Leveraging Data Science in eCommerce: 7 Projects to Try

As an online retailer, how can you improve your business? Of course through providing a better customer experience. An e-commerce company needs to have a well understanding of the following factors:

• Latest trends
• Niches in which they should focus more
• Customer preferences
• User interface- whether it is simple or user friendly

How do e-commerce businesses do that?

Through analyzing data with the aid of machine language, through the aid of various data science projects.

Usually, online platforms like Amazon are visited by millions on a daily basis. For them, more clicks mean more data. All these data cannot be analyzed by the human workforce. For example, take the case of customer reviews. Each day thousands of reviews are being uploaded for each category of products. Some of them are real, some are fake. In order to understand them accurately, we need to implement a data science projects for e-commerce. This will help in better decision making.

Top 7 E-commerce Data Science Projects That Can Benefit Online Retailers

Here we are discussing 7 e-commerce data science projects for that every e-commerce business should be doing so that you could become one of the top players in this industry.

1. Pricing Optimization Strategy 

When it comes to pricing, an e-commerce company should offer better than what is offered by their competitors. In addition to that, we also need to consider other price-determining factors like cost analysis and market segmentation too. Using all these analyzed data points, e-commerce businesses can come up with their price optimization algorithms with the aid of e-commerce data science. This can also help the retailers to predict how customers will respond to the new price ranges. This way through using the proper pricing strategy they can improve the overall conversion rates.

A perfect example of an e-commerce company with a good pricing strategy is Amazon. They offer comparatively lowest prices in the industry for their products. Its quick-delivery pricing offers customers the delivery of products within two days shipping if they can pay an additional premium. Also, their dynamic pricing is making its competitors far behind. It is estimated that amazon changes product pricing every 10 minutes.

In case if you are wondering how can your company reach margins if the products are sold in such rates, Amazon scan teach you the solution also. You can yield a profit by selling related services or accessories.

2. Recommendation System

Customers love recommendations. An e-commerce website can access the shopping behavior of the user. You can check the

• Past behavior
• Purchase history
• Average time spent on each product 
• More browsed categories
• Profile info

From the above data points, you can make use of any of the recommendation techniques below:

Collaborative Recommendation: Here recommendations are made by comparing the behavior data of the intended user with other users.

Content-Based Recommendation: This recommendation is made purely based on the customer’s past behavior and profile info.

Hybrid Recommendation: A combination technique of both collaborative and content-based recommendation.

Using these techniques with the aid of data science, you can predict the interests and future behavior of the customer, thus you can show them to your customer whenever he/she visits the online shop. You can also sell add-ons.

For example, say a customer wants to buy a new mobile phone. They checked out various mobile phones on your e-commerce platform. This will give you all the data needed. Thus you can provide the user with a list of mobile phones that matches their budget and all other features they want in the recommendation list. This will make the customer impressed and there is a higher chance that he/she will buy it. You can also sell additional accessories like an earphone to them using recommendation features

3. Customer Service Improvement

For every e-commerce company, customer service complaints are a teacher, when it is not resolved in time. Making customers go through different stages of automatic customer service can sometimes result in losing a potential customer. It becomes extremely hard if you are a large company with a large volume of customer-related data.

E-commerce scraping is a good option in such conditions. Scrap all the reviews, complaints and other feedback given by the users. Analyze them with advance data science techniques like WordClouds and NLP(Natural Language Processing). This will give you a complete insight into the customers. Thus you can provide them with on-point solutions and hence improving the customer service.

When it comes to real-life again Amazon is showing us how to be better in customer service. Whenever a customer faces an issue, they have a dedicated help page where you can post your complaints and can expect a reach out in real-time. They have all the info on your previous purchase review and complaints with them to make things better.

4. Customer Lifetime Value Modelling

Customer Lifetime Value Modelling is aimed at generating maximum revenue from a customer within his/her customer life cycle using a prediction of a future relationship with the customer. The online retailer can also cross-sell or upsell various products to the customer. This predictive analysis is carried out using the scrapped data which represents the customer’s purchase behavior and interactions with the aid of e-commerce data science. The formula is,

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

For example, take the case of your online business. Using the above customer lifetime modeling, you can predict how much revenue each customer can bring you. Using this you can plan your future strategies.

5. Fraud Detection

Even though all e-commerce platforms have their own strict rules and regulations, fraudsters always find some loophole in it. It mostly happens with return policies. There are also sellers who post fake reviews to build their product reputation or tarnish the competitor company. If you are a big company it will be difficult to follow up and find these fraudsters. That’s where e-commerce data science comes to rescue. Through scrapping the e-commerce data and analyzing them you can find out the fraudulent incident happening in your business.

If a customer is making multiple returns or false claims to obtain money from your business, you can for his identity through spotting anomalies in the data obtained through scrapping in real-time. 

6. Inventory Management

Inventory management is a difficult job for giant e-commerce companies spreading over a large area like Alibaba or Amazon. They will need multiple large warehouses. So, in order to keep the most popular items in stock at the right time, you will need the help of an efficient data algorithm. 

For this, the business has to collect data on the most popular or demanding item on each season or region. Then check its availability. You can stock it in your warehouse when it is available in bulk. This way you can ship them in time when the orders come in.

7. Customer Retention

Retaining a customer is two times harder than getting a new customer. The chances of losing a customer after his first buy are very high. As every company needs its customers to remain loyal.

Companies can implement Churn Model for identifying customers who are most likely to shift to another eCommerce website. This will give you an insight into why the customer is choosing the other one. For implementing this model you will need to analyze the scrapped e-commerce data using different algorithms. 

Using this insight you can try out various techniques like discounts, special discounts or offers, more filtered recommendations to bring the customer back.

Data science applications for e-commerce can help in the growth of e-commerce companies to a large extent. Through implementing the right action at the right time using the right data can save your business, even bring you to the top of the e-commerce industry.