Amazon Data Analysis: Key Metrics, Tools & Best Practices

In the era of big data analytics and AI, business decisions and strategies no longer need guesswork. Everything depends on what the data says. So, understanding Amazon data is imperative for businesses to stay competitive and optimize performance.

However, navigating 150 reports from Amazon Seller Central, Vendor Central, and digging through advertising data and customer preferences is no small feat. From data processing to using different types of data analytics tools and techniques, it requires a lot of work to derive meaningful insights.

To make things easier, this blog provides a holistic approach to Amazon data analysis, covering various aspects such as sales metrics, customer reviews, competitive intelligence and more.

So, let’s begin!

Understanding Amazon Data

Amazon provides abundant data through its Seller Central and Vendor Central platforms, as well as through third parties and APIs. These data points can be broadly categorized into sales metrics, operational metrics, customer feedback, and competitive intelligence.

First, let’s go through all the data that Amazon provides. In the coming Part 2 of this post, we will discuss three ways how an Amazon ad automation software brings this data together.

Part 1: Amazon metrics

Sales Metrics

Sales metrics are required to assess the overall performance and growth of an account. Key metrics include:

• Sales Revenue: Total revenue generated from product sales.

• Units Sold: Number of units sold over a given period.

• Average Order Value (AOV): Average price at which products are sold.

• Sales by Product Category: Distribution of sales across different product categories.

Operational Metrics

Operational metrics provide insights into the efficiency of various processes and can help identify areas for improvement. Examples of operational metrics on Amazon include:

• Inventory Levels: Quantity of stock available for each product.

• Inventory Turnover: Rate at which inventory is sold and replaced.

• Fulfillment Metrics: Measures of order fulfillment performance, such as order defect rate and late shipment rate.

• Advertising Metrics: Performance metrics for Amazon advertising campaigns including ad spend, click-through rate (CTR), conversion rate, and advertising cost of sale (ACoS).

| Related Read: 3 Metrics Beyond ACOS to Improve Amazon Ad Performance

Customer Feedback

Customer feedback is crucial for understanding customer satisfaction and identifying opportunities for product improvement. Key sources of customer feedback on Amazon include:

• Product Reviews: Written reviews and ratings left by customers.

• Seller Feedback: Feedback left by customers regarding their experience with a seller.

• Customer Questions & Answers: Questions asked by customers and answers provided by sellers or other customers.

• Customer Service Metrics: Metrics related to customer service interactions, such as response time and resolution rate.

| Related Read: How to Drive Customer Acquisition on Amazon

Competitive Intelligence

Analyzing competitors' strategies and performance to address customer needs is essential for staying ahead in the competitive Amazon marketplace. Key aspects of competitive intelligence include:

• Competitor Pricing: Analysis of competitors' pricing strategies and price positioning.

• Competitor Sales Metrics: Assessment of competitors' sales performance, including revenue, units sold, and market share.

• Performance cookies: Leveraging data on how users behave on competitor websites, such as the site features they're browsing, popular content they interact with, areas of friction, and overall user experience.

  
  

Part 2: Analytical tools and techniques

Having mentioned all the data that Amazon provides, it’s time to discuss how atom11 brings these raw data points together to analyze them effectively. Here are the three important levers that atom11 offers:

Analytics dashboard

The analytics dashboard is a unique combination of sales and operational metrics. It helps you analyze time series trends on 23 parameters, including total sales, total orders, inventory day on hand, inventory units on hand, AOV, best seller rank, and all ad metrics.

The below dashboard image shows that a 1$ increase in price led to a 50% drop in sales for a product. This information saves a brand’s (or agency’s) efforts to optimize advertising performance. Instead, they can work on fixing the root cause (i.e., pricing) at the right time to stop the decline in sales.

An extension of this dashboard is the compare tool, where customers can select two time periods and compare data from 28 different parameters.

Alerts

Data visualization and analysis is a deep-dive activity that teams usually do once a week. But what if you got daily alerts regarding sales fluctuations? Using these alerts, you can immediately take corrective actions rather than waiting for a week’s analysis to figure out what to do.

Alerts help you prioritize which ASINs to take action on. The best part—you can set up alerts the way you want, and they get delivered right to your email every morning!

Refreshable Google Sheets

Most of Amazon’s reporting happens on Google Sheets. Agencies that want to create custom reports for their advertisers and brands/sellers use Google Sheets or Looker Studios. However, creating reports from scratch on either tool is time-consuming. 

On the other hand, atom11 provides custom refreshable reports for data in any format.

Here is what Luum, a brand aggregator and agency had to say about atom11 custom reports feature:

Read how Luum improved ACOS by 10% & increased sales by 22% with atom11.

Best Practices for Holistic Data Analysis

Implementing tools like Amazon Redshift for data warehousing is not enough to derive actionable insights from Amazon's data. Here are a few best practices that businesses should follow:

1. Set clear objectives: Define specific goals and objectives for the analysis to ensure focus and relevance.

2. Use multiple data sources: Combine data from various sources, including data lakes, AWS services, such as Amazon EMR or Amazon OpenSearch Service, and third-party sources for a more comprehensive analysis.

3. Contextualize data: Consider external factors such as market trends, seasonality, and competitive landscape when interpreting data.

4. Iterate and refine: Continuously iterate on analysis methodologies and refine strategies based on insights gained.

5. Collaborate across teams: Foster collaboration between different teams within the organization, such as marketing, sales, and product development to leverage diverse perspectives and expertise.

Conclusion

Analyzing Amazon data holistically is essential for businesses looking to thrive in the competitive e-commerce landscape. However, it’s not easy.

To ensure in-depth data analysis, you must leverage sales and operational metrics, customer feedback, and competitive intelligence. Further, you should employ data analytics techniques, analytical tools, and best practices to gain valuable insights that can help you optimize performance, drive growth, and enhance customer experience. 

As the e-commerce landscape continues to evolve, mastering the art of holistic data analysis will be critical for maintaining a competitive edge in the marketplace. And that’s exactly why you need platforms like atom11. 

Atom11 can enhance your ability to analyze and act on Amazon data effectively, offering powerful analytics and customizable reporting that streamline decision-making. Book a demo with atom11 today and start optimizing your Amazon strategies with precision.

Frequently Asked Questions

Does Amazon have an analytics tool?

Yes, Amazon has an analytics tool. Amazon Brand Analytics provides sellers insights into customer behavior, search performance, and brand advertising attributes. It is only available to sellers registered with Amazon’s Brand Registry program.

How does Amazon do data analysis?

Amazon maintains extensive data warehouses. It uses real-time analytics to process millions of customer interactions in no time, providing immediate insights into inventory, pricing, customer purchase patterns, customer preferences, etc. It uses machine-learning models and advanced algorithms for personalized recommendations, demand forecasting, and supply chain optimization.