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This is Personal: The Do's and Don'ts of Personalization in Tech

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Jun 14, 2021

I have a rule about using Netflix in my household: We watch dark, scary shows on my partner’s account, and humorous sitcoms on mine.

I claim that this is to maintain an easy differentiation for the recommendation algorithm and so that, depending on our mood, we can pick the relevant account and start watching right away. But to be honest, the real reason I insist on this separation is that it makes me feel good about beating Netflix at its own game.

This way, as far as Netflix knows, I am a bright person with a sunny disposition, who only watches positive, uplifting comedies of 20-minute durations, hardly ever binge-watches, and will happily return to old favorites such as Modern Family and Friends every few months. My partner, on the other hand, is a dark personality who watches crime shows and thrillers (sometimes through the night), loves getting into the minds of psycho killers, and will consume anything that matches this description.

But who are we really? Well, I am not spilling the beans here and I definitely don’t intend to solve this mystery for Netflix. 

Me 1, Netflix 0. Or so I think.

But who else do I hide my true self from? My fitness app? My grocery shopping app? Amazon? Spotify? As more and more platforms go down the path of using data to personalize the customer experience, this cat-and-mouse game will only get more interesting. 

Why does personalization work? What are its limits? How does psychology make an appearance in this complicated tech story? In this article, I’ll be breaking this down.

References

  1. Gomez-Uribe, C. A., & Hunt, N. (2015). The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems (TMIS)6(4), 1-19.
  2. Spotify Technologies, Form F1, Submitted to Securities and Exchange Commission
  3. www.theverge.com/2017/8/30/16222850/youtube-google-brain-algorithm-video-recommendation-personalized-feed
  4. MacKenzie, I., Meyer, C., & Noble, S. (2013). How retailers can keep up with consumers. McKinsey & Company18.
  5. Making It Personal: Pulse Check 2018, Accenture. Available at – https://www.accenture.com/_acnmedia/PDF-77/Accenture-Pulse-Survey.pdf
  6. https://marketoonist.com/2016/09/journey.html
  7. Berger, J., & Milkman, K. L. (2012). What makes online content viral?. Journal of marketing research, 49(2), 192-205.
  8. Haddock, G., Maio, G. R., Arnold, K., & Huskinson, T. (2008). Should persuasion be affective or cognitive? The moderating effects of need for affect and need for cognition. Personality and Social Psychology Bulletin, 34(6), 769-778.
  9. Jeong, E. S., Shi, Y., Baazova, A., Chiu, C., Nahai, A., Moons, W. G., & Taylor, S. E. (2011). The relation of approach/avoidance motivation and message framing to the effectiveness of charitable appeals. Social Influence, 6(1), 15-21.
  10. Research at Spotify: https://research.atspotify.com/just-the-way-you-are-music-listening-and-personality/
  11. https://www.forbes.com/sites/kashmirhill/2014/05/14/shutterfly-congratulates-a-bunch-of-people-without-babies-on-their-new-arrivals/?sh=6bde1841b089
  12. https://www.amazon.com/Personalization-Mechanics-Targeted-Content-Teams-ebook/dp/B00UKS4PYE#:~:text=Drawing%20on%20interviews%2C%20product%20evaluations,team%20implementing%20it%20to%20a
  13. Teeny, J. D., Siev, J. J., Briñol, P., & Petty, R. E. (2020). A review and conceptual framework for understanding personalized matching effects in persuasion. Journal of Consumer Psychology.
  14. https://www.inskinmedia.com/blog/infographic-environment-matters-improving-online-brand-experiences/
  15. https://www.theguardian.com/media/2018/oct/20/netflix-film-black-viewers-personalised-marketing-target#:~:text=But%20now%20the%20streaming%20giant,is%20targeting%20them%20by%20ethnicity.
  16. https://www.theguardian.com/commentisfree/2020/aug/08/amazon-algorithm-curated-misinformation-books-data
  17. https://www.newamerica.org/oti/reports/why-am-i-seeing-this/introduction/

About the Author

A smiling woman in a sleeveless pink dress stands against a red brick wall next to a metal fence. Part of a red sign is visible in the top right corner.

Preeti Kotamarthi

Staff Writer · Grab

Preeti Kotamarthi is the Behavioral Science Lead at Grab, the leading ride-hailing and mobile payments app in South East Asia. She has set up the behavioral practice at the company, helping product and design teams understand customer behavior and build better products. She completed her Masters in Behavioral Science from the London School of Economics and her MBA in Marketing from FMS Delhi. With more than 6 years of experience in the consumer products space, she has worked in a range of functions, from strategy and marketing to consulting for startups, including co-founding a startup in the rural space in India. Her main interest lies in popularizing behavioral design and making it a part of the product conceptualization process.

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