Data Science plays a vital role in the success and progress of e-commerce businesses. Just like any other businesses, e-commerce also relies on information collected and provided by customers. This information is critical for increasing profitability of e-commerce business, as this information or data will enlighten the retailer about its customer needs & demands and how the customer is interacting & responding towards its product.
Whenever a customer opens the e-commerce website and click on any item to either know about it or purchase it the information (data) related to that specific click is tracked, catered and stored in the retailer database, which will be sorted and analyzed later by the retailer to extract the productive and crucial information about consumer’s behavior. Without Data Science, it would have been impossible for the retailers to learn about what the customer is looking for and when do he/she needs it.
How Is Data Science Helpful for E-Commerce?
Data Science is like a gold mine for the retailers because it provides all the necessary information about the consumer. This information can be used to gain the attention of new customers as well. The data in e-commerce business has been tracked via multiple “Trackable Points” which are associated with customer’s mouse clicks or touchpoints such as viewing an item and adding it to cart to make a purchase, clicking an ad or simply ratting a specific product or service. All the useful & productive data is then extracted from this massive data by identifying and using specific patterns. The business strategies, objectives, and achievements are then analyzed and future predictions are made under the light of this information.
Data Science – a Game Changer for E-Commerce Businesses
The data science has very valuable applications for the succession & progression of e-commerce business. The retailers can benefit from data science in various ways which will eventually increase their profitability by attracting new customers. It helps the retailers too,
- Identify & Discover
- Valuable Customers
- Finding Reasons Why Customer Stop making the Purchases (Churn/Drifting Customers)
- Sales Driven Strategies to increase profitability
- Automatic Data Extraction
Now let’s review all these data science applications in detail and learn about how the retailers can benefit from these applications.
Identifying & Discovering the Customers:
Customers are the key players in any business, therefore the focus of the business revolves around the needs and demands of customers. The customers are of 3 types, the new ones, the regular ones aka valuable customers, and the churn customers. Data science aids the retailers with information on all customers.
Information on Valuable Customers:
The valuable customers are the ones who affect the revenue of the business greatly. Hundreds of people visits and checks the e-commerce website in a day and it is necessary to identify the “Valuable/Potential” customers of your business. The retailers can predict and identify their potential customers by using “Customer Lifetime Value (LTV) Modelling”. This is a data science approach which not only identifies each potential customer but helps to predict the impact on future revenue. This approach also helps to predict the average future revenue, purchase values, whether an individual remains the customer or not, and how often they are making purchases within a certain time period.
The retailer can also observe the consumer behavior with LTV modelling. All these facts and information improves the marketing strategies of the business.
Finding Reasons Why Customer Stop Making the Purchases (Churn/Drifting Customers)
The new customers are expensive and difficult to acquire. And therefore the e-commerce businesses try to maintain relationships with their existing consumers. To retain the existing customers the business need to consider and put first the needs of customers. A model of data science is implemented as a “Customer Retention Strategy” driven by data, the Churn Model.
This model identifies the reasons why potential customers stop making purchases and using the products/services provided by the retailer. The model calculates & measure the churn risk and probability of individual customers impacting the revenue in a given length of time. With this information, the business can initiate the “Customer Retention Focused” email campaigns and make the customer desire changes in products/services.
Sales Driven Strategies to Increase Profitability:
Data science is quite valuable for increasing the sales of an e-commerce business. The e-commerce businesses today use “Recommendation Engines” which collects all the information on the products the customers are looking for. The recommendation engines use ‘wish lists, recommendations’ etc, based on browsing & purchasing activities of customers. These recommendation engines also measure the “Average Orders” of customers. With all this information, data science increase average sales driven by Intelligent Product Recommendations for e-commerce business.
Automatic Data Extraction:
A way to learn about customer behavior for product/services is via “Product Reviews” & “Rating” which contains useful information about the changes which customers want in the product/services. IT is difficult to extract the useful information out of hundreds or thousands of reviews and ratings. But Data science resolves this problem by offering techniques which sort and extracts the information useful for the business automatically. The data scientists use a set of techniques, known as “Natural Language Processing” to scrape out useful information out of hundreds of customer reviews. This technique allows them to learn about the reasons why a certain customer is rating their product from 1 star to 5 stars. This information is then used to improve the product/services by making necessary changes.
Ultimate Use of Data Science in E-Commerce:
Data science uses various techniques to ensure the maximum consumer satisfaction. Data science allows the retailers to learn about the changes which they require to make in their products/services and timely update of features and specifications in their product to retain the positive customer impact on business revenue in a given length of time.
Although the future behavior and impact of consumers on business & revenue is uncertain data science still helps the retailers to predict the possible sales and strategies & preventive measures to retain the potential customer traffic for the business. Summing up all the discussion, data science brings great value to e-commerce business in multiple ways.
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