A Machine Learning-driven organization may say things like…
“We deliver content curated for the individual.”
“People like you also liked <insert name of recommendation>.”
“We predict that you’ll respond better to text messages than a phone call.”
“We seek to get to the N of 1.”
Great. But that’s no longer good enough.
As a music listener, there are times where I will turn on Vivaldi, Dave Matthews, or Taylor Swift (…like you don’t?). Yet one day, pulling up Spotify on my work computer, I clicked on a playlist called “Recommended for you.” The first few songs were nice, soft classical pieces that helped me settle in for heavy focus session on my work. But then the playlist took a turn for the unexpected — “Louis and Dan and the Invisible Band” (a great children’s music duo – I might add). I nearly spit out my coffee as the rockers passionately conveyed their desire for hot dogs.
Seriously? I’ve never listened to kids music on my work computer. In fact, I have only ever listened to soft piano music. You can do better than this, Spotify!
Now, Spotify wasn’t completely wrong. We do listen to kids music occasionally at my house. But that is almost exclusively on our Kitchen Echo device after 5pm on weekdays. So the content wasn’t “wrong” for the individual in general. What was wrong, was the time and the place. As humans, we are complicated. We have moods and sometimes we have routines. Therefore, the traditional data science approach to treat us all as one-dimensional vectors will largely miss the mark.
We are in the Post-Individual world. What is true for an individual now, may not be true for that same individual 5 hours from now. And data scientists need to begin taking situational clues into account for their applications.
One good example is Apple. Iphone users may have noticed that upon connecting to a specific bluetooth device, or joining a specific wifi, the user is prompted with an “app” recommendation commonly associated with that context event. Traditional data science might say “Your favorite app is Reddit – so let’s present the Reddit app!” Apple knows, however, that when I connect a bluetooth speaker, I am not looking to pull up Reddit.
Many digital companies are aware that nothing frustrates customers more than those who purchase a product or service at full price, and then a day later are served an ad with a discount. Traditional data science may have predicted that “this person is likely to convert.” But a smart digital marketer will have hooks in place to control the timing of the ad based on context events, and will even suppress the ad when appropriate.
Companies can begin to move beyond the N of 1 (maybe toward an N of 0.1, I guess) by gathering a set of situational data which can help describe a person’s mood or patterns of behavior. There is plenty of digital information made available from web experiences and even more from IoT devices. Life events are critical sources of data that can change a person’s values, power, and ultimately decisions. A birth, home purchase, accident, divorce, or graduation are all major events that will fundamentally change a customer’s needs and availability. Companies should look to capture all of this data, and store it in real-time. Rules and models can be deployed that trigger an immediate recommendation upon recognizing a particular pattern or event. This way, we don’t simply have a “Customer Record,” we have a record for every possible mood for that customer, and a set of models and scores for that mood. Perhaps a set of records for different times in the day for that customer, so that we can recommend the best experience or service for that customer at any given moment. Learning algorithms can be deployed to learn routines about the individual and even understand which devices are being engaged with at various times throughout the day. Whatever it may be, remember that humans are complicated. Unpredictable (pun intended). But our ability to capture situational context will help create moments of comfort (and dare I say trust) between a brand and the individual.
(originally published to LinkedIn 5/31/2022)
(Photo Credit: Kat B Photography, licensed under CC BY-NC 2.0 )