Extracting Valuable Amazon Insights to Optimize Performance

In a digital age, where there is more data than we know what to do with it, it is imperative for businesses, especially in the eCommerce industry, to understand how to leverage their data to extract valuable insights, identify trends, and make sense of patterns. Of course, such as task is easier said than done. 

Given the massive amounts of data a company collects in just a short period, knowing where to look and what tools to use to pull those valuable insights can seem overwhelming and tedious at best. This article will explain why and how Amazon sellers and other major eCommerce brands should take a more data-driven approach in 2023 to optimize daily operations, become more efficient, increase revenue, and gain a competitive advantage.

Why Amazon Sellers Should Leverage Data Insights 

Amazon data can represent major pivot points when it comes to marketing and sales. The data analytics boom has unleashed terabytes worth of potential. Those companies that leverage analytics have proven successful in turning data into market growth and are more likely to excel at using analytics to identify business opportunities, transform insights into stronger products, and deliver those products more effectively to consumers and the marketplace at large. 

Regardless of which areas of your Amazon business need optimizing, making data analysis a regular habit, be it automated by tech or manually by a team, can yield significant performance improvements. According to a study conducted by McKinsey, a global management consulting firm, “companies that inject big data and analytics into their operation show productivity rates and profitability that are 5 percent to 6 percent hight than those of their peers.” Now, that is an advantage no Amazon seller can afford to ignore.

What Constitutes a Valuable Amazon Insight?

A valuable Amazon insight is any information that can be extracted from data and used to inform business decisions and improve performance. Some common examples of valuable Amazon data insights include but are not limited to the following.

• Sales Trend Insights – Identifying which products are selling well, which are not, and why. This can inform product development and marketing strategies.
• Search Engine Optimization Insights – Monitoring BSR data can help Amazon sellers identify patterns in how the trajectory of their sales and keyword usage impacts their ranking as a seller.
• Customer Demographics Insights – Understanding who is buying products on Amazon and their purchasing behavior can help businesses target their advertising and promotions more effectively.
• Competitor Analysis Insights – Studying competitors’ products, pricing, and marketing strategies can help businesses identify opportunities and threats in the marketplace.
• Product Listing Analysis Insights – Dissecting all areas of listings quality to pinpoint weaker areas and optimize them accordingly to improve a product’s overall performance and visibility.
• Product Reviews Insights – Tracking customer reviews can provide insights into product strengths and weaknesses, as well as areas for improvement.
• Advertising Performance Insights – Analyzing the performance of advertising campaigns on Amazon can help businesses optimize their ad spend and improve their return on investment.
• Business Operations Insights – Examining everyday eCommerce operations can help businesses save time and become more efficient in the way that they conduct daily business operations.

Tools for Extracting Valuable Amazon Insights

There are several tools sellers can use to extract valuable insights from their Amazon business data, including Amazon’s own analytics tools like Amazon Redshift, data integration and visualization tools, and AI-based tools provided by third-party software.

Amazon’s Analytics Tools

Amazon Redshift is a fully managed, petabyte-scale data warehouse service provided by Amazon Web Services (AWS). It allows users to store and analyze large amounts of data using a variety of tools and technologies, such as SQL. Redshift is based on columnar storage architecture and uses advanced compression and encoding algorithms to improve query performance and reduce storage costs.

Redshift also allows users to easily scale their data warehouse up or down as their needs change and to use a variety of data integration and management tools, such as the AWS Data Pipeline and AWS Glue, to move and transform data. Additionally, Redshift integrates with other AWS services, such as Amazon QuickSight, Amazon Machine Learning, and Amazon EMR, to allow customers to perform analytics and machine learning on their data stored in Redshift.

Data Integration Tools 

DataHawk Connections is a data collection and storage service that doesn’t require sellers to connect their Seller Central accounts. By enabling Connections on the app, Connections consolidates all the data DataHawk collects in a cloud database it manages for users. It allows users to easily create and publish interactive dashboards and visualizations that are fully customized to their needs within their favorite BI tools. In doing so, users can perform ad-hoc analysis of data from myriad of destinations, including Tableau, Power BI, Excel, and Looker Studio, among many others.

Connections also offers a variety of pre-built Google Sheets dashboard templates that can be used to quickly build data visualizations and extract valuable insights from their Amazon data without the need for coding or specialized skills. Additionally, DataHawk Connections allows users to share their visualizations and dashboards with other team members to delegate analysis according to areas of expertise: operations, SEO, content, market, ads, and finance.

Extracting Amazon Insights From Dashboards 

This section will demonstrate how sellers can extract valuable Amazon insights data analytics by using DataHawk’s prebuilt Google Sheets dashboards as an example. Take, for instance, the Profit and Loss Dashboard, which allows sellers to analyze historical and up-to-date sales, expenses, and profitability metrics. Amazon sellers can gather any number of insights ranging from sales trends to net profitability; they just have to know where to look.

Examining finance analytics, such as the ones presented in the Profit and Loss dashboard in the image above, can help a business identify which products are performing well and which are not, which can inform product development and marketing strategies. In addition to product performance, sellers can glean Amazon insights about their revenue, costs, gross profit, net profit, inventory management, and sales trends. 

Revenue: The total amount of money made from sales of products on Amazon, including any fees or commissions.

Costs: The total expenses associated with selling on Amazon, including the cost of goods sold (COGS), fulfillment costs, and any other expenses.

Gross profit: The difference between revenue and costs, which shows the profitability of the business.

Net profit: The final profit after taking into account all expenses and taxes.

Product performance: Information about the sales and profitability of individual products, including best sellers and underperforming items.

Inventory management: Information about inventory levels, including the number of units sold, the number of units remaining, and order numbers.

Sales trends: Information about sales trends over time, including daily, weekly, and monthly sales, can help sellers identify patterns and make informed decisions about product pricing and inventory management.

Artificial Intelligence Tools 

For those Amazon sellers with less data analysis experience, automated AI tools can be leveraged to generate valuable Amazon insights. DataHawk’s Intelligence product, for instance, has an AI-backed feature called Insights, which allows Amazon sellers to receive several proactive and reactive notifications that are based on automated data exploration. They cover all aspects of an Amazon seller’s business, such as ads, products, sales, SEO performance, and market.

The three categories of Amazon insights sellers can receive include Findings, Anomalies, and Good Practices. Findings are business-savvy insights that help sellers manage specific aspects of their business. They can include actionable recommendations that advise sellers on how to optimize the corresponding metric brought into question.

Anomalies are insights that shed light on outliers in data. They notify sellers of unexpected changes that have been detected and monitor the evolution of various metrics to spot any irregularities in account data. Good Practices are a curated selection of eCommerce essentials that are ideal prerequisites to help Amazon sellers improve their business metrics. While they are not based on a user’s personal data, they cover basic industry knowledge.

Leveraging Amazon Insights From AI

Perhaps the greatest benefit of relying on AI to generate Amazon insights is the time-saving factor. For example, with DataHawk’s Insights feature, sellers can receive a message informing them that AI has examined the seller’s product listings and determined that one listing, in particular, can be improved organically by ranking higher for a specific keyword. Avoiding any data analysis, the seller would have to do otherwise to reach this conclusion. After receiving the Amazon insight, the seller can just reincorporate the given keyword into their listing more frequently to rank higher for it.

The image above is DataHawk’s AI-generated Amazon Gross Sales Anomaly Insight, which indicates that the software detected an unusual or unexpected change in the user’s Seller Central account’s gross sales data. This simple observation can prompt a seller to reevaluate their metrics as there may be a potential issue with their business’s operation or sales strategy.

For example, this type of insight could forecast a problem with the company’s product or marketing efforts. On the other hand, a sudden increase in gross sales could indicate that the company has found a successful new sales strategy or that demand for its products is growing. Identifying and analyzing gross sales anomalies can help a business make informed decisions about how to improve its operations and increase its revenue.

Conclusion 

In short, data analytics is a powerful tool that can help Amazon sellers and other eCommerce brands optimize their daily operations, increase revenue, and gain a competitive advantage. However, extracting valuable insights from the massive amounts of data collected can seem overwhelming.

By understanding the importance of data-driven decisions, identifying valuable insights such as sales trends, SEO, competitor analysis, product listing analysis, product reviews, advertising performance, and business operations, and using tools such as Amazon’s own analytics or third-party software, businesses can leverage their data to make informed decisions and drive their success in the coming year. With the help of data analytics and AI technology, businesses can stay ahead of the competition by extracting the valuable Amazon insights they need to achieve long-term growth.