The amount of data available online grows bigger after every click. To make sense of the huge amount of information, eCommerce business relies on various data science techniques. If you wish to learn from your customer’s past activities or purchases to provide them with a new service, data science can help. And if your main goal is to boost customer retention, there are plenty of other data projects you can try.
In truth, eCommerce businesses are some of the biggest consumers of data science techniques, and the ones that don’t experiment with them are more prone to fail. In this article, we prepared the top nine projects you should try if you run an eCommerce business.
1. Recommendation System
If you ever made a purchase on Amazon or any other eCommerce website, you most likely noticed the “Recommended products” section as you scrolled a page. Over the past few years, more eCommerce businesses have started implementing sophisticated recommendation systems to add value to the company and generate more revenue.
A recommendation system filters choices for specific users depending on their past searches or purchase data. It allows a user to have a personalized website experience by being offered products they’re interested in based on their previous consumer action.
There are plenty of ways to use the recommendation system. A user looking for a new iPad on Amazon will most likely be interested in frequently used accessories like a detachable keyboard, protective covers, etc. These items can be represented as “Recommended products,” which the consumer can easily add to the basket.
Three main ways to set up a recommendation system include collaborative filtering, content-based filtering, and hybrid filtering.
Collaborative filtering is about giving recommendations based on user activity data on the website as well as by analyzing their similarity to other users’ activity. This is a frequently used technique as it only searches to find similarities among different interests of users.
Content-based filtering offers recommendations based on an item’s description and the user’s profile. The recommendation depends on the products users have browsed for or liked in the past.
Finally, hybrid filtering is the combination of the two methods above. Many eCommerce owners can make separate predictions with the two techniques and combine the results later or use the results from one technique as input for the other.
2. Customer Lifetime Value Model
Many eCommerce businesses rely on the customer lifetime value model to predict the net profit coming from their future relationship with a customer. This model calculates how much someone can bring to the company’s revenue in the long term. The figure is calculated with the help of the purchaser’s history and interaction with the eCommerce website or similar businesses.
The formula is pretty simple – you just need to multiply the average order value, number of repeat orders, and the average customer lifespan. The average customer lifespan is the amount of time during which the person would remain your customer.
The customer lifetime value model makes it easier for companies to determine which type of customers to focus on and how much revenue they could get in the future. If a certain type of customer is sure to bring more than $3k in revenue each month, while others could only bring $500, the company can retarget their marketing efforts towards the first group.
There are other benefits of this approach as well:
- Business marketing strategy optimization and adjustment
- Definition of objectives for a company
- Bringing cross-selling or up-selling decisions following a purchase
- Helps decide the cost of attracting customers
3. Customer Retention-Churn Model
If you’re an eCommerce business owner and want to add value to your company, consider starting a Customer Churn model. This project is highly related to customer retention, which is all about the ability to keep your customer for a longer period.
Once you have high retention, you can expect more regular income and more sales from a single client. Also, those customers often bring new clients as word of mouth is by far the best marketing tool. Finally, a high retention rate is a sign that your customers love your marketing efforts and products.
The churn model is the most effective way for you to achieve higher customer retention. In a nutshell, this project helps you identify clients who are likely to switch to a competitor’s website. When you identify such a user, you can take action to keep them with you. This model works with a range of metrics, including the number and percent of the lost customers, percentage of recurring value lost, and value of recurring business lost.
Some benefits of this model include helping maintain customer lifetime value, identifying churn customers, or helping track business progress.
4. Detecting Fraud
Privacy and security are becoming a hot trend in recent years. Ecommerce businesses that only focuses on bringing more customers to generate more revenue without protecting their security is more likely to fail. Fraud detection is a multi-million-dollar business that only grows in popularity year after year. With more companies being victims of some sort of economic crime, it’s becoming imperative to invest in fraud detection projects.
The most common types of fraud include identity theft, clean fraud, triangulation fraud, chargeback fraud, friendly fraud, and merchant identity fraud. This list gets bigger by the day. So, to make sure your eCommerce channel runs successfully and without fraudulent activity, it’s crucial to start applying security measures to avoid losing customers.
In order to use data science to solve these issues, you first must identify potential fraud segments. This can include shipping addresses that differ from the billing ones, multiple same-item orders, unexpected international orders, and multiple same-address orders with different cards.
You can detect these activities with the help of data science and machine learning. For example, data mining can help you validate, detect, and correct errors as well as fill up incorrect or missing data. Clustering and classification can also help detect anomalies. Finally, you can match algorithms to eliminate false alarms, predict future transactions, or estimate risks.
All these activities are crucial for boosting your company’s revenue and customer retention, as well as reducing the number of questionable transactions.
5. Improve Customer Service
A happy customer translates to a happy business. To be successful in your eCommerce business, you need to maximize your efforts in building an easily accessible, comprehensive, and helpful customer service.
For example, more businesses have started implementing chatbots into the customer service experience. These bots use natural language processing to make communication easier and can also serve as great sources of audio marketing.
Also, data science can help you extract the ratings and reviews from your website and understand why you received negative feedback. Your data scientists can segregate the reviews and perform sentiment analysis to boost your customer satisfaction by leaving a more positive impact on the clients.
6. Price Optimization
Product price has a huge impact on its demand and profit. eCommerce businesses can rely on big data analytics to predict customer segmentation with a price change response. It’s also advisable to analyze the cost of the competing products to come up with a suitable price for a product or service.
Properly done, price optimization can boost your profit and revenue and let your eCommerce business grow its market share for particular products.
7. Inventory Management
Customer retention depends on product availability. It’s crucial to meet the needs of your customers on time, and inventory management can help you achieve that. This is the process of stocking goods in a healthy condition and place for future use.
However, keeping track of a supply chain is becoming more challenging in the modern globalized world. Using inventory data science and analytics can help you get rid of product scarcity when you most need them.
The combination of data analysis, predictive analysis, and machine learning can help detect patterns and supply chains for your inventory management. You can track the patterns for your most in-demand items and set up inventory strategies with machine learning algorithms.
8. Warranty Analytics
Warranty claim analytics is important because it comes with information about the reliability and quality of products. This can help manufacturers identify early warnings and abnormalities in their products and prevent them from failing in the business.
Two main techniques here include data mining and text mining. These tools can help identify problem areas in claims patterns for a product and convert data into insights, recommendations, and real-time plans.
9. Customer Sentiment Analysis
Customer sentiment analysis is a widely used tool for eCommerce websites that helps gather all feedback from customers. Data specialists and marketers have found new ways to help retailers transcend old-fashioned in-person surveys and now use social media, analytics, and machine learning for gaining deeper insight into client sentiment.
Most analyses of this type use natural language processing, negative or neutral sentiments, and text analysis. The project involves extracting data from different forms, social media, online reviews, or online surveys.
Data Science as the Essential eCommerce Companion
Thriving in an eCommerce world today is virtually impossible without proper data science techniques and projects. As the amount of information available online is becoming too overwhelming for a person to handle, sophisticated algorithms and machines can help.
Fortunately, data science has developed so fast over the years that you can now use it to enhance almost every aspect of your business. Hopefully, this article has given you some good ideas to help you get started.
