Data Privacy Concerns Usher in New Era of Hyper-Personalization

Privacy has always been a hot topic, but recently temperatures have been rising. From the Facebook Files to Netflix’s documentary The Social Dilemma, we now understand more than ever how social networks and search engines utilize our data not only to sell us products and services but also to promote ideology, opinions, and political views. 

As consumers, enterprises and governments evolve to a privacy-first mindset, the retailers and brands that allow the consumer to dictate how, when and what they are marketed will develop a stronger, trust-based relationship that will be far more beneficial for all parties.   

Currently, many ecommerce retailers are scrambling, trying to figure out how to build these new relationships. Because big tech has seen the writing on the wall, two of the biggest sources of consumer data — Apple and Google — have taken it upon themselves to give consumers more control over the use of their personal data.  Beginning with iOS 14.5, Apple started requiring app users to opt in to allow apps to track the user across other apps and the internet. 

This new effort to protect Apple consumers’ data has been mostly lauded yet comes at the expense of, most notably, Facebook/Meta, which has relied on cross-app tracking to sell advertising to billions of consumers. Facebook remains one of  the largest advertising and customer acquisition platforms globally, even though retailers and advertisers’ customer acquisition costs have skyrocketed while views have plummeted.


Together with Google’s imminent end to third party cookies — online trackers that allow advertisers to target consumers with product suggestions based upon previously visited  websites or ecommerce sites — these major developments would seemingly be a big win for consumers and a potential bottom-line problem for retailers.  

On the bright side, these changes in the consumer privacy landscape can be a game-changer for retailers that take the time to truly understand their consumer.  The ones that cultivate these relationships will have a brighter future by using Artificial Intelligence (AI) and Natural Language Processing (NLP) to gain a greater understanding of what their customers want. Accenture found that 91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them, meaning that some understanding of an individual’s personal preferences is beneficial to all.

According to Boston Consulting Group, businesses that are able to deliver personalized, relevant experiences to customers at multiple moments across the purchase journey achieve cost savings of up to 30% and revenue increases of as much as 20%.  But how can businesses benefit from this in this new era of privacy protection? One way to increase relevance is for online retailers to leverage AI to understand how to display exactly the right products or ads the consumer will find valuable, based upon their behavior profile rather than their demographics. 

Google is testing a new product called Topics API that uses Interest Based Advertising (IBA) to give internet publishers data on a consumer’s interests based upon her online behavior.  For example, if she has been searching for kitchen renovation tips and scouring interior design sites, she may be presented with an offer for kitchen cabinets or new sofas.  This methodology uses only the consumer’s top five searches each week, and then that data disappears after three weeks.  Overall, IBA makes it difficult for advertisers to pinpoint one specific user, which ultimately protects consumers’ identity.

Behavioral intent data is definitely about to have its time in the sun. Retailers can look to Spotify’s AI for inspiration to develop winning strategies to offer personalization in this way. Spotify uses Collaborative Filtering to help it make highly personalized music and artist recommendations.  Essentially what collaborative filtering does is that it creates patterns that can be discerned from a consumer’s behavior.  Instead of using demographic look alikes — which has been Facebook’s primary way to bucket groups of consumers together based upon their demographic profile (gender, age, household income, geographic location) — collaborative filtering buckets consumers based upon their and others’ behavior.

When ecommerce brands leverage an artificial intelligence tool that uses collaborative filtering, they can offer consumers more relevant suggestions based upon their behavior, not simply past purchases or relying on segmentation and third-party data for profiling.  When more relevant products are displayed to consumers, both conversion and loyalty increase.

The future of ecommerce personalization marries shoppers’ intent with machine learning, which will not only protect individual identities but also generate higher sales, foster trust and maybe even convince them to hand over data…willingly. 

Klevu CEO and Co-founder Nilay Oza is an entrepreneur with expertise in developing innovative machine learning software. His passion is to make a difference through continuous learning-driven, software-led innovation. Oza previously served as a Project Director at the University of Helsinki and as a Senior Research Scientist at VTT, a leading research and technology company in the Nordic region. He holds a PhD in Software and Business Engineering from the University of Hertfordshire.

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