Thanks to evolving technology, retail marketers nowadays have access to wide range of data and analytics tools, which is making everyone’s life easier. It gives businesses a chance to solve problems and provide excellent customer experience.
The ability of machines to process data and perform tasks in a way that mimics human intelligence such as voice and visual recognition or decision making is opening many doors for online retailers, and brand owners look for ways to utilise AI for their eCommerce businesses.
Knowing that more companies have insights to data based on customer’s behavior, competition is arising, and it is crucial to screen analytics in order to be successful in the world of eCommerce.
Having data on hand is one thing, but better understanding, strategy implementation and improvement based on it is another challenge. In this article we will discuss data science uses in eCommerce and why it is important for every online retailer.
Uses of Data Science in Ecommerce
Recommendation System
As the name suggests, it is a system which filters the information and predicts user’s preferences, while they’re browsing the Internet. It analyses people’s previous searches and screen their purchases in order to come up with relevant products. There are three main recommendation techniques:
- Collaborative Filtering- the most popular technique used in eCommerce. It collects data and finds similarities between activities and interests of different users.
- Content Based Filtering- this technique finds recommendations based on product descriptions of items liked by users.
- Hybrid Recommendation Filtering- it uses the 2 techniques described above and combines their results or use one technique’s results as input for another technique.
Customer Lifetime Value
It is a prediction of how much can one customer bring to revenue of a company during their lifetime. It is calculated by shopper’s previous purchases and their interaction with a specific eCommerce site. How to calculate customer lifetime value? This is a simple formula:
(Average Order Value) x (Number of Repeat Orders) x (Average Customer life span)
- Average order value- based on previous orders
- Number of Repeat Sales- number of times that order was placed
- Average Customer Life Span- How long a person has remained your customer
Improved Customer Care
Every eCommerce business owner knows how important customer service is. Data science helps companies improve it by extracting rating and reviews from the website. After extracting, it is possible to segregate them and do Sentiment Analysis for better understanding why bad reviews were given in the first place. It helps eCommerce businesses to efficiently go through all of the reviews and work on improvement and user satisfaction, prioritising the problems mentioned by unhappy customers.
Predictive Analytics
Consumers nowadays look for more personalized experiences and every brand needs to be able to predict what users are looking for on their eCommerce platform. Every customer interacts with a website in a different way and have individual preferences. Similar to Recommendation System, Predictive Analytics screen consumers’ shopping patterns and their interaction with the site, providing access to all of the insights. Ecommerce business then, are able to provide better customer experience and decide on minimum and maximum prices for their products.
Big Brands Use Big Data
We have analysed how big brands use data to boost their sales and achieve brand success.
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- Amazon
This world-known online retailer has access to customer’s information such as their names, search histories, payments and addresses. Amazon uses this data for efficient customer care and personalized recommendations, having all of your details on hand.
- Netflix
The video streaming service has access to all of the insights and viewing habits of all users around the world. Netflix analyses the data and chooses the content that will be appealing for any audience globally and picks specific films or series that will perform well with certain individuals.
- Starbucks
How does Starbucks stay so successful with all of their branches? They analayse big data to decide on every new opening location by area demographic, traffic and customer behavior. It helps them to determine whether opening a new store will be successful for the brand and bring them significant profit.
- Amazon
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Summary
Benefits of accessing and analysing data in eCommerce are endless and it is crucial to understand the insights of customer’s behavior and interaction with the website, in order to be successful. By collecting data, you will be able to improve customer care and provide personalized experience, boost your sales, optimise product’s price or even decide on your new store’s location. Consider using the techniques below to beat your competitors:
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- Recommendation System
- Customer Lifetime Value
- Improved Customer Care
- Predictive Analytics
