Personalization and Privacy – A Paradox

Personalization and Privacy – A Paradox

Personalization requires data collection and analysis, but data privacy often hinges on restricting these practices. Companies can and should ask and collect data transparently, keeping it safe. First-party data — audience data signals acquired directly from user interactions on brands’ own digital assets — will now become the cornerstone of successful precision marketing and personalization.

While personalization is meant to happen behind the scenes, customization is driven by the user. Depending on your industry and business, there may be ways to let users decide the level of personalization they want (and therefore the amount of data they want to share) and let them shape their own customer journey and experiences.

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%. Accenture also 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. In short, when more relevant products are displayed to consumers, both conversion and loyalty increase.

Google’s imminent end to third party cookies, and other developing changes in the consumer privacy landscape can be a game-changer for retailers that take the time to truly understand their consumer. 

Behavioral intent data is finally about to have the attention it deserves

In this new era of privacy protection, how can businesses benefit from the use of AI? One way to increase relevance is for 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. 

When brands leverage artificial intelligence tools that use collaborative filtering, they can offer consumers more relevant suggestions based on their behavior, not simply past purchases or relying on segmentation and third-party data for profiling. 

Collaborative filtering is a way to make better use of aggregate and anonymized data. It creates patterns that can be discerned from a consumer’s behavior. Instead of using demographic lookalikes based upon their demographic profile (age, gender, geographic location etc) collaborative filtering buckets consumers based upon their and others’ behavior.

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. 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.

Shruti Chawla
Shruti Chawla Reed