personalization engine

How a personalization engine works

Personalization engines are based on data science -- the study of where information comes from, what it represents and how it can be turned into a valuable resource for businesses and IT strategies. As a result, personalization tools often employ techniques like machine learning, data mining and data visualization.

Three different types of personalization engines are currently available, each operating in slightly different ways to fulfill different business needs.

• Collaborative filtering engine. This type of personalization tool gathers data on all customers' interactions with a business -- such as their past purchases, whether they purchased online or in store and when they made their purchases. This collection of information is used to draw similarities between customer profiles to predict when a specific customer is most likely to buy.
• Content-based filtering engine. This personalization software focuses on the keywords that are used to describe a product or service. Customer profiles are created to identify the type of product or service the buyer is most likely interested in according to the keywords they've searched. Recommendations are then made based on previous purchases as well as browsing behavior.
• The third type of personalization tool combines the methods of both collaborative and content-based filtering engines. This approach is sometimes the most effective since it incorporates a wider array of customer data. However, these personalization engines can make it difficult for data gathering and analysis to begin if they are not provided with enough data -- an issue known as a cold-start problem.

Regardless of the method used, personalization engines must start by creating unique profiles for each visitor in Real Time as they actively browse the company's website. As the system captures log files, tracks where people are clicking and records transaction data, the personalization software will continue to collect, add to and adjust the profile.

Artificial intelligence (AI) is necessary for effective personalization engines. AI is used to cluster and classify data as it enters the system, making it easy to discover during queries. Natural language processing (NLP) functions, like named entity recognition (NER), should also be applied -- to determine if data contains the names of people, places or products that may provide valuable insights.

AI will also help the engine whenever a user makes a query. The query intent can be predicted using the context around the domain, user and question. A knowledge graph -- also known as a semantic network -- can be used here to help identify domain-specific entities such as people, places, topics, phrases, synonyms, acronyms and misspellings.

Overall, the personalization engine must continuously self-learn and adjust the relevancy of its results to better predict user intent, which is integral for personalizing each customer experience.

In addition, personalization engines must be scalable, capable of supporting several thousands of simultaneous users (and hundreds of thousands of queries) per second. They should also show system administrators and e-commerce merchandisers how users are interacting with the system. This includes data visualization and other means of understanding individual buyer journeys -- from becoming aware of a product or service, through their final purchase.

Key components of a personalization engine

According to Gartner, the critical capabilities which personalization engines must possess are:

• Data and analytics
• Targeting and triggering
• Marketing channel support
• Testing and optimization
• Measurement and reporting
• Digital commerce support
• Customer experience support

Furthermore, in order to qualify as a personalization engine, a product must:

• unify customer data across different customer experience channels;
• produce and deliver customized user experiences through various channels;
• enable users to create customer personalizations;
• incorporate machine learning, segmentation and A/B testing when creating customer profiles.

Some of the top personalization engines currently on the market include:

• Optimizely
• Dynamic Yield
• Evergage
• Qubit Pro
• Zeta Market Platform
• VWO Insights
• AddShoppers

Benefits of personalization engines

Personalization engines can benefit marketing teams and ensure their campaigns are effective and memorable -- by automating the segmenting and testing processes, as well as the distribution of one-to-one marketing efforts. Email marketing and Content marketing assets also can be tailored to a specific customer, making simple pieces -- like receipts and newsletters -- personal notes to each customer.

Personalization engines will also increase a company's revenue, while increasing customer satisfaction by offering personalized content. The tools allow companies to present their customers with personalized recommendations while shopping, thus providing consumers with options they may not have been aware of. Tracking interaction data helps the personalization engines understand searcher intent for each individual customer, to accurately predict what the customer is likely to buy. Then at the right time and place, it can offer individualized sales recommendations with a high likelihood of converting. This also makes it easier for customers to find what they want. When properly implemented, these personalized experiences frequently increase Brand loyalty and customer retention. 

Customer data platform (CDP) vs. personalization engine

Personalization engines can be built on or integrated into customer data platforms (CDPs) to create a powerful marketing strategy. While the two solutions both personalize data and increase conversions and revenue, their use cases differ.

A customer data platform is a type of software application that provides a unified platform of customer information that can be collected, viewed or accessed by other systems. On the other hand, a personalization engine is a way of applying the context of an individual and their circumstances to create customized collateral for marketing, digital commerce and the customer experience.

The primary difference between the two is that a personalization engine is used mostly by marketers while a CDP can benefit the entire organization. For example, CDPs provide loyalty, customer support and experience teams with the ability to easily reference the most accurate and current customer profiles. While personalization engines focus on customizing a user's experience specifically while interacting over the internet, CDPs have a broader approach to data collection, and provide customer data that is more useful for an organization beyond marketing activities.