The MRXplorer Approach

What you get, customized to your team's specific needs and capabilities. Contact us today to get started.

Capabilities Assessment

A survey and 1:1 interviews designed to assess current usage and familiarity with generative AI, so that training is on par with current capabilities on the team, instead of being too basic or too advanced. Each of the following modules is then customized depending on the results of the assessment.

Module 1: AI Foundations for Market Research

Not a generic AI overview, but a grounding in how large language models actually work, what that means for how you use them, and why market research is a particularly important context for thinking carefully about outputs. 

• How generative AI works (at the level that matters for using it well)

• Critical thinking about AI (understanding its limitations, such as biased responses)

• What AI is genuinely good at in MR workflows, and what it isn't

• Common failures in MR-specific contexts, and how to catch them

Module 2: Responsible AI Use in Insights

Addresses the professional credibility and data responsibility questions that are specific to market research work. Your team already understands the importance of data security, and this builds on that with practical guidance on how it applies specifically to AI tools: what to put in, what to keep out, and how to build habits that protect clients and maintain research integrity.

• The Enterprise license myth

• Data handling in AI workflows: what goes in, what never does, and why

• Protecting client confidentiality when using AI for analysis and reporting

• Tool selection discipline: why free consumer versions of AI tools are not appropriate for client work

• Evaluating and verifying AI outputs to maintain research quality standards

Module 3: Claude in Practice

This is where we get specific. We work through prompts vs Claude skills, context setting vs Claude projects, and how to show what Claude is doing behind the scenes to know what it's doing. Also covers the difference between Claude, Claude Cowork, and Claude Code and what each does.

• Prompt engineering for MR workflows (with hands-on practice)

• Getting consistent, reliable outputs across your most common task types

• Building prompt libraries and skills, and when to use each

• Session and context management

Module 4: Claude Cowork and Claude Code for Insights Managers

Claude Code opens a new door: the ability to build lightweight tools without a development background. This layer demystifies it and gives your team a practical foundation for thinking about automation opportunities in their own work.

• What Claude Cowork is and when to use it versus Claude Code 

• The Claude Code window on Mac versus the CLI (and what the heck is a CLI?)

• Reading and understanding what Claude Code produces (you don't need to write code to work with it)

• Practical use cases for MR: data formatting, report templates, simple automations

• How to collaborate with Claude Code without getting lost

• Context files and other behaviors to maximize tokens