How Data Science is transforming the Ecommerce industry

 

According to a recent SalesForce study, e-commerce store users are 4.5 times more likely to complete a purchase process after clicking on a recommended product. Another Bariliance study reveals that product recommendations make up about 31% of all revenues from the worldwide Ecommerce industry.

However, personalized product recommendations from well-known sites such as Amazon and eBay are just one of the many ways that Data Science and Machine learning technologies are transforming the Ecommerce customer experience.

Beyond product recommendations, data science is impacting the Ecommerce industry in many other ways, including customer service, digital reputation and marketing.

This post proposes a summary of the impact that Data Science is having on different areas of the world of Ecommerce.

 

Cross-sales

 

When we think about product recommendations, we often imagine that Ecommerce stores simply present us with different products related to each other (such as toothpaste for those who have just bought a toothbrush).

In reality, companies such as Amazon, eBay and Zalando use powerful Big Data algorithms that rely on demographic and behavioral data from millions of users to determine the likelihood that a customer who has bought product A will buy product B.

A simple example to understand the importance of this type of algorithms when recommending products is to look at the case of customers who have bought winter gloves. Imagine that we only consider the type of product (in this case, accessories that shelter from the cold), and that we do not take into account other data such as seasonality. As a result, it is likely that our Ecommerce store will end up making poor suggestions, such as for example recommending a scarf in July to a customer who bought gloves in January.

Obviously, this is a very simple example to understand the importance of contextualizing the sales process when making cross recommendations. In reality, sites like Amazon and eBay analyze a set of extremely varied and complex parameters (known as Big Data) to achieve an optimal product recommendation experience.

 

Digital Advertising

 

Data Science also has a very important role when planning Digital Advertising investments.

Ecommerce companies often invest considerable amounts in digital advertising tactics known as Retargeting, which consists in re-impacting the users who visited our website, serving them ads on Facebook and on Google affiliated pages.

The more information we have about our users, the better we can invest our money in Retargeting campaigns.

An example of how companies use user behavior data to optimize their advertising campaigns can be seen in the analysis of the use of an Ecommerce page. Machine Learning algorithms allow us to detect patterns of behavior in users who are more likely to buy our products. Once these patterns or touch points have been detected, we are able to steer our Retargeting investment towards those users whose way of interacting with our website indicates greater conversion potential.

The Customer Lifetime Value (CLV) is another indicator that we can use to guide our Retargeting investments. This piece of information can be saved in the form of Cookies that are updated at each purchase made by a customer, and in this way we can choose to serve ads to our most loyal customers through Facebook, Google GDN and RTB / Programmatic.

 

User reviews

 

Ecommerce stores usually have their own customer rating channels such as Ekomi or Trustpilot, in addition other channels such as Facebook and Google Reviews. The result is often impossible to measure and difficult to manage, due to the excessive volume of data generated by these thousands of user reviews scattered over different review aggregators.

With Data Science, we can automate the measurement of everything that is said about our brand and our products, categorizing each review according to positive and negative sentiment criteria. This allows us to get a global vision of our clients' sentiment, as well as to detect the flaws and the virtues of each product. Moreover, a data scientist will be able to replicate this analysis for competing products, to help us understand if people speak better or worse about our products compared to our competitors' products.

 

Customer care

 

Data science also improves customer service and the resolution of enquiries in the pre-sale phase. A good example of Data Science helping users during the purchase process is the Amazon system that offers answers taken from the reviews made by previous buyers. If I want to know some details that do not appear in the description of a product, I can ask it before finishing the purchase, and Amazon's algorithms are able to relate my question to answers from other users thus saving their Customer Care team valuable time.

 

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