Overview

Consumer Optimisation Uses ‘What-if’ Simulation to De-risk Decision-Making

To make good decisions one must understand the consequences.

Traditional insight and analytics – be it market research or big data analytics – doesn’t have much to say about consequences. It can’t; it never really understood causation.

That leaves decision makers to shoulder the burden – and risk – of every decision.

Consumer Optimisation flips the script.

It starts by understanding, then mimicking consumer thoughts and actions under alternative, counterfactual scenarios.

It identifies risk. It reveals opportunity.

It enables true evidence-based decision-making.

Think of it as a decision support system – built for humans.

From Insight to Intervention

While Causal AI focuses on objective business metrics – explaining root cause and enabling causal inference – we address a parallel challenge in the more nuanced realm of consumer perceptions.

By transforming static, retrospective consumer research data into dynamic simulation, we shift the focus from ‘what is’ to ‘what could be.’

Purpose-built for experiential domains like customer experience, brand, and communications, this approach moves beyond passive reporting to enable proactive strategy.

It empowers teams to explore alternative futures, test interventions, and make confident, evidence-based decisions.

By contrast, legacy solutions rely on non-causal pattern recognition, so they cannot anticipate shifts in perception or translate these into changes in consumer thoughts and actions.

Such tools create a dangerous illusion of certainty: decisions are made based on yesterday’s static correlations.

From ‘What is’ to ‘What could be’

We all know ‘what is’:

  • 12% of customers are dissatisfied with your app’s functionality
  • 34% of consumers perceive your brand as “fair”
  • 7% of customers had a poor delivery experience
  • 68% of employees feel their salary is uncompetitive

It’s informative. It adds context. But it’s not enough.

Consumer Optimisation takes the same data and asks a different question:

‘What could be?’:

  • If we improve the app, how much will NPS actually rise – and is it worth the investment?
  • Should we keep promoting ‘fairness’, or pivot to another brand value? What’s the risk of change?
  • What’s driving poor delivery experiences – and what’s the lowest-cost way to improve perceptions and protect the brand?
  • If pay rises aren’t possible, what interventions could preserve morale and retention?

Because it’s the ‘what could be’ that empowers confident, evidence-based decisions – not just contextual understanding, but foresight.

This Isn’t Just a ‘Nice to Have’

Consumer Optimisation delivers immediate, tangible value – without the need for new research.

  • Commercial benefits: Strategic clarity, reduced risk, and better outcomes across CX, brand, and communications.
  • Operational impact:
    • Speed up and standardise decision-making
    • Maximise ROI of CX and brand investment
    • De-risk interventions with confidence
    • Integrate new research and data into an existing solution
  • Instant activation: Apply to existing data sets – no new fieldwork required.

Because the cost of getting it wrong is too high.