CASE STUDY
Streamlining Workflows for Adobe AdCloud Users
Adobe Advertising Cloud for Search is Adobeβs platform for search marketing management. AdCloud provides marketer's with the tools needed to manage and optimize campaigns that hit performance goals.
As an Adobe employee, AdCloud is one of my most utilized products. When Adobe began prioritizing Generative AI this year, including the launch of Sensei for the Experience Cloud, I was surprised to see AdCloud excluded from the product rollout.
There is an opportunity for Adobe to build out Generative AI capabilities across all solutions, including AdCloud, and brand themselves as consistent leaders in the ever-evolving tech space.
TIMEFRAME
6 weeks
TOOLS
Figma
MY ROLE
UX Designer
UI Designer
THE PROBLEM
High volume of companies competing to launch the next big AI product
Adobe's AdCloud has not been included in Adobe's generative AI rollout
Daily task management in AdCloud is very manual and time consuming
THE SOLUTION
Design an AI-backed feature integrated into Adobeβs AdCloud platform
Build a new AdCloud feature that improves user workflows in AdCloud
RESEARCH
How do marketers use AdCloud to optimize campaigns?
Understanding AdCloud super users
To kick off the research phase I recruited five Adobe colleagues that are also Marketing Managers. All five participants currently use AdCloud and have tried AI technology before.
Interviewing marketerβs with a mix of seniority and experience, I was keen to understand what aspects of campaign management they owned and how they incorporated AdCloudβs existing feature set.
βIf there were any AI tools to help with reporting, iβd jump on it.β
β KN, Growth Marketing Manager
βI mostly use AdCloud for pulling reports, and then iβll review the data to map out key insights before creating an optimization planβ
β JB, Senior Marketing Manager
βMy team spends 6+ hours a week pulling manual reports from AdCloud into excel - looking for insights is very time consumingβ
β AR, Search Marketing Strategist
Marketing tasks remain very manual
Data analysis proven the most time consuming task
As a former digital marketer, I know how time consuming campaign optimizations can be. I wasnβt surprised to find that most of my colleagues felt the same way.
The majority of participants struggled the most with analyzing data and synthesizing insights. This task is completed multiple times a week and is very time consuming, leaving less time to innovate.
The majority of participants were also looking for a better way to QA campaign errors and track changes. This is a valuable part of campaign management that often gets overlooked.
Last, all participants were excited about the idea of using AI technology to supplement their work.
4 out of 5 participants say reporting & analyzing insights was the most time consuming part of the optimization process
5 out of 5 participants want to review all AI recommendations first before implementing
5 out of 5 participants were excited at the idea of using AI to supplement their work
4 out of 5 participants say reporting & analyzing insights was the most common optimization in AdCloud
Who benefits the most from AI-enhanced AdCloud?
Developing junior and senior-level personas
Based on the research findings, I developed two personas. These personas represent two types of marketers who use AdCloud - a Manager and an Analyst. Managers drive the strategy and collaborate with stakeholders, while Analysts drive the day-to-day campaign performance and optimizations.
Persona 1
The Manager, Strategist
Persona 2
The Analyst, Insight Finder
Persona user journey mapping
Visualizing a day in the life
Mapping out the user journeys for each persona was helpful in identifying their largest pain points. For both personas, easy access to campaign performance and insights were essential to their daily roles. Managers need to report performance back to their stakeholders, while Analysts need to make accurate campaign optimizations using data.
Persona 1
The Manager, Decision Maker
Persona 2
The Analyst, Optimizer
How do competitors use AI technology?
Understanding the competitive landscape
To go a step deeper, I analyzed four competitors that offer AI-based solutions for the most frequently mentioned needs (i.e. data visualization, ad creation, keyword-content mapping, keyword recommendations).
STRATEGY
Dual focus
Designing for the entire team
It became apparent after the research phase that better reporting and optimization tactics were the most central need across all marketerβs. βHow might weβ statements were created with both analyst and manager personas in mind.
How might we help analysts build better reporting processes that generate more impactful insights?
How might we improve manager optimization strategies to drive stronger campaign performance?
Key Features
Defining the MVP
After reviewing the research results with the team, we spent some time ideating on features. It was important to enhance the existing feature set by brainstorming on new ideas. After gathering the list, I started prioritizing what features were necessary to launch the product.
Must Haves
The core set of features I would need to design
Data Filters
Report Dashboard
Notifications Page
AI Chat Box
Change Log
Nice to Haves
Additional features that are build in addition to the core feature set, timeline permitting
Edit Report Insights
Share Access Button
Share Report Button
Reporting Templates
Instructional Pop-Ups
Minimize Reports
How will users complete key tasks?
Building three primary task flows
Tasks flows were built out for three core tasks. It was important to understand when and where a user would interact with the primary feature - an AI chatbot. Defining what a GenAI chatbot could and couldn't do within AdCloud was essential to staying within the project scope.
Ultimately, I decided that AdobeChat could be able to create and change both notifications and reporting views. These aspects of AdCloud are very rules-based and would be realistic for GenAI technology to actually implement.
REVIEWING NOTIFICATIONS
CHECKING IN ON PERFORMANCE
SHARING REPORTING
DESIGN
Ideating on feature layout
Low Fidelity Wireframing
Sketching out the core pages, I tested multiple layouts for the must-have features. Experimentation with filters, tabs, notifications, and icon type ultimately let to selecting the layouts that were easiest for users navigate.
Building the layout
Mid-Fidelity Screens
Three core tasks flows representing the most essential features were mapped it. I incorporated a combination of the low fidelity elements to create the most harmonious layout that best aligned with the style guide.
REIVEWING & EDITING NOTIFICATIONS
REVIEWING & EDITING REPORTS
V1 Designs
High-Fidelity Screens
Testing
In-Person Usability Testing
Validating the design with usability testing
The goal of testing this prototype was to understand how users would utilize a generative AI chat assistant to customize notifications and reports. Five Adobe employees and AdCloud super users were taken through two key tasks;
Check-In on Notifications (Review, Remove, and Add Notifications)
Check-In on Performance (Review, Update, and Share Reports)
Positive Usability Feedback
Analyzing the test results
While one user did not successfully complete all of the tasks without error, overall product feedback was extremely positive. All of the participants found AdobeChat enjoyable to use and expected the feature to reduce their manual workflow significantly.
4 out of 5 users completed both tasks without any errors
5 out of 5 users found the AdobeChat feature enjoyable to use
5 out of 5 users believe that using AdobeChat would reduce their time spent on manual task work
Optimizing for Visibility
Frequency to Severity Mapping
Using a frequency to severity map I was able to organize participant feedback and prioritize iterations I would make. The more frequent and severe the feedback, the more likely I was to implement the iteration.
Making Iterations
Naming Conventions
The change log title needed to be simplified to help users clearly understand the features purpose
Automated Instruction Pop-Ups
Instructional pop-ups were added to key screens to inform users of the new AI chatbot feature and teach them how to use it
Saved Notifications
The saved notifications were added to the Change History to be able to track previous and existing notifications in one place
Reporting Metric Drop-Down
Create an option to select different reporting metrics to provide easier report customization
Edit Icon
The edit icon was added to the reporting insights to help users quickly edit and customize their reports
Final Prototype
Key Learnings
Users want to be informed at each step of an interaction
When a new technology is released within a mature product, like AdCloud, it can often be overshadowed by the existing feature set. Long time users will stick to their routines and are less likely to test out a new feature. This was a huge insight identified during usability testing that led to the addition of pop-up notifications for clearer education and direction.
Future Considerations
Because this project was completed in a 6-week sprint, there are a few things I would do differently with more time and resources
Align with Adobe employees working on the GTM strategy for GenAI features to better understand the company priorities and refine the feature roadmap
Interview a larger set of digital marketing professionals, outside of Adobe employees
Perform another round of usability testing with the post-iteration screens