20 Jul 3 Examples of Hyper Personalized Marketing Campaigns
Creating personalized customer experiences has become a winning strategy for brands in engaging customers with their brands and products. In our content-driven and channel fragmented digital world, it means providing each user a unique content experience – one that matches his or her interests – across all your marketing touchpoints (email, social, web etc). How can brands contextualize their rich media assets for different customers? And how can they do this at scale? We handpicked 3 examples of successful personalized marketing campaigns from which we can draw inspiration.
While personalization has been a marketing trend – and driver – for many years, it exists in many forms and has become more and more individualized over time. The practice is not only a necessary, but a powerful tool for engaging, converting and building relationships with customers. Today’s marketing strategies extend far beyond conventional use cases such as segmenting lists, using a customer’s first name in a promotional email or offering relevant ads on a website. To deliver truly personalized customer experiences to each unique individual, companies are finding more innovative ways of tailoring the online experience. One example is that of “hyper personalization,” a marketing personalization approach that combines customer data, artificial intelligence (AI) and automation to display the most relevant content for every user
What is hyper personalization?
Hyper personalization, also referred to as one-to-one marketing, involves leveraging all forms of customer data, such as location, online behavior and user preferences, in order to display relevant content and customized messaging to customers, at scale. This type of personalization reaches a level of granularity that allows brands to more easily capture a customer’s attention. By identifying the customer’s interests or needs, you can for example, not only provide a personalized product recommendation, but also customize the content – such as the title or image that accompanies the product – in order to entice that specific user. Hyper personalization therefore goes beyond showing the right product at the right time – but also within the right context and sometimes creative approach – by serving a combination of content that will effectively compel the user because it is specifically relevant to him or her.
While there are many examples of successful personalized marketing campaigns, we highlight three that show the true power of customer data and technology in creating exceptional content experiences.
3 examples of hyper personalized campaigns
Deutsche Bahn: Combining the worlds of programmatic and creativity
German Railway company, Deutsche Bahn, is a prime example of how brands can creatively respond to customers’ interests and pain points. Their social media marketing campaign, ‘No Need to Fly,” encouraged Germans to book train tickets to travel within their home country rather than take an expensive flight to a remote destination. Using an AI algorithm, the company identified what destinations people were searching for when making travel plans, and identified their German lookalike photographs. They then juxtaposed these iconic international destinations with their similar-looking German locations, and using customer data on social media like Facebook and Instagram, targeted travel enthusiasts.
With geo-targeting and AI, the company was able to pinpoint a user’s current location, as well as their nearest and desired destination airport, and in real-time, deliver a side by side price comparison between those two destinations. With thousands of dynamic and personalized ad variations produced, the No Need to Fly marketing campaign was highly successful, resulting in an +850% click through rate as well as a 24% increase in sales revenue for Deutsche Bahn, as travelers, unsurprisingly, opted for the cheaper train ticket to the location that looked almost identical to the exotic landscape they originally searched for.
Netflix: From personalized recommendations to personalized product images
Netflix is famous for its powerful recommendation engine, so it’s not surprising to see it appear in our top 3. The online video streaming company works diligently on providing its users a unique customer experience, recommending titles of TV shows and films that might interest users based on their viewing history and rankings. The company continues to bolster its recommendation engine, using machine learning algorithms and data to enrich its personalization mechanism. One notable example of how it does this can be seen in the number of ‘landing cards’, or thumbnails Netflix creates for a specific movie or TV show.
For the same TV series, Netflix creates a number of teaser images and displays the one that will best appeal to a specific user, thereby maximizing their interest and click through rate. This type of hyper-personalized marketing is evidenced by the huge success of Stranger Things, which leveraged data to present different artwork or imagery highlighting the same title but to different Netflix subscribers. For example, one person may receive an image showing an actor that they recognize, while another person sees an image that captures an action-like or horror-like scene depending on the genres or themes of movies they typically watch on Netflix.
These personalized images are a perfect example of how brands can optimize the customer experience using customer data, going beyond recommendations that are simply based on an audience with similar tastes and characteristics, but rather on an individual’s unique interests and preferences.
O2: Delivering marketing ads with personalized messaging
UK telecommunications provider, O2, is another example of a brand performing hyper personalized marketing. The company leverages customer data in order to deliver customers personalized ads with tailored messaging. On social media, customers can, for example, receive a marketing ad with the same image, but with a slightly different offer. For example, a user whose contract ended would receive a different personalized message from someone approaching the end of their contract or who had recently upgraded their phone. The company also extends its personalized marketing campaigns to video ads, generating more than 1,000 versions of a video with messaging varying in real-time according to the user’s device and location. These personalized ads perform 128% better in terms of click-through rate than generic video.
By dynamically changing the content shown to customers, whether that’s by using the same imagery but adapting the messaging or offer – like in the case of O2 – or by illustrating different imagery for the same offer – like in the case of Netflix – you end up with a similar outcome: you boost real time conversions by creating experiences that are specifically customized to the context of your customers.
Powering marketing personalization at scale
As we’ve seen, brands that are hyper personalizing the user experience to maximize sales or increase user engagement are giving new meaning to data and content. In each case, we observe the importance of personalizing the content experience – the creative media used, the message displayed, or a combination of both – in order to resonate with users more effectively.
But creating these one-to-one experiences in real time across all touchpoints requires investment in data, tools and content. In order to streamline and scale your personalization efforts, you need to switch from a manual to programmatic personalization strategy. That’s where Digital Asset Management (DAM) and Digital eXperience Management (DXM) come in. DAM platforms allow you to house all your media files and content in one central hub and connects to your entire marketing ecosystem while DXM ensures that when this content is delivered, it is personalized in real-time using data-driven and even AI-powered contextual adaptation. When it comes to hyper personalization, these technologies help you do the heavy lifting as you avoid the manual creation of hundreds of variations of an asset, and instead benefit from automated dynamic adaptation of content, which can vary according to any user context (location, preferences, behavior etc).
To find out more on how DAM & DXM are uniquely placed to help brands achieve their personalization goals, check out our latest article: supporting your personalization strategies with a DAM.