Applications of Data Science in Banking

Data Science Projects Every E-commerce Company Should Do

The data is increasing with every single click on the internet. In order to make sense of this huge data and use it for company’s benefits etc, we need help from different Data Science techniques.

Every single day people buy and sell things online, with a single mouse click, but in order to keep the customers engaged with the website or to improve customer’s experience, companies use Data science/Machine Learning, i.e. on amazon website, when you are looking for a product, you see number recommendations. These recommendations generate through Machine Learning algorithms. It learns from user’s past activities and purchases. The companies store the data of every click customer make, every reviews customer read, every story customer share on social media etc, and use this data to learn about their customer or create a platform to help new customers.

How does it start?

When you are shopping online, do you ever feel like, why they have made this thing in certain way or why is this stuff showing here? or thought, how does this thing know what am I looking for? There is only one answer to all these questions and that is- Data Science. E-commerce is one of the biggest consumer of Data Science/Machine Learning techniques and those who does not implement these techniques obviously are on fall.

In this post, we will discuss about 5 main projects which, an E-commerce company should do, in order to enhance the customer experience as well as their revenue or business.

1.Recommendation System

Do you remember seeing recommendations on Amazon, Netflix or any eCommerce website? In the past few years recommendation system has taken over the internet based businesses by adding values to many businesses.

Before understanding the benefits of recommendation systems in eCommerce, let’s clear the basics of recommendation system.

Wikipedia Definition,

A recommendation system is a subclass of information filtering system that seeks to predict the “rating” or “preference” a user would give to an item.

The recommendation system is more than what above definition describes. It is used to filter choices for particular user on the basis of their past searches or other customer’s search or purchase data. It gives users a personalized view on the eCommerce website and help them to select relevant products. E.g. - While looking for a new phone on Amazon website, there is a possibility that you might want to buy a phone cover too. Amazon will decide this possibility by analyzing previous purchases or searches data of their customers.

Popular Recommendation Techniques

There are a number of ways to setup a recommendation system. Each of these techniques filter or provide recommendation in different manner. There are three main and known techniques as below-

1.Collaborative filtering

2.Content Based Filtering

3.Hybrid Recommendation Filtering

In the Collaborative Filtering, recommendations will be given on the basis of collected data about user’s activities on the website and by finding similarity between their activities with other user’s. It is the most popular technique among eCommerce companies as this particular technique does not need to know about the item before recommending it to the customer. It just try to find the similarities between different user’s interests.

Unlike Collaborative filtering, Content Based Filtering provides recommendations on the basis of user’s profile and the item description. This technique can filter out products for users on the basis of what they liked in the past.

The Hybrid Recommendation System is the combination of Collaborative and Content Based Filtering. Hybrid technique can be used in many different ways. We can make predictions separately using Collaborative and Content Based Filtering, later combine their results or make predictions using one of technique use its results as the input for another technique. One of the best example of Hybrid is Netflix.

As now, we have a clear picture about what are Recommendation Systems, we will further discuss how do they add values to businesses.

Importance of recommendations in eCommerce site

There are a number of eCommerce websites and some of them are hard to differentiate as they sell similar kind of products. Here eCommerce businesses will need to think how they can keep their customers engaged with the website/product. I bet most of you must be thinking why are we talking about this here in Recommendation system.

Imagine, you are shopping online for clothes on a e-commerce website1. The website1 doesn’t have any recommendation system implemented and for that reason as a user you have to go through a lot of different products. This might put customer off from website1, as it is very time consuming to shop on website1. On the other hand, their competitor website2 has recommendation system, resultant website2 will become more engaging than website1. Every time user click on a product, he or she will see similar or related products as recommendation on the website.

It has been observed that the more engaging a website is, the more people will shop there. This will eventually increase the revenue of the eCommerce company.

2. Customer Lifetime Value Modelling

Many of you might have heard of the term “Valuable Customer”. What does it mean? What is it that make a customer valuable?


Wikipedia Definition,

Customer lifetime value is a prediction of the net profit attributed to the entire future relationship with a customer.

The definition clearly states that Customer lifetime value modelling is, calculating how much a customer can bring to the revenue of a company during his/her lifetime. Moreover, it is a calculated figure which is predicted by the customer’s purchase and interaction history with the eCommerce website(or any other businesses)

Before we try to understand why is it important for a business to know a customer’s value, let’s see how it can be calculated.

Calculate Customer Lifetime Value

There are a number of articles which describe the steps of calculating customer lifetime value. In order to keep it simple here we will discuss the formula which was used in the optimizesmart article.

In the article states the basic formula to calculate customer lifetime value i.e

(Average Order Value) x (Number of Repeat Orders) x (Average Customer life span)

Average Order Value- Average value of all previous orders

Number of Repeat Sales- Number of times, the orders were placed

Average Customer life Span- How long a person remains your customer

Importance of Customer Lifetime Value in eCommerce site

The customer lifetime value is a predicted amount which customer will bring into the company. But how much a single customer can bring in and why do we care about this?

Lets say a company has 2k regular customers, by calculating future cash flow for all these customers, the company can predict the future revenue. Why do companies want to know their future revenue? The companies decide their strategies for future work e.g. how much they can take up or how much extra work they need to do etc, on the basis of their predicted future revenue. Not just this, but the companies can also decide on which customer to focus on. Say, customer ‘A’ would bring in 5k in revenue in the next ten years, whereas customer ‘B’, who will only bring 1K. Looking at the numbers, the companies will decide marketing strategy and will try to retain the incoming cash flow from the customer ‘A’ .

Moreover, CLV helps eCommerce businesses in many ways -

1.Defining objectives for the company- growth, expenditures, future sales, net profit etc.

2.Optimise business marketing strategies.

3.Adjusting campaign and advertisement.

4.Decide cross sell and up sell according to customer’s purchase.

5.CLV helps to decide customer acquisition cost, the cost of attracting customers.

It is one of the essential metric which needs to be taken into account in any eCommerce business. It helps businesses in deciding their spends and know about their loyal customers.

3.Customer Retention- Churn Model

Churn Model is one of the project, which every eCommerce business should consider implementing in order to add the values to their businesses. As Churn model is related to customer retention, we need to first understand what is customer retention?

Wikipedia definition,

Customer retention refers to the ability of a company or product to retain its customers over some specified period.

Customer retention is an important aspect for business but why? Once a customer go to a eCommerce website and order something there is a possibility that he/she would come back and buy more things too(only if they are happy). Customer retention helps in generating higher customer lifetime value. It is good to have new customers but existing customers bring more revenue than the new ones.

There are a number of benefits of having loyal customers -

1.Having a solid numbers for existing customers, it helps businesses to expand their market.

2.Customer appreciate your marketing strategy and are ready to try new things.

3.Real time feedback received from the customers.

4.Existing customers bring more new customers, they are the best source of marketing.

5.Customer retention also help in attracting new customers. Seeing a company give rewards and extra benefits to their existing customers, it attracts more people.

As now we know how does customer retention benefits the businesses, we will now try understand how can we achieve customer retention.

There are many ways to achieve customer retention but the most commonly used model is- Churn Model.

Churn Model

Churn Model helps identifying customers who are most likely to switch to different eCommerce website. Once identified the companies can take actions in order to keep its existing customers. Now the question is, how does Churn model identify these customers? The model can be used to calculate the churn rate and depending on the nature of business, different metrics can be used. Few common metrics are -

1.Number of customers lost

2.Percent of customers lost

3.Value of recurring business lost

4.Percent of recurring value lost

Importance of Churn Model in eCommerce

The Churn Model benefits businesses in many ways. Few advantages of implementing Churn Model in eCommerce are -

1.Churn rate can help identifying churn customers and accordingly businesses can run retention campaigns.

2.Churn Model can help business to maintain CLV.

3.It helps businesses to track the progress.

4.The inputs received from Churn Model can be very helpful for BI activities.

4. Fraud Detection

Most of the eCommerce businesses focus on acquiring more customers and generating more revenue. In order to achieve their targets, the companies want their site to be efficient. The efficiency won’t be able to save the business if the companies will fail to provide the security.

Wikipedia definition,

Fraud is a billion-dollar business and it is increasing every year.

The PwC global economic crime survey of 2016 suggests that more than one in three (36%) of organizations experienced economic crime.

Seeing that the Fraud risk is so high, the another project that online businesses should consider implementing is, Online Fraud Detection. Living in a digital world where millions of transactions happen with every single click, it seems easy to get mugged online.

There are a number of ways a fraud can happen online -

  • ● Identity theft
  • ● Chargeback fraud
  • ● Friendly fraud
  • ● Clean fraud
  • ● Triangulation fraud
  • ● Affiliate fraud
  • ● Merchant identity fraud
  • ● Advanced fee and wire transfer scams
  • The list of online fraud is huge and fraudsters are getting smarter day by day. So, in order to have a successful eCommerce business, the companies will need to consider implementing the security measurements. For example, ordering a product online and not receiving the product which was shown online. The customer who ordered the product will not use the website again and probably will provide bad reviews. This can eventually put new users off and can also affect the business revenue.

    Now the question is how can these companies detect the frauds? With the help of Data Science and Machine Learning Techniques, these fraudsters can be found easily. In order to use Data Science techniques, the companies will have to come up with a list of any probable frauds. Some examples of suspicious behaviors indicating potential fraud are -

  • ● The shipping address differs from the billing address
  • ● Multiple orders of the same item
  • ● Unusually large orders with next day shipping
  • ● Multiple orders to the same address with different cards
  • ● Unexpected international orders
  • The above suspicious behaviors can be detected using DS/ML. Some of the common techniques that are used -

  • ● Data Mining — Detection, validation, error correction, and filling up of missing or incorrect data
  • ● Time Series Analysis
  • ● Clustering and Classification to find associated groups in the data. This helps in anomalies detection
  • ● Matching algorithms to avoid any false alarms, estimate risks, and predict future of current transactions or users
  • Importance of Fraud Detection in eCommerce

    Any company who cares about their customer’s security and business credibility will definitely consider having Fraud Detection System within their company. The Fraud Detection System can help companies in various ways -

  • ● Increase customer retention
  • ● Increase company revenue
  • ● Decrease unidentified transactions
  • ● Help increasing company’s brand value
  • We saw how online companies and their customer can suffer because of Online Fraud and how this Data Science/Machine Learning can help.

    5. Important Reviews- Improved Customer Service

    Many companies use content marketing in order to attract customers but in order to retain loyal customers, it is important to provide best service possible. What does improved customer service mean here? and How can it be achieved? Also, how does data science helps in improving customer service?

    Businesses are running customer services since a long time. The traditional approach of customer service is to contact customers through emails, posts and telephones and ask them to provide feedback on company’s products and their services. These days companies especially online businesses, have ratings and reviews section on their websites for their products. But, it is not easy to read each and every given review online manually. Not just this, but sometimes it becomes difficult to make sense of those reviews also, e.g the reviews containing incorrect spellings or shorthand words etc. This is where Data Science comes into picture.

    Using Data Science techniques e.g NLP(Natural Language Processing) the ratings and reviews from the website, can be extracted. This technique helps to retrieve user reviews and understand why bad reviews were given. For example- WordClouds are a popular way of displaying how important words are in a collection of texts and N-grams helps looking for words association. These techniques and others help Data Scientists making sense of reviews.

    Once the reviews are extracted, Data Scientists can further segregate them and do Sentiment Analysis. With this information, eCommerce can efficiently maximize user satisfaction by prioritizing product updates that will have the greatest positive impact.

    Are you ready to discover the endless possibilities hidden in the sheer volume of your business data?

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