Top 7 Big Data Use Cases in E- Commerce

Big data Use cases in E-Commerce Industry

#1. The use of Predictive Analytics

This analytics can be used to analyze what will be the trend and what will create a buzz in social media by predicting the future with forecasting algorithms. Also, predictive analytics is used to determine what a  customer can buy in future this can be known as sentimental analysis, which will analyze what a customer discussing e-commerce products on social media.

#2. Optimizing the Product Price  

Using real-time analytics in big data can help retailers to enable the best price for goods by tracking through the transactional records, competitors, and other things. So this is why the pricing of a particular product in an e-commerce store often varies. 

#3.Helps to Forecast the Demand

For websites like Amazon, it is very important to execute accurate forecasting on demand, because it is very complex to manage their inventories on shelves. Amazon uses some real time forecasting tools to track the historical data and those tools will have the provision of assessment in demand fluctuations. 

#4.To bring Better Customer Experience 

I. 89% of customers are refused to buy a product in an e-commerce portal, after experiencing poor service with them. 

II. 83 % of customers often require immediate customer support while purchasing a product online. 

Big data lets the business to create a 360-degree view on users in order to bring stressless and optimized customer experience by compiling the previous online/offline transactions, social media discussions, product reviews and etc.

#5.To create Personalized Stores : 

Big data can help e-commerce businesses to create dynamic websites that are filled with the relevant products by tracking the previous purchase history and browsing details of a customer.

#6.To Increase Conversion Rates and sales 

Big data can be used to personalize the purchase by avoiding the cart abandonment. A statistics report that a  huge volume of customers has been failed to make purchases at the last minute, even the product has been listed into the cart.  Businesses and retailers can use big data to offer personalized customer experience in order to prevent such cart abandonment.

#7.Can increase the decision making on micro-moments

Micro-moments are the trend and hot topic on the e-commerce industry last year, and this will be the trend of 2019 and 2020 too. Every customer looks for an immediate solution while purchasing. Around 70% of sales are made through smartphones. So retailers focused to improve the micro-moment decisions by connecting the smartphone technologies with big data analytics. Refer : medium.com

Conclusion

It is clearly shown that big data is directly impacting the increase in sales conversion, increase in customer experience, increase in revenue through advertisements, and finally increase in ROI. As listed above there is ‘N’ number of use cases can be derived while using big data in e-commerce. We have mentioned the top use cases of big data in e-commerce. 

Read more at : how big data can be used in e-commerce industry

Data Analytics Vs Data Science : Understand The Difference

If you are a learner to the big data industry, then i am damn sure you will be get confused with these two terms. That is data science and data analytics. These two terms seems to be pretty similar to each other, but as your secondary mind thinks, this two topics are having huge variations between them. So this article is for the novice people who are getting struggled to understand the difference between data science and data analytics..

The Similarity Between Data Science and Data Analytics

Before understanding the difference we have to fully accept the similarity between these two fields. Yes, these two fields are totally focused on analyzing the data. This is the only and primary similarity to both data science, and data analytics(business analytics). But, inside the data engineering cycle, this two process starts to vary between each of us, as they are occur in two different phases.

Data Analytics :

It is a systematic process, to extract valuable information’s various structured and unstructured data source. Getting the past, present and future business performance through collected business information’s. Determining and explaining the best statistical &data driven business model to the concern business owner

In simple terms :

Collecting, extracting, visualizing the business insights from various structured and unstructured business data, and helping business owners to take timely, data driven, and logical business decisions.

Data science :

Designing, developing, and deploying logical, automated, machine learning algorithms that should support any business intelligence tools inorder to analyze a huge volume of data. it is the foundation for data analysis, through which an applied business problem can be solved.

Image source : The Ultimate Comparison: Data Science vs Analytics

Originally published on : What is the difference between Data Science and Data Analytics?