5 Best Applications of Data Science in E-Commerce

Data science is a relatively new area, having only been around for about 40 years. It is defined as the scientific discipline that explores the collection, integration, analysis, and interpretation of data in order to understand and interpret human behavior. This might seem like an odd definition - there is no mention of the actual use of data. 

To most people, data science is all about predictive analytics, machine learning, and business intelligence. However, there are other amazing applications of data science in e-commerce. In fact, companies from all around the world are using these applications right now to make their businesses more efficient and profitable.

Here is a list of the five best applications of data science in e-commerce that major e-commerce companies are currently using to gain a competitive advantage.

1- Predictive Customer Behavior Analysis

Conducting market research and surveys is a norm for all major businesses, which provides them with valuable insights about consumers' needs and wants. However, at the end of the day, people make decisions based on their emotions and not facts and figures. For instance, you will buy a new smartphone because you're bored of your old phone, not because it has better technical specs than other smartphones in the market.

Also, humans are habitual creatures by nature who like to stick with their comfort zones. This adds another layer of complexity because now companies need to take into account historical data and integrate it with behavioral patterns before making any decision. No doubt, this is creating new career paths in data science and computational engineering because companies are looking for a workforce trained in statistical modeling. 

Amazon is currently using predictive customer behavior analysis to provide customers with precisely what they want (to the extent possible) at the right time. For instance, Amazon has two new services – "My Mix" and "You Should Also Buy". My Mix allows Amazon to identify and recommend personalized products for customers, and "You Should Also Buy" attempts to establish a connection between two different items and suggest the other one based on customer behavior.

Similar applications can be found in Netflix (recommendation engine) and Google (search results based on past searches).

2- Customer Sentiment Analysis

Customer sentiment analysis takes data science to a whole new level – it can be used in multiple industries, including e-commerce. For instance, one application of customer sentiment analysis is in movies, where companies use Twitter feeds to measure public opinion about different movies. Here are some ways how the analysis is carried out:

• Tracking trends on social media – Companies monitor keywords associated with particular movies and categorize tweets as positive, negative, or neutral to determine public opinion.
• Tracking hashtags – People use different hashtags for similar movies. For instance, if there are two movies released simultaneously, i.e. "Iron Man 3" and "Man of Steel", then #ManOfSteel and #IronMan3 will be two different hashtags that can be tracked separately.
• Twitter reactions during the course of a movie – A positive reaction at a particular point in time indicate excitement while a negative reaction indicates dissatisfaction with some aspect of the movie. These data points can then be used to make necessary changes for future movies.

In a nutshell, customer sentiment analysis is extremely valuable to e-commerce businesses because it offers a real-time view of how customers feel about a product or service. This information can enhance customer satisfaction and help identify dissatisfied customers who have stopped using a particular service/product. 

3- Click-Through Rate Optimization

Click-through rates are the number of times a link is clicked on an advertisement divided by the number of times it is shown, typically expressed as a percentage. The higher the click-through rate, the better your ad performed. Businesses use click-through rates to determine their ads' performance and which ad performed better.

While several factors affect an ad's click-through rate, one of the most important ones is price. For instance, say you are running an ad for a new smartphone on an e-commerce site. You want users to see your ad and click on it so that they land on your website and see the product details. Now, you will have to decide what price to use for your ad – $300 or $400?

This is where data science comes in – companies like Amazon use sophisticated models (that can be built using machine learning) to analyze historical sales patterns along with consumer behavior to determine the perfect price point. The idea is similar to Google Adwords – you've got limited slots, and you want your ad for a particular product to be clicked on the most number of times.

For instance, Amazon has used data science to inform decisions like setting prices for their new products Fire TV Stick and Dash Buttons. It has also used data science to optimize pricing for items sold at Whole Foods – a grocery store acquired by the company in 2017. In addition, it also used price optimization to make its Prime membership from 100 million subscribers to 200 million.

4- Product Recommendations 

One of the prominent reasons why e-commerce growth exploded is because it was able to offer tailored experiences to users. Recommender systems are automated applications that predict likely outcomes (clicks, sales, ratings, etc.) when presented with a particular set of inputs. These systems are powered by machine learning models that use statistical algorithms to analyze historical data and make accurate predictions.

Recommender systems help companies increase engagement on their websites since they bring in more targeted traffic based on likely customer preferences. The more personalized the recommendations, the higher the likelihood of increased conversions (clicks, downloads, sales, etc.).

Netflix is one example where personalized product recommendations have contributed to massive growth. Netflix has over 100 million streaming subscribers today - up from less than 10 million in 2011. The massive increase can be attributed to smart data science-driven decisions about what kind of content to make available on the platform. Netflix started using data science around 2007 for decisions related to what kind of content to commission and also how many episodes to include in a season.

Netflix uses collaborative filtering algorithms (like user-user, user-item, and item-item) to identify popular combinations based on customers' preferences and viewing habits. For instance, if a user has watched an older season of South Park along with Big Mouth, the system will recommend other seasons/shows involving Matt Stone and Trey Parker.

Data science is also used to help Netflix identify popular combinations for shows that are yet to be released so they can acquire more subscribers. The company even uses data science to personalize its Twitter posts to its users.

5- Site Search Analytics

Ecommerce companies are leveraging data science to offer personalized experiences using site search analytics to improve their conversion rate. While there are several factors that affect conversion rates (like pricing and product availability), one of the most crucial ones is site search analytics.

If a user can't find what they want on your e-commerce site, they will leave. And most users won't return back to your site to purchase the product they wanted. This is why e-commerce companies need to improve search analytics with data science that can help them identify what products are being searched for most often.

Companies like Amazon use different data science solutions to determine popular combinations of search terms and then recommend similar products on the site. They can also use product recommendations to show on search engine results pages (SERPs) using paid ads.

E-commerce stores have won the hearts of their customers by personalizing the shopping experience. The process starts with collecting customer data, which is then used to improve targeting and product recommendations across all channels. Using data science, it's possible to create an algorithm that will recommend products based on a customer's purchase history, demographics, and even social media interactions, therefore increasing the e-commerce store's average order values.

Conclusion

Data science is set to play a crucial role in e-commerce in the coming years. Top brands have already started leveraging the power of data science to improve customer experience and increase average order value. In 2022 and ahead, more and more businesses will explore the opportunities in the data science sector to take their profits to the next level.