End consumers are starting to perceive the usage of Machine Learning (ML) in products and services in their lives. Their reactions range from “Curious” to “Concerned” to “Alert” to “Obsessed” ! Businesses will need a differentiated strategy to address these 4 types of end consumers.
Consumers and Machine Learning
Consumers instinctively calculate the impact of an event along two dimensions . When the lights go off suddenly in the house, is it just “Me” or are “All of Us” in the neighbourhood without power supply ? A hurricane warning half-way across the world is a “Distant Issue” while a localised flood warning is an “Intimate Topic”.
Businesses deploy machine learning to innovate and ultimately for commercial gains. Depending on the area of impact on the two dimensions businesses also need to anticipate and address, in a calibrated manner the ethical and emotional impacts on their consumers. Broadly , there are 4 types of consumers: Obsessed, Alert, Curious and Concerned.
Curious Consumer
While some live to eat, most eat to live. So advances in using Artificial Intelligence based weeders to reduce the need for universal herbicides are unlikely to attract much attention. The consumers are mildly Curious as “All of Us” are likely to see this as a “Distant Issue”. Here the producers need to communicate the current levels of herbicide usage which meet current norms and then show further improvements over time.
Concerned Consumer
Billlions of dollars are spent on medical research with a significant part spent on cancer research. So when a consumer reads that AI can identify cancer cells she could be Concerned enough that this is an “Intimate Topic” for “Me”. Here the key engagement would be the potential to make it available as a diagnostic tool at my health-care provider.
Alert Consumer
Grocery shopping is mostly a chore; perhaps it is a pleasurable one when shopping for unique ingredients to cook a special meal. In any case, consumers want grocery delivery, for that matter all goods delivery, to be hassle-free and drones are on the threshold of going commercially large. In this experience of using drones in Australia, Noise Pollution has become a key concern. The Local communities are very Alert as “All of Us” are going to be impacted on an “Intimate Topic” on a daily basis.
Obsessed Consumer
If there is one ML application that has caught the attention of millions of consumers, it is facial recognition. Since it’s demo in 2005 (Yes that long ago!) for mobile devices, facial recognition has gone main-stream in mobile phones. And now consumers are Obsessed that they are tracking “Me” on an “Intimate Topic” by age and gender in a mall in Canada. In this case, the consumers were allegedly unaware of this technology being used and were suitably outraged. In the ensuing uproar, the real estate company suspended the use of cameras and a Federal investigation had been launched.
A prior consumer engagement on the initiative would have helped. Perhaps, the initiative may have been scuttled but the company would be in a better place than now. An interesting side bar question would be on the protection of the data collected, stored and inferred during the two months or so of the operation.
It would be interesting to note the reaction of the same consumers , while boarding a flight, to be told that airport authorities were using facial recognition technology to identify known terrorists.
Summary
When companies discuss Machine Learning, a lot of time and effort is spent on technical details of data models, weights and algorithms. It is a classic case of missing the woods for the trees. Executive Decision makers would be well advised to consider the potential emotional and ethical impact on customers along with the obvious commercial bene
