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Personalization enables brands to serve their customers with segmented offerings. The goal is to ensure customers get content that fits their needs and tastes. Because personalized offerings are more relevant, customer experience typically becomes more positive. The (desired) result of personalization is increased customer loyalty and ROI.

Traditionally, personalization is achieved through manual group segmentation, created by analysts and marketers. Today, there is a branch of personalization tooling that automates the process. In this article, you will learn about the machine learning algorithms that power personalization technology, and the brands that successfully implement this technique.

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1. What is Customer Personalization?

Customer personalization is a marketing strategy that uses customer data to increase engagement. Typically, this data is fed to machine learning (ML) models which then produce profiles for individual customers or subsets of customers. Brands can then use these profiles to predict customer behavior, better understand customer preferences, and deliver curated content. 

Customer personalization enables brands to intentionally deliver specific content in specific ways to customers. For example, recommending products based on previous purchases or redirecting customers to specially curated pages.

When customers feel that a brand is paying special attention to them it can strengthen brand relationships and increase customer loyalty. This often leads to increased revenue, new customers through recommendations, and a more positive brand image. It also tends to produce a greater ROI on marketing investments since it is cheaper to retain existing customers than to court new ones.

2. Examples Of Machine Learning Used In Personalization

Many of the biggest brands today are using machine learning to personalize the experiences of their customers. For some, this personalization is part of what made them so successful in the first place. 

Here are a few prominent examples of machine learning in action:

  • Disney parks—guests are equipped with MagicBands that serve as tickets, payment methods, and room keys. These bands collect information on guest activities which is then used to deliver special offers or recommendations to guests through a mobile app.
  • Netflix—customers create profiles that collect information about viewing histories and preferences. Customers are also encouraged to rate titles. This information is then used to correlate viewer interests between titles and to serve personalized recommendations based on a combination of others’ ratings and customer preferences.
  • Starbucks—offers a customer loyalty app that records customer purchase preferences and uses those preferences to provide recommendations or serve special offers. 
  • Instagram—ML models are used to serve targeted ads to users based on posts that they have interacted with or the accounts they follow. Models are also used to help identify content deemed offensive or inappropriate through content analysis of comments. 
  • Nike—offers customers the chance to customize shoes according to personal preference. The customizations the customer selects are then used to develop preference profiles and to recommend similar products or styles. 

3. Machine Learning Methods for Prediction and Personalization

There are many models that marketers and marketing tools can apply for personalization and prediction of customer behavior. Below are a few types that are commonly used. 

3.1 Regression

Regression models enable you to predict the relationship between a dependent and independent variable. These models are at the root of many machine learning analyses and can be used to predict customer behavior, model events over time, and determine causal relationships between events or behaviors. 

There are multiple types of regression models that you can use but linear and logistic regression are the most common. Linear regression attempts to find the best fit line between a dependent variable (often customer behavior or preference) and one or more independent variables. For example, how much a customer might like a specific product based on previous purchases. It is useful for determining correlations but not causal relationships.

Logistic regression attempts to find the probability that an event occurs or doesn’t occur. For example, whether a customer makes a purchase or doesn’t. It is useful when there is no linear relationship between variables but requires large sample sizes to produce accurate results.

3.2 Classification

Classification models are models that use characteristics from known groups and apply those characteristics to new data to determine a match. There are a variety of classification models you can use depending on the type of data you have and what type of outcome you expect. For example, you can use decision trees if you have discrete variables that combine to determine multiple possible outcomes. You can use these models for natural language processing, biometric identification, and image analysis.

Decision Tree

For personalization, classification models can be used to identify content that matches customer preferences. For example, evaluating the content of newsletters that a customer has read and comparing it to the content of a proposed newsletter to determine similarity. It can also be used to categorize customers into profile groups based on profile pictures, communication styles, or other characteristics.

3.3 Association Rule Learning

Association rule learning models enable you to determine relationships between items in a data set. It is useful for determining a directional relationship between two values. For example, if a customer purchases item A, they are likely to purchase item B. However, this does not mean that events occurring in the opposite order are true.

A common use of this method in personalization is for product or service recommendations. In these cases, large amounts of customer data are evaluated and patterns between selections or purchases are identified and assigned probabilities. These probabilities are then used to recommend products that a customer is likely to be interested in.

Decision Tree

3.4 Reinforcement Learning

Reinforcement learning is a class of models in which ML algorithms actively collect and apply data. This is in contrast to supervised learning models, like those above, which are fed and trained on specific data sets. Reinforcement learning models are also known as bandit models. 

Reinforcement models require analysts to balance the collection of valuable data with the consistent application of predictions. For example, allowing some questionable recommendations through to customers to gain additional feedback and improve the model. This enables models to continuously improve their accuracy and incorporate a wider range of contextual information.

Decision Tree

Conclusion

In today’s highly demanding eCommerce landscape, brands compete over customer attention, engagement, connection, and loyalty. To draw new customers and retain repeating clientele, brands are using personalization techniques. However, manual personalization can often turn into a tedious and time-consuming process. Data scientists can help solve these challenges, by training and improving personalization and prediction machine learning algorithms.

Thank you for reading!

Gilad David Maayan

Gilad David Maayan

Author

Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Ixia, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.