Who pours their heart and soul into journey maps that end up gathering dust...

Exactly no one of course!

Sure, we all want our journey work to have more impact, faster.

But the question is how?

That's exactly what we're exploring in the Journey Management Playbook series in collaboration with TheyDo.

What makes this series different is that it's built on the real-world lessons learned from companies who are deep in the trenches of implementing journey management.

Success leaves clues – clues we're sharing with you.

Just like in the first episode, this second part dives into a step that many professionals (myself included, sometimes) tend to skip, overlook, or take for granted.

Yeah, it's the kind of oversight that often comes back to haunt you later on.

This crucial step is all about grounding your journey efforts in solid evidence, blending the what with the why. It’s what separates informed action from guesswork.

Getting this right brings clarity on what truly deserves your attention and what noise you can safely ignore.

Without it, you’re essentially sailing in the dark, hoping for the best.

And let's be honest, hope isn't the most reliable strategy when stakeholders are looking for tangible results.

So, join Tingting Lin and me to learn how to prevent your journey management efforts from failing to make the impact they deserve.

I thought I already had things pretty well dialled in with my own journey management, but this conversation was an eye-opener. Tingting showed how looking into a few simple things differently could help me ground my journeys in even stronger evidence.

Take care,

~ Marc

[RESOURCES]

• LinkedIn - https://www.linkedin.com/in/tingting-lin-8728308/
• Journey AI Episode: https://youtu.be/ImPCICkw9CI
• TheyDo - ⁠https://www.theydo.com/

[EPISODE GUIDE]

00:00 Episode Preview

04:00 Meet the Expert: Tingting

06:30 EP 1 Recap: Business Challenge

08:30 Data in Workflow

11:30 Analyzing with Data

16:30 Data Types & Sources

18:00 Siloed Data Challenge

19:30 Numbers vs Emotions

21:30 Why Data Quality

22:30 Understanding Quant Data

25:30 Correlations & Indicators

28:00 Comperehensive data

32:30 Adding Data Context

35:30 Actionable Data Context

37:45 Quant Data Takeaways

39:00 Qualitative Data Example

41:00 Churn Example with Qual

43:00 Unstructured data

45:00 Naturally occurring data

48:00 Key qualitative data insights

49:45 AI for Quality Analysis

50:45 Putting Data Together

53:00 Data Combination Insights

56:00 Data in Large Businesses

57:30 The Business Journey Tool

58:30 Episode 3 Preview

1:00:00 Audience Q&A (EP 1)