Improve Ecommerce ROI with Personalized User Navigation
Posted: Wed Dec 04, 2024 8:08 am
Each of these pieces of information can define a rule or a filter that allows you to create increasingly refined customizations, based not only on the user's online behavior (if you saw this product then you might like this other one), but also on a latent demand that adds multiple variables at the same time. How is it possible to do this?
Second step. From historical data to contextual data
First of all, the past and the present should not be information managed in separate silos. Online navigation is a contextual fact, therefore personalization cannot be based only on static and historical information but must be dynamic, that is, take into account what is happening at that precise moment.
This is especially true when managing e-commerce with a large product catalog, which does not necessarily correlate easily starting from the last product purchased. Users buy a certain product but it does not mean that they want to hear only about that. It is therefore necessary to try to reason on complex segments that take into account who the user is and also what he wants at that moment (for example: he bought a television of a certain brand, he has a certain spending potential, but now he wants a new vacuum cleaner and not a remote control!).
As we have already written in this post , there are at least 3 good reasons to focus on personalization : from increased sales to creating a loyalty relationship with the user. For this reason we must be able to intercept ever new needs. Marketing automation helps us in this : equipping ourselves with solutions that learn in real time and automate based on what is actually happening.
Third step. Moving from “who you are” to “how you are”
An example is the brand Sephora sale leads, armenia email address which, for 4 years now, has implemented a technology distributed in physical stores that allows it to classify its customers according to skin type, with the aim of offering them makeup products that are more suitable for their natural colors.
This translates into a series of suggestions for online purchases born with the assumption of supporting the customer in selecting a product closer to the need and therefore more satisfying. The classification occurs with the support of sales assistants who, simply by photographing the visitors, enrich the database with fundamental information. (The whole story can be found at this link shade-matching-skin-care-tool-boosts-brand-loyalty/ )
The Sephora case presents us with a paradigm shift. The personalization of online browsing is not just a matter of product correlation. Sephora starts from “who you are” to get to “how you are”. The further consideration to make is therefore whether it is not possible to use data to imagine segments of users who are similar to each other, with similar behaviors, which helps us to propose interesting alternatives that they are not thinking about. Are you a user of this type? This is the best answer we have developed to meet your need.
Naturally, the greater the complexity we want to achieve, the greater the effort needed to collect data, normalize it in order to automate actions that reflect our marketing and sales strategy. Working on multiple variables at the same time will require structuring a dynamic offer that updates in real time, in a virtuous circle of continuous learning. It is called artificial intelligence and it is the engine of personalization not only during navigation of the e-commerce site but, as we will also see in all subsequent phases, on direct and third-party channels such as.
Second step. From historical data to contextual data
First of all, the past and the present should not be information managed in separate silos. Online navigation is a contextual fact, therefore personalization cannot be based only on static and historical information but must be dynamic, that is, take into account what is happening at that precise moment.
This is especially true when managing e-commerce with a large product catalog, which does not necessarily correlate easily starting from the last product purchased. Users buy a certain product but it does not mean that they want to hear only about that. It is therefore necessary to try to reason on complex segments that take into account who the user is and also what he wants at that moment (for example: he bought a television of a certain brand, he has a certain spending potential, but now he wants a new vacuum cleaner and not a remote control!).
As we have already written in this post , there are at least 3 good reasons to focus on personalization : from increased sales to creating a loyalty relationship with the user. For this reason we must be able to intercept ever new needs. Marketing automation helps us in this : equipping ourselves with solutions that learn in real time and automate based on what is actually happening.
Third step. Moving from “who you are” to “how you are”
An example is the brand Sephora sale leads, armenia email address which, for 4 years now, has implemented a technology distributed in physical stores that allows it to classify its customers according to skin type, with the aim of offering them makeup products that are more suitable for their natural colors.
This translates into a series of suggestions for online purchases born with the assumption of supporting the customer in selecting a product closer to the need and therefore more satisfying. The classification occurs with the support of sales assistants who, simply by photographing the visitors, enrich the database with fundamental information. (The whole story can be found at this link shade-matching-skin-care-tool-boosts-brand-loyalty/ )
The Sephora case presents us with a paradigm shift. The personalization of online browsing is not just a matter of product correlation. Sephora starts from “who you are” to get to “how you are”. The further consideration to make is therefore whether it is not possible to use data to imagine segments of users who are similar to each other, with similar behaviors, which helps us to propose interesting alternatives that they are not thinking about. Are you a user of this type? This is the best answer we have developed to meet your need.
Naturally, the greater the complexity we want to achieve, the greater the effort needed to collect data, normalize it in order to automate actions that reflect our marketing and sales strategy. Working on multiple variables at the same time will require structuring a dynamic offer that updates in real time, in a virtuous circle of continuous learning. It is called artificial intelligence and it is the engine of personalization not only during navigation of the e-commerce site but, as we will also see in all subsequent phases, on direct and third-party channels such as.