clearly aligning each Center of Excellence with the corresponding stakeholder departments.

• Addressing business challenges with a focus on “low-hanging fruit.”

• Ensuring baseline analytical coverage for all products and business areas:

• Dashboards with product, business, marketing, technical, and loyalty metrics.

• EDA (Exploratory Data Analysis) for discovering quick insights to improve UX and mechanics.

• Product event tracking for behavioral analytics, funnel building, and Customer Journey Mapping (CJM).

• User segmentation and personalized offers to address customer pain points and increase profitability.

• Automation of regular reporting processes.

• A Data Platform built from scratch now serves 30 Data Office members and approximately 200 BI users, fully addressing the data needs of the entire company.

• A Data-driven culture has been successfully integrated into Click’s ecosystem, supported by regular meetups and demonstration sessions that showcase departmental use cases.

Key measurable outcomes:

• Automated business reporting, reducing the Finance Department workload by 65%.

• Embedded data-driven culture, requiring hypothesis validation through both qualitative and quantitative methods.

• Implementation of comprehensive research and analytics practices, including:

• Market research (TAM, SAM, SOM), Lean Canvas design.

• Product health metric trees and hypothesis prioritization.

• Unit economics and P&L modeling for ROI optimization.

• UX prototyping, corridor testing, JTBD, and focus group research.

• Product event tracking and staged feature rollouts (Friends & Family, fake doors, partial and full releases).

• 360° Marketing Analytics, enabling real-time budget reallocation and cost optimization.

Measured performance improvements:

• 6% increase in conversion rate: Installation → Add Card

• 5% increase in conversion rate: Installation → Transaction

• 5% increase in conversion rate: Registration flow optimization

• 12% increase in conversion rate: Improved UX identification

• 10% organic growth attributed to App Store Optimization (ASO) improvements

• 8% increase in retention rate through gamification and referral mechanics

• Threefold reduction in Customer Acquisition Cost (CAC) achieved through marketing performance analytics

• 5% monthly profit growth resulting from the implementation of the Merchant Monitoring System

• Increased Average Revenue Per User (ARPU) and retention achieved through Customer Value Management (CVM) and Recency, Frequency, Monetary, and Duration (RFMD) segmentation initiatives

• DWH: MSSQL

• OLAP: ClickHouse

• BI Tools: Apache Superset, Tableau

• Orchestration: Apache Airflow

• ETL Tool: Airbyte

• MMPs: AppMetrica (UX Analytics + A/B testing), AppsFlyer (Marketing Attribution)

• CVM (Customer Value Management) — the next evolution of CRM: Maestra

• Firebase + Google Analytics 4 + BigQuery

• Lack of qualified data specialists in Uzbekistan.

• The absence of an established data-driven development culture.

• Initial skepticism regarding the value of data analytics.

• Limited product development practices (e.g., market discovery, hypothesis testing, unit economics, and OKR-based planning).

As indicated in the preceding section, the project demonstrates significant analytical potential, with 14 years of accumulated data supplying the Data team with a substantial foundation for their work.

The supporting data stack includes:

• DWH: MSSQL

• OLAP: ClickHouse

• BI Tools: Apache Superset, Tableau

• Orchestration: Apache Airflow

• ETL Tool: Airbyte

• MMPs: AppMetrica (UX Analytics + A/B testing), AppsFlyer (Marketing Attribution)

• CVM (Customer Value Management) — the next evolution of CRM: Maestra

• Firebase + Google Analytics 4 + BigQuery

This stack supports the smooth operation of 30 data specialists and ~200 BI users.

We are currently in the process of transitioning to a new Data Platform in response to increasing data volumes and internal requests. By 2026, we plan to expand the Data team by 2.5 times.

The new platform focuses on:

• Advanced data modeling and real-time data processing.

• Scalability and optimized computation.

• Secure data storage and archiving.

• Role-based access, data governance, and data quality control.

• Integration of ML Platform and AI-ready data infrastructure.