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3 powerful ways to increase loyalty in e-commerce

First of all,
keep in mind for the rest of our life, that modern consumer is very sophisticated and what is more – super busy. Thus, showing content or sending offers that are not relevant to him is a real failure. Just imagine, a customer goes to an online store and selects a vacuum cleaner. He has around $100 as a budget for this purchase and a great desire to stick to the allotted limit. What will be his disappointment, if all the information left about himself, will lead just to the offer with “discounts for the Karcher appliances” received? The online store did not understand what the potential consumer wants or did not find it necessary to understand.

This approach can cause a client loss for this online store. In order, to prevent this from happening, it is important to make the potential consumer feel special. In offline shops, it is achieved by the consultant. He listens to wishes of the client and offers suitable goods or services. Within the e-commerce segment, personalization is a key to solving the problem of individual approach. So, read below to find out about what is it, and how it affects the loyalty of customers.

Personalization and machine learning
Personalization is a selection of content, products, method, and channel for a communication with a specific person. Marketers personalize mailings, sites, and landings, as well as advertising campaigns. All the information presented in such campaigns tells about what the company offers in a unique way to every segment of the audience. Segments are recognized by similar characteristics, such as:

marital status
behavior on the site, etc.
The data about users can be of any type. It takes a lot of time and effort to analyze such amount of information manually, as well as it will be pretty cost-inefficient. Thus, machine learning comes as a solution.

How does it work?
David needs a vacuum cleaner for his new apartment, his budget is $100 for it. He looked at several options under $100, however, he liked Phillips device the most for $115. The cost of vacuum cleaner goes beyond David’s budget for now. So all he does — leaves his e-mail address to get an email, if some special deal appears. Next — he closes the page. Within a few hours, David receives an offer with the Phillips vacuum cleaner he looked at.

The machine analyzed the behavior of David on the site: determined that he was looking for vacuum cleaners up to $100, but looked for a long time at Phillips device, which costs $115.

It is impossible to incorporate and use all the data without marketing experts. They analyze the data that the algorithms have collected in order to use them for selling. If some data about the user were missing, and now they appear, the newly obtained information will be added to the machine learning model. Thanks to the collected data, users are clustered together with a similar set of behavioral characteristics.
There were Bob, Sally, and Natalie together with David in the target group. They have a budget for a vacuum cleaner, too, no more than $100, and they also liked that perfect Phillips product as well, which does not fit into this budget. All of them live in Minneapolis-St. Paul, MN, their average monthly income is just above $4, 500. They all are married.

The task of the online store is to make sure that all of the targeted group buy vacuum cleaners of their dreams. How to solve it using machine learning?
May 6 '19 #1
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