How Amazon Uses Data Science to Enhance Customer Experience

“The customer is king” means putting the customer’s needs and wants at the center of a company’s strategy. It is a reminder that businesses exist to serve customers. The business should aim to provide the best possible experience for the customer with everything it does

In other words, Meeting and fulfilling customer expectations must be the key to every successful business. Put, A business that values the customer is more likely to be successful than one that doesn’t. Customers are why businesses exist; without them, there would be no revenue or profits.

By putting the customer first, businesses can build strong relationships, increasing loyalty and repeat business, resulting in long-term success and growth.

Why is Customer Experience Important for Any Product?

The Customer experience (CX) can directly influence sales and revenue, given that contented customers tend to make repeated purchases and recommend the product to others. A serious CX strategy is crucial to build a customer-oriented product with a great customer experience. Along with other product-driven attributes, customer interaction plays a very important role in keeping a check on customer experience.  

• Good customer experience leads to repeat purchases and recommendations, boosting sales and revenue.
• Positive customer experience builds brand loyalty and improves reputation, giving a competitive edge.
• Customer feedback provides insights for innovation and upgrades.
• Customer experience helps meet target audience needs, improving product-market fit.
• Good customer experience lowers the cost of acquisition, reducing churn and generating positive word-of-mouth.
• Poor customer experience results in negative reviews, damaging reputation and causing lost sales.
• Customer experience influences product selection on Amazon, and positive reviews and feedback can increase sales and foster customer loyalty.
• Amazon is convenient with one-click ordering, fast shipping, and easy returns.

Key Highlight of the Blog:

• Amazon is the world’s largest online retailer. 
• It is a customer-facing business – both buyers and sellers are the customers of Amazon. 
• Amazon uses big data and data science to enhance the customer experience.
• Type of data collected by Amazon to improve user experience.
• 10 Tips to Build the best customer-centric addictive product
• How to measure customer experience?
• It collects and analyzes vast amounts of customer data to gain insights into customer preferences and behaviour.
• Amazon uses data science to personalize product recommendations based on past purchases and browsing behaviour.
• Improve the customer experience through features such as one-click ordering and same-day delivery.
• Amazon’s logistics and supply chain management systems use data science to optimize delivery routes and inventory levels.
• Amazon’s use of data science creates a more personalized and convenient customer shopping experience.
• Amazon is convenient with one-click ordering, fast shipping, and easy returns.

Some Facts Related to Amazon

Amazon operates in 13 countries and offers Prime in 10 of them. The Prime-eligible countries are the United States, United Kingdom, Spain, Japan, Italy, Germany, France, Canada, Austria, and India.

• Active Amazon users: 300 million (Amazon)
• Paid Amazon Prime users: 200 million (Amazon)
• Unique visitors per month: 213.56 million (Statista)
• Countries Amazon is present: 200+ countries (Amazon)

Around 46% of Amazon’s customers are from the age group 25-44 years. The number of Prime subscribers has been increasing continuously over the past few years, as the company reported 150 million members at the end of the fourth quarter of 2019. Amazon saw an increase of 33% in its Prime user base in just one year, mostly because of the pandemic.

How is Amazon Improving its Customer Experience Using Data Science?

Amazon places the customer at the centre of its business, using data science to optimize the customer experience.

• Collects vast customer data for its big data analytics, including demographic info, purchase history, and online behaviour.
• Uses machine learning to personalize and recommend offers.
• Analyzes customer feedback using data science for insights into satisfaction and areas for improvement.
• Trains employees to provide great service.
• Equips employees with customer data, analytics, and support systems.
• Tracks performance metrics and shares feedback with employees for innovation and improvement.
• Encourages innovation and feedback to improve customer experience.
• Puts the customer first and uses data to enhance satisfaction and increase profits.

Amazon is constantly improving its customer experience using data science. Leveraging data from various sources and applying advanced analytics techniques to gain insights that help them make data-driven decisions. Some ways that Amazon uses data science to improve its customer experience include:

Read More: 10 Tips to Build the Best Customer-Centric Addictive Product.

If you are wondering how you will measure the customer experience? Here’s a blog – “How to measure customer experience?”

Here’s How Amazon uses data science to enhance customer experience

Personalization

Amazon uses customer data to train their machine learning model and personalize the shopping experience. Numerous studies have demonstrated the high effectiveness of this approach. This technology generates product recommendations. They account for approximately 35% of Amazon’s total revenue.

Amazon uses data from previous purchases, browsing history, and search queries. This information is analyzed to provide personalized product recommendations. Consequently, customers are inclined to make purchases based on these personalized recommendations. This not only boosts the customer experience but also generates revenue for the company.

How does Amazon use Personalization to improve User Experience?

• Amazon uses machine learning algorithms to personalize website and marketing campaigns
• Analyzes customer data such as browsing history, purchase history, search queries, demographics, and ratings
• Makes improved personalized product recommendations
• Curate customized homepages
• Offer personalized promotions
• Tailor-fit email campaigns with relevant products and offers

Follow the below table to understand how Amazon is improving its Customer Experience in terms of Personalization using Data Science:

Personalization	What Data Does Amazon Collect	How Amazon Uses the Data to Personalize	Examples of How Amazon Personalizes the Customer Experience
Product Recommendations	Customer purchase history, browsing history, search queries, demographics, and ratings	Machine learning algorithms analyze customer data to make personalized recommendations for products.	Amazon’s recommendation engine suggests products based on a customer’s purchase and browsing history, search queries, and demographic information. Customers can also rate products, which helps Amazon refine recommendations further.
Customized Homepage	Customer purchase history, browsing history, search queries, demographics, and ratings	Amazon uses machine learning to curate a personalized homepage based on a customer’s activity on the site.	Customers who log into their accounts see a homepage tailored to their interests and preferences. This includes products they have recently viewed or purchased. It also includes personalized recommendations for new products based on their purchase and browsing history.
Personalized Promotions	Customer purchase history, browsing history, search queries, demographics, and ratings	Amazon analyzes customer data to offer personalized promotions and discounts on products.	Amazon sends personalized promotional offers to customers based on their purchase history and browsing behaviour. For example, customers who frequently purchase pet products may receive pet food or toy discounts.
Customized Email Campaigns	Customer purchase history, browsing history, search queries, demographics, and ratings	Amazon uses customer data to personalize email campaigns with relevant products and offers.	Amazon tailors its email campaigns to the interests and preferences of each customer. For example, customers who frequently purchase electronics may receive emails about new product releases or exclusive offers on tech gadgets.

Shipping:

According to a survey by Salesforce conducted in 2020, 84% of Amazon customers said delivery impacts their overall experience. As of 2021, Amazon had over 250 fulfilment centres worldwide, allowing faster delivery times. Amazon has also implemented AI-powered delivery routes to optimize delivery times and minimize errors. Resulting in a reduction in delivery-related errors.

For an extended period, Amazon has provided free shipping and handling for orders meeting a specific limit, typically $25 or $35. Nonetheless, Amazon’s policies are subject to alteration, and new information may emerge.

Here’s an Example of How Amazon Tried to Improve its User Experience by Tackling Shipping-Related Issues Using Data Science:

• Using data science to improve the shipping process and reduce delivery times
• Collecting and analyzing data such as order data, tracking information, carrier data, and location data
• Predicting potential delivery delays and proactively notifying customers
• Providing compensation if necessary
• Using computer vision and image analysis tools to detect package damage during delivery
• Machine learning predicts package loss and aids recovery/compensation.

Let’s understand how it works in more detail:

Shipping issue	What Data Does Amazon Collect	How Amazon identifies the issue	How Amazon tackled the issue using data science
Late Delivery	Order data, tracking information, carrier data, location data	Automated alerts triggered by missed delivery dates, customer complaints, carrier data	By utilizing machine learning algorithms, the company can anticipate possible delivery delays and take proactive measures such as notifying customers and providing compensation. These algorithms use historical shipping data to identify patterns that may indicate a delay. It can even adjust delivery routes in real-time to prevent delays.
Damaged Packages	Customer feedback, package tracking information, inventory data	Customer complaints, automated alerts from package tracking data	Vision tools spot delivery damage and shipping process improvements. These tools can automatically flag packages with visible damage and provide feedback to delivery drivers and warehouse staff. By analyzing this data, Amazon can improve packaging materials and processes to reduce the likelihood of damage occurring in the future.
Incorrect Address	Customer input, order data, carrier data	Automated address validation, customer service calls	The company uses natural language processing algorithms to identify common address input errors. These algorithms can then provide suggestions for correction to customers. Algorithms use past data to suggest address corrections for pre-orders. In addition, customer service representatives can use this data. They can provide personalized support and ensure the correct address for future deliveries.
Lost Packages	Package tracking information, carrier data, location data	Automated alerts triggered by prolonged delivery time, customer complaints	Machine learning algorithms predict potential package loss. They can then proactively work with carriers to locate and deliver the package. If necessary, the company may provide compensation as an alternative.These algorithms analyze historical delivery data to identify patterns. These patterns may indicate a lost package. Additionally, they can automatically alert the appropriate parties.Amazon’s support team to investigate. In addition, Amazon can use real-time location data to track the package. The company can use this data to work with carriers and redirect the shipment to the correct destination.

Customer Service:

Amazon is known for high-quality customer service. They focus on fast and efficient support. Additionally, Amazon takes customer feedback into account. They use machine learning to track customer complaints on social media, online reviews, and customer support tickets. This allows Amazon to quickly identify customer satisfaction issues and take the appropriate steps to address them. Amazon uses machine learning to identify customer service trends. It allows them to proactively address potential issues before they become major problems.

In a 2020 Statista survey on customer satisfaction, Amazon US was ranked as the top online retailer, scoring 73 out of 100.

Furthermore, Amazon’s use of machine learning to track and respond to customer feedback has been effective. In 2021, Amazon reported on the effectiveness of its machine-learning algorithms. They reduced customer service response times by 40%.

Also, their automated customer service systems resolved over 70% of inquiries without human intervention.

Amazon can proactively address issues and efficiently resolve customer concerns by analyzing customer service inquiries and complaints with machine learning.

This is How Amazon Uses Data Science to Improve its Customer Service:

• Using machine learning algorithms to improve customer service
• Analyzing customer data such as customer feedback, call transcripts, and purchase history
• Pinpointing opportunities for improving customer service.
• Providing personalized support to customers
• Using natural language processing algorithms to identify common customer complaints
• Providing suggestions for the resolution to customer service representatives

Let’s explore further to see how Amazon improves the customer experience in terms of customer service:

Customer Service	What Data Does Amazon Collect	How Amazon Identifies the Issue	How Amazon Tackles the Issue Using Data Science
Call Transcripts	Call logs, customer feedback, transcripts	Natural language processing (NLP) algorithms identify common complaints and keywords	NLP algorithms provide suggestions for a resolution to customer service representatives
Product Defects	Customer feedback, product ratings, reviews, returns data	Machine learning algorithms analyze patterns in data to identify common defects	Machine learning algorithms provide insights to inform product design and improve quality control processes
Delivery Issues	Order data, tracking information, carrier data, location data	Data analysis predicts potential delivery delays	Proactive notifications to customers, providing compensation if necessary
Return Process	Return data, customer feedback	Analysis of customer feedback and return data identifies common issues	Personalized support and suggestions for the resolution to customer service representatives
Language Barriers	Customer feedback, call logs, transcripts	NLP algorithms analyze transcripts and feedback to identify language barriers	Multilingual customer service support and language translation tools

Fake or Poor-Quality Products:

Amazon has faced criticism for selling fake/poor-quality products on its platform. In 2020 the Government Accountability Office (GAO) found that 20% of products purchased from third-party sellers on Amazon were counterfeit. Moreover, a study by the consumer advocacy group Which? The UK found that 97% of the top Amazon reviews for certain unbranded electronics products were unverified, indicating that they may have been fake.

Amazon uses data science and machine learning algorithms to combat this issue to identify and remove counterfeit products from its platform. This removed over two billion suspected bad listings and closed hundreds of thousands of suspect seller accounts in 2019. Additionally, Amazon has implemented programs such as Transparency and Project Zero, which help prevent the sale of counterfeit goods on their platform by using various technologies such as unique codes and machine learning algorithms to ensure that products are authentic and of high quality.

This is How Amazon Uses Data Science to Improve its Fake or Poor-Quality Products:

• Using data science to combat fake or poor-quality products on the platform
• Collecting and analyzing data such as product ratings, reviews, and purchase history
• Identifying potentially fraudulent or counterfeit products
• Removing them from the platform
• Using machine learning algorithms to detect fake reviews
• Improving the overall quality of reviews on the platform

Let’s explore further to see how Amazon improves the customer experience in terms of fighting against fake or poor-quality products using Data Science:

Issue	What Data Does Amazon Collect	How Amazon Identifies the Issue	How Amazon Tackles the Issue Using Data Science
Fake Products	Customer reviews, seller information, product details, order history	Machine learning algorithms analyze customer reviews to detect potential fake products and identify patterns of fraudulent behaviour among sellers	Amazon suspends fraudulent sellers and products, provides refunds to customers, and works with law enforcement to take legal action against fraudulent sellers
Poor-Quality Products	Customer reviews, product details, order history	Machine learning algorithms analyze customer reviews to detect patterns of poor-quality products and identify factors that contribute to poor quality	Amazon works with sellers to improve product quality and offers refunds or replacements to customers.

Pricing and Deals:

Amazon leverages data science and machine learning algorithms to adjust prices in real-time dynamically. According to a study by Profitero, a global e-commerce analytics firm, Amazon changes its prices on average every 10 minutes, resulting in more than 2.5 million daily price changes. Several factors, such as demand, competition, and inventory levels, drive these price changes. Using data science and machine learning algorithms to analyze these factors, Amazon can optimize its prices to maximize profits and remain competitive.

This data-driven pricing strategy benefits both customers’ and Amazon’s business decisions. 

For customers, dynamic Pricing can lead to lower prices and greater accessibility to products. According to a study by Boomerang Commerce, a retail analytics company, Amazon’s algorithmic Pricing resulted in 12% lower average prices than its competitors. This benefits Amazon’s business decisions by enabling the company to maintain a competitive edge and maximize profits. Additionally, by analyzing sales and pricing data, Amazon can identify market trends and adjust its inventory and pricing strategies accordingly.

This is How Amazon Uses Data Science to Improve its Pricing and Deals:

• Using data science to optimize Pricing and deals
• Analyzing data such as customer purchase history, competitor prices, and demand fluctuations
• Making real-time pricing adjustments to remain competitive
• Offering personalized deals and promotions to customers
• Using machine learning algorithms to predict demand for products
• Optimizing Pricing to maximize revenue

Let’s explore further to see how Amazon improves the customer experience in terms of Pricing and deals:

Issue	What Data Does Amazon Collect	How Amazon Identifies the Issue	How Amazon Tackles the Issue Using Data Science
Pricing Disparities	Customer purchase history, product details, competitor pricing data	Machine learning algorithms analyze pricing data to detect potential disparities and identify products that cost above or below market value	Amazon adjusts prices to be competitive and notifies customers of price changes
Deal Availability	Customer purchase history, product details, deal terms and conditions	Machine learning algorithms analyze deal availability and identify products that are frequently out of stock or unavailable	Amazon restocks popular items and adjusts deal terms to meet customer demand

Some Improvements in Amazon’s Customer Experience

Improvement	Example
Faster Delivery	Amazon uses its army of drones and delivery trucks to bring you your precious packages faster than before. Because apparently, waiting two days for free shipping wasn’t fast enough for you.
Personalization	Amazon collects all the data about you to create a personalized shopping experience. It’s like having a creepy stalker who also sells you stuff.
Artificial Intelligence	Amazon is using chatbots to talk to you when you have a problem. Because who needs human interaction, right?
Customer Service	Amazon has created an army of customer service reps ready to handle your complaints and problems 24/7. So you can complain to someone at 3 am when you can’t sleep. Lucky you.
Returns Process	Amazon has made it super easy to return things you don’t like. So buy that inflatable sumo wrestler costume and return it the next day. We won’t judge.

Conclusion:

In conclusion, Amazon uses data science to enhance the customer experience in various ways. Amazon prioritizes the customer in their business by utilizing big data, AI, and machine learning to drive personalization and innovation. By utilizing data science, Amazon can provide exceptional service. By analyzing customer data and using it to inform their business decisions, Amazon can consistently improve the customer experience. This strategy has enabled them to establish a dedicated customer following and rank among the most prosperous corporations worldwide.

Contributed by: Aman Kumar