Last updated - October 7, 2020
Today e-commerce businesses are using data science in many different areas to stay ahead of the competition. For instance, eCommerce sites are investing funds into personalizing shopbots to enhance customer experience and recommending products to buyers based on browsing habits and previous purchases.
Selling the best products only works if eCommerce businesses can identify who wants to buy them and recommend them when these customers are ready to make a purchase. Here are some ways e-commerce businesses are utilizing data science to enhance the customer experience.
Retain customers
One concern for every eCommerce business is customers switching to other e-commerce websites. Customer retention is crucial if a business is to expand and grow. There are many benefits from having loyal customers, such as receiving real-time feedback from them and having them recommend products or services to others.
A churn model provides metrics such as the number and percentage of customers lost to the business as well as the value and percentage of this loss. When a company is able to identify customers who are most likely to switch to a different eCommerce site, it can take actions to try and keep them.
Give product recommendations
Using big data analytics offers a way to understand the shopping behavior of customers and predict patterns. For example, being able to establish which brands or products are most popular when spikes in demand for certain products occur or times of the year when customers shop more can help to determine the right strategies.
Recommendation filters for a particular user are based on past searches, purchase data, reviews read, etc. and allow a personalized view. This helps users with the selection of relevant products.
For example, if you’re looking for a mobile phone on an e-commerce site, there is a possibility that you might want to buy a phone cover too. Deciding whether this is a possibility might be based on analyzing previous purchases or data searches of customers.
Analyze customer sentiment
Gathering customer feedback is very important for e-commerce sites. Using social media analytics, data science and machine learning, companies can perform brand-customer sentiment analysis. Natural language processing, text analysis, data from online reviews and online surveys are just some ways to analyze customer sentiment.
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Here is A Guide to Customer Sentiment Analysis (and Why It Matters).
Predict the lifetime value of customers
eCommerce businesses can benefit from knowing what net profit a customer is likely to bring to the company. Being able to predict the lifetime value of a customer can help with factors such as defining objectives for expenditure, optimizing marketing strategies and deciding cross sell and up sell according to customer purchases.
By using data science models to collect and classify data, e-commerce businesses can predict future buying behavior and have more understanding when formulating business strategies. They know which customers are most loyal and can decide where spending money on advertising etc. will offer the most return on investment.
Manage Inventory
Proper management of inventory is essential for eCommerce businesses. When customers are unable to get what they want when they want it, it’s a major deterrent to retaining them. They will simply move on to the next company that can offer this. They want to receive the right goods at the right time and in perfect condition.
The maintenance of the supply chain has become complex today and using inventory data analytics enables businesses to manage inventory effectively. Using machine learning algorithms and predictive analytics enables patterns to be detected that can define inventory strategies.
Detect fraud
Living in a digital world where millions of transactions are taking place consistently makes fraud detection essential. Many different forms of fraud are possible and fraudsters are becoming smarter every day.
eCommerce businesses can detect suspicious behavior by using data science techniques. Signs of suspicious behavior could include a shipping address differing from a billing address, an unexpected international order or multiple orders of the same item.
Common data science techniques to detect such behavior include:
- Matching algorithms to estimate risks and avoid false alarms.
- Data mining to address missing or incorrect data and correct errors.
- Clustering and classification to help detect associated data groups and find anomalies.
A fraud detection system helps companies to decrease unidentified transactions and increase company revenue and brand value.
Improve Customer Service
A customer is central to any business, especially eCommerce. Personalizing services and giving customers what they really want and need is essential to keeping them happy. Big data analytics offers businesses the potential to enhance their processes so that customers enjoy transacting online.
Natural language processing allows customers to communicate with voice-based bots and data can be stored for future purposes. When businesses know more about their customers and what they want, they are able to devise the best strategies to improve their customer service.
Optimize prices
Data-optimized pricing is making some retailers plenty of money. Many online retailers, such as Amazon, Home Depot, Discover and Staples, vary their pricing based on secret formulas. Cost analysis, competitor analysis and market segmentation are all critical when it comes to pricing.
Pricing of products can impact a business in many ways when it comes to market share, revenues and profits. A key for retailers is to be able to figure out the right price and with big data analytics, they are not only able to determine that number for the market in general but also calculate it with some precision for individual customers.
Make online payments easy
Many eCommerce sales are made via mobile platforms and online payments must be secure and safe for customers. Big data analytics helps to identify anything that threatens the process and helps to make online shopping safer.
Various payment options make the online payment process easy and convenient for customers.
Determine the quality and reliability of products
eCommerce stores usually provide warranties for products that allow customers to deal with any problems at no cost during the warranty period. Analytics relating to warranty claims can help to determine the quality and reliability of products.
If manufacturers are able to identify early warning of possible problems, they may be able to address them in time to avoid serious damage to the business.
Text mining and data mining are two techniques that can be used to identify patterns relating to claims and problems with products. The data can be converted into real-time insights and recommendations.
The bottom line
We’ve taken a look at the ten ways that data science models can impact e-commerce. There are so many eCommerce websites and many of them sell similar types of products. Data science helps eCommerce businesses to understand and analyze customer behavior and provide ways to enhance customer service.
When companies understand what they do best and who their loyal customers are by using data science, they are able to improve product designs and customer service, formulate better pricing strategies, manage inventory effectively and provide secure online purchasing and payment options.
