27 June 2023

Why It’s SO Much Harder To Grow A Startup In 2023… (#133)

2Frameworks

9Insights

Frameworks in this episode

Strategy5 steps

Magic Wand Barometer

Test whether perfect execution would create a material result

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Marketing6 steps

Transactional–Influence Marketing Model

Balance measurable conversion with harder-to-measure market influence

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Insights & moments

The myth-busts, hot takes, explainers, and tools worth keeping.

Myth Buster· 2

Myth Buster05:00

B2B Marketers Undervalue Social's Influence on Enterprise Buyers

Kipp cites a large Google buyer study in which social ranked as the most influential channel for enterprise B2B buyers, followed by peer word of mouth and online video. The finding challenges teams that dismiss social because its direct-response performance is difficult to observe.

Social ranked first for influence among enterprise B2B buyers
Peer word of mouth ranked highly
Online video was another major influence
Social functions partly as visible word of mouth
Direct-response attribution can obscure social's real value

“Social number one, I think word of mouth was number two, peers, word of mouth I think was number two, and then online video.”
Kipp Bodnar · 05:30

“Most companies very much undervalue the influence of social.”
Kipp Bodnar · 06:00

#social media#b2b#buyer behavior#word of mouth

Myth Buster22:00

Anyone Claiming to Know the New Growth Playbook Is Lying

The hosts describe the current marketing environment as a transitional limbo rather than a settled system with proven, repeatable plays. They expect clearer tactics to emerge over the next 18–24 months, but reject claims that a dependable replacement playbook already exists.

The current distribution environment has not stabilized
Reliable replacement plays are still emerging
Marketers must operate without a complete playbook
Experimentation and adaptability matter during the transition
Confident claims of certainty should be treated skeptically

“We just don't have those today”
Kipp Bodnar · 22:30

“Anyone who's telling you they do”
Kieran Flanagan · 22:30

#growth#marketing playbook#uncertainty#strategy

Hot Take· 3

Hot Take08:00

Exact Marketing ROI Is Increasingly a Fiction

Kipp argues that the margin of error in marketing ROI has expanded as buyer journeys and channels have become harder to observe. ROI models can still support relative investment decisions, but treating their outputs as perfectly accurate disguises assumptions as facts.

Marketing ROI now has a larger margin of error
Exact returns often depend on unverifiable assumptions
Relative channel comparisons remain useful
Modeled influence is not the same as direct conversion
Measurement claims should reflect uncertainty

“I think looking at ROI as an absolute is total bullshit now.”
Kipp Bodnar · 08:00

“I think you could look at them on a relative basis and say, oh, directionally this is a better place to invest my money than…”
Kipp Bodnar · 08:30

#roi#attribution#measurement#analytics

Hot Take11:30

Fear Pushes Marketing Teams Into an Unproductive Defense

During difficult economic periods, marketers seek perfect certainty before spending and become highly risk-averse. Kipp argues that this defensive posture redirects time from customers and market action into excessive internal analysis.

Hard times reduce teams' willingness to take risks
Defensive marketing seeks impossible spending certainty
Risk aversion increases internal analysis
Teams spend less time influencing and inspiring customers
New market plays can create more learning than marginal model refinements

“When times get hard, you don't take as much risk. You feel like you need to perfectly spend that money, and you go from playing…”
Kipp Bodnar · 11:30

“We're doing it to validate ourselves not to help our customers.”
Kipp Bodnar · 13:00

#risk#marketing strategy#decision making#customers

Hot Take26:00

AI Agents Could Automate Much of Premium Health Coaching

Discussing Kipp's personalized fitness service, Kieran predicts that AI agents could interpret blood tests and combine them with a person's age, weight, height, and goals. The system could then automate much of the work involved in producing individualized health and workout plans.

AI could interpret blood-test results
Personal attributes and goals could shape recommendations
Workout plans could be generated automatically
Premium personalized services may face AI-driven automation
Human coaching may shift toward oversight and accountability

“this is a perfect business model for AI”
Kieran Flanagan · 26:00

“AI agent is able to like extrapolate all of the things you should do based upon blood tests.”
Kieran Flanagan · 26:30

#ai agents#health#fitness#automation

Explainer· 2

Explainer02:30

Why the Golden Age of Easy Digital Distribution Is Ending

Google once provided a huge, measurable front door to internet demand, while digital advertising made acquisition scalable and attributable. Search fragmentation, closed social platforms, familiar viral mechanics, and declining measurement quality now make customer distribution harder.

Distribution means acquiring demand, leads, and new customers
Google concentrated large-scale, measurable intent
Digital advertising made acquisition more scalable and attributable
Search and social are becoming more fragmented or closed
Established product-led viral mechanics are widely known and copied

“So distribution, acquiring demand for your product, acquiring leads. It's like the thing that drives, it's the fuel that drives your business.”
Kieran Flanagan · 03:00

“we were lucky that Google became the defacto front door of the internet”
Kieran Flanagan · 04:00

#distribution#search#advertising#growth

Explainer18:30

Fast AI Adopters May Get a 12–18 Month Distribution Tailwind

Kieran expects AI to help fast-moving marketers produce search content, advertising creative, video, and personalized email with smaller teams. That advantage may last 12–18 months before widespread adoption commoditizes the capabilities and turns the tailwind into a headwind.

AI can let one person operate a sophisticated content system
Small companies may replicate larger SEO engines
Google and Facebook are adding AI-assisted advertising capabilities
Email personalization should become easier
Early adopters may benefit before the tools become commoditized

“I think for the first 12 to 18 months for fast movers, it's actually a tailwind.”
Kieran Flanagan · 19:30

“they've actually built that exact system that a single person can do it all.”
Kieran Flanagan · 19:00

#ai#distribution#seo#advertising

Story· 1

Story20:00

How a Google Update Forced HubSpot to Reallocate Its SEO Team

A Google algorithm update placed greater weight on content freshness and page performance, causing HubSpot's search traffic to take a substantial hit. The company shifted people and time from producing new content toward updating existing pages and improving technical performance.

HubSpot was hit hard by a Google algorithm update
The update increased the value of freshness and page performance
Existing-content updates became a higher priority
Technical work focused largely on content pages
Algorithm changes can force rapid resource reallocation

“We got hit hard.”
Kipp Bodnar · 20:00

“we had to reallocate time and people to, much more updating of existing content versus creating new content.”
Kipp Bodnar · 20:30

#google#seo#content#algorithms

Takeaway· 1

Takeaway21:00

AI Is Already Changing Marketing Through Other Platforms

AI's impact is not limited to marketers directly using generative tools. Competitive pressure is also changing search and social algorithms, making existing distribution performance less predictable and requiring teams to diagnose changes and adapt quickly.

AI disruption can arrive through search algorithms
Social algorithms may also change more rapidly
Performance declines may reflect platform-level shifts
Teams need agility to diagnose and respond
New distribution channels matter during periods of uncertainty

“the disruption AI caused is not just directly AI-related”
Kipp Bodnar · 21:30

“this game is unpredictable”
Kipp Bodnar · 21:00

#ai#algorithms#agility#distribution