Consumer Product Innovation and the Opportunities for Data Analytics

Heather Burgess, Kripa Rajshekhar and Wlodek Zadrozny

Chapter 2 in Innovation Analytics:Tools for Competitive Advantage, 2023, pp 19-39 from World Scientific Publishing Co. Pte. Ltd.

Abstract: The exponential increase in consumer behavior and product marketing data has not led to a corresponding increase in innovative output at leading Consumer Packaged Goods (CPG) companies. A number of practical barriers remain to the use of Big Data by innovation teams, including limited in-house data science talent, siloed data sources, mismatch in timescales between product development and rapidly changing consumer trends, and lack of integrated e-commerce and Web/Social insight mining capabilities.On the other hand, many promising advances in machine learning, e.g., Natural Language Processing and Third-Wave AI, can help accelerate the convenient use of abundant consumer data for quicker and more efficient CPG innovation.Using a combination of web surveys and face-to-face interviews, this chapter has identified practical concerns of leading innovation executives and their recommendations for technologists, e.g., long-term trend prediction and more direct links to sales/share metrics, as well as going beyond incremental performance marketing to more creative use cases.Responding to practitioner needs, we offer examples of the most promising tools, areas of technology, and illustrative case studies. Starting with practical problems in mind, this chapter offers consumer business leaders a glimpse of the art of the possible, with easy-to-apply examples of how AI-enabled tools and processes can help synthesize and mine e-commerce/Web/Social data for smarter, faster, and more efficient growth and innovation.

Keywords: Innovation; Analytics; Business Management; Product Development; Process Innovation (search for similar items in EconPapers)
JEL-codes: O31 O32 O33 (search for similar items in EconPapers)
Date: 2023
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