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big data measurement has high potential. Combining surveys with experimentation and big data collection can yield targeting information, sales response,
and attitude changes. Systematic experimentation
on pricing can lead to new pricing and promotion decisions. Mapping social networks and seeing how they respond to new products and new viral messages can lead to new insights and response effectiveness knowledge. Identifying in uencers, role models, and bloggers allows targeting them with incentives. This can activate not one consumer, but an extended social network.
In the balanced view, big data is supplemented by market experimentation and controlled panel behavioral research to support tactical and strategic decisions.
The Future of Big Data
It is safe to predict that big data will continue to increase. Today’s data base will look small in three years. Just think about integrating biomedical identity data. Finger prints and eye scanning can record in real time a range of behaviors wider than purchasing. India already has 750 million people using biometric IDs to access government services in its AADHAAR program.18 New insights can be gained by mining such
data. Facial recognition is becoming more accurate. This will allow automatic identi cation as a consumer enter stores and may be used to customize messaging and pricing. Even PC cameras may be able to accurately Identify speci c consumers while internet shopping and link their facial expressions and mood to the shopping experience.
Virtual design of products by users is facilitated by the internet and the data tracking of customer designs
can generate speci c new product creations and improvements or need input to enable the rm to design products (Aral and Walker 2011and Von Hippel 2009). For example, the consumer generated data base of Nike consumer shoe designs provides speci c running shoes as well as need input on style and functionality. Contests to design products, social media content, complaint les, and product ratings provide
a more comprehensive data base will support more effective product design. But the creative designer and marketing manager input will remain important in the new product development process.
Advances in arti cial intelligence will allow rapid processing of response and allow deep learning algorithms to optimize performance. For example, an AI system built at MIT nds the best creative banner
copy to show a customer after they have been targeted (Urban, Hauser, and Liberali 2014). This is done by continuing experimentation of alternate ad copy and updating of probabilities of response by a speci c consumer to banners that are designed for consumers with alternate cognitive styles (e.g. analytic vs.
holistic or rational versus emotional). Such “Machine Learning” algorithms will relieve managers of many repetitive decisions so they can concentrate on creative and innovative marketing strategies.
The future of big data and marketing analytics is exciting and should be welcomed by marketing managers. But there are many issues that will have
to be addressed. Privacy and security of data will become more salient. Can you use my credit card, click data, and facial IDs to set my price for a product? “Opt in” permission is likely to be more common and consumers will only allow the use of their data by trusted rms who will use that data in the customer’s interest. Globalization means even bigger data bases, but national political and protection policies may make the access and structure heterogeneous and dif cult to integrate for marketing analytics.
Conclusion
Big data will present big opportunities and along with market experimentation and behavioral research, managers will have new resources to draw on to develop more innovative and effective products and services. Marketing is at an exciting point in its history and big data growth will drive further innovation.
References
1. Aral, Sinan and Dylan Walker (2011), “Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer In uence in Networks” Management Science (Volume 59, Issue 9), pp. 1623-39
2. Brynjolfson, Erik and Lynn Wu (2013), “The future of Prediction: How Google Search Foreshadows Housing Prices and Sales” MIT Sloan School working paper (MIT, Cambridge, Ma)
3. Little, John D.C. and Peter m. Guadagni (1983), “A logit Model of Brand choice Calibrated on Scanner Data”, Marketing Science (Volume 2, Issue 3), pp. 203-38
4. Urban, Glen L., Gui Liberali, Erin MacDonald, Robert Bordley, and John R. Hauser (2014), in Marketing Science, (Volume 33, issue1), pp. 27-46.
5. Von Hippel, Eric (2009) “Democratizing Innovation: The Evolving Phenomenon of User Innovation,” International Journal of Innovation Science (Volume 1, Number ), pp. 29-40.
Author: Professor Glen Urban, David Austin Professor in Management, Emeritus Professor of Marketing, Emeritus Dean Emeritus; Chairman, MIT Center for Digital Business
18 http://www.innovationiseverywhere.com/this-is-aadhaar-indias-750-million-biometric-and-online-identity-database-and-its-future-as-an-ecosystem- of-innovation/
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