Hyper-personalization in Advertising
Hyper-personalization utilizes artificial intelligence (AI) and data harvesting to deliver targeted advertising content that is closely tailored for each user. It can be a useful tool in an advertiser’s toolbox; however, it also raises several privacy concerns, and there is an inherent risk of alienating consumers.
Hyper-personalization is an evolution of personalized marketing, which consisted of such practices as adding a first name to an email opening line. However, personalized marketing never attempted to understand the individual preferences of each customer. Instead, it merely created the appearance that marketing messages were aimed at the individual. On the other hand, a hyper-personalized version of that same email might take a customer’s browsing behavior and location data into consideration, to offer products or services that would likely be of more interest to that particular customer. A hyper-personalized approach could also make use of real-time data, to make recommendations that were perhaps only relevant for a small window of time. For instance, a person who spent their morning googling car reviews, might be more receptive to an ad that promoted a local car dealership.
Before venturing into hyper-personalization, relevant consumer data must first be gathered. This data can be harvested from a variety of sources, including social media, browsing tracking, purchase history, consumer trends, and data from IoT (internet of things) devices. Take Amazon for instance: their hyper-personalized recommendation engine is responsible for over 35% of their conversions. The engine’s algorithm is called ‘item-to-item collaborative filtering’ and it suggests products based on four data points:
- Previous purchase history
- Items in the shopping cart
- Items rated and liked
- Items liked and purchased by other similar customers
Using these data points, all harvested from their own site, Amazon can create a detailed user profile and then email this user ads that are specifically relevant to them. Thus, greatly increasing the likelihood of success.
Once consumer data has been collected and analyzed, companies can then segment their consumers into different subgroups. These subgroups can be based on a variety of factors, including demographics, location, brand interaction history, satisfaction, average amount spent, etc. Segmenting will allow for targeted communication, which will increase the potential for conversions. When customers are given offers that are unique to them, it’s likely that a solution will surface to their problem.
Returning to the idea of using hyper-personalization to promote local car dealerships, let’s take a look at an innovative way it could be used. Imagine a car dealership wanted to increase sales, they could target local consumers who had recently searched for car reviews, directions to a car dealership, or directions to a mechanic (if they are experiencing car problems, they may respond positively to messages to buy a new car). Once the car dealership had a group of local consumers that were likely interested in buying a car, they could then segment this group into subgroups based on their income levels, demographics, and browsing behavior. This would allow the dealership to send promotional content that included the specific vehicle (perhaps even in the specific color) that would mostly likely appeal to that consumer.
There are challenges when it comes to hyper-personalization, however. Hyper-personalization requires a modern digital infrastructure. Most legacy systems fail to offer this capability, so many traditional mom-and-pop retailers (who may not have the resources to overhaul their existing platforms) have trouble adopting this new approach. There are also huge limitations that come from increasingly strict privacy regulations and consumer adoption of data protections. Consumer privacy laws, such as the California Consumer Privacy Act (CCPA) and the General Data Protection Regulation (GDPR), are putting cookie tracking at risk (the key component to tracking consumer behavior online). Free adblocking plugins and proxy servers can also be utilized by any consumer concerned about their data privacy, nullifying data collection and making hyper-personalization impossible. Users also often share devices, making it difficult to create a reliable user profile based on browsing behavior in those cases. Then there is the danger of eroding trust in brands if they appear to be too invasive. A poorly designed hyper-personalization campaign could push consumers away, if the communication makes it seem as though the brand has been spying on them. A recent survey showed that 86% of consumers are concerned about their data privacy, with 40% claiming that they don’t trust companies will use their data ethically.
Even with these challenges, hyper-personalization can be a profitable tool when utilized intelligently. Knowing the behavior and interests of your audience in real-time is very valuable, and new methods of capitalizing on this information are still being developed. Brands who are unwilling or unable to put hyper-personalization to use in their advertising strategies, will be at a disadvantage to brands who use it successfully to cleverly communicate with their audience.