Data Science and Personalization: A Strong Bond?

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Personalized customer experiences are the new norm. If you get it right, it can be very profitable — but you first need to know what your shoppers want to provide tailored experiences.

This is where data science plays a big role. Data science focuses on generating insights from data, it’s a no-brainer to use it for personalization. In this article, we will dive deeper into how exactly data science can help make tailored experiences for your customers into a reality.

Are Data Science and Personalization Connected?

Personalization is one of the biggest drivers of customer satisfaction and engagement. The logic is to analyze customer behavior and create tailored experiences based on it. But what makes it tick?

The short answer is data. The way that a customer makes their journey through a store to find what they’re looking for can be used to understand their needs and behaviors. While it can be done manually for a couple of in-store clients, it’s barely possible for businesses with large audiences, especially online, because there’s too much data — so what do you do?

This is where data science comes in: analyzing customer data, and drawing insights from it that can be turned into decisions. Data scientists structure and analyze data to see the logical patterns in it. These patterns lead to the understanding of trends, customer interests and needs.

The more information you have, the more value you can extract from it. There are a lot of ways to get insights into what customers like, so let’s talk about the most popular ones.

Data Science Personalization: Essential Technologies Explained

The variety of technologies for data-driven personalization is astonishing. Here, we will cover the most popular ones.

The most popular AI technologies that can boost your personalization activities

Machine Learning (ML) Algorithms

With the rise of AI, new tech advancements pop up almost every day. One is machine learning algorithms. By applying AI’s ability to learn and analyze data, businesses can get valuable insights into customer’s behavior and preferences.

Four types of algorithms in particular work well for data-driven personalization:

• Clustering algorithms analyze unlabeled data and separate it into groups with similar traits. One of the most popular uses for clustering algorithms is recommendation engine development and anomaly detection;
• Regression analysis identifies relations between target data and independent variables, and is very useful for forecasting and trend prediction;
• Association rules uncover relationships between units of information in huge datasets. This allows for the creation and definition of relations between different users to determine how they relate to each other;
• Markov chains show a possible sequence of events based only on the current state of the process. Markov chains work best in combination with other ML algorithms.

Recommendation Engines

These tools gather customer data to provide suggestions for the most fitting items by constantly tracking user interactions, such as clicks and views, feeding the engine more and more data and allowing the tool to adapt to changes.

Recommendation engines can provide customers with new categories of products and more. For example, if the user abandons their cart, the engine can suggest items similar to the ones the customer abandoned.

Predictive Personalization

This approach is similar to using recommendation engines, but a little more complex. Predictive personalization uses data about a customer’s past behavior, such as what they’ve viewed or bought before, along with information from other similar users, to guess what they might want in the future.

For example, if a person often buys mystery novels, a website might suggest more mystery books to them. Or if a user watches a lot of action movies, a streaming service might recommend new action films the person hasn’t seen yet.

Customer Data Platforms (CDPs)

Customer Data Platforms (CDPs) are software systems that aggregate and manage customer data from a variety of sources:

• CRM systems;
• Transaction databases;
• Websites;
• Mobile apps.

The data collected is then used to create a full picture of the customer’s behavior and interests. A CDP analyzes this data and focuses on overlapping cases. Using Customer Data Platforms is a must if you want a complete portrait of each customer for tailored experiences.

Behavioral Analytics

A big part of creating personalized experiences is to study customers’ behavior. This process involves:

1. Data collection: Engineers need a wide range of complex data from users, including interactions with customer support and details about their journey through your services;
2. Analytical methods: By leveraging machine learning and predictive modeling, behavioral analytics reveals hidden patterns and trends within the data;
3. Actionable insights: Businesses receive valuable insights that can inform strategies, drive product development, and help understand customer preferences.

As you can see, there are several useful options to consider in terms of data science personalization. With the right combination of tools, you can achieve the customer engagement and satisfaction you need.

And we don’t just know theory — we have experience to refer to. Let’s open up our original article and look at the application of data-driven personalization along with the common development challenges and how to solve them.