American Automobile Association (AAA)
CRM Design and Implementation Strategy
Sr. UX Architect | AAA ClubLabs | UX Research | 10 Weeks | 2019
Overview
I was tasked by the UX Design Manager to independently research the current email marketing strategy, and understand why the metrics told a story of a highly ineffective approach. Based on my findings, I was to present analytics, propose solutions, and proofs of concept to the Vice President of ClubLabs, the digital incubator for AAA, for a mandate to test the strategy.
Having explored data automation via AI and ML on my own prior to working with ClubLabs, I interviewed the Vice President of Marketing, the Head Quality Assurance Engineer, Lead Product Managers, and the Manager of Digital Messaging to gain insight and understanding around AAA's existing CRM approach.
Problem
Differentiating a highly commoditized product
The current email marketing strategy is incomplete, impersonal, confusing, repetitive, and uninspiring. Overall metrics for auto insurance email campaigns continue to decline Y.O.Y. indicating a disconnect in engaging potential customers. Optimal A/B testing, personalization in messaging, and efficient internal workflow are seemingly non-existent, while overall revenue is not sufficient to meet business goals.
The current email marketing strategy is incomplete, impersonal, confusing, repetitive, and uninspiring. Overall metrics for auto insurance email campaigns continue to decline Y.O.Y. indicating a disconnect in engaging potential customers. Optimal A/B testing, personalization in messaging, and efficient internal workflow are seemingly non-existent, while overall revenue is not sufficient to meet business goals.
Interactive, targeted messaging, emphasis on brand awareness, attractive visual design, as well as clear and transparent value propositions, optimized user flows, and workflows, are all currently lacking in AAA email marketing campaigns.
Solution
The current email marketing strategy is incomplete, impersonal, confusing, repetitive, and uninspiring. Overall metrics for auto insurance email campaigns continue to decline Y.O.Y. indicating a disconnect in engaging potential customers. Optimal A/B testing, personalization in messaging, and efficient internal workflow are seemingly non-existent, while overall revenue is not sufficient to meet business goals.
Interactive, targeted messaging, emphasis on brand awareness, attractive visual design, as well as clear and transparent value propositions, optimized user flows, and workflows, are all currently lacking in AAA email marketing campaigns.
Process
• Learn
• Discover
• Define
• Discover
• Define
Deliverables
• User Flows
• Wireframes
• Interactions
• UI Design
• Wireframes
• Interactions
• UI Design
Team
• Design Director
• Visual Designer
• Technical PM
• FE Manager
• Visual Designer
• Technical PM
• FE Manager
Tools
• Trello
• Figma
• Chime
• Figma
• Chime
Discovery
PRFAQ Summary
• Lowers entry barrier to Machine Learning (ML) technologies
• Allows data scientists to focus on more complex problems
• Democratizes ML and reduces bottlenecks for data scientists
• Aggregates, compiles and cleans data from multiple sources
Goals + Objectives
The product solves less complex prediction model needs. Simplifying the nine steps of the data modeling experience per the current SageMaker experience was a top priority. My challenge was simplifying the "Import Data" and "Join Data" processes, collaborating with the other designers to make sure that the entire process was seamless.
The product solves less complex prediction model needs. Simplifying the nine steps of the data modeling experience per the current SageMaker experience was a top priority. My challenge was simplifying the "Import Data" and "Join Data" processes, collaborating with the other designers to make sure that the entire process was seamless.
• Get output in front of test customers immediately
• Deliver key experiences for data analysts
• Fits it into Amazon Web Services Ecosystem
• Focus on designing the "happy path”
Metrics for Success
• Functions with other data scientists tools
• Streamlines the data modeling process
• Informs end user via heuristics
• Simple, consistent and polished
User Persona
Persona A: Data Analyst: Wants to predict business churn. Has the data and needs Data Science support.
Role
• Business Manager
• Operations Manager
• Financial Manager
• Accountant
• Business Manager
• Operations Manager
• Financial Manager
• Accountant
ML Experience
• No coding experience
• Familiar but not technical
• Comfortable with data
• Understands ML value
• No coding experience
• Familiar but not technical
• Comfortable with data
• Understands ML value
AWS Experience
• No experience needed
• Familiar with BI tools
• Works with Data Scientists
• No experience needed
• Familiar with BI tools
• Works with Data Scientists
Functionality
1. SSO Login
2. Home
3. Import Data
2. Home
3. Import Data
4. Join Data
5. Create Model
6. Train Model
5. Create Model
6. Train Model
7. Post-Train Model
8. View Predictions
9. Collaborate
8. View Predictions
9. Collaborate
Features
Data Join
With guidance from Amazon’s Product Manager and Front-End Manager who provided screen references from Amazon Snowflake, I gained an understanding of data joining using SQL. Data joining is the process of combining datasets from multiple sources side by side where at least one column in each data set must be the same. There are four types of Joins: Left, Right, Inner and Full.
With guidance from Amazon’s Product Manager and Front-End Manager who provided screen references from Amazon Snowflake, I gained an understanding of data joining using SQL. Data joining is the process of combining datasets from multiple sources side by side where at least one column in each data set must be the same. There are four types of Joins: Left, Right, Inner and Full.
User Stories
"As a user, I want to join multiple datasets without code and train a new model."
"As a user I want to preview the data in order to validate it's the right data."
Design Challenges
Due to the short timeframe of this project, it was difficult as first to gain and understanding of the data modeling process and SQL and apply it immediately to low-fidelity wireframes for the "Join Data" user experience. There were several early iterations where I missed the mark, including:
1. Starting the process from the incorrect screen
2. Focusing too heavily on data visualization
3. Utilizing too many screen transitions
Style Guide
Design Aspirations
Beautiful, Different, Accessible, Extensible, Breathable
Beautiful, Different, Accessible, Extensible, Breathable
The style guide created was based on the AWS design system, and shares some components from its library. But SageMaker Canvas required new components that were simpler, more colorful and engaging. Illustrations were also created for landing pages.
Project Output
• Streamlined the priority features
• Simple, consistent and polished interface
• Completed on schedule
Project Impact
• Solved data prep and model build gap
• 3x faster machine learning training time
• Added to the popularity of SageMaker Studio
• Fits seamlessly into the AWS ecosystem
In Retrospect
Learn and unlearn
Having spent the previous several years working with more autonomy on projects that required chart and graph data visualization, it took some time to adhere to Amazon's "working backwards" design process.
Having spent the previous several years working with more autonomy on projects that required chart and graph data visualization, it took some time to adhere to Amazon's "working backwards" design process.
Ask more difficult questions
With the data modeling and SQL coding process, I assumed things or tried to figure them out on my own. I could have relied more on the Technical Program Manager for answers.
With the data modeling and SQL coding process, I assumed things or tried to figure them out on my own. I could have relied more on the Technical Program Manager for answers.
Media Links
1. Understanding the Supply Chain Business Model
