How Data Science increased the profitability of the e-commerce industry?

Data Science helps businesses provide a richer understanding of the customers by capturing and integrating the information on customers web behaviour, their life events, what led to the purchase of a product or service, how customers interact with different channels, and more.

Data Science Use Cases in Ecommerce

Data Science Applications in E-commerce

1) Product Recommendations for Customers

“The future is going to be so personalised, you’ll know the customer as well as they know themselves”   said Tom Ebling, President and CEO, Demandware

Promotions and Recommendations are highly effective when they are based on customer behaviour.Customers these days are dependent on recommendations whether it is for products to purchase, news on recent launches, restaurants to visit or services to avail. Most of the ecommerce websites like Walmart, Amazon, eBay, Target have a data science team that considers the type, weight, features and various other factors to implement some kind of a recommendation engine under the hood .The recommendation engines implemented through data science have two major motives-

  • Cross Sell-You are purchasing an iPhone 6 so you possibly might be interested in one of iPhone cases to protect it.
  • Up Sell- For instance, you are looking at a LED TV, here is the next version of the TV which is even more awesome and is available just for a few dollars more.

Data science algorithms learn the various attributes and correlations among the products; learn the tastes of customers to predict the needs of customers. Data science algorithms help in personalizing customer experience by changing the gallery pages for a specific customer or by changing the order of products in the search result of the mobile app or website.

Puneet Gupta, chief technology officer, Brillio (a US-based technology consultant and software developer) said -“With predictive analytics and the use of machine learning, e-commerce players can now derive a clear understanding of consumer behavioural patterns, spanning purchase history and performance of different products on the site.”

The best example for this is Amazon’s Recommendation Engine that uses predictive modelling. Amazon’s recommendation engine discovers and mathematically represents those discovered relationships in historical data to make classifications or predictions about future events.

2) Gaining Customer Insights for customer retention, up selling and cross selling

With changing shopping habits, diminishing customer loyalty and high expectations-gathering customer insights has become extremely important for ecommerce businesses in order to survive.

Any ecommerce website or mobile app has products to sell but the answers an ecommerce business needs to focus on is-

Who are the people buying their products?

Which location do they live?

What kind of products they are interested in?

How the business can serve them better?

What makes them buy?

The answers to all the above questions can be generally be provided by the data analysts in a group dedicated to customer insights within the product space. Data science algorithms can add value with more advanced analytics like classifiers, segmentation, unsupervised clustering, predictive modelling, and natural language processing together with topic modelling and keyword extraction.

Blue Yonder, a German Software company has developed a self-learning technology using data science tools and techniques that helps Otto (European Online Fashion Giant) – to self-learn about customers as they walk into the physical store or log in to the retailers Wi-Fi or connect with the mobile app or website. Customers are sent push notifications based on the location of stores, weather conditions and tons of other factors.

3) Defining Product Strategy for the optimum product mix

Ecommerce businesses have to deal with various questions like-

  • What products should they sell?
  • What price should be offered for the products and when?

Data science algorithms help ecommerce businesses define and optimize the product mix. Every ecommerce business has a product team that looks into the design process where data science algorithms can help the business with forecasting like-

  • What are the loopholes in the product mix?
  • What should they make?
  • How many quantities should be ordered as initial batch from the factory outlet?
  • When should they halt the supply of those products?
  • When should they sell?

Data scientists help ecommerce businesses with more advanced predictive and prescriptive analytics whereas data analysts will merely look into the retrospective analysis like how much did the business profit by, what are the products that are worthless, etc.

4) Predicting the Supply Chain model for effective delivery

For ecommerce businesses to sell products, they need the right amount of products in the right place at the right time. In ecommerce or any retail business, some products might have a very short demand window  (think of customised “Merry Christmas 2014” products on Jan 1, 2015) and if the business misses that window for a given product they might end up piling up useless stock inventory in their warehouses. Data science algorithms perform detailed analysis to develop advanced predictive models that help ecommerce businesses optimize customer satisfaction, reduce the risk factor and inform strategy.

5) Personalized Marketing Strategies

Data science plays a critical role in personalized marketing programs. Ecommerce businesses are always looking for novel ways to encourage existing customer to make more purchases or finding out strategies to attract more customers. Data Scientists can contribute to it through ad retargeting optimization, channel mix optimization, ad word buying optimization, etc. By designing data science algorithms for employing these various strategies, data scientists can help an ecommerce business reach dizzying heights which will earn worthy rewards for business.

Data science is at the core of ecommerce business and can also be used for Fraud Detection, Web Analytics, and HR.Can you think of any other data science applications in the ecommerce industry that are revolutionizing e-tailing? Let us know in comments below.