Balancing Personalisation and Privacy in the Digital Age
Balancing privacy and personalisation in the age of hyper-personalised experiences. Learn how brands can protect consumer privacy with zero-party data and AI-ML.
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In the current business landscape, companies are faced with adhering to new privacy standards while simultaneously operating in a highly competitive environment where people are being intentional about how they spend their money.
Consumers today expect hyper-personalised experiences when interacting with businesses, whether in-store, online, or during customer support interactions. Capturing and maintaining customer attention and engagement has never been more challenging.
At the same time, people are wary about how their information is used and won’t purchase from companies that take liberties with their data. Qualtrics research found that 66% of consumers care about how companies are using their personal data and have cut off contact with at least one brand because of its privacy policy. Just under 60% of people believe third-party cookies should be disabled altogether.
Balancing privacy and personalisation
Data privacy entered the mainstream with the passage of GDPR in May 2018, but more than 100 countries around the globe have passed new data privacy laws aimed at protecting personal data. More recently, we have seen Apple and Google introduce significant privacy-related changes, such as allowing users to opt out of data tracking and ending third-party cookies in the Chrome browser.
In response, many brands are trying to get ahead of customer perception by implementing their new privacy policies and approaches, sacrificing their ability to leverage old data collection tactics to promise improved consumer trust.
So how can brands protect consumer privacy by providing hyper-personalised experiences?
Start with zero-party data
New technologies are helping companies get more direct feedback and preference data from their customers, sometimes without even having to ask.
Experience data is a form of zero-party data—which is defined as the information a person intentionally volunteers to a brand in exchange for something that might improve their experience with a company. This kind of data is the primary alternative to third-party cookies, and it allows brands to gather the information they need to deliver personalised services transparently. This data could be captured through pre-built surveys, the like/dislike buttons on an app, interactive polls, chats and email engagements and more.
Thanks to advances in conversational analytics technology, companies can infer and understand key aspects of experience, such as customer intent, emotions, sentiment and personal preferences, by simply analysing what those customers share as they interact with the company and during calls to a contact centre, social media posts, online reviews—and everywhere in between.
Over time, brands can build up detailed profiles of their customers that outline exactly what they like, need, and how every interaction with their company made them feel.
Enhancing personalisation the AI-ML way
AI and ML are essential tools for making sense of all the unstructured first-party data that companies are trying to wrap their arms around. Companies can take all the data they’ve captured about what people said, how they feel, and what they did and use it to model the impact of the different actions they can take to personalise or change their products and services.
The goal is to surface recommendations that can be delivered in plain language to people across the business so they know exactly what to do to design and improve the experience and provide the personalisation that customers today expect.
These modern AI tools will help the customer service teams, product designers or marketing leaders understand what actions to take next and where to invest their resources. Because they are modelled using first-party data provided directly by customers, there is less risk in terms of compromising customer privacy.
Personalised experiences lead to differentiation
American Express is a great example of a company tapping into its experience data in near real-time to understand better how it can better serve its customers.
If you go back to the beginning of the pandemic, their team began to proactively curate customer commentary from specific products, countries, and customer groups. They found that the unstructured nature of the customer commentary highlighted opportunities for new offerings that would provide value to customers under the pandemic-induced circumstances.
For instance, the company introduced streaming and internet deals to supplement travel and entertainment offers that were temporarily obsolete. It also made short-term financial relief planning offers available to support customers experiencing economic hardship.
The AMEX team rapidly proliferates those data insights throughout its business, allowing the organisations leading key customer initiatives to gauge the reception of new offers in real time. This helps them quickly iterate based on customer feedback and arrive at more favourable, personalised outcomes.
Ultimately, these innovations empower companies to meet customers’ demands for privacy while delivering personalised experiences that fit in the context of the customer journey.
