How has data science improved the profitability of e-commerce stores?

Before you hop on to enrol in a masters in data science in Germany, you might want to get your reasons straight.

Data science is an incredibly fast-growing industry with applications across several other industries including e-commerce. Data scientists are some of the highest in-demand professionals within the e-commerce domain.

You can check some data science eCommerce projects on ProjectPro Data Science Projects.

What’s the reason? Data science has been responsible for revolutionising the industry and making it more efficient and profitable.

However, what’s been achieved till now is too little too late. According to an eCommerce study, only 23% of retailers in e-commerce can make sense of their customer data to make informed decisions.

Another study published in KDnuggets.com reports that 50% of retailers are unable to harness their data for a business decision due to the lack of qualified data science professionals.

However, the situation isn’t unoptimistic. Data science and machine learning algorithms have been the reason for the success of e-commerce giants like Amazon and eBay.

Let’s dig deeper into how the e-commerce industry is gaining from data science applications and algorithms.

How is data science helping e-commerce industries?

Primarily, data science can help e-commerce companies by providing them with a richer understanding of their customers from their web behaviour and online purchasing decisions.

Companies can also learn more about how customers interact with different e-commerce channels and what motivates them to buy a specific product.

Using these insights to create appropriate marketing strategies can help e-commerce companies improve their sales and revenue.

Here are some of the primary ways in which data science is enhancing the performance of e-commerce companies.

1. Providing accurate recommendations for customers
2. Providing reasonably accurate customer insights that can improve cross-selling and up-selling
3. Accurately predicting the required supply chain model for deliveries
4. Defining product strategies for the optimum product mix
5. Personalise marketing strategies as per marketing goals

How are e-commerce companies effectively using data science algorithms?

There are several success stories within the e-commerce industry of data science algorithms usage. Here are the most famous ones.

1. L’Oreal uses data science to find out the effect of different chemical ingredients on skin textures and compositions
2. Amazon uses data science to single out platform defaulters and block them from misusing the platform
3. Rolls Royce uses data science to analyse the maintenance schedules for their luxury cars
4. Fruition Sciences, a renowned wine company, uses data science algorithms to determine how much to water their grape crops for their optimum growth.
5. eBay uses data science programmes to detect customer frauds and flag them to compliance officers.

You can find several more examples of successful usage of data science algorithms in e-commerce through a relevant data science programme.

The programme can also prepare you for a data science career in other industries if you aren’t particularly interested in joining the e-commerce industry.

Start searching for appropriate data science courses from reputed universities around you today to become a well-paid and successful data scientist in the future.