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Automated Product Suggestions with Needs-based Configurators

This article introduces a consumer-oriented approach to the future of automated product suggestions. It investigates a new type of product configurator: so-called needs-based configurators (NBCs). Such configurators analyze consumers’ product-related needs and create analysis-based automated product suggestions. Johanna Hasenmaile and Philipp Scharfenberger discuss the specific qualities of NBCs and show why automated product suggestion systems potentially simplify product choices (especially for consumers with little product knowledge). Their 10 recommendations for action are relevant for practitioners and future research alike. By Johanna Hasenmaile



Imagine buying a new car, yet feeling you lack product knowledge. You visit your preferred manufacturer’s website, select your choice vehicle from a vast range, and navigate key features (e.g., drive variants, the vehicle’s exterior and interior), each comprising dozens of subcategories with several hundred choices. How do you feel?

Increasingly complex product portfolios are requiring many companies to find new ways of efficiently suggesting the right product to consumers. One key tool for meeting this challenge is product configurators. Mass customization toolkits enable consumers to customize products (e.g., sneakers, cars) online. Customization, however, can be extensive, particularly for complex products. Even rather simple products (e.g., cereals) offer consumers up to 566 quadrillion configuration possibilities (mymuesli, 2020).

Most traditional configurators rest on attribute-based choice architectures. These enable choosing from diverse product attributes, based on which the customized product is developed (Hildebrand, Häubl, & Herrmann, 2014; Huffman & Kahn, 1998). Previous studies have focused largely on attribute-based configurators (ABCs) and discussed the advantages (Schreier, 2006) and limits of mass customization systems (Zipkin, 2001). Customizing each and every attribute can be onerous (Hildebrand et al., 2014), may entail feature fatigue (Thompson, Hamilton, & Rust, 2005), and probably fails to satisfy every consumer. This poses a major challenge for companies with complex product portfolios: How might future systems be designed to refocus attention on the buying experience and to make sales more fun and efficient? How can companies optimize the complex matching process between highly diverse customers and manifold product offerings?

In cooperation with Audi AG, Johanna Hasenmaile and Philipp Scharfenberger investigated a new type of product configurator: needs-based configurators (NBCs). Such configurators analyze product-related needs and then create automated product suggestions (Randall, Terwiesch, & Ulrich, 2007). In their Marketing Review St. Gallen article, they embed NBCs theoretically and develop specific recommendations for their utilization based on a qualitative study and a best-practice analysis.


Recommendations for Action

#1: Position NBCs prominently and as a new, entertaining, fast, intuitive, and uncomplicated means of configuration beside traditional attribute-based configurators (ABCs).

#2Provide clear instructions on which configuration type provides the best starting point. Guide consumers with high expertise to the ABC, those with low expertise to the NBC.

#3: Limit the number of questions and offer additional topics that enable optional customization.

#4: Include questions in your NBC that ensure novices receive a high perceived recommendation fit. Make sure all questions are actually needs-based (e.g., ask about travel habits instead of fuel type).

#5: Integrate info buttons and a chat function for further information.

#6: A mobile-optimized website is best suited to an NBC. Visualize technical details and enable users to skip steps.

#7: Communicate pricing transparently and clearly establish what is included in the price. Include a budget question and different financing options (lease or purchase). This can also help convert leads to sales.

#8: Show three different model recommendations that match the stated needs. Be transparent and highlight which needs are (or are not) considered and explain how the answers to previous questions lead to these recommendations.

#9: Program and test all algorithms very carefully.

#10: Clearly highlight the next steps (e.g., continue your configuration or contact your dealer) to reduce configuration terminations and integrate a call to action to convert leads into sales.



This research was conducted in cooperation with Dieter Kopitzki, Head of Development/Rollout Digital Retail Modules and Configurator at AUDI AG. The authors wish to thank AUDI AG for granting permission to publish the results and insights of this study.


More Information


Audi (2020). Modellempfehlung. Retrieved from

Hildebrand, C., Häubl, G., & Herrmann, A. (2014). Product Customization via Starting Solutions. In Journal of Marketing Research, 51,6, pp. 707-725. 

Huffman, C., & Kahn, B. E. (1998). Variety for Sale: Mass Customization or Mass Confusion? In Journal of Retailing, 74, 4, pp. 491-513. 

Mymuesli (2020). Discover our diverse range of products. Retrieved from

Randall, T., Terwiesch, C., & Ulrich, K. T. (2007). Research Note—User Design of Customized Products. In Marketing Science, 26, 2, pp. 268-280. 

Schreier, M. (2006). The value increment of mass-customized products: an empirical assessment. In Journal of Consumer Behaviour, 5, 4, pp. 317-327. 

Thompson, D. V., Hamilton, R. W., & Rust, R. T. (2005). Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing. In Journal of Marketing Research, 42, 4, pp.431-442. 

Zipkin, P. (2001). The Limits of Mass Customization. In MIT Sloan Management Review,42, 3, pp. 81-87.