Why Data Science is Important for E-Commerce

by Guest Author on August 1, 2019

in Guest Posts

If you want your company to succeed in the e-commerce industry, you need to invest in data science and analytics.

Why?

Today’s advanced technology has allowed us to use a wide range of machine learning and data analytics tools to analyze the data your company has collected, in order to produce valuable insights about your business processes, customers, and products. Advanced algorithms built by data scientists have allowed computers to process information in ways that a human would, in order to make deductions faster than a human could.

You likely have amassed a large amount of data on your clients from their visits to your sites, in Google Analytics and Search Console, and other data collection forms. However, if you allow your data to sit and choose to do nothing with it, you will fall behind. Because the utilities of data science and analytics are much more accessible in today’s society, competition among ecommerce sites is increasing. Through data analysis, companies will have a better understanding of customer behavior. Companies that understand their customer’s behavior can improve their strategies, and will rise above the competition, while you will fall behind.

Here are reasons why data science is important for any ecommerce retailer. 

Customer Journey Analysis

A Customer Journey Analysis is a helpful tool that allows a company to see its product and services through the eyes of the customer. This journey is mapped out, and is a summation of the experiences the customer took while interacting with the company. This mapped out journey can be analyzed in context, to draw valuable insights about the customer’s experience. Sophisticated analysis of this data can be used to improve business processes that will allow customers to achieve their objectives faster and more efficiently, which will in turn lead to greater conversions and purchases.

Recommendation System

A recommendation system is a process in which information is filtered to predict user preferences while they are using the internet. This system analyzes a user’s previous internet searches in order to screen their purchases to provide them with the most relevant products – the products that they are most likely to buy. Customer preferences are drawn from cookies in order to provide this service. 

Predictive Analytics

Because every customer that comes upon your website interacts with it in a different way, ecommerce companies must account for preferences. Predictive Analytics uses customer data to analyze a customer’s shopping patterns and interactions on the site. This insight can allow companies to access insight such as what type of product pages customers spend the most time on, which products lead customers to actually place it into their shopping cart, and which pages on the site cause the customer to lose interest and exit from the site. Companies like Amazon use Predictive Analytics to create more efficient personalized recommendations and customer care, and even use it to send limited deals to customers in their inbox based on these insights.

Guest article written by: The 4 Sights Team

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