Game Theory in Growth Strategy: Pricing, Positioning and Strategic Interactions

When building growth strategy in a competitive market, I do not just think about what works in isolation — I think about how others will respond. Competitors, platforms and even customers behave strategically. That is why I often use concepts from game theory to model pricing, campaign rollouts, offer timing and more.

Game theory gives me a structured way to think through interactions where multiple actors are trying to optimise their own outcome. It is not theoretical fluff. It is a method for predicting and planning in dynamic, reactive environments.

In this article, I explain how I apply game theory to growth work, with particular focus on:

  • Nash equilibrium
  • Dominant strategies
  • Zero sum vs non-zero sum games
  • Payoff matrices and reaction modelling

The Setup: Strategic Interdependence

Most growth decisions are made in an environment of strategic interdependence. Your outcome depends not just on your choice, but on the choices of others.

For example, if two ecommerce brands both run steep discounting campaigns during a seasonal window, neither benefits. If one discounts and the other holds price, one wins and one loses. If both hold, margins stay healthy. This is a classic setup for a prisoner's dilemma — one of the foundational games in game theory.

Nash Equilibrium: Where No One Wants to Move First

A Nash equilibrium occurs when each player’s strategy is the best they can do, given what the other player is doing. No one has an incentive to change unilaterally.

Suppose we have two brands, A and B, choosing whether to discount or hold price. The payoffs could look like this:

B DiscountsB Holds Price
A Discounts(3, 3)(5, 1)
A Holds Price(1, 5)(4, 4)

In this case:

  • If both discount, they split market share but lose margin (3, 3)
  • If one discounts and the other does not, the discounter wins (5, 1) or (1, 5)
  • If both hold, margins remain (4, 4)

The Nash equilibrium is (4, 4) — because if either player switches from that strategy unilaterally, they lose. This outcome is stable.

Dominant Strategies: What to Do Regardless of the Other

A dominant strategy is one that is best no matter what the other player does.

In practice, very few marketing games have dominant strategies — most are conditional. But dominant strategies do exist in highly asymmetrical situations. For example:

  • If you have vastly lower cost of delivery, you might always undercut price
  • If you control the search channel, you might always bid aggressively on branded terms

I map this using payoff matrices. Each cell in the matrix shows the result of a strategic pair. I often build these in spreadsheets or Python to simulate scenarios.

Zero Sum vs Non-Zero Sum

A zero sum game means one player’s gain is another’s loss — the total payoff is constant.

Example: bidding on the same ad slot. If your ad shows, mine does not.

A non-zero sum game allows for cooperative gain — both players can improve simultaneously. For example, growing the overall category via joint education.

Growth hacking is full of both kinds. Referral programmes, loyalty schemes and industry events are non-zero sum. Bidding, price wars and exclusive deals are often zero sum.

Modelling Competitor Reactions

I build reaction functions to anticipate competitor response. For example, in dynamic pricing, I might assume a competitor updates their price based on a trailing window of my price.

I model:

PC(t+1)=f(PM(t),D(t),E(t))P_C(t+1) = f(P_M(t), D(t), E(t))

Where:

  • ( P_C ) is competitor price
  • ( P_M ) is my price
  • ( D ) is demand
  • ( E ) is environmental noise (e.g. inflation, platform change)

This helps me plan not just what to do, but what happens next.

Practical Example: Pricing Defence in Subscription SaaS

A SaaS client faced a new entrant offering a similar product at half price. They were considering matching the offer. I modelled this as a dynamic game.

We assumed that:

  • The new entrant would raise prices over time
  • Price-sensitive users were likely to churn, but power users cared more about feature set
  • Aggressive pricing could trigger a race to the bottom

Instead of price matching, we segmented users by sensitivity, added value to the high-end plan, and let the competitor absorb the low margin base.

The result: churn stabilised, average revenue per user increased, and the competitor eventually narrowed their offer to a single freemium tier.

Game theory guided that outcome.

Strategic Tools I Use

  • Payoff matrices: simple grids to visualise strategy pairs
  • Backward induction: in multi-stage games, work backwards from end goals
  • Best response functions: model what the optimal reaction is to each possible move
  • Mixed strategy equilibria: when players randomise across options (e.g. ad timing or discount cadence)

These tools allow me to build growth plans that anticipate pressure, avoid traps, and exploit strategic blind spots.

Why This Matters in Growth

In competitive markets, growth is not just about doing the right thing — it is about predicting and shaping the response environment. Game theory gives me that lens.

I use it to:

  • Decide when to lead, and when to let others move first
  • Build resilience into pricing strategy
  • Avoid zero sum waste
  • Identify stable, high margin equilibria

Most importantly, it helps me move from guessing to simulating.

If you are launching into a space where others already operate, or you want to defend position without reacting blindly — I can help you map the game, play to your strength, and grow with strategy, not just tactics.