
HOW WE WORK
We are a fully transparent company and do not believe in black boxes. Everything we do, we will detail, explain, and review, if needed. We are analytically rigorous and brand savvy. We use proven methods that have been validated in academic research.
All of our work is led and conducted by senior leaders (we do not hand off work to junior researchers). We love innovation and constantly look for ways to do things more efficiently, and all-around better.
We use AI in two different ways
ANALYZE OPEN-ENDED TEXT
AND RESPONSES
We use AI to analyze open-ended text responses (such as we use in our brand density analysis). In analyzing open-ended responses, we start with a manual human coding step. Once we develop a human-coding-based dictionary of relevant meaningful terms, we use models such as the BERT model to score the entire dataset.
GENERATE SYNTHETIC
RESPONSES AND RESPONDENTS
We use AI to generate synthetic responses & respondents. This can be done in two ways:
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We can generate synthetic respondents for a subset of variables using techniques such as SMOTE. See our paper on how we use these to improve segmentation typing tools.
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We can generate synthetic responses to open-ended questions such as our brand density question. Our BD AI solution is based on this. Here we use a variety of AI tools such as ChatGPT, Llama3, and BERT.

WE ARE INNOVATORS
Kwantum has pioneered many new brand intelligence approaches, including:
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Brand Density analysis
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Latent Class Conjoint
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Holistic Conjoint
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The Switchable Consumer Approach
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Implicit Brand Drivers
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Consumer Surplus Method
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New approaches in using AI
Our Clients














Our Teams
KWANTUM'S BRAND STRATEGY TEAM



MARCO VRIENS
Founder and CEO
ERIC KOIVISTO
EVP Brand Strategy
JEFFREY KALTREIDER
Director Brand Strategy
KWANTUM'S ANALYTICS AND CONJOINT TEAM



MARCO VRIENS
Founder and CEO
FELIX EGGERS
Chief Consumer Science Officer
GARGI CHAUDHURI
PhD Director Modeling
Published Works & Resources
BRANDING, BRAND DRIVERS, AND MARKETING MIX
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Eggers, F., Vriens, M., Verhulst, R., Talwar, J. & Collis, A. (2024) Why You Should Be Tracking Customer Surplus Value. Harvard Business Review.
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Bayesian Networks for Key Driver Analysis (2021). Kwantum white paper.
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Vriens, M., Chen, S. and Schomaker, J. (2019). The evaluation of a brand association density metric. Journal of Product and Brand Management, 28, 1, 104-116.
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Vriens, M. Chen, S. and Vidden, C. (2019). Mapping brand similarities: Comparing consumer online comments versus survey data. International Journal of Market Research, 61, 2, 130-139.
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Vriens, M. and A. Martins Alves (2017). Modeling the implicit brand: Capturing the hidden drivers. Journal of Product and Brand Management, 26, 6, 600-615.
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Vriens, M., Martins Alves, A. and Chen, S. (2017). Brand segmentation using implicit measures. Applied Marketing Analytics, 3, 2, 172-182.
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Vriens, M., Vidden, C., Chen, S., and Kaulartz, S. (2017). Assessing the impact of a brand crisis using Big Data: The case of the VW diesel emission crisis. In: DMA Annual Analytics Journal, 107-118.
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Franses, P.H. & Vriens, M. (2004). Advertising effects on awareness, consideration and brand choice using tracking data. Working Paper.
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Vriens, M., Grigsby, M. & Franses, P.H. (2002). Time Series Models for Advertising Tracking Data. Canadian Journal of Marketing Research, 20, 2, 62-71.
CONJOINT
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Vriens, M. & Eggers, F. (2025). Using conjoint across the marketing value chain. Applied Marketing Analytics.
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Vriens, M. & Eggers, F. (2024). Holistic conjoint. In Sawtooth Software Analytics & Insights Conference Proceedings, San Antonio, 313-319.
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Vriens, M., Mills, D. & Elder, A. (2023). Integrating consumer goals in conjoint using Archetypes. In Sawtooth Software Conference Proceedings.
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Haaijer, R., M. Wedel, M. Vriens, and T. Wansbeek (1998). Utility covariances and context effects in conjoint MNP models. Marketing Science, Vol. 17, No. 3, pp. 236-252.
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Vriens, M., J.R. Bult, J.C. Hoekstra and H. Van der Scheer (1998). Conjoint experiments for direct mail optimization. European Journal of Marketing, Vol. 32, No. 3-4, pp. 323-339.
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Vriens, M., H. Oppewal and M. Wedel (1998). Ratings-based versus choice-based latent class conjoint models: An empirical comparison. Journal of the Market Research Society, July, Vol. 40, No. 3, pp. 237-248
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Wedel, M., M. Vriens, T. Bijmolt, W. Krijnen and P.S.H. Leeflang (1998). Assessing the effects of abstract attributes and brand familiarity in conjoint choice experiments. International Journal of Research in Marketing, Vol.15, pp. 71-78.
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Vriens, M., Loosschilder, E. Rosbergen and D.R. Wittink (1998). Verbal versus realistic pictorial representations in conjoint analysis with design attributes. Journal of Product Innovation Management, Vol. 15, No. 5, pp. 455-467.
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Vriens, M., M. Wedel and T. Wilms (1996). Metric conjoint segmentation methods: A Monte Carlo comparison. Journal of Marketing Research, Vol. 33, no. 1 (February), pp. 73-85.
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Loosschilder, G., E. Rosbergen, M. Vriens and D.R. Wittink (1995). Pictorial stimuli in conjoint analysis - to support product styling decisions. Journal of the Market Research Society, 37, 1, 17-34.
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Vriens, M. (1994). Solving marketing problems with conjoint analysis. Journal of Marketing Management, 10, 37-55.
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Wittink, D.R., M. Vriens and W. Burhenne (1994). Commercial use of conjoint analysis in Europe: Results and critical reflections. International Journal of Research in Marketing, 11, 1, 41-52.
ANALYTICS GENERAL
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Vriens, M. & Vidden, C. (2022). From data to decision: Handbook for the modern business analyst, 2nd edition, Cognella Academic Publishing.
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Vriens, M., Rademaker, D. & Verhulst, R. (2020). The business of marketing research. Cognella Academic Publishers.
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Vriens, M., Bosch, N., Talwar, J. (2022). How to build better segmentation typing tools: The role of classification and imbalance correction methods. In Sawtooth Software Proceedings, May 2022, Orlando.
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Vriens, M., Bosch, N., Vidden, C. & Talwar, J. (2022). Prediction and profitability in market segmentation typing tools. Journal of Marketing Analytics, https://doi.org/10.1057/s41270-021-00145-4
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Martins Alves, A., Vriens, M. & Ramos, T.G. (2021). Designing efficient assortments: A branch-and-bound method to optimize volume and satisfaction. Applied Marketing Analytics, 6, 4, 377-386.
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Vriens, M., Vidden, C. & Bosch, N. (2021). The benefits of Shapley-Value in key driver analysis. Applied Marketing Analytics, 269-278.
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Vriens, M. & Vidden, C. (2019). The Linux compete strategy: An analytics case study. Applied Marketing Analytics, 5, 129-136.
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Vidden, C., M. Vriens, S. Chen (2016). Comparing clustering methods for market segmentation: A simulation study. Applied Marketing Analytics, 2, 3, 225-238.
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Vriens, M. and A. Martins Alves (2015). Integrated competition and customer analysis: Managing market share efficiently. Applied Marketing Analytics, Vol. 1, No. 4, pp. 350-362.
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Chen, S., Vidden, C., Nelson, N., and Vriens, M. (2018). Topic modeling for open-ended survey responses. Applied Marketing Analytics, 4, 1, 53-62.
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Vriens, M. and S. Sinhara (2006). Dealing with missing data in surveys and databases. In: Grover, R. and M. Vriens (eds.), Handbook of Marketing Research, Sage Publishing, Thousand Oaks, CA.