Web Strategist Subscription

Those working as strategists need an understanding of the foundation, value, and strategies of experimentation to drive a culture of digital transformation within their company. This subscription of eLearning courses is ideal for program managers, C-Level positions, and/or experimentation contributors.

eLearning
Program Manager
This learning path is designed for the experimentation lead, who we call The Maestro. You will learn strategies involved in setting up an experimentation program, how to design data driven hypotheses, the basics of creating experiments, and how to interpret and share results across your company. Concepts addressed in the program manager courses:
 
- Foundations of experimentation cycle and program structure
- Align experimentation to your business goals
- Build an optimization team
- Understand the purpose of a testing charter
- Decide what is important to include in your charter
- Build your own testing plan using our starter templates
- Connecting revenue to experimentations
- Primary, secondary, and monitoring metrics
- Using direct and indirect data to develop experiment ideas
- Identifying and defining problem statements
- Crafting hypotheses using the Problem > Solution > Result framework
- Account structure
- Setting up projects
- Manage Collaborators
- Project and account settings
- Calculating sample sizes
- Prioritizing ideas with a defined framework
- Best practices for:
      *Defining experiment components: Pages, Events, Extensions (to add), Audiences
      *Building experiments with those components
      *Creating variations using Visual and Code Editors
      *Basic experiment QA using the Previewer
      *Includes Full Stack and Web
      *Managing hypotheses
      *Statistics for experimentation
 
Data Analysts
This learning path is designed for data analysts, who we call Data Geeks.  You will learn how to align experimentation goals with business goals  (including connecting revenue to experiments),  as well as  use direct and indirect data from experiment results to help identify, define, craft, and manage hypotheses. Concepts addressed in the data analyst course:
 
- Foundations of experimentation cycle and program structure
- Aligning experimentation goals with business goals
- Connecting revenue to experimentations
- Primary, secondary, and monitoring metrics
- Statistics for experimentation
- How Stats Engine is different from other statistical models
- Reading the Optimizely results page
- Taking action on winning, losing, and inconclusive results
 
C-Levels
This learning path is designed for executive sponsors and leaders on a team, who we call Experimentation Evangelists. You will learn to use experimentation results to drive company-wide strategic objectives and support a culture of experimentation Concepts addressed in the C-Level course:
 
- Foundations of experimentation cycle and program structure
- Align experimentation to your business goals
- Build an optimization team
- Statistics for experimentation
- How Stats Engine is different from other statistical models
- Reading the Optimizely results page
 
Idea Contributors
This learning path is designed for idea contributors, who we call Idea Gurus. You will learn strategies to align experimentation goals with business goals, use metrics to help drive future experimentation ideas, define problem statements, and craft effective data-driven hypotheses. Concepts addressed in the Idea Contributor course:
 
- Foundations of experimentation cycle and program structure
- Aligning experimentation goals with business goals
- Connecting revenue to experimentations
- Primary, secondary, and monitoring metrics
- Using direct and indirect data to develop experiment ideas
- Defining problem statements
- Crafting hypotheses using the Problem > Solution > Result framework
- Prioritizing ideas with a defined framework
- Best practices for managing hypotheses
- Documenting and collaborating on hypotheses in Optimizely Program Management
- Best practices for creating business requirements documents
- Defining experiment components: Pages, Events, Audiences
- Basic experiment QA using the Previewer