Simulation for experience & perception

Consumer Optimisation simulates the consequences of strategy before you act.

Reduce risk and improve outcomes by understanding how changes to experiences, messaging, and brand cues ripple through perceptions into behaviour.

Built for Customer Experience, Brand, and Communications teams.

Product

Built for experience and perception-driven disciplines

Consumer Optimisation simulates the consequences of strategy before you act: reducing risk and improving outcomes. It’s built specifically for experience and perception-driven disciplines: Customer Experience, Brand, and Communications. Unlike conventional modelling, it maps cause and effect, revealing how consumer behaviour shifts in response to your actions. This shifts insight from passive, historic dashboards to forward-looking “what if” scenario simulation.

From dashboards to decisions

Instead of simply reporting what has happened, Consumer Optimisation lets you explore what could happen next. See how proposed changes propagate through perceptions into behaviour, and compare alternative strategies before committing resources.

“For a genuinely game-changing innovation check out Phil Wright’s ‘Consumer Optimisation’ work deploying network analysis and simulation in an amazingly accessible, dynamic and commercially relevant way. It is the work of a true genius. A hidden Gem. If you want to really understand how to change your brand check it out.”

— Dr. Nick Baker, Global Chief Research Officer at Savanta and Past-Chair, MRS Board

How it works

Quasi-causal simulation for real-world data

Consumer Optimisation uses a quasi-causal simulation engine built for real-world market research data. It uncovers how experiences influence perceptions, how perceptions cascade through the system, and how these changes ultimately flow through to behaviour. For any potential intervention, it then simulates both the direct and halo effects, revealing consequences that conventional modelling cannot resolve. This lets you explore scenarios, test alternatives, and understand consequences before you act.

Cause and effect, not just correlation

The engine is designed to respect the structure of your data and the realities of consumer decision‑making. It focuses on how changes to experiences and signals are likely to shift perceptions, and how those shifts propagate into behaviour. Because it models cause and effect rather than simple associations, it can show the consequences of a change - not just the variables that happen to move together - revealing how targeted interventions lead to meaningful behavioural outcomes.

Direct and halo effects

Even when an intervention targets only a few perceptions, its effects extend beyond these. Consumer Optimisation simulates both the direct impact of a change and the ripple effects that propagate through connected perceptions, revealing second‑order consequences that conventional modelling cannot detect.

“The million-dollar question ‘what drives purchase (consideration, loyalty etc.)’ isn’t answered by a simple linear relationship; it comes from a network of causal factors that determine whether a customer doesn’t buy, buys, and buys again. To identify the levers of customer loyalty amongst CX, brand and messaging you need to understand this. Every company in the world is trying to do this, yet most still rely on legacy analytics, gut feel and experience rather than evidence.”

Phil has built a data-driven solution, a network that captures all the elements that pull customers toward or away from purchase, and in doing so he finally answers the million-dollar question.”

— Caroline Dieleman, Director Data Science & Analytics, Metyis

Applications

Where Consumer Optimisation is used

The same engine supports multiple disciplines, each with its own language and decision context. Customer Experience, Brand, and Communications & Behaviour Change share a common need: understanding how actions today will shape perceptions and behaviour tomorrow.

Customer Experience depends on knowing what to fix next. Consumer Optimisation reveals which improvements – from broad touchpoints to specific micro interactions – create the greatest uplift, how changes ripple through perceptions into behaviours, and where ROI is strongest. It lets CX teams simulate scenarios, compare alternatives, and optimise the experience before committing resources.

“If we invest $1M, which three actions deliver the greatest uplift right now?”

“If we can’t fix this expensive issue, what can we do instead that protects satisfaction?”

“We need to lift CSAT by five points, so set targets we can achieve on the current budget.”

Brand strategy starts with understanding how each brand cue shifts consumer perceptions and preferences. Consumer Optimisation models these shifts – including unintended effects – and how they translate into outcomes such as consideration, preference, and brand equity. It accounts for direct and halo effects, identifies which levers the brand can realistically move, and lets teams compare and optimise brand positioning before committing investment.

“How can we increase consideration without creating perception shifts that weaken brand equity?”

“Does our ethical stance create an association with higher prices, and how strongly does that weaken preference in segment 2?”

“What is the optimal brand position for us, and which specific actions – campaigns, experiences, and other signals – will move perceptions into that space?”

Behaviour change depends on understanding which interventions shift the perceptions and beliefs that drive action. Consumer Optimisation models how different interventions – including messaging – update these underlying perceptions and how those changes translate into behaviour. It shows where the real behavioural leverage sits – which perception shifts drive meaningful changes in action – and allows intervention options to be tested, compared, and refined before they are put into practice.

“What drives destination consideration for each traveller type, and how do we adjust messaging to move perceptions and consideration in line with our tourism strategy?”

“Which messaging and intervention shifts will most effectively increase consideration and adoption of public transport?”

“What intervention mix increases compliance among self-employed workers while keeping compliant individuals confident and nudging non-compliant ones into the system?”

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