Integrate Customer Data for Personalized Advertisements.
Customer Data Platform | B2B | SaaS | 0-1 Feature
Duration
March - June 2024,
3 months.
Role
Lead Product Designer
Team
1 Founder, 1 Designer, 1 Marketing Specialist, 6 Engineers
Status
Launched 🚀
Overview
Context
AdsGency AI leverages GenAI to craft advertisments by analyzing user-inputted text.
For example:
👗
Product Descriptions
Affordable, Stylish, Dress
👩
Targeted Audience
18-25, Female, Students
📈
Business Goals
Customer Acquisition
User Problems
The generic input about the targeted audience is too broad to resonate with their needs and interests, hindering the achievement of high conversion rates.
Business Objectives
Fewer users have registered for the Business Plan compared to the Brand and Startup Plans. We aim to introduce a new feature to encourage users to upgrade.
Startup $79/month — Email marketing and a limited number of social media platforms.
Brand $179/month — Startup Plan + video and infographic generation.
Business $399/month — Brand Plan + unlimited social media platforms.
Solutions
Integrate customer data to analyze and create customer segments for tailored Ads.
Impacts
1k+
upgraded users in a month
25%
improved retention rates
$2.4M
raised funding
Core Experience
Upload Customer Data
Users can connect to their datasets to upload customer data for analysis.
Customer Insights
Users can gain insights into their customers and create targeted customer segments based on their data.
Customer Segments
Users can utilize data-driven customer segments and profiles instead of inputting text to generate tailored marketing content.
… How did we get there…?
Research
User Research
How do users gain a deep understanding of their customers, and how does it impact marketing success?
Sent 150+ E-mails to reach out
Received 30+ Screener Surveys
Conducted 6 Interviews
key Insights
Marketing specialists struggle with analyzing customer data timely and effectively.
Sally is a Digital Marketing Specialist at a medium-sized retail company.
"I want to better understand my customers to create personalized marketing strategies that drive sales without wasting time and money on data analysis."
😖 📊
Data Fragment
Customer data is often scattered across multiple platforms (e.g., social media, CRM, email marketing tools), making it difficult to compile a complete picture.
😰 ⌛
Lack of Real-Time Insights
Users may rely on outdated or static data, which doesn’t reflect current behaviors or preferences.
😵💫 👬
Inaccurate or Insufficient Segmentation
Users often struggle to divide their audience into meaningful segments due to limited analytics capabilities or incomplete data.
Design Goal
How might we integrate data from multiple platforms to deliver real-time insights and empower users to perform effective customer segmentation?
In-Depth Research
I discovered that our users lack knowledge of what a customer data platform is. Therefore, I requested to hire a marketing specialist to conduct SME (Subject Matter Expert) interviews. I also conducted a competitive analysis and met with engineers to figure out the user flow and technical constraints.
Conducted 10+ SME interviews
Competitive analysis
Meetings with engineers
User Flow
👩💻 Step 1 — Upload and View Data
As a user, I want to upload datasets from various sources and view their schema to understand their structure.
Connect Source
Manage Connection
View Table
📊 Step 2 — View Customer
As a user, I want to gain valuable insights into customer behavior and preferences from the uploaded dataset.
Customer Overview
Customer Profile
Analyze Customer
👭 Step 3 — Create Segments
As a user, I want to create segment from the dashboard and effortlessly generate or launch content tailored to it.
Create Traits
Create Segments
Generate Ads.
Design
Prototypes
After creating the initial prototypes, I conducted a design review with engineers and team members to discuss about the feasibility and seek feedback.
Key Insights
There are different types of data which require various sources and amounts of time to obtain. Due to time constraints, we couldn't display the prediction data.
For example:
Direct Data
6/24/2024
Last Visit Date
Category
20-30
Age Group
Calculation
55%
E-Mail Open Rate
🥲 Prediction
$5200
Lifetime Value
Problems
😵 Raw data analysis for dashboards has limited customization options.
Raw data analysis
e.g. Total number of purchases made in a month.
Unprocessed data for basic insights.
🤯 Predictive traits for creating segments brings high complexity.
Predictive Traits
e.g. Budget-Conscious Shopper: A customer who frequently purchases during sales
Higher-level, predictive insights that describe behaviors and preferences.
Question
Is there anything between raw data analysis and high-level predictive traits that can be used to analyze customer data and understand their behaviors?
Solutions
🧐 Use attributes to analyze customer data and create segments.
Attributes
e.g. Purchase Frequency: Purchased more than five times in the last three months.
Specific characteristics derived from raw data.
Iterations
Create attributes to set up their own metrics for analyzing customers.
Reduced the complexity compared to creating traits to make it more intuitive for users.
Increased the flexibility of metrics to meet the diverse needs of users.
Use attributes on the dashboard to make it more customizable.
Reorganized the structure of the dashboard, including tabs, buttons, and dropdown menus, to make it more effective and better suited for analysis by attributes.
Added the "Top 10 Customer Activities" to provide quick insights into customer behaviors.
View attributes on the customer profiles to understand customers further.
Added attributes for detailed information on the customer profile, which helps avoid mixing different types of data and reduces confusion.
Added dividers to the table to make it easier for users to scan and read.
Combine attributes to create customer segments.
Designed to enable users to use attributes instead of general filters for selecting and analyzing customer groups, making the process more accurate.
Simplified the process to enable users to create segments using attributes, making it more straightforward compared to using traits for segment creation.
Results
Results
Users can add detailed and specific customer segments and profiles instead of generic input text to generate targeted marketing content for achieving high conversion rates.
Impacts
This feature was successfully launched and has helped our users achieve substantial revenue growth and drive business expansion.
If I have more time…
I want to conduct usability testing to ensure the product to meet users needs effectively.
I want to establish a feedback loop for continuous improvement to stay on top of evolving user needs and ensure the product remains relevant and valuable.
What I learned…
UX projects often face unforeseen challenges. Being flexible and adaptable is essential to navigating these obstacles. The ability to pivot and adjust based on new information allows teams to stay agile.
It is important to seek early feedback throughout the design and development process. Engaging stakeholders early on helps identify potential issues, gather diverse perspectives, and align expectations. This proactive approach not only improves the quality of the final product but also saves time and resources by addressing challenges before they become significant roadblocks.
