Personalization
What it is and why it matters
Personalization uses data and analytics to tailor marketing messages and customer experiences. By analyzing preferences, demographics and behaviors, businesses can adapt communications and offers to fit individual preferences. As a result, customers feel as though brands are speaking directly to them.
How personalization evolved
Personalization has been around since the earliest shop owners tailored their services to individual customers. In the 1800s, cobblers used customer information cards to craft custom shoes based on sizes, preferences and spending habits. This personal touch faded with the Industrial Revolution's mass production. Even with the rise of the internet, personalization remained minimal, with marketing treating all customers the same.
The customer experience shifted when web-based companies like Amazon appeared. They pioneered web personalization by delivering product recommendations through a “customers who bought this item also purchased …” feature. This began the age of the recommendation engine.
Companies began grouping customer segments based on merchandise preferences that could be shown to other buyers. Segmentation paved the way as a starting point for successful personalization techniques.
Personalization in today’s world
Personalization is a keystone for building great customer experiences. Learn more with these resources.
Balancing personalization and privacy
Technological advances, including AI and machine learning, can collect more customer data than ever. While personalization is important for delivering hybrid digital-physical experiences, there’s another element that’s equally important: privacy.
Whether it’s a security breach or government use of personal information, consumers are increasingly wary about how their personal information is being used.
Marketers must find that delicate balance between tailoring content to improve customer engagement and ensuring privacy to build consumer trust and loyalty.
Who’s using personalization?
How personalization works
Personalization is mainly accomplished by using advanced algorithms, AI and machine learning. Algorithmic complexity varies from basic to advanced, but they all offer a degree of differentiation. Basic algorithms might present new products or bestsellers to a buyer. More advanced algorithms for personalization will be able to identify specific things about a customer and recommend similar items.
For example, Netflix uses an algorithm that looks at the shows you watch in real time, and then recommends shows based on your viewing data. Decision trees direct you to different paths to find more shows related to your known interests.
There are multiple approaches to personalization. Here are five ways it's commonly used.
Contextualization
Contextualization focuses on gathering behavioral data (such as past purchases, page views or clicks) to deliver personalized offers or recommendations. For example, a person visits an airline website to check prices on flights to Las Vegas. Later, the airline sends an email reminding them to book the flight or look at other flights.
Hyperpersonalization
Hyperpersonalization uses real-time data, advanced analytics and machine learning to process vast amounts of customer data. Because it uses real-time data, this technique takes personalization to a new level. As a result, offers can be adapted in real time as a customer navigates through a company’s website – delivering an enhanced customer experience.
Real-time interactions throughout the entire customer journey
Personalization offers benefits throughout all phases of the customer journey. For example, it can also be used in retention activities, such as customer support. Real-time decision-making capabilities deliver personalized experiences instantly, such as next-best-action recommendations that call center agents can use while interacting with customers.
Customer loyalty programs
Customer loyalty programs are a great way to increase customer engagement and satisfaction, plus build a positive perception of a brand. Personalization is a fundamental component when implementing loyalty programs – including techniques such as real-time decisioning, predictive analytics, customer data integration and customer journey mapping – that deliver targeted and relevant incentives and rewards.
Ad personalization
Personalized advertising allows brands to deliver targeted ads based on individual preferences and behaviors. Ad personalization is accomplished using advanced analytics, AI and real-time decisioning. Marketing and advertising data are integrated from various sources to build detailed customer profiles to ensure that timely and relevant ads are delivered across multiple channels, including websites, mobile apps and social media. Examples of well-known advertising platforms include Google Ads and Facebook Ads.
