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Generate insights you never imagined when the survey was invented.

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Generate volumetric and financial projections.

Volumetric models allow us to speak to our clients in the language of business. We build virtual shopping environments, like a virtual restaurant menu or grocery store, and experiment with the product or pricing mix to understand how these changes affect consumer purchasing.

We then calibrate our models to actual in-market volumes and report the effect of product or pricing innovation in terms of units, revenue, cannibalization, incrementality, and profit.

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Find patterns within ideas.

We enter into an innovation test for a client with a list of individual ideas. One of the key outputs is an Idea Map. This map groups ideas into themes, allowing for a strategic view of the underlying structures across that inform a range of opportunities.

Applying this visualization to innovation, these maps highlight potential product and positioning territories that our clients leverage to focus their efforts.

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Find patterns within populations.

What makes our approach to consumer segmentations unique is our consultative approach; we look at multiple solutions, talk to you about the differences and the impact of each solution on your business, then we recommend a path forward.

Once all of that is done, we conduct cross-functional sessions in which we translate the segmentation into strategy and tactics. The result is a memorable, actionable segmentation and a roadmap for your business.

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big data

Marry surveys with big data.

Data often exist in silos. We might know how people behave, but we don’t know why or what might motivate them to change their behavior. In these situations, we apply an attitudinal segmentation to a customer database. The result is an understanding of the motivations of each of your customers rendered at scale and the ability to individually tailor communications and offers to be highly relevant.

This sets the stage for marketing that goes beyond demographics and current behaviors and instead speaks to what your customers want and need.

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Uncover causal drivers.

Traditionally, we've learned that correlation is not causation. However, with machine learning techniques, we can uncover the causal relationships between attitudes and your customers’ likelihood to recommend or buy your product.

Should you focus more on building trust with your customers, or improving your tech support? We can simulate changes to each to see where you will get the best bang for your marketing buck.

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Machine learning

Automate insight generation.

We produce analyses that formerly required statisticians days or weeks to build because we've invested in software engineering. Examples include our own in-house portfolio modelling and conjoint simulators.

We also leverage parallel processing to speed up access to these results, writing our own algorithms when performance is still not fast enough. Big models that formerly took a week to run now finish in minutes!