Data privacy is a growing concern of governmental bodies everywhere. Europe’s General Data Protection Regulation (GDPR) regulation has been enforced now for close to four years, and while the US has enacted consumer privacy rules on a state-level, there is legislation in progress to set federal-level data privacy regulations nationwide.
Ensuring that your private data is not used without your permission should be a right to individuals everywhere, yet we are leaving personal information across the internet every time we shop, binge watch or chat on social media. And, the trail of information we leave behind isn’t always malicious. Sometimes, the data we leave behind can make sure our vendors or service providers really see us as individuals, and it allows them to personalize the message.
The Netflix Personalization Effect
Netflix may be the poster child for personalized content. There was a time when, upon finishing a movie, it would ask you to provide a rating between one and five stars. I don’t know about you, but I found that to be a little too much work — especially after a binge session that went late into the night.
More recently, the streaming service simply asks you provide a thumbs up or thumbs down rating. Not only is that totally manageable, but it achieves the same end-goal, which is to let Netflix know if you liked the movie or not. Eventually it sees a pattern — you really like adventure movies, but documentaries, not so much. While the common thinking is that more data enables greater personalization, maybe it’s not always needed — sometimes you don’t need more data points but simpler and more actionable ones.
In actuality, while we may think we are being targeted as unique individuals, unlike everyone else in the world, that might not always be the case. There’s a good chance that we are being broken down into a manageable number of persona types. Maybe it's romcom watchers, period dramas or documentary lovers. We get recommendations based on the types of movies we watched and liked, and it's proving to be very effective.
Related Article: 6 Ways Marketers Need to Balance Privacy, Personalization
Keeping Personalization Simple Increases Engagement and Privacy
Not only does keeping it simple provide the most effective route to engagement, it also helps companies avoid the data privacy issues that are on the rise. After all, extreme personalization requires vast amounts of data that people may not be willing to give up so easily.
Keeping it simple in order to provide new recommendations, however, only works for companies that don’t have an ulterior motive. While Netflix only wants you to enjoy the experience to keep loyal watchers, Facebook and other big tech firms, for example, have an army of websites to get data from you at any cost. It buys troves of data in order to monetize your personal data.
When your personal data becomes the business instead of a customer service then the issue of data privacy becomes real. Unfortunately, too many people have fallen prey to this practice — where the data gathered on them is sold to third parties. This not only causes mistrust across the board, but also data privacy fines or punishments.
So beyond keeping it simple, how can companies truly looking to boost the user experience gain trust and the cooperation of users? Consider the following four ways:
Communicate the Value
People are willing to share information about themselves if the sole intent is to boost their experience, and they need to see the value in doing that.
Whether you’re an ecommerce site, a bank or a streaming service, you should let customers know how the information they are sharing is benefitting them.
For any business collecting customer data, there should be a customer disclaimer, letting them know that their data will never be sold to a third party and that outlines the measures you are taking to keep their data secure — whether through encryption, data aggregation or by destroying data once it is leveraged.
Anonymize the Data (De-Identify)
Targeted data does not have to indicate the name of the customer, and there are ways to keep the individual anonymous while still developing a customer profile of likes and dislikes.
Leverage Synthetic Data in Your Algorithms
Insights about customer behavior, buying or activities are formed when machine learning algorithms are fed lots of data to uncover specific patterns. Not all of this data, however, has to come from real customers.
Synthetic data-sets can supplement the real data to inform business insights that can help you target specific customers and provide relevant offers accordingly. For example, feeding an algorithm with synthetic data could show you that 95% of people go to a grocery store on Saturdays. With this information, you can target shopping ads or specific food brands to them on Fridays so it’s fresh in their mind on shopping day. Leveraging synthetic data, you are not infringing on customers’ data privacy since the data is anonymized.
Conclusion: Consumer Data's Not the Product
Despite growing data privacy requirements, targeted offers and content that show customers that the companies they do business with understand them on a personal level is now tablestakes. Yet, when customer data becomes the product, it’s crossing the lines. It’s no longer a personalized customer experience, but a monetary gain.