Top Applications of Data Science in E-commerce

Companies all over the world are implementing data science applications in their businesses. Data Science helps them to analyze the data and make better decisions.

For example, Amazon uses various data science methodologies to improve their shopping experience.

Data science has revolutionized eCommerce businesses in many ways. With the help of data science, businesses can make forecastings, and determine customer behavior and shopping patterns to improve customer experience.

Data science has found a crucial place in the retail and e-commerce industry. It helps companies to predict their profits, losses, purchases and even push customers into buying additional products based on their behavior.

Data science has a big role in the e-commerce & retail industry.

Anyways, let us discuss the 9 interesting uses of data science in e-commerce / data science applications.

1) Recommendation Engines

Recommendation engines are one of the most important tools in a retailer’s arsenal. Retailers use these engines to drive a customer towards buying the product. Giving recommendations helps the retailers in increasing their sales and to dictate trends.

Let us take an example- Whenever you shop on Amazon you see product recommendations on the basis of what you are searching for…right?

How do they do this? Let’s crack this down!

The engines are made up of complex deep learning and machine learning algorithms. They are designed in such a way so that they can keep a track record of the individual behavior of customers, analyze their consumption patterns and give them suggestions based on this data.

Another usual example is Netflix;

After you finish watching a movie or series on Netflix, it gives you some more recommendations of the same category.

This same thing works with youtube too, based on your interest and watch history, it recommends you a few more videos.

All of this is happening with the help of Recommendation Engines using the KNN Algorithm.

It is a very complicated process and involves a great deal of data filtering and reading, and all this passes through the machine learning algorithm.

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2) Market Basket Analysis

Market Basket Analysis is one of the most traditional tools of data analytics. Market basket analysis works on this simple concept- if a customer buys one group of items, they are most likely to buy another set of related items as well.

For example, You went to a restaurant and ordered starters, then there are huge chances that you will also order the main course or desserts. The set of items the customer purchases are known as an itemset, the conditional probability that a customer will order the main course after starters are known as the confidence.

In the eCommerce industry, customers buy products based on impulse, and market basket analysis works on the concept of predicting what the chances of a customer making a purchase are and for what item.

This mostly depends a lot on how the product is being marketed by the retailers. And in the realm of e-commerce, customers’ data is the best place to look for potential buying impulses.

Just like search recommendations, market basket analysis also works with a machine learning or deep learning algorithm.

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3) Retain customers

One of the biggest concerns for every e-commerce business is customers switching to other e-commerce websites. Retaining the customers is crucial if a business is to expand and grow. There are many benefits of having loyal customers, such as receiving real-time feedback and having them recommend products or services to others.

A churn model provides crucial metrics like the number and percentage of customers lost to the business as well as the value and percentage of this loss. If a company is able to identify the customers who are most likely to switch to a different e-commerce site, it can take action to try and keep them.

4) Price management with a data-based approach

Managing pricing policy based on real insights is always fruitful rather than depending on intuition. Making policies backed up data with the help in overall business growth be it, boosting sales at times of low demand, or increasing it during seasonal peaks.

With this initial classification and feeding the data to algorithms and machine learning models, it would be possible to:

• Making the discount coupons or pricing policy based on the user demographics: the data analysis is efficient for detecting common patterns and then identifying customer clusters based on their previous online behavior and purchase history.

This way, it is possible to make promotion strategies tailored for the different types of users found, and therefore, increasing the conversion rates.

• Define prices by segments: The cost of products and services is set taking into account wider audience segments.

For example, one segment would be to have average market prices for the majority of users. Another segment would contain more aggressive discounts aimed at those customers who make their purchase based on the price. Lastly, they would include premium offers & services for customers who seek extra security.

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5) Warranty Analytics

With the help of warranty data analytics, retailers and manufacturers can keep a check on their product’s potential lifetime, problems, and returns. Warranty data analytics also helps them to keep a check on any fraudulent activity.

Warranty data analysis depends upon the estimation of failure of distribution based on data like the age and number of surviving units in the field and the age and number of returns.

After analyzing the data, retailers and manufacturers can keep an eye on how many units have been sold and among them how many have returned due to issues. They also concentrate on detecting frauds in warranty claims. This way retailers can turn warranty challenges into actionable insights, and price their warranties.

6) Inventory Management

Inventory refers to the stock of goods a company keeps in the stores/warehouse in order to ensure a smooth supply chain that can continuously cater to customer demand on a regular basis.

Inventory management is critical for any organization that is especially for the manufacturing industry because an organization/retailer invests a lot of money in purchasing/manufacturing the goods and that capital is lying idle till it is sold.

It is crucial for retailers and manufacturers to stock the goods in the right quantities in order to meet the customer demand for their products.

To obtain this goal, the stock and supply chains are analyzed thoroughly.

Powerful machine learning and deep learning algorithms are used to analyze the data between the elements and supply in a detailed manner to detect patterns and correlations among purchases.

This data is further analyzed by the analysts and they come up with various strategies to ensure timely delivery and managing the inventory, and even to increase sales.

7) Location of new stores

All the E-commerce companies need to open a few stores and warehouses to showcase and store their products. They will have to decide where to open up their stores/warehouses on the basis of various factors and for that, they have to do a full-on analysis and figure out the best locations to set up their stores/warehouses.

Location analysis is a vital part of data analytics. The algorithms used in Location analysis are simple, yet effective.

The data analysis is done on the basis of demographics. A thorough analysis of zip codes and demographic information is done which gives the basis for understanding the labor price, transportation, competition, and the potential of the market.

8) Customer sentiment analysis

Customer sentiment analysis means measuring the attitude of the customers towards the products and services that we sell which they describe in text.

For example:

Negative Sentiment/Attitude:

“Amazon’s interface is too heavy, user experience is horrible, not gonna shop from amazon again”

Positive Sentiment/Attitude:

“I love to shop from amazon”

Customer sentiment analysis or we could say customer review analysis has been in the business world for a long time. But now, machine learning algorithms have taken the charge. Machine learning algorithms help to simplify, automate, and save a lot of time while giving accurate results.

Social media platforms are the most readily and easily available tools to perform customer sentiment analysis. An analyst uses language processing to identify words carrying a negative or positive attitude of the customer towards the brand. Hence, collecting those feedbacks help the brand to meet consumer needs and improve the product and services.

9) Customer Lifetime value prediction

Customer lifetime value refers to the total value of the customer’s profit to the company over the lifetime of this customer-business relationship.

Usually, the algorithms collect the data concerning customer preferences, classifies, and cleans it. Customer’s spends, recent purchases, and behavior act as the input.

After the data is processed, a presentation of the possible value of the existing and possible customers is received. It helps a company to decide its marketing budgets and further helps to increase sales.

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10) Prevent fraud

Having great products and providing exceptional customer experience is not enough for eCommerce businesses to succeed. They must ensure customers’ security. Most e-commerce payments are done online, and the payments must be secure and safe for customers. Big data analytics helps to identify threats and makes online shopping safer.

Online frauds not only bring loss to the company but also harms the brand image of your company, thus customers’ trust.

To solve this issue, eCommerce businesses can use the combination of machine learning and data science techniques to detect unusual behavior.

Online frauds such as phishing or account thefts, shipping, and billing-related scams are on a continuous rise. In fact, in 2018, consumers lost about $1.48 billion related to fraud complaints.

11) Hot or Not: How Big Data Is Used to Spot the Next Big Trend

In 2015, According to an article published in Tech Times, An algorithm was able to predict with almost 65 percent accuracy, whether or not a song would be a Top 10 hit.

Though these kinds of things are relatively easy to predict with the help of data science. But in retail, the challenge is finding the hits before they get to market, and stocking up on and pushing those items.

Big players like Google are hard at work on just that problem. Google has in-house “fashion data scientists” along with the founder of Shopintelligence & product manager from Zappos.

By analyzing the search trends and geographic data, Google was able to correctly predict the spring fashion trends for 2015.

Shopintelligence claims to provide a massive uplift in conversion rates and average order value to their retail clients that have subscribed to their predictive analytics services.

12) Personalized Marketing

Gone are the days where you could get away with emails like “Dear Valued Customer”.

We are in the era of modern shoppers with high expectations, and when your customers are sophisticated, that means that your marketing efforts need to get sophisticated as well.

Big data has transformed digital marketing in ways that make the customer feel like a particular product/service is specially designed for them and the brand really cares about their taste, interests, and preferences.

Suppose, you are running a grocery shop and a website for the same. And you have a customer that only buys wheat flour from your site/shop. Now, with the help of data science, you can keep an eye on how often he buys the wheat flour, and after detecting the patterns, you can send him a personalized email telling him that “Your wheat flour is about to finish, and he can order it from your site and get up 10% off”.

Conclusion

Data Science in eCommerce helps businesses to get a deeper understanding of their customers by capturing and analyzing the information about the customers, the events that happened in their lives, what drove them to purchase a product/service, etc.

Here are some of the use of data science in eCommerce industry–

• 60% of people research and engage with brands on various channels like social media, in-store, websites, etc. before making a purchase
• A survey by eCommerce found that only 23% of UK retailers use data to make informed decisions.

These trends show the rising boom of data science in the eCommerce industry. Data Science holds the power of enhancing the shopping experience of customers that can provide eCommerce businesses with an improved marketing mix and enhanced profitability.

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