During one of the discussion forums, one of the panelists had asked the audience “How do you feel about state-of-the-art technologies and their implications on the customer data?”. I had jokingly given an analogy that the new technology is no less than a creepy antagonist from the movie who used to stalk their victims with a note “I know what you did last summer”. Be it Artificial Intelligence/Machine Learning, Big Data or Internet-of-Things (IoT), most of these novel technologies are keeping a trail and capitalizing on the user’s digital footprints.
I vividly remember once during a conversation with my friend, he was bubbling with exuberance about his newly bought “Siri”. What stupefied him more was Siri’s recommendation of his exact favorite songs at his voice commands for which he was a die-hard fan since his teens. I smirked to myself because I knew the underlying reasons. Data exhaust through findings from Web search engines, likes/comments on Facebook, blogs, searches from YouTube and Twitter following, is evolving Customer Knowledge Management (CKM) that can make intelligent guesses and provide the customers with the recommendations.
Pervasive nature, openness, and ease of access through “social software” has revolutionized the way products and services are customized and marketed, based on customers’ liking. Just like the case of Starbucks in which customer’s preferences, likes, future desires, trends, and needs are captured and new products/services are developed based on the above information. On the flip side, people getting attracted through promotions and advertisements on social media could have serious implications like the case of “Fyre Fest” in 2017 – regarded as one of the biggest scams of the 21st century, in which millennials were scammed through social media (with the promise of the biggest party of the century at Bahamas island).
Nevertheless, there are endless possibilities of cutting-edge technologies especially AI and Machine Learning. It really fascinates me to note how logistic regression and the neural network can translate into a model and through regular reiteration/test/practice of the model, it can predict the outcome with higher accuracy. But food for thought – to what extent we should use this prediction as recommendation/decision-making?? The case of “Target customer recommended of baby-related products (while she was in high school!)” could give chills to any parents. It could also have life-threatening implications like the case of “Palestinian worker arrested by Israeli police because his FB post says attack them”, which was caused because of wrong selection by automatic translation software (choosing “attack them”, instead of “good morning”).
REFERENCES:
https://doi.org/10.1108/13673271311315196
https://www.newscientist.com/article/2166207-discriminating-algorithms-5-times-ai-showed-prejudice/
https://www.flixwatch.co/movies/fyre-the-greatest-party-that-never-happened/