E-Commerce is one of those industries that collects huge volume of data. This is relating to customers, products, sales, operations, finance and supply chain. Having high volume of data has many benefits but is also challenging at the same time. E-Commerce is an industry that is constantly changing to align itself with the changes in business, economy, regulations and technology. Having a constant stream of data poses a big challenge to analyze it and derive insights. This has brought out a revolution in Data Science for E-Commerce that has a significant impact on business and technology. The value addition of having successful Data Science projects will provide a significant competitive advantage.
So, what are some of the high value Data Science projects that can be taken up?
Won’t it be nice if we can predict what product(s) the customer is most likely to buy next. To be able to predict this a thorough analysis of the customer purchase behavior has to be done and factors that influence the purchase decision have to be identified. Based on the past purchase history and future trends a model can be developed to predict the next purchases. The business impact here is not only to improve revenue but also to decide which products must be added to the assortment, which products to stock, what kind of promotions to run and how to optimize the delivery cost and the time of delivery.
Most customers purchase products from multiple categories and sub-categories. Analyzing that purchase behavior can help in identifying the affinity for products in the same category and also across other categories. Based on this data a predictive model can be developed to determine which cross sell and up sell products a customer is likely to buy and when. The business impact here is to come up with attractive product assortments which not only will induce buying but will also optimize the inventory carrying cost. It will also help in providing prior information to sellers to stock the right products. Promotions can also be designed by taking into account the affinity of the products across categories.
Product returns is one of the risks in Ecommerce that has to be managed well. Returns are not avoidable but it helps to minimize them. After analyzing the behavior of customers who correspond to a high rate of returns and also the products that are encountering a high rate of returns, the factors that trigger high returns can be identified. We can then build a predictive model to know which customers will mostly engage in returns. Further, it will predict which products will usually have a high rate of returns. By taking proper steps in this and similar data science projects the returns can be minimized and the supply chain can also be fine tuned based on expected returns.
Many customers purchase in bulk during promotions, festivals or from categories which sell products that offer cost advantage in purchasing in bulk. While this is a good news from the sales point of view, it can become a bad news from the customer engagement point of view. Therefore from the customer experience perspective, it is one of the important data science projects to take up.
It is often noticed that these bulk purchasers deplete the stock fast, especially during promotions. They significantly impact the purchasing opportunity of other customers turning them away to competition. These bulk buyers also get additional benefits such as coupons and vouchers. It will always be in the best interest of the business to provide an equal opportunity to all customers to make a purchase. It will be possible to build a model that can identify the bulk buyers from existing customers. Appropriate action can then be taken for this data science project to provide a fair playing area to all customers.
Read our Blog Top Data Science Projects in eCommerce Part 2
Coming up with the right price at the right time for the right customer is the most challenging decision in business. In E-Commerce the volume of customers and products makes this even more challenging. Making the price of products competitive enough is another area of significant challenge. Adopting a reactive approach to setting prices will not work and a more proactive approach will be required. This can be effectively done by building a dynamic pricing model that can generate the most attractive price at the right time in order to influence the purchase decision. The model will have to make real time pricing decisions by feeding on real time signals from various sources.
Read our Blog: Top Data Science Projects for eCommerce – Part 2
Similar to the data science projects on pricing a product right it is also critical to adopt the most optimal discounting strategy. It is for products which will not just increase the sales but also reduce the loss. Many a times, coming up with the right discount is a matter of gut feeling and competition rather than using maths and science to take the decision. A certain segment of loyal customers may make a purchase irrespective of the discount level. Or they will purchase beyond a level of discount without the need for additional discounting. These customers can be provided with additional benefits to increase loyalty rather than discounting heavily. Giving high level discounts that do not generate enough sales have negative impact on business. We can build a predictive model that will take into account the-
- Past purchase history
- Competitive discounts
- Customer profile, etc.,
to predict the most optimal level of discount for a product and a customer.
In a future blog we will continue listing more of these high value and challenging projects in E-Commerce.