E-Commerce is a large consumer of data science/machine learning techniques because they provide a way to beat the competition.
Businesses that do not implement these techniques fail to make sense of all the data they accumulate to improve customer experience. Here are nine data science projects businesses are putting into use to make a difference in customer experience challenges.
1. Recommendation systems
A recommendation system filters information and predicts the preferences of a particular user based on past searches or purchases. For example, a user looking for a new camera on the Amazon website may want to buy camera accessories as well, and Amazon will decide on this possibility by analyzing search data and previous purchases.
A recommendation system can be set up in different ways. Collaborative filtering is a popular technique that involves giving recommendations based on user activity data and the similarity between this and the activities of other users.
As the assignment help puts it, content-based filtering offers recommendations to users based on products they liked in the past. A hybrid technique combines both collaborative and content-based filtering.
2. Customer retention model (Churn model)
Studies prove that existing customers bring in more revenue than new customers. If customers go to an eCommerce website and make a purchase they are happy with, they are likely to return and make another purchase.
Having a solid number of loyal customers helps companies to receive real-time feedback and happy existing customers tend to bring in more customers. Existing customers who are ready to try new things help businesses to expand their market.
A Churn Model identifies customers who are most likely to leave and companies can take action to try and keep them.
3. Customer lifetime value (CLV) model
This model calculates how much net profit a customer can contribute to the revenue of a company throughout his/her lifetime. This figure is calculated by taking the average value of each previous order and the number of repeat sales over the average customer life span.
As derived by experts at EduGeeksClub and bestdissertation.com, CLV helps companies to decide which customers to concentrate on in terms of marketing. It also helps them to define their objectives for company growth in terms of factors like expenditures and future sales.
4. Price optimization system
A price optimization system involves analysis to find out how customers will respond to different prices for products. Businesses may need to raise their prices to optimize their profit or lower them to improve their sales volume.
Marketing providers from the term paper writing service highlight this case. Most eCommerce sites use unique formulas to value their items and some of the factors that influence them to include competitor actions, market conditions, ability to pay, input costs, and trade margins. If they want to improve their revenue and their market share, they must optimize their prices.
5. Fraud detection system
With millions of digital transactions taking place daily, many types of fraud can happen online, such as identity theft, friendly fraud, chargeback fraud, and merchant identity fraud.
Various suspicious activities can help to identify fraud, such as a shipping address differing from a billing address, multiple orders of the same item, very large orders with next day shipping, and many more.
Today, eCommerce companies need to ensure that their customers’ details and transactions are safe. With the use of data science and machine learning, it is possible for them to detect suspicious activity.
As the term paper writing service mentions, online payment methods need to be safe and secure without loopholes that can affect the safety of funds. A reliable payment platform must combine effective payment processing and anti-fraud protection.
6. Customer sentiment analysis
Customer ratings and reviews provide social proof to influence other customers today and so they are very important to eCommerce businesses. Sentiment Analysis is a part of Natural Language Processing (NLP) that allows these businesses to gain valuable insights into reviews and ratings about a given product.
Sentiment analysis enables the identification of certain words that indicate a negative or positive attitude and this feedback helps businesses to improve their products. They can also resolve customer issues and enhance the overall customer experience.
Prioritizing product updates that will have the most positive impact is another benefit of sentiment analysis.
7. Quality assurance system
If eCommerce stores can identify quality problems in the manufacture of products early on, they can deal with them quickly and prevent future problems. They usually offer warrantees to customers for products and that means customers are able to deal with problems without paying during the warranty period.
Analytics relating to warranty claims is a way to find out the reliability and quality of products. Retailers and manufacturers can keep a check on how many units are returned due to issues.
Detecting anomalies in warranty claims is also a way of turning challenges into insights businesses can act on. A quality assurance system, therefore, increases company credibility and customer confidence.
8. Merchandising analysis
Merchandising is basically coming up with strategies that help to increase product sales. It influences the decision-making of customers through the use of visual channels.
Branding and packaging can help to enhance the visual appearance of goods and keep customer attention but rotating merchandise is also important. Merchandising algorithms go through sets of data in order to pick up insights and take various elements such as trends, seasonality, and trends into account when displaying merchandise to customers.
9. Inventory management system
The maintenance of supply chains has become more complex today and managing an eCommerce business means being able to manage inventory effectively.
Customers will simply move on to another provider if a business fails to provide them with what they need when they need it. Predictive analytics and learning algorithms are essential in order to detect patterns and follow the right inventory tactics.
A final word
With the help of data science, eCommerce businesses can take customer experience to the next level. They can make personalized recommendations, determine shopping patterns, prevent fraud, analyze customer sentiment, optimize prices, manage inventory, and much more. By enabling businesses to leverage the data they collect, these ever-evolving technologies are helping eCommerce businesses to make better decisions to enhance customer experience and drive the tremendous growth of eCommerce all over the world.
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