Customer Centric LLC

Redesign Onboarding Flows using Causal Chain Thinking

John Gusiff

Managing Partner, Customer Centric LLC

Krzysztof Czubak

Founder UserMindful.Design and Behavior System Architect

Learn why users stall even when steps look simple — due to hidden beliefs, anxieties, or behavioral friction.

Where onboarding breaks — and why users stop moving

See how causal chain thinking reveals the actual causes behind hesitation, delays, abandonment, or disengagement.

Workflow vs. Causal Chain Thinking in real product scenarios

Discover how to transform your onboarding from task-lists into confidence-building moments that pull users forward.

How to redesign onboarding for momentum and progress

You'll walk away with more confidence about what you want next and a roadmap for getting there.

Work/life expert and bestselling author

Simone Stolzoff

IDEO Design Lead and bestselling Author of The Good Enough Job

Cohort 10

Cohort 11

Design Thinking is a creative approach to finding clarity amidst ambiguity. In this course, you'll be guided through how to apply Design Thinking to your own career exploration.The course itself is broken down into four sessions: Ground, Diverge, Converge, and Plan which will help you articulate your values, explore the career possibilities available to you, design experiments to figure out what you want, and plan your short, medium, and long-term next steps.Instead of spinning your wheels about what's next for your career, you'll follow a step-by-step process to determine what you value and plan your next move.

Clarify what you want with the help of Design Thinking

A step-by-step framework for intentional career design

Tips for navigating job searches and career transitions

Research-backed tools becoming more fulfilled and resilient 

Curious explorers who are ready for a change in their life but aren't clear on exactly what they're looking for

Overworked professionals who move a mile-a-minute but don't have the space to take a step back and reflect

Passionate generalists who feel pulled in multiple directions but aren't sure which path they want to choose

Go from trial-and-error prompting to systematic AI-assisted building. Ship functional projects with a workflow you can use on every project

Cohort 2

AI Design Leader |  x-Uber/Google/Amazon

Stanford University

Amazon

Google

Uber

Rupa Chaturvedi

AI Design Leader | AI enablement for UX orgs | ex-Uber, Google, Amazon, Stanford

Cohort 1

The job posting asks for a designer who can research, prototype, code, and ship. One role, three people's worth of expectations. Design teams got smaller. The work did not. And now every product roadmap includes AI features that need to be designed by people who were never trained to design for probabilistic systems.The pressure is coming from two directions. Inside your organization, you are expected to move faster with fewer resources while somehow incorporating AI tools into workflows no one has figured out yet. Outside, the profession itself is shifting. AI generates interfaces, writes copy, produces prototypes in minutes. The execution work that built your career is compressing. The question is no longer whether designers need to adapt—it is whether you are building the skills that will matter in two years.Claude Code for Designers answers both pressures at once. You move faster because you can build functional prototypes yourself. You stay relevant because directing AI to build is the new execution skill. You leave with a deployed project and a workflow system you can use on anything that comes next.

Master Claude Code. Build functional projects. Develop a reusable workflow.

An AI-First Build Workflow: From project scoping through deployment

A Reusable Workflow System: Templates, skills, prompt libraries, MCP configs

A Deployed Application: Ship a functional project from your own idea

Portfolio artifacts and fluency to discuss AI-assisted design in interviews

Design-to-Code Translation: Turn your design system into functional components

This course assumes working knowledge of design systems, user research, and product workflows.

5+ years of professional design experience

Experience using AI assistants in your workflow. The course moves quickly and assumes baseline comfort.

Familiarity with AI tools (Claude, ChatGPT, Copilot)

Product Designers. You design in Figma and wait on engineering. You want to build and ship functional prototypes yourself.

Design Leaders. Your team needs to move faster and integrate AI workflows. You want firsthand fluency before rolling this out.

MXDAI Alumni. You have the strategic frameworks for AI design. Now you want the execution skill to match.

Bad Learning Design Is Costing You Customers — Here's the Science Behind It

Learning Scientist | L&D Strategist

Vanorsdale Learning Lab

Great Game of Business

Docebo

National Alliance on Mental Illness Florida

English Like a Native

Dr. Christie Vanorsdale, Ed.D, Ms.Ed.

Researcher. Practitioner. 15 years translating learning science into results

You've updated the onboarding. Rebuilt the knowledge base. Added walkthrough videos, help articles, live training sessions. Your customers still aren't adopting the product the way you need them to.The instinct is to fix the content. More of it. Make it Better, or Shorter. More visual. But, the problem isn't the content.It's that content was never designed to produce learning in the first place.Human beings don't retain complex knowledge because they watched a video or completed a walkthrough. Retention requires intentional sequencing, cognitive scaffolding, and an architecture that reduces friction rather than creates it. Most customer onboarding violates every one of those principles — not because the people who built it weren't smart, but because they were never taught what learning science actually says about how knowledge transfers.This workshop gives founders and leaders the diagnostic framework to identify exactly where their customer education breaks down — and the research-backed principles to fix it. You'll leave with a clear line of sight between learning architecture and the revenue outcomes you actually care about: adoption, retention, and renewal.

Learn the science behind knowledge transfer and leave with a diagnostic framework that connects learning architecture to business outcomes.

Understand why customers aren't retaining your product using learning science

Pinpoint exactly where your customer onboarding breaks down

Apply three evidence-based principles to improve product adoption

Connect learning design decisions to churn, adoption, and renewal.

The Scaling Founder Complex product. Customers churn before reaching value. Onboarding feels broken but there's no framework to diagnose why

The Customer Success Leader Owns onboarding and renewals. Content exists but adoption is flat. Needs a framework and internal buy-in.

The L&amp;D Manager Fixing customer education with no infrastructure or alignment. Knows training theory. Missing the revenue impact language

Master Causal Inference to Make Credible Decisions from Messy Real-World Data

Seasoned data scientist & educator

Atlassian

Pinterest

eBay

Dropbox

Anirban Bhattacharyya 

Data scientist and educator with 10+ years in experiments and causal inference

Most data scientists are asked to answer high-stakes “what caused what?” questions long before they have a perfect A/B test: Did this feature actually improve retention? Did the campaign drive incremental revenue? Did policy changes reduce churn, or were users already trending that way?This workshop helps you move beyond surface-level correlations and build the judgment to make credible causal claims from messy, real-world data. You’ll learn how to frame business questions as causal questions, choose the right method when experimentation is limited, identify common sources of bias, and communicate assumptions clearly to stakeholders.By the end, you’ll have a practical toolkit for making better product, growth, marketing, and policy decisions when randomized experiments are unavailable, delayed, or incomplete

Data scientists &amp; analysts who need to answer “what caused what?” using messy product, growth, or business data.

Product, growth &amp; marketing teams making decisions when clean A/B tests are unavailable, delayed, or incomplete.

Students &amp; early-career data pros who want practical causal inference skills for interviews and real-world data work.

Agentic AI

Coding with AI

AI Workflows

Claude Code

OpenClaw

Vibe Coding

AI Evals

AI Transformation

RAG & Search

AI for PMs

AI for Engineers

AI for Designers

AI for Marketers

AI for Founders

Product Sense

Product Discovery

User Research

Prototyping

Growth

Analytics

Tech Foundations

Strategy

Influence

Leadership

Career Growth

Machine Learning

LLM Ops

Context Eng

Security

System Design

Figma

Design Systems

Visual Design

Design Strategy

Automation

Content Marketing

Demand Gen

Go-to-Market

Product Marketing

Positioning

Social Media

Brand

B2B Marketing

SEO & AEO

AI for Leaders

AI Governance

Communication

Management

People Operations

Exec Presence

Storytelling

Goal-setting

Personal Brand

Fundraising

Everyone

Operators

Data Scientists

Business Analysts

User Researchers

Customer Success

Project Managers

HR Professionals

Sales People

Lawyers

Finance

Investors

Real Estate

Educators

Creators

Welcome and Agenda

The Onboarding Problem and Causal Chain Thinking

Speaker Introductions: John and Casey

Introducing the Make It Toolkit for Behavioral Design

Defining the Onboarding Problem: Low Activation Rates

Product Logic vs. User Logic

Workflow vs. Causal Chain: The Invisible Logic

The Three Core Questions of Causal Chain Thinking

Example 1: Applying the Three Questions to a Fitness App

Example 2: Applying the Three Questions to a FinTech App

Case Study Introduction: Nexus AI Automation Platform

Walking Through the Nexus Onboarding Flow

The Three Layers of Analysis: Workflow, Analytics, and Causal Chain

A Rigorous Method for Causal Chain Audits

The Audit Process: Defining Business and Audience Profiles

Deep Dive: The Nexus Audience Profile

Step-by-Step Causal Chain Audit of the Nexus Flow

Formulating Design Interventions with the Make It Toolkit

Recap: A Beautiful Flow on a Broken Causal Chain

Next Steps and a Challenge to the Audience

Q&A Session

Product managers who drive strategy, development, and launch of products.

User experience design — information architecture, interaction design, usability, and end-to-end experience flows.

Redesign Onboarding Flows using Causal Chain Thinking

What you'll learn

Why this topic matters

You'll learn from

John Gusiff

Krzysztof Czubak