59. What should a marketing tech stack look like in 2024?

59. What should a marketing tech stack look like in 2024?

In this episode we discuss: what should the marketing tech stack look like in 2024. We are joined by Mark Farnell, Head of Marketing Operations at DeepL.

Love The Operations Room? Please support us by rating and reviewing it here.

We chat about the following with Mark Farnell:

  • How is AI impacting marketing operations?
  • Where to start with building a marketing tech stack.
  • Why it's important to consider the complexity and scalability of current platforms.
  • Regularly reviewing and optimising the marketing tech stack is crucial for success. Use the first year or two to understand if a tool is a long-term solution before committing to a multi-year contract.
  • What value does Intent data platforms provide?
  • Why sales enablement is crucial for getting the most value out of marketing automation tools.
  • The build vs. buy decision based on your current situation and long-term plans.

References
  • https://www.linkedin.com/in/markfarnell/

Biography

A senior marketing operations leader with more than 12 years experience in marketing technology and process. I build and scale the platform and insight needed to understand and grow predictable pipeline.

To learn more about Beth and Brandon or to find out about sponsorship opportunities click here.

Summary

6:42 Introduction and Background

17:36 The Impact of AI on Marketing Operations

18:01 Challenges of AI in Marketing Content

19:02 The Value of AI in Marketing

20:26 Building a Comprehensive Marketing Tech Stack

23:26 Vendor Management and Tech Stack Optimisation

24:29 Introduction and the Importance of Long-Term Tool Evaluation

25:44 Essential Tools for a Startup's Series A Toolkit

34:39 Challenges and Value of Intent Data Platforms

35:08 The Build vs. Buy Decision

37:14 Importance of Clean Data and Data Research Approaches

39:14 Key Takeaway: The Importance of Data



This podcast uses the following third-party services for analysis:

Podcorn - https://podcorn.com/privacy

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