The Secret to Mass Personalization & Personalized Content with AI

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The Secret to Mass Personalization & Personalized Content with AI

The Secret to Mass Personalization & Personalized Content with AI – 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!


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reference – The Secret to Mass Personalization & Personalized Content with AI

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