The complete glossary for marketers
Personalization is the practice of creating personal interactions and experiences for existing and prospective customers through the use of digital marketing technologies. A somewhat ubiquitous term, personalization is often used blanketly to describe segmentation, targeting, and optimization techniques, while purists equate it to 1-to-1 personalization or individualization.
Personalization is achieved through the deployment technologies and strategies that deliver personalized customer experiences. Over the years, as technologies have improved and strategies have been refined, users have discovered more opportunities to deliver improved customer interactions.
With personalized customer experiences, each specific user is served dynamic content using a machine learning algorithm that leverages behavioral targeting and predictive analytics. The personalization technologies used may vary based on the type of digital property, user intent, and the desired conversion action like an ecommerce sale or lead.
A segmentation-based approach requires identifying a subsegment of people within your larger audience to target a specific experience to. A good example would be targeting loyalty customers with specific messaging that you would not want to show to non-loyalty visitors.
The aim of segmentation is to produce better business outcomes by delivering more relevant customers experiences that encourage the customer behavior you want - increase conversions, revenue, etc.
One of the challenges of segmentation-based personalization is that in order to produce ever greater outcomes, you have to target smaller and smaller segments. You will eventually hit a plateau, where marketing spend to manage the segments and all the creative for them is greater than the return.
That being said, segmentation-based personalization can produce substantial results if your segments are large enough and your experiences tailored well enough.
Product recommendations are used widely within retail ecommerce and other industries. The recommendations are determined by algorithms that analyze customer behavior to curate products that may be of interest to the customer. A user is shown related products, offers for specific product categories, or similar purchases by other users. The data to create personalized content is gathered through a variety of ways:
1-to-1 Personalization describes the practice of delivering the optimal experience for each individual customer or prospective customer using all the data available about each person. Deployment of 1-to-1 Personalization requires rapid data aggregation and analysis, cross-channel deployment, and machine learning optimization.
The term "1-to-1 Personalization" is derived from the term "personalization," but denotes a focus on delivering optimal experiences to individuals rather than customer segments. The term “Individualization” carries the same connotation.
1-to-1 personalization technology aggregates a user’s behavior such as site visit history, browsing activity, geographic location, type of device, and other data. The artificial intelligence personalization engine analyzes the aggregated data and pushes the best most relevant experience for each specific user.
1-to-1 personalization is best use to achieve business outcomes when the best experience for each user is difficult to predict ahead of time. The machine learning algorithm can analyze each user, track how each user behaves, and make adjustments very quickly.
The Monetate Intelligent Personalization Engine's powerful decisioning capabilities can make complex decisions in real-time. Our machine learning algorithms decide which data is most relevant and which action will have highest probability of achieving a specific outcome such as increased average order value (AOV) or conversion rate.