4 Must-Have Features for Your Personalization Project
Today, personalization is more important than ever, since customers have access to content from many different channels. Personalized content talks directly to the consumer. Personalized content can attract consumers’ interest among the overload of information in all digital channels.
According to a study, there is a unique reaction in the brain, when people hear their own names.
A similar reaction happens in the brain when people get personalized messages. People tend to remember personalized content for much longer. This article reviews how personalization works, and what features you need to include in your personalization project
What is Customer Personalization
Customer personalization is a procedure for designing products and services that meet the unique requirements of each customer. Marketing personalization can be a simple personalized greeting message, or it can be customized services and offers. Eventually, a personalized customer journey can lead to increased customer loyalty and satisfaction. Customer loyalty is a measure of how likely a customer will do repeat business with you.
Generating personalized content for first time website visitors can sometimes be complicated because personalization is based on historical customer data analysis. However, you can use custom short links to get an in-depth insight into who’s clicking on your links. You can also use a dynamic linking feature of short URLs to personalize the destination of the link based on individual user behavior.
How personalization engines work
Personalization engines are often integrated with Customer Data Platforms (CDP), and Digital Customer Experience Delivery (DCED) platforms. These platforms utilize A/B testing software and content experience software to generate customized content creation and distribution cycle.
Personalization engines leverage machine learning to find the source of data, provide context to this data, and turn it into a valuable business or IT resource. Personalization engines are based in the following concepts:
- Creating unique profiles for each visitor—by collecting and analyzing log files, tracking clicks, and records transaction data. The personalization engine will continue to collect this data until the profile is created.
- Artificial intelligence (AI)—personalization engines use AI to classify data that enters the system. Classification enables you to easily find the data during queries. Personalization engines also use Natural Language Processing (NLP) models like Named Entity Recognition (NER) to determine if the data contains particular names, products, or places that can provide valuable insights.
- Knowledge graph—also referred to as a semantic network, can help personalization engines to identify domain-specific entities such as places, people, phrases, topics, synonyms, and misspellings.
- Self-improvement—personalization engines need to continuously self-learn and improve their results to enable better prediction of user intent.
Must-Have Features for Your Personalization Project
Each personalization project is different. You can use personalization in emails, videos, images, eCommerce stores, and more. But, there are features that every personalization must have.
Self-learning
Self-learning is the ability to recognize patterns, learn from data, and become more intelligent over time. AI systems with self-learning ability can automatically improve their predictions based on past experiences without human intervention.
Modern personalization engines need to have integrated AI or deep learning systems that can analyze the behavior of hundreds of millions of users. This analysis can then be used for self-learning and improvement of future predictions.
Dynamic scaling
Scaling is the ability to analyze and provide personalized content to a constantly growing number of users. Digital merchandisers, for example, need to understand the behavior of shoppers on the site and then use these insights to update the business rules.
Personalization engines use machine learning, classification, and clustering to eliminate many repetitive tasks. As a result, marketers can turn their attention to develop new solutions and improve products. Personalization engines should also be able to scale and analyze results without involving IT.
User intent analytics
User search intent is the main goal a user has when typing a search query. Common search intent types include commercial, informational, and navigational. People often use different search strategies. They use different words to mean the same things. Some people can’t spell so well.
That is where analytics comes in. You can better analyze user intent by capturing transaction data, log files and clicks. Personalization engines leverage artificial intelligence to cluster and classify data and make it more discoverable. In addition, make sure to implement NLP functions to determine if a search query refers to people, products, places, or some other entity that you find important.
Intuitive analytics
Modern personalization engines consist of variable, dynamic, and interconnected components. You can improve or optimize each component individually. However, the whole does not equal the sum of its parts.
Therefore, personalization systems should be able to show how customers and business users interact with the system. The system should visualize data, and understand individual user or customer journeys. Make sure that your personalization system includes connectors to analytics tools like Tableau to enable data engineers to interact with data with familiar interfaces.
Conclusion
Customer expectations carry a lot of weight. People know what they want, and they expect to get it whenever they want and wherever they are. To meet those demands, companies need to create personalized offers and experiences. However, they need to do it in a non-invasive way.
There are ways to show your audience that you’ve heard their needs and understand just what they’re looking for. For instance, you can use intuitive analytics, user intent analytics, dynamic scaling, and self-learning.
