IBM Pearl
IBM has built a management system around marketing efficiency to highlight the company’s ROI on marketing spend, how best to spend in future and to make marketing decisions data-driven.
ROLE
Design/Product Owner
Members of our team split up work in the dashboards, research and designing features under the Sr. Product Manager
SUMMARY
Pearl is a management data visualization system around marketing efficiency created in response to the requirements of the CMO to know the company’s ROI on marketing spend, how best to spend in future and to see which programs are delivering to the business.
THE PROBLEM
IBM lacked a centralized place to help global marketing teams optimize all of their live campaigns and make more data-driven decisions.
Largest user pain-point was that there were too many analytics tools at IBM
THE PROCESS
Conducted sessions with power users, IBM leadership, and dev to create a management system This was used to define the MVP. Prototypes were created to assess the value with the SMEs and leadership. We established an agile process of working sketch sessions, followed by simultaneous data prototyping and wireframes. Once we had validated with our users, we developed the feature and created a method for via API for other teams. In accordance with the agile workflow, we presented new features and roadmap every 2 weeks to stakeholders and continued to re-iterate.
The Design Process
DESIGN THINKING WORKSHOP
The teams aligned on project goals, objectives and scope; discuss + define user groups. We sought out to think about pain-points, opportunities, and blue-sky ideas
Our Hill: Marketing & Sales teams will have one centralized platform to quickly and efficiently see the returns of their programs to the business
USER INTERVIEWS
Research with users were conducted to better understand the current experience, identify pain points and opportunities for enhancements in functionality (15 interviews with Pearl users)
EXISTING DATA ANALYSIS
Review current user data to inform IA and design update needs. Findings were integrated alongside qualitative findings from generative research activities to provide a more comprehensive picture of key behaviors. Quantitative findings helped prioritize and size different opportunities relative to user needs and business outcomes
CONCEPT CREATION
After the initial round of user research, we began creating high fidelity design concepts. Design prototypes were created, reviewed, refined and finalized for testing
INFORMATION ARCHITECTURE
Products are best utilized with a streamlined Information Architecture. Users will be more inclined to use tools that are frictionless. What is holding back users? What are some current ideas for improvement? We identified some “blue sky” ideas that would be considered innovative in the marketing analytics space
CARD SORTING
A total of 54 participants took part in the card sorts. This statistically placed our data in a 95% confidence interval. Participants were diverse in several geographies and business units with various roles
TOOLS USED
Design Thinking Methodology (Post-its & whiteboards)
Mural
Trello
Invision
Sketch
Adobe Creative Suite (Photoshop/Illustrator)
Results
Optimize return of every dollar spent: 22% improvement in market campaign efficiency
Significant incremental marketing investments shifted from sales
7pts faster growth than other channels on flat spend
Used by 5,000 marketers across 170 countries
12+ data systems, 100+ reports and tools
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