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Ecommerce & Data Science

Since its arrival in 2001, Ecommerce has changed a lot. In initial days, one could sell anything and make good profit. Competition was least and everyone could not afford technology. Limited access to the product, left end consumer with almost no choice.

With affordable technology reaching everyone through smart devices has turned the game on its head. With so many Millennials buying online from small home essentials to large apartments to luxurious cars, competition has limited choice. Data Science for Ecommerce is essential to remain ahead of the consumers and competition, both!

Today, a larger share of consumers is purchasing online which is resulting in generation of multiple data points at various nodes be it customer data, delivery data, logistics data, inventory data among others. With data science, this data can be managed to offer customized products, deliver products in the best optimized route and better inventory management, help companies to cut costs and generate more revenues.

Why Data Science cannot be ignored
  • Customer Churn: Companies try very hard to retain their customers. It becomes harder from Ecommerce as it is not easy for them to make this calculation. Since there is no definite action that will define customer churn, arbitrary cut-off date method is used. But arbitrary is just a best guess.
  • Customer Life Time Value: The customer lifetime value is an important Business Input that ecommerce companies base their future strategies on. Data Science helps ecommerce businesses with Predictive models that help them forecast how much month will a single customer bring into their company and in what time frame?
  • What to Sell: Ok! Now we have a great marketplace! But what to sell? Getting the customer to stay on marketplace one of the key challenges of all merchandisers. With plethora of products coming to their desk every day from promotions, page visibility and cross selling, most of them a rendered confused. Data Science frameworks help merchandisers know what their current buyers are interested in buying. Predictive models enable them to identify “Sweet Price Points” and “Volume Movement” in a given time.
  • Lack of Recommendation Engines: More ecommerce and large retail organisations are looking to base their business decisions on “Recommendation Engines”. A thorough analysis of vast amount of data that influences the customer decision making is yet to come to mainstream. Recommendation Engines are based on Data Science.
  • Tedious Methods for Customer Sentiment Analysis: With emergence of Data Science, customer sentiment analysis need not depend on focus groups and customer polling. Machine learning algorithms provide the basis for sentiment analysis. However, sentiment analysis is a fairly new area for Data Science. It is based on Language Processing to track information over various social media platforms.
  • Rising Fraud Instances: Frauds in e-commerce industry at multiple customer touch points. It includes delivery, returns, abuse of rights, credit risk etc. that ruin the reputation of the Ecommerce company. Reputation loss puts business at risk. Only way the companies can reduce fraud instances is when they can predict and arrest it.

Key E-Commerce Pulse using Data Science

Here are the top 8 key E-Commerce Pulse you can know immediately after implementing Data Science tool(s) in your selling platform:

  • One Time Buyer vs. Returning Buyer
  • Cart Abandonment Rate
  • Average No. of Orders per Customer
  • Checkout Completion
  • Customer LTV
  • Average Order Value
  • Revenue Per Visitor
  • Conversion Rate by Device

Advantages of an upskilled Workforce

Ecommerce is all about staying ahead of the competition. An upskilled force implements latest Data Science tools and techniques that help Ecommerce businesses get actionable insights based on the “Data of Sold” to know “What to Sell Next”!

How Can SkillUp Technologies help you leverage Data Science?

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