Hyper-personalization leverages Artificial Intelligence (AI) and real-time data to display unique content customized for individual users. The content is displayed automatically with a high level of granularity. This is typically done via one-to-one marketing platforms.
The main goal of hyper-personalization is to provide unique and tailored messaging for each website visitor. Instead of using traditional personalization segments users with rule based personalization, you can use AI to serve each user unique content.
In this article, you will learn about cutting-edge hyper personalization systems and the algorithms that power them.
Hyper-Personalization Systems: Key Functionality
Hyper-personalization systems use AI technology to deliver personalized and much more relevant content to users by analyzing and monitoring their behavior. To create a useful hyper personalization system, you need to implement the following functionalities:
- Audience selection—define the audience you want to target for specific reasons like sales or retention. This customer selection can be based on parameters like, purchase history, customer lifetime value, spend behaviors, income levels, and brand loyalty.
- Event definition and detection—decide what events should trigger a real-time customized message. For example, events like a loyal customer posting negative reviews, a single purchase exceeding a certain threshold, or a customer approaching contract expiry. You need to have processes and tools to continuously monitor these events for further decision-making.
- Messaging decisions—event monitoring can trigger messages and actions based on predefined strategies. This includes things like achievement celebration messages leading to up-sell, special offer decisions for customer retention, or appreciation messages to strengthen the relationship.
- Channel decisions—determine what channels trigger the real-time messaging. This could be based on the customer’s historical interactions, media budget limits, or Click-Through Rate (CTR). You can also make channel decisions for a multi-channel approach like email, push notifications, and SMS.
- Response data collection and analytics—collect and analyze the response data from the customer interactions to ensure that the process is always optimized. You should send the insights from the analysis to the decision-making algorithms. Measure the results of the analysis based on predefined KPIs and benchmark values.
Hyper-Personalization Algorithms and Models
Hyper-personalization algorithms like collaborative filtering can provide a personalized experience to your users. To help you choose the best algorithm, you can find a description of each one below.
Collaborative Filtering (CF) is one of many personalised content recommendations model offerings. CF recommends relevant content to users, based on collaborative aspects like user reviews, or purchases.
Collaborative filtering can be categorized into 3 different approaches:
- User-User—recommends an item to a user if similar users liked this item before. The similarity between two users is computed from the number of items they have in common.
- Item-Item—recommends items that are similar to the ones you previously liked. The similarity between two items is calculated using the amount of users they have in common.
- User-Item—combines both approaches to generate recommendations. For example, you can cluster items or users based on their preferences.
Sequence-aware recommenders use Recurrent Neural Networks (RNN) to process a sequence of inputs. A classic example from the field of natural language processing is document classification. The document is represented by a sequence of words. The algorithm predicts possible topics, sentiments or intents. In the travel industry, the algorithm can recommend a country to visit based on the user’s past history of bookings.
Classification models convert visitor characteristics into a number that represents the probability of that user to take a certain action. Classifiers are considered to be supervised learning models. This means they use existing data for training. The algorithm tries to find the probability of response to certain triggers with predetermined characteristics or features. These predictions are then applied onto live data.
Supervised vs. Reinforcement learning models
Most personalization applications are based on supervised learning models. Supervised learning algorithms like logistic regression or matrix factorization need input and output examples to learn the correlation between them. Those examples are usually collected by a system that has nothing to do with the learning algorithm. For example, logs of the website visits.
The reinforcement learning algorithm actively participates in the data collection process. The learning objective has to match with the goal of getting the correct data points. For instance, you can give a suboptimal recommendation, if the algorithm believes that it can learn something valuable for its long term performance.
Deep learning personalization algorithms combine collaborative filtering and content-based models. Hybrid deep learning algorithms enable you to learn closer interactions between items and users.
Deep learning models are non-linear, therefore they do not over-simplify a user’s preference.
Deep learning models can represent complex preferences for many different items. This includes datasets that cover datasets from multiple domains. For example both movies, music, and TV shows.
Hyper-personalization is becoming a key enabler of one-to-one marketing campaigns. Personalization makes targeting much more accurate by analyzing large volumes of customer attributes and behavior patterns from a variety of sources. Hyper-personalization algorithms like collaborative filtering and content based models can target customers with the right message through the right channel at the right time.