Design

Complex data systems

Research

In-app Purchase Report Suite

Design

Research

Complex data systems

Design

Research

Complex data systems

An all-in-one report suite with valuable market data

The In-app Purchase Behavior report suite is a powerful reporting tool designed to decode in-app purchases, offering key insights into user spending patterns. From casual users to high spenders, this tool empowers businesses to optimize their strategies and drive revenue growth in the competitive mobile market.

Customer Research

"We need to track new versus returning revenue and cross-app purchases to anticipate trends in user interests and enhance retention."

"We want to optimize revenue by examining Average Revenue Per Paying User (ARPPU) and cross-app purchase behavior for targeted monetization."

“We want to deepen insights into share of wallet and wallet size metrics to better understand high-paying users in our games sectors.”

Client	"I want to"	1st Priority Metrics	Wish List	Trial
Scopely	Understand which IAP strategy may be working best: Subscription vs one time purchases and which is performing better Non-renewable subscription campaigns	Average LTV (Lifetime Value)	Where do paid users go next - showing changing trend of paid users’ interests (CRITICAL) What's the first purchase and when (week, month, d3?) and what type of player do they become	Yes
OutFit7	Understand user lifecycles in different markets: Cumulative average user purchasing history Whale user purchasing history Regular user purchasing history Casual user purchasing history Source of user acquisition (Paid/Organic) that end up becoming a type of purchasing user	Avg. Share of Wallet Wallet size of Paying Users	Renewal Rate (for subscriptions) Shorter Time LTV (1 week to 1 month)	Yes
JoyCastle	To perform revenue forecasting: Understanding if projected revenue goals are on track or need to be adjusted Understand competitors’ trends and predict potential competitors based on In-app purchase strategies Understand or predict life-cycle of an IAP item (possibly non-subscription) Predict revenue and strategy for specific “One-Time Purchase" items	ARPPU (Avg. Revenue per Paying User) Average LTV Cross App Purchase	No answer	Yes
CrossField	Understand the In-app Purchase lifecycle: Upgrade/Downgrade History Churn rate for renewable subscription items First-time spenders over time vs revenue per user over time	Paid User Churn Rate ARPPU Average LTV	No answer	Yes

Page 1 of 2

Client interview matrix excerpt

"Our highest priority is to focus on reducing paid user churn and improving lifetime value by analyzing spending patterns and engagement."

“Our goal is to enhance paying user retention and share of wallet by understanding user preferences across apps and maximizing SKU performance.”

Creative problem solving with business imperatives in mind

Synthesizing research and developing a first product concept

Through initial research and delineation of our business imperatives, we determined that the most successful and cost-effective implementation of this new SKU involved integrating Nielsen's new data set across multiple existing products. These included our flagship reports and the new Dashboard system.

"We frequently analyze revenue distribution by user segments to optimize retention and drive targeted monetization efforts, but we don’t have a way to do it at a market level."

Creating a product that uncovers new monetization opportunities

User research and customer validation provided the necessary feedback to forecast a $4.5M ARR opportunity, with the gaming sector having the highest revenue potential of $2.76M followed by other segments such as Financial Investments. I focused design and market research to extract the main user goals to pair with our intended business opportunities.

Resolving a major conflict with business goals in mind

During the design exploration phase we determined that this approach was not attainable, as the UDW/ETL Engineers and Data Science could not reconcile the data mapping between Nielsen’s partnership and data.ai’s own sources without causing a major loss in data reliability.

Designing a robust report system to handle a variety of complex use cases

A large effort during the design process of this project involved building complex information architecture maps to account for and optimize all of the dimensions, breakdowns and filters relevant to our target use cases.

Scopely Usecase sample

"I'm interested in getting into a new gaming sector and understanding which players drive the most monetization"

01

I need to refine my analysis to target the relevant market segment

02

I need to identify a group of apps with high-conversion customers who have significant wallet sizes, maximizing spending potential.

03

I want to further understand these apps performance by analyzing their Average Revenue per Paying User (ARPPU)

04

This group of apps is great per my criteria. Now I need to analyze what are the most successful monetization strategies they use

Project Workstreams

Designing as part of a task force

I worked closely with a team of experienced contributors from across the organization. Our goal was to fast-track a new product SKU with time-sensitive deliverables and ensure a strong ROI on our partnership investments.

When is it important to build full prototypes?

It's Playtime

Open Figma prototype

Optimized for Google Chrome

Given the complexity of this report system, I developed a detailed prototype to gather customer feedback during usability interviews. This prototype also helped effectively communicate the design and scope to company leadership and served as a key reference for Front-End engineering, detailing the implementation of UX systems and visualizations.

Using lateral thinking to come up with imaginative solutions

Quickly pivoting our strategy, I decided to step back and re-evaluate our initial research on user goals and business objectives. After collaborating closely with the PM, I determined that the clearest path forward was to isolate this data set within its own report suite. I then designed new interaction systems to support a wide variety of use cases that aligned more closely with achieving a higher ROI.

Exploration

Planning

Execution

Launch

Post-mortem

Data Acquisition

Project Management

Service Integration Engineering

Data Science

Technical Project Management

FE Engineering

UDW / ETL Engineering

Product Design

Simplifying complexity

To maximize the product’s ROI while retaining its compact, single-report structure, I focused on versatility. The report was designed to support a broad range of dimensions and breakdowns, including unified and individual apps, app store categories, and App IQ/Game IQ classifications and tags. This ensured it could provide actionable insights across diverse use cases without compromising clarity or introducing visual clutter in the control panel.

Report Types

Report Dimensions

Report Breakdowns

Establishing the basics

Left-to-right, top-to-bottom control hierarchy

I established a simple, consistent rule across data.ai's report system which I then brought into this project: first select dimensions, then breakdowns, followed by filters, which can span multiple rows. Menus dynamically adjust based on your initial selections, but elements to the left or below never influence those positioned earlier in the flow.

The art of tabs and dropdown menus

Balancing signal-to-noise ratio, and creating visual anchors

When choosing menu types, I focused on cognitive awareness, which emphasizes how users perceive, process, and retain information. This approach ensures users can easily grasp the report’s context, even when interrupted or navigating between tabs. By exposing dimensions first, I helped users immediately recognize what dimension they chose to use for their analysis. Meanwhile, all additional controls were layered in dropdown menus to maintain a clean interface, reducing distractions while keeping critical data accessible.

Contextual options

Based on the new data set and the nature of data estimates for the mobile app market, not all dimensions and breakdowns are made equal. Selecting one branches the user's following options which can increase complexity if not layered properly under the right type of menu.

Eye tracking heatmap

Dimensions & Breakdown flow

Designing for a cross-dimensional analysis

The database provided through this new partnership allowed for support of relational filtering, enabling comparisons across dimensions and breakdowns by defining conditional relationships between datasets (e.g., "users who bought in X, also bought in Y"). This system was previously designed for Data.ai's existing Cross-App/Web Usage report, but poorly executed and littered with UX issues. I took the time to re-invent the control panel to power this new Cross-Purchase report in a way that would be easy to understand without compromising functionality.

Uncovering functionality through research

User interviews in the exploration stage of the project helped build the functionality for this report. Initially, my hypothesis was based on building a single dimension system

(e.g. Buyers in App A, also bought in App B).

Buyers in common between 2 apps

However, during several 1-hour customer interviews, I uncovered the need to build a system that allowed the cross examination of all of our dimensions (e.g. Buyers in App A, also bought in sub-genre Y. Or, buyers in apps with Advertising revenue strategies also bought in apps with an app store category)

Buyers in apps with IQ Tag "Monetization - Battle Pass" also made purchases in apps within the game category "Hyper-casual - Racing"

Report

Datasets

Breakdown Categories

Map of all available cross-purchase permutations

"We need to expand our user acquisition opportunities based on where our consumers are most likely to spend their money outside our own app."

Subway Surfers Acquisition Manager

User need statement

Structured query

Buyers in 'dataset type' then 'dataset source' also purchased in 'data aggregate type' then 'data aggregate filter'

User needs shaping the control panel design

After several iterations of the control system for this report, I decided to approach the UX design by rephrasing the user needs uncovered during our research phase into a query statement. This exercise allowed me to normalize our user goals into consistent statements that became the foundation of the final design.

Dimensions and Breakdowns

Apps

Start your analysis by viewing the top app players in the U.S. market and dive in deeper by selecting app store categories or data.ai exclusive industry taxonomies

Classifications

Analyze all major app industries by app store or data.ai classifications

IQ Tags

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Bringing forward all the key visualizations

Top Performers

Start your analysis by viewing the top app players in the U.S. market and dive in deeper by selecting app store categories or data.ai exclusive industry taxonomies

Cohort Analysis

Analyze all major app industries by app store or data.ai classifications

Cross-Purchase Overview

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Purchasing 3D Insights

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Powerful high-level report with all key insights in one place

The Overview System

Maintaining Cohesion Within the Product Ecosystem

I created a new report system that brings together all the data schema of data.ai’s Market Intelligence, and provide a one-stop-shop for users focused on understanding In-app purchase behavior in the market and within the top performing apps.

Report Types

Report Dimensions

Unifying Data Visualizations Across Reports

The In-app Purchase Overview page introduces four visualization types: an LTV curve line graph, a bar chart for top-player metrics, a bubble chart, and a cross-purchase widget. These visualizations are seamlessly carried into the Market Analysis, App Analysis, and Cross-Purchase reports. This ensures familiarity and continuity across pages, making insights easier to interpret and reinforcing a cohesive design.

Full report Lifetime Value visualization

Overview Report Lifetime Value widget

Design Highlights

Design execution through the project lifecycle

I worked through each stage of the design process, collaborating closely with the taskforce to align efforts across design, product, and engineering teams. From defining early concepts and prototypes to usability testing and final validations, every step was synchronized with the broader project goals.

Exploration

Planning

Execution

Launch

Post-mortem

Market Research

Initial Concept Validation

Rapid Prototyping and Divergent Explorations

Usability Interviews

Final Prototypes

Cross-Disciplinary workshops

Alpha & Beta Testing

Design Compliance Review

User Onboarding & Marketing

Roadmap Prioritization & Initial V2 Conceptualization

Project Summary

This project expanded data.ai's offering with a new report suite powered by partnered datasets. As part of a task force of experts, I tackled the project's complexity and compressed timelines, delivering a cohesive, user-friendly, and impactful report system.

ROLE	TIMEFRAME	TEAM
Principal Designer, Researcher	Q4 2022 - Q1 2023	8 Disciplines, 13-16 people

View all specifications

Design Processes

Rapid Prototyping

HiFi Prototyping

User Flow Mapping

Advanced Data Mapping

Concept Mapping

Cross-disciplinary workshops

Tools Used

Figma

Pendo.io

Miro

Spreadsheets

Presentation Decks

Tableau

Asana

Jira

Research Methodologies

Team

Market Analysis

Competitive Analysis

Personas

User Research

Usability Interviews

Surveys

Journey Mapping

Data Acquisition Lead

Parnership Director

Principal Project Manager

Sr. Service Integration Engineer

Lead Data Scientist

Senior TPM

Front-end Engineer Manager

Senior Front-end Engineers

UDW/ETL Engineers

Principal Product Designer

Project Specifications

ROLE	TIMEFRAME	TEAM
Principal Designer, Researcher	Q4 2022 - Q1 2023	8 Disciplines, 13-16 people

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Simplifying complexity

To maximize the product’s ROI while retaining its compact, single-report structure, I focused on versatility. The report was designed to support a broad range of dimensions and breakdowns, including unified and individual apps, app store categories, and App IQ/Game IQ classifications and tags. This ensured it could provide actionable insights across diverse use cases without compromising clarity or introducing visual clutter in the control panel.

Report Types

Report Dimensions

Report Breakdowns

Establishing the basics

Left-to-right, top-to-bottom control hierarchy

I established a simple, consistent rule across data.ai's report system which I then brought into this project: first select dimensions, then breakdowns, followed by filters, which can span multiple rows. Menus dynamically adjust based on your initial selections, but elements to the left or below never influence those positioned earlier in the flow.

The art of tabs and dropdown menus

Balancing signal-to-noise ratio, and creating visual anchors

When choosing menu types, I focused on cognitive awareness, which emphasizes how users perceive, process, and retain information. This approach ensures users can easily grasp the report’s context, even when interrupted or navigating between tabs. By exposing dimensions first, I helped users immediately recognize what dimension they chose to use for their analysis. Meanwhile, all additional controls were layered in dropdown menus to maintain a clean interface, reducing distractions while keeping critical data accessible.

Contextual options

Based on the new data set and the nature of data estimates for the mobile app market, not all dimensions and breakdowns are made equal. Selecting one branches the user's following options which can increase complexity if not layered properly under the right type of menu.

Eye tracking heatmap

Dimensions & Breakdown flow

Designing for a cross-dimensional analysis

The database provided through this new partnership allowed for support of relational filtering, enabling comparisons across dimensions and breakdowns by defining conditional relationships between datasets (e.g., "users who bought in X, also bought in Y"). This system was previously designed for Data.ai's existing Cross-App/Web Usage report, but poorly executed and littered with UX issues. I took the time to re-invent the control panel to power this new Cross-Purchase report in a way that would be easy to understand without compromising functionality.

Uncovering functionality through research

User interviews in the exploration stage of the project helped build the functionality for this report. Initially, my hypothesis was based on building a single dimension system

(e.g. Buyers in App A, also bought in App B).

Buyers in common between 2 apps

However, during several 1-hour customer interviews, I uncovered the need to build a system that allowed the cross examination of all of our dimensions (e.g. Buyers in App A, also bought in sub-genre Y. Or, buyers in apps with Advertising revenue strategies also bought in apps with an app store category)

Buyers in apps with IQ Tag "Monetization - Battle Pass" also made purchases in apps within the game category "Hyper-casual - Racing"

Report

Datasets

Breakdown Categories

Map of all available cross-purchase permutations

"We need to expand our user acquisition opportunities based on where our consumers are most likely to spend their money outside our own app."

Subway Surfers Acquisition Manager

User need statement

Structured query

Buyers in 'dataset type' then 'dataset source' also purchased in 'data aggregate type' then 'data aggregate filter'

User needs shaping the control panel design

After several iterations of the control system for this report, I decided to approach the UX design by rephrasing the user needs uncovered during our research phase into a query statement. This exercise allowed me to normalize our user goals into consistent statements that became the foundation of the final design.

Dimensions and Breakdowns

Apps

Start your analysis by viewing the top app players in the U.S. market and dive in deeper by selecting app store categories or data.ai exclusive industry taxonomies

Classifications

Analyze all major app industries by app store or data.ai classifications

IQ Tags

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Bringing forward all the key visualizations

Top Performers

Start your analysis by viewing the top app players in the U.S. market and dive in deeper by selecting app store categories or data.ai exclusive industry taxonomies

Cohort Analysis

Analyze all major app industries by app store or data.ai classifications

Cross-Purchase Overview

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Purchasing 3D Insights

Use the most extensive set of feature classifications, revenue, advertising strategies or game mechanics to analyze the market.

Maintaining Cohesion Within the Product Ecosystem

I created a new report system that brings together all the data schema of data.ai’s Market Intelligence, and provide a one-stop-shop for users focused on understanding In-app purchase behavior in the market and within the top performing apps.

Report Types

Report Dimensions

Unifying Data Visualizations Across Reports

The In-app Purchase Overview page introduces four visualization types: an LTV curve line graph, a bar chart for top-player metrics, a bubble chart, and a cross-purchase widget. These visualizations are seamlessly carried into the Market Analysis, App Analysis, and Cross-Purchase reports. This ensures familiarity and continuity across pages, making insights easier to interpret and reinforcing a cohesive design.

Full report Lifetime Value visualization

Overview Report Lifetime Value widget

Design Highlights

Target User

This project expanded data.ai's offering with a new report suite powered by partnered datasets. As part of a task force of experts, I tackled the project's complexity and compressed timelines, delivering a cohesive, user-friendly, and impactful report system.

KPI's

This project expanded data.ai's offering with a new report suite powered by partnered datasets. As part of a task force of experts, I tackled the project's complexity and compressed timelines, delivering a cohesive, user-friendly, and impactful report system.

KPI's

This project expanded data.ai's offering with a new report suite powered by partnered datasets. As part of a task force of experts, I tackled the project's complexity and compressed timelines, delivering a cohesive, user-friendly, and impactful report system.

In-app Purchase Report Suite

An all-in-one report suite with valuable market data

Customer Research

Understand which IAP strategy may be working best:

Subscription vs one time purchases and which is performing better

Non-renewable subscription campaigns

Average LTV (Lifetime Value)

Where do paid users go next - showing changing trend of paid users’ interests (CRITICAL)

What's the first purchase and when (week, month, d3?) and what type of player do they become

Understand user lifecycles in different markets:

Cumulative average user purchasing history

Whale user purchasing history

Regular user purchasing history

Casual user purchasing history

Source of user acquisition (Paid/Organic) that end up becoming a type of purchasing user

Avg. Share of Wallet

Wallet size of Paying Users

Renewal Rate (for subscriptions)

Shorter Time LTV (1 week to 1 month)

To perform revenue forecasting:

Understanding if projected revenue goals are on track or need to be adjusted

Understand competitors’ trends and predict potential competitors based on In-app purchase strategies

Understand or predict life-cycle of an IAP item (possibly non-subscription)

Predict revenue and strategy for specific “One-Time Purchase" items

ARPPU (Avg. Revenue per Paying User)

Average LTV

Cross App Purchase

No answer

Understand the In-app Purchase lifecycle:

Upgrade/Downgrade History Churn rate for renewable subscription items

First-time spenders over time vs revenue per user over time

Paid User Churn Rate

ARPPU

Average LTV

No answer

Client interview matrix excerpt

Creative problem solving with business imperatives in mind

Synthesizing research and developing a first product concept

Creating a product that uncovers new monetization opportunities

Resolving a major conflict with business goals in mind

Designing a robust report system to handle a variety of complex use cases

Scopely Usecase sample

01

I need to refine my analysis to target the relevant market segment

02

I need to identify a group of apps with high-conversion customers who have significant wallet sizes, maximizing spending potential.

03

I want to further understand these apps performance by analyzing their Average Revenue per Paying User (ARPPU)

04

This group of apps is great per my criteria. Now I need to analyze what are the most successful monetization strategies they use

Project Workstreams

Designing as part of a task force

When is it important to build full prototypes?

It's Playtime

Optimized for Google Chrome

Using lateral thinking to come up with imaginative solutions

Dimensions and Breakdowns

Bringing forward all the key visualizations

​The Overview System

Design Highlights

Design execution through the project lifecycle

Project Specifications

Sorry for the inconvenience!

Dimensions and Breakdowns

Bringing forward all the key visualizations

Design Highlights

The Overview System