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How Amazon’s Radical New Customer Experience is Also Poised to Transform Healthcare

Last week Amazon announced the opening of its beta store Amazon Go in Seattle, a radically different food and convenience store without lines, check-outs or registers. Customers simply swipe their phone with a free Amazon app as they enter the store, grab the products they want, and then go. In-store sensors, cameras and microphones capture products taken by the customer in real time and a virtual shopping cart on the app charges the customer.

Amazon Gains Access to Enormous Customer Data

For marketing and digital strategy practitioners always on a quest for deeper customer insights and ways to shape brands, messages and engagement accordingly, Amazon Go represents an incredible experiment in combining immersive, observational data with the ability of pattern recognition and the predictive analytics of quantitative methods.

Do Thick Data Finally Meet Big Data?

The relative merits of “Thick Data” collected from the field are defended by ethnographic researchers – on the basis that the hyper-qualitative data collected via immersive, observational research methods offer insights into real-life behaviors in a way that traditional research survey based on laboratory-style observational studies cannot match. User Experience (UX) research leverages these ethnographic methods with its hyper-qualitative observational destination studies. At the other end of the spectrum, “Big Data” has gained enormous attention in recent years and is increasingly being applied to gain real-life and real-time behavioral insights, sentiment analysis, segmentation and personalized offers and messaging across a wide range of settings, including social media, the airlines industry, consumer retail, financial services and the fast food industry.

With Amazon Go, qualitative thick data and quantitative big data are merging, with the new store essentially operating as an ongoing destination study, adding sensor fusion, computer vision and deep learning algorithms to the cameras and microphones of ethnographic field studies. At the same time, Amazon Go is leveraging the big data processing capabilities built by Amazon’s eCommerce business, fusing data capturing product movement, customer physical movements and characteristics and in-store sounds, into a complete picture of in-store customer behavior. As analytics and insights emerge from this always-on consumer laboratory, it will be interesting to see what type of mass personalization will result from the Amazon Go experiment.

The Value Exchange

While the technology appears to work as advertised, it still remains unclear how many consumers will feel comfortable having their every move tracked while they are shopping at Amazon Go. Surveys reveal that when data is used to improve a product or service, consumers generally consider the benefits a fair trade for their data (Harvard Business Review May 2015. Customer Data: Designing for Transparency and Trust by Timothy Morey, Theo Forbath and Allison Schoop). And of three categories of data, basic self-reported data was found to be valued the least, digital exhaust data somewhat more, and the most value was ascribed to profiling data used for predictive purposes and extracted from the above and other data sources

The Value for Customers Is an Effortless Customer Experience

The data customers provide to Amazon Go are self-reported and digital exhaust data, which are data categories found to be fair trade for consumers against a product benefit. For the data provided, consumers gain convenience and time. In busy urban areas with crowded lunch hours, this benefit is tangible. The question is how this technology will translate into other business settings and how data privacy concerns will evolve over time. Certainly for healthcare and education settings, it’s not difficult to imagine scenarios in which this type of deeply immersive tracking environment would be incredibly powerful. For example, imagine if Emergency Room waiting areas or ICU rooms were equipped with the same type of sensors and tracking – allowing for real-time tracking of vitals – simultaneously linked to big data feedback and monitoring from the cloud.

The Value for Amazon Is Leap-frogging to the Brick & Mortar of the Future

While retailers are increasingly turning digital, with robotic shelf stocking and warehousing and mobile payment systems, Amazon Go allows Amazon to enter the brick-and-mortar retail setting with all the data power of its eCommerce and cloud computing power. While it took Amazon a long time to make a profit (it finally did in Q2 2015, for four straight quarters), the tech giant looks poised to win long-term as a result of its persistent and aggressive investment in the future. While we wait to see how the Amazon Go experiment unfolds, you can sign up to be notified when Amazon Go opens to the general public.

SOURCES

https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#q=sign+up+to+be+notified+when+Amazon+Go+opens

http://www.greenbookblog.org/2015/11/16/goodbye-big-data-hello-thick-data/

https://www.quora.com/At-what-size-does-data-become-big-data.

https://freerangeresearch.com/2013/05/23/what-is-the-role-of-ethnography-and-microanalysis-in-online-research/

https://datafloq.com/read/amazon-leveraging-big-data/517

http://www.tableau.com/sites/default/files/media/Whitepapers/whitepaper_top_10_big_data_trends_2017.pdf?ref=lp&signin=ef294fa613dad52c98492bf7e9457646

http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

http://www.usatoday.com/story/tech/news/2016/12/06/amazon-go-surveillance-cameras-shopping-grocery-supermarket/95055200/

http://venturebeat.com/2016/12/05/amazon-launches-amazon-go-a-brick-and-mortar-grocery-store-that-does-away-with-checkouts/

https://www.wired.com/2016/12/amazon-go-grocery-store/

http://www.tableau.com/sites/default/files/media/Whitepapers/whitepaper_top_10_ big_data_trends_2017.pdf

http://info.usertesting.com/Modern-UX-Research-in-Action.html

https://www.dezyre.com/article/5-big-data-use-cases-how-companies-use-big-data/155

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Lone Harboe
A Director of Commercial Strategy and Innovation for Cadient, Lone is a well-rounded pharmaceutical marketing professional with over 15 years of experience at all stages of drug development and commercialization: from early stage new product commercialization; through pre-launch planning and post-launch repositioning. She has led launch teams and launch planning efforts in the US and in Europe at global, regional and national market levels. Lone holds an MBA from Middlebury Institute of International Studies at Monterey.

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