March 30, 2020
The Secret to Mass Personalization & Personalized Content with AI (2018) | AI for Business #3

The Secret to Mass Personalization & Personalized Content with AI (2018) | AI for Business #3

Hi and welcome back to another of our
videos on artificial intelligence. We know that personalization is a powerful
way of influencing consumer behavior but a few years ago the generation of
personalized content used to require much more effort on the company side. Now, the increased availability of artificial intelligence, combined with
marketing automation, made sophisticated segmentation less costly and faster to
implement. So, let’s get into depth and see how AI can improve your
personalization strategy. First of all, computers are now able to perform
profile and classification of customers, based on the data that they actively or
passively provide, the so called digital footprints. In the last few years this
practice became known as personalization at scale. McKinsey has summarized what
personalization means to customers according to this simple formula. So
let’s analyze this relationship. It is directly related with the relevance of
the content, for example consumers have reported to preferred suitable
recommendations that they wouldn’t have thought by themselves. The traveling
industry makes use of text mining to create and test recommendation systems,
based on the similarity of the destinations. For this, they use the
reviews that previous travellers have made, their most co-occurring words to
describe a particular destination. With that they can set retargeting campaigns
suggesting similar destinations to previous travelers. Another factor in the
previous formula is timeliness. Users report to prefer to be approached when
they are in a shopping mode and because nowadays people are always using their
smartphones to check their emails. Sending automatic messages at midnight
might not seem very timely, or it just don’t make sense to their daily schedule.
So, how do we make these things in a way that does not deteriorate trust and does
not interfere in their privacy? After all, what are the boundaries of digital mass
persuasion? Recent research has shown that people
usually do not behave logically when it comes to privacy. For example, we often
share intimate information with strangers, while we keep secrets from our
close ones, the so called privacy paradox. This helps to explain why that on
average just 65 like Facebook pages allow behavior
analysts to understand someone’s personality traits better than friends
do, 120 to understand them better than their family members an 250 to understand them
better than a partner or spouse. Nevertheless, behavioral science has
identified some factors that predict whether people would be okay with the
use of their personal information. I will illustrate this using some
experimental examples. What happens when we know that a friend has reviewed
something personal about us to others. We usually get upset and those norms can
also be applied to our digital life. The researchers use the dimensionality
reduction method to find groups of practices that consumers tend to dislike.
They did that based on a list of common ways in which Google and Facebook use
consumer personal data to generate ads. The results suggest that obtaining
information from third party platforms and deducing information about someone
from analytics, are more frequently disliked. Previous research has also
tested whether varying the copywriting would also affect consumer behavior,
within the same ad but with different disclosure designs. So in one design a
group of participants saw an ad that had the following copywriting – you are seeing
this ad based on information that you provided about yourself. A second group
of subjects saw – you are seeing this ad based on information that we inferred
about you and a control group saw the business as usual no disclosure ad.
Participants who viewed the ad framed as inferred behavior analytics
showed much less interest in purchasing than the other groups did. Also, tested variations of copywriting to check which was more
effective to increase conversions in an email marketing campaign. In their case
the less intrusive variation was less effective than the one obtained by an
LDA model, a type of natural language processing applied to the users reviews.
This variation said that based on your past trips their team of travelers
scientists thought that you probably have a passion for a romantic landscape,
local food or shopping. I find this example interesting because it shows how
important it is to test different variations of copywriting and looking at
how humans react to them. Artificial intelligence is helpful here because
machine learning improves our ability to predict what person will respond to what
persuasive technique, for which channel and at which time. This
combination between behavior analytics and automation is now called digital
nudging. For example, digital nudging can help some companies to reframe their
services within a personal advice approach. This can make it easier for them
to acquire customer data. Also, it helps to comply with the GDPR regulations,
concerning privacy issues and information sharing. If you want to know
more we have a video about that so check the link in the description. Also in a
recent podcast hosted by the channel behavioural grooves, Rebecca blank the
chief behavioral officer at merits, discussed in detail how customers might
react differently, according to the way companies communicate with them through
personal content. In summary there are three takeaways. Any disclosure is less
creepy and will convert better than no disclosure. Deduced information on the
customer will convert less than an open explanation of why they are seen a
specific ad and finally, this might seem corny, but trusts is a key factor in
dealing with this new world of shared information and data gathering. In
conclusion we see that personalization is an efficient way to influence
consumer behavior, especially when it’s powered by artificial intelligence. But
we have to mitigate backlashes by testing different layouts, image and text
that provide information on how your personalized content was generated. What
do you think about data privacy? For example I think it’s psychological
profiling but please let us know your opinion in the comments below!

11 thoughts on “The Secret to Mass Personalization & Personalized Content with AI (2018) | AI for Business #3

  1. GDPR making you sweat? Find out how it affects marketers in this video >>

  2. Leveraging GDPR to become a Trusted Data Steward (BCG Group) is another good reading to complement the video.

  3. Nice video. Currently I'm doing MBA in finance I have no knowledge about coding or computer language what course would be best for me??? Keeping future prespective in mind. I am thinking about taking SQL course in finance is it good or is there any other better course I should take. I would appreciate if you could help me.

  4. Youtube's AI is bogus epic fail .. When folks want to sell trash like t-shirts, rap videos and yoga-lessons it works fine .. yet when it comes to matters of planetary importance it has zero responsibility or concern … After all Youtube's owners are mostly involved in PROFIT MAXIMIZATION …

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