The 1% Insight

How To Run a B2B Influencer Programme with Chris Peters & Will Beech of Moon at Dawn | #27

Jack Regan Episode 27

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0:00 | 28:29

If you're marketing a B2B tech company, you've probably watched the influencer conversation grow on LinkedIn and wondered whether any of it applies to you. Chris Peters and Will Beech of Moon at Dawn make the case that it does, but not in the way most tech brands assume.

In this episode we get into why picking creators on "vanity and vibes" is the fastest way to waste budget, why your own customers are your most credible advocates and how peer content is now shaping the answers buyers get from LLMs. 

We also cover how to set up a programme from scratch when you've got nothing in place, how to measure it against the campaigns you already run and what a fully integrated influencer engine looks like twelve months in.

For B2B tech marketers trying to build trust with an out of market audience, this is a practical look at a channel most of your competitors are still getting wrong.

Key insights for B2B Tech Marketers:

→ Influence in B2B goes far beyond content creators. Subject matter experts, academics and key opinion leaders are the under-tapped opportunity while everyone else chases creators.

→ Start with your customers. They already know the product, so they can speak to it more authentically than any paid creator and they'll often do it for free.

→ Peer content is now feeding LLM answers. Getting trusted voices to validate your research and brand directly influences what buyers see when they ask an AI.

→ "Vanity and vibes" selection is a common failure. Build a business case with real signals behind every influencer, not follower counts and gut feel.

→ Activate a cohort, not a single bet. Twelve to fifteen smaller, relevant voices beats betting the house on one big creator.

→ Measure against what you already run. Benchmark an influencer pilot against your existing awareness or lead gen activity.

→ The goal is integration, not a side project. A mature programme runs horizontally through events, content and PR not as an adjacent experiment.

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