E-commerce has experienced a significant rise in recent years, becoming an integral part of modern-day business.
Statista estimated global e-commerce sales at $5.2 trillion in 2021, projected to reach $8.1 trillion by 2026.
Frankly, this has led to increased competition and innovation in the e-commerce industry. Businesses must constantly improve their online presence and offer a seamless shopping experience to customers.
But how are e-commerce store owners aware of AI and data? What are the trends and use cases of data science applications in eCommerce?
Let’s uncover them all in this article.
What is E-commerce Data Science?
E-commerce Data Science refers to using customer data analysis and machine learning algorithms to extract critical insights from eCommerce data.
Data, after collecting, processing, and analyzation, will serve multiple purposes, from improving customer experience to pushing up sales and optimizing business operations.
Data science proves essential for e-commerce because of its capability to help companies make data-driven decisions. This will result in increased revenue and customer lifetime value rate.
Steps to Analyze eCommerce Data
Applying data science in eCommerce involves several steps, including:
- Data collection – Gather data from various sources, such as customer transactions, website visits, and social media interactions.
- Data cleaning – Remove any errors or inconsistencies in the data, for example, missing values or duplicates.
- Data analysis – Explore the data to identify purchase patterns and trends.
- Model building – Create machine learning models to predict purchaser behavior, optimize pricing, and improve supply chain management.
7 Use Cases of Data Science in eCommerce
AI and Data Science are so well-integrated into the scenario that you may not even realize their usage. The followings are critical use cases and highlights of how your eCommerce stores can leverage data to improve the bottom line and enhance the customer lifetime value.
#1 Recommendation Engines
Have you ever been in a situation where you search and order an item online, and similar products keep appearing and attracting your interest the next time you stream?
That’s exactly what data science and machine learning algorithms do, calling “product recommendations” or “recommendation engines.”
Briefly, recommendation systems employ data science algorithms to suggest products to customers based on their browsing and purchase history. It also involves other relevant data like demographics and location.
E-commerce businesses rely on this to promote relevant items to customers and push sales. It enhances the shopper experience by making finding what they are looking for easier.
Take Netflix as an example. Its personalized movie and TV show recommendations use viewers’ viewing history and ratings. If you watch an action film like “Atomic Blonde,” this streaming service will put up similar series, “Fast Five” or “Black Knight.”
Sephora’s “Recommended for You” feature, Spotify’s “Discover Weekly” playlist, and Pinterest’s recommendations are worth mentioning too.
The former learns from buyers’ past purchases, skin types, and advice on beauty products. Meanwhile, the latter suggests new music via the user’s past searches, listening history, and preferences.
#2 Inventory Management and Optimization
Many eCommerce companies are reaping the benefits of data science to manage and optimize inventory levels. And you aren’t outside the race, are you?
Entrepreneurs utilize data science algorithms to analyze historical sales data, current inventory levels, and other relevant data to predict future demand and optimize inventory accordingly.
This technology puts an end to traditional inventory management methods. It empowers e-commerce stores to reduce costs, increase efficiency, and improve customer satisfaction. You can avoid stockouts and overstocking and reduce lost sales or excessing inventory that ties up capital.
An online grocery store is a case in point. Looking over sales data for a particular product, such as milk, gives the manager the prediction of the milk units in the upcoming week. They can then adjust their inventory management, ordering enough milk to meet demand without overstocking.
On top of that, data science and machine learning algorithms can also be in charge of inventory placement and routing optimization.
By studying customers, suppliers, and warehouse locations, businesses are able to fully control inventory routing to reduce transportation costs and improve delivery time
#3 Customer Segmentation
If you treat all customers in the same way, you’re definitely ruining your online business. It’s necessary to divide them into distinct groups based on customer preferences and characteristics.
This is what data science does, called “customer segmentation.” Not only does the technique allow your stores to understand customers better, but you can tailor marketing efforts to specific types.
Depending on your products or business fields, there are various ways to segment customers.
You can use demographic data (age, gender, and location) or behavioral data (purchase history, browsing behavior, and engagement with marketing campaigns). Once customers are classified, you’re able to create targeted marketing campaigns for each set, such as personalized emails or social media ads.
For instance, a clothing retailer may categorize its customers by age and purchase history.
They can promote the young who have bought items from the summer collection clothes similar in style. The older, having purchased winter coats previously, should be introduced to new winter styles items.
What’s more, customer segmentation enables businesses to figure out areas for improvement in their products and services.
Finding customers’ common pain points and dissatisfaction is no longer your business’s pain point. You can analyze their behavior and preferences and then make changes to improve their shopping experience.
#4 Pricing Optimization
If pricing products somehow rues into your current concerns, you have data science at hand. Luckily!
It uses meaningful data on customer behavior, market trends, and competitor pricing to determine the optimal price for a product or service. This data analysis can take into account factors such as customer willingness to pay, the cost of production, and the market competition level.
By using data to decide a fair and optimal price and implement pricing strategies, businesses can maximize their revenue potential and stay ahead of the competition.
A hotel may take out customer behavior and competitor pricing data to control the price of a room. Factors like the time of year, hotel location, and amenities are worth your consideration.
Additionally, online businesses make use of data science to implement dynamic pricing strategies. This includes adjusting prices in real time based on changes in demand, supply, or other factors.
An airline adjusts ticket prices relying on the time of day, day of the week, and the number of seats available on a particular flight
#5 Customer Sentiment Analysis
Another practical application of e-commerce data science is identifying customer feedback to determine their sentiment towards a product or service. This technology does wonders for understanding customer needs, preferences, and pain points and improving their satisfaction.
To break down the customer sentiment analysis operation, data science algorithms gather buyers’ feedback from various sources, like social media, reviews, and support interactions. Then, they recognize patterns in the data to evaluate whether the input is positive, negative, or neutral.
This method is helpful for specific customer needs and preference identification. You can learn from their comments and improve or change your products/services to better meet their demands.
#6 Market Basket Analysis
As the name suggests, the market basket analysis learns from customers’ carts and transactions to find products frequently purchased together. Stores aware of market basket analysis can make informed decisions about product placement, marketing, and promotions.
What’s more, data science does a great job of spotting new product opportunities. Imagine customers often buy some products together. If you promote them as a bundle, the sales rate will soar like an eagle.
Another way to make use of data is by exploring buyer behavior and sales data to sort out which products are selling well and which are not. It permits you to shape your online business’s right product mix, placement, and pricing strategy.
The general principle behind this method is studying sales data, customer actions, and market trends.
Based on this, you would be able to decide which products to stock, how to price them, and where to place them in the store. The more product offerings are optimized, the higher sales and profitability.
On the other hand, merchandising analysis gives a greater image to your store layouts. Knowing which products are frequently purchased together, online retail can place them in close proximity to increase sales and make customers happier.
Ready to Make the Most from Data Science in eCommerce?
Data science is of indispensable necessity in the eCommerce industry. It provides online shops with valuable insights into customer behavior, product performance, and market trends.
We’ve walked you through seven data science applications demonstrating their versatility in eCommerce. You can predict customer churn, market basket analysis, optimize product recommendations, and more.
By leveraging data science algorithms and machine learning algorithms, online businesses can make great choices that enhance the customer experience, increase sales, and drive profitability.
As the eCommerce industry continues to grow and evolve, data science will undoubtedly play a critical role in helping businesses stay competitive and meet the customers evolving needs.