Using AI as a Time Machine for Brand Value
How much do people really value a brand in their everyday lives, not on a survey scale, but in terms of what they’d actually give up?
How much do people really value a brand in their everyday lives, not on a survey scale, but in terms of what they’d actually give up? That’s the question Felix Eggers (Copenhagen Business School) and collaborators have been working on, and it leads to a very different way of thinking about brand tracking: one where AI can reconstruct brand value over time, simulate alternative futures, and turn tracking from static snapshots into a dynamic “time machine.”
From “do you like this brand?” to “would you give it up?”
Traditional tracking asks people how much they like a brand or how likely they are to recommend it. Useful, but indirect. This research asks something much more concrete:
“Would you give up access to [Brand X] for one month if we paid you $Y?”
By varying the amount, we see what people actually need to be paid to temporarily give up a brand. That give-up price is a direct, behavior-anchored signal of the brand’s real value in their lives.
Training AI to behave like thousands of consumers
In work presented at the MSI conference, Felix showed how to go a step further:
- Collect incentive-compatible survey data. Real consumers make real trade-offs.
- Train a large language model on these choices. The model learns to behave like realistic consumers, segment by segment and year by year.
- Use the AI as a synthetic consumer panel. Once calibrated, it can simulate thousands of realistic choices across conditions and time periods.
The key result: the AI’s simulated choices align closely with real survey data, which means it can credibly fill in missing years, explore what-if scenarios, and track brand value through time.
Why this matters for brand tracking
A deeper brand value metric. “What would you need to be paid to give this up for a month?” captures how embedded the brand is in people’s lives: a richer core KPI than stated liking.
Faster, more affordable tracking. Use real survey waves as anchor points and let the calibrated model generate high-quality synthetic data between waves, grounded in real respondents, without exploding the budget.
Trends, not snapshots. Build a brand-value time series: how value moved around launches, campaigns, or crises; whether a competitor’s entry eroded your value or just your share of voice.
Scenario planning. Simulate how usage shifts, bundle or positioning changes, regulation, or new competitors might affect brand value, stress-testing strategy before the market does it for you.
From research to product
This “AI time machine” approach is built into BrandSight™: track not just what people say but what they’d need to be paid to give you up, combine survey rigor with AI-generated synthetic data, and explore historical trends and future scenarios in one platform.
Want to explore it for your category? Book a demo to discuss a pilot or an integration with your existing tracking.