The Economics of Relationships

In the short term, optimizing for metrics outpaces the monetary gain of the relationship-centered approach. Extending the time horizon, however, reveals a different story.

But why does this happen? How?

No matter the approach, fundamentally, we need to...

• make a thing (features)
• attract people to the thing (aquisition)
• get people to buy/use the thing (conversion)
• hopefully get them buy/use it again (retention)

How we choose to go about these ends reveals how they yeild such different outcomes.

Old playbook:

Make a thing

• We often decide what to make we by the crosss section ofprojected revenueand least time/cost to implement.

Attract people...

• We often rely on paid aquisition to get people to the thing.

Get people to buy/use the thing

• We often choose conversion tactics over experience to get people to buy/use the thing or pad pockets.

Get people to buy/use it again

• We often focus on incentivizing repeat purchase through monetary incentives, gamification or more features.

New playbook:

Make a thing

• We make a thing that is useful, desirable and easy to use for a specific user. Who we serve is extremely clear.

Attract people...

• We use value driven organic social to attract people that share our values.

Get people to buy/use the thing

• We use leverage relationship captial we've built to make strategic asks. Our business model supports and incentivizes building well for this group.

Get people to buy/use it again

• We use referrals a the main proxy for success, thus we can't incentivie it. We do howeever make it prominent.

Even if you don't do paid media and opt for a 100% organic approach it will always cost more to aquire a new user than to retain an existing one.

It's also true that the most effective path to your next user is through the word of mouth of an existing user.

The New Product Playbook understands this.

Through service of a specific user, we can make a product worth using. When we do this extremely well, we make an experience worth organically telling others about. When people refer others, we get a head start on a positive relationship for them to do the same.

When we ask why we're doing the things we're doing in the old playbook the answer often amounts to short-term monetary gain. The new product playbook offers a morally satisfying answer; long term service. Pardoxically, the new product playbook results in more money in the long run.

Why optimize for relationships instead of metrics?

It's generally more honest, more cost effective, and amounts in a much more resilient business.

Which costs more? Keeping a user? Or acquiring a new one?

Which is more valuable? A user who loves your product? Or a user who is just using it because they have to?

Which converts better? A referred user or paid acquisition?

Which of these is more likely to churn?

Optimizing for relationships results in the compound interest of trust and the exponential growth of your business, by way of a genuinely solved problem and genuine referral.

As a business, optimizing for metrics results in the logarithmic returns of traditional approaches: quick initial gains leading to eventual plateau.

One of the reasons A/B testing as a default framework is so insidious is it's ability to handcuff product teams into short term thinking and local maxima.

An A/B test really only measure the impact of one feature and in a short time horizon.

This puts tremendous pressure on the solution to deliver quick wins.

These quick wins are often inconsiderate of the the entire experience or worse, opposed to the entire experience.

• Logarithmic returns of traditional approaches: quick initial gains, eventual plateau
• Exponential returns of relationship-centered approach: slower start, accelerating growth
• Visual comparison of the two growth curves over time
• Why retention economics favor relationship-centered products
• How word-of-mouth growth creates exponential adoption curves
• The diminishing returns of optimization vs. the compound interest of trust
• Long-term financial benefits: higher retention, lower acquisition costs, premium pricing power

GPT

Traditional approaches to product growth often prioritize immediate optimization—rapid feature launches, aggressive A/B testing, and short-term conversion tactics. This mindset yields quick initial gains, as immediate friction points are smoothed out and surface-level user needs are addressed. However, such methods quickly run into diminishing returns. Early optimizations soon become commoditized, making incremental improvements increasingly marginal. The result is a logarithmic growth curve: steep at first, then flattening into a plateau as competition intensifies and users demand deeper value.

In contrast, a relationship-centered approach to product development invests heavily in building enduring user trust from the outset. This method appears slower initially, as it requires substantial upfront investment in thoughtful design, deliberate feature rollouts, and consistent user-focused decision-making. But unlike traditional optimization strategies, relationship-building compounds over time. Trust isn't easily replicated or commoditized, creating a moat against competitors. Consequently, growth in relationship-centered products doesn't plateau—it accelerates, creating an exponential trajectory over the long term.

Why optimize for relationships instead of metrics? It's generally more honest, cost-effective, and results in a significantly more resilient business.

Consider these comparisons:

Which costs more? Keeping a user or acquiring a new one?

Which is more valuable? A user who loves your product or a user who is just using it because they have to?

Which converts better? A referred user or paid acquisition?

Which is more likely to churn?

Optimizing for relationships results in the compound interest of trust and the exponential growth of your business through genuinely solved problems and genuine referrals.

One significant pitfall of relying heavily on A/B testing as a default framework is its inherent tendency to trap product teams into short-term thinking and local maxima. An A/B test typically measures the impact of one feature over a limited time horizon, creating tremendous pressure for solutions to deliver quick wins. Such quick wins are often inconsiderate of—or even detrimental to—the overall user experience.

Visually, these two approaches tell distinct stories. The traditional model spikes quickly but flattens sharply, signaling diminishing effectiveness. Relationship-driven growth, however, starts gently and curves upward dramatically as trust accumulates, creating enduring user loyalty.

Retention economics overwhelmingly favor relationship-centered products. High retention rates significantly reduce churn, decreasing the need for expensive ongoing user acquisition efforts. Loyal users not only remain longer, providing a stable revenue base, but they also engage more deeply, unlocking greater lifetime value. Furthermore, trust-based relationships empower premium pricing strategies—users willingly pay more for products they deeply trust and identify with.

Word-of-mouth growth further amplifies these exponential returns. Trust-rich products naturally create vocal advocates. Satisfied users organically share their experiences, driving exponential adoption curves that traditional marketing cannot replicate. This organic, self-sustaining growth cycle dramatically lowers customer acquisition costs while simultaneously enhancing market penetration.

Ultimately, the diminishing returns inherent in continual optimization stand in sharp contrast to the compounding interest of trust. Optimization is transactional, quickly replicable, and limited by diminishing margins of improvement. Trust, however, is relational, uniquely defensible, and infinitely scalable through word-of-mouth.

The long-term financial benefits of relationship-centered products are clear: significantly higher retention, dramatically lower acquisition costs, robust pricing power, and sustainable exponential growth. In an increasingly crowded and commoditized digital landscape, the future belongs to companies that understand and leverage the exponential returns of trust-based relationships.