7 steps to picking the right Amazon analytics tool
As Amazon continues to share more data with third-party (3P) Sellers and first-party (1P) Vendors, it becomes more important to take your analytics tool choices seriously. But making the right decision has never been harder. More data means more options, and more third-party vendors touting the benefits of their specific tool.
Although we highly recommend our solution, this article isn’t really about what we do. It’s about providing a framework that will allow you to assess your business needs and align that with a better understanding of what the market has to offer — and whether or not you need to invest in an analytics tool at all.
Step 1: Establish your business objectives
Every investment should start with outcomes. If you want to improve your Amazon analytics capabilities, that should be done in order to drive specific business objectives. That could be:
- Grow your business by 30%
- Increase profitability by 10%
- Successfully roll out a new product
- Increase market share
These high level goals will help you deploy the software you eventually pick. But it will also help you hone in on the features that are most important. For example, if you want to increase your market share, look for a tool with sophisticated competitor analysis capabilities. If you want to increase profitability, you should look for a tool that helps you drive down marketing costs, upsell to customers, and better understand customer lifetime value.
Establishing your business objectives is just a starting point. But it’s a critical step that you shouldn’t overlook. Fundamentally, it’s an important thing to understand regardless.
Step 2: Investigate what Amazon offers for free
Amazon offers a number of free data reporting and analysis tools. Seller Central and Vendor Central both offer large amounts of data. There are the Amazon Advertising reports, and data within Amazon MWS — which (to be fair) you often need a third-party tool to access. But probably the most sophisticated analytics option is ABA — Amazon Brand Analytics, which comes free if you qualify and enroll with the Amazon Brand Registry program.
Before deciding to invest in a third-party option, it’s worth investigating whether or not these solutions offer enough capability to get the job done. But while there is a lot of great data here (it’s where most third-party tools pull their information), these reports present two significant challenges. There is a:
- lack of hard numbers
- lack of context
Most of the native Amazon reports don’t provide you with specific data points. Instead, they focus on comparative analysis. For example, the ABA Search Term Report will tell you the most-searched-for phrases, and the most clicked on ASINs, but not search volumes or how many clicks.
You also don’t get much product-specific information, or insights into what you should do in order to improve outcomes. To do that, you will need to manually look up product listings and augment the data using spreadsheets and manual review.
Fundamentally, Amazon’s reporting functions are great for small businesses. But if you have a lot of product listings, the process of going from data to action is pretty unwieldy, and more sophisticated solutions will serve you well.
Regardless, ABA, specifically, is an important tool to understand. If you want to learn more about best practices, check out our free eBook — Mastering Amazon Brand Analytics.
Step 3: Match the size of your operation to your data needs
Larger operations benefit from more sophisticated tools. The more products you sell (both in terms of product lines and product volumes), the more variables you have to deal with and the more value you will get out of data analysis. More sales means more data, and more data means more accurate analysis and more challenging manual processes.
Starting out, you can go a long way analyzing the performance of your Amazon listings with spreadsheets and Amazon’s own tools. All of the data reports offered by Amazon can be exported in .csv files, and you can schedule emails to deliver them in regular intervals. Amazon Brand Analytics data is not available indefinitely, so exporting this data is also essential.
Manual processes will let you track simple long-term trends and craft personas that go beyond aggregate demographic data. But the volume of data flowing through Amazon will eventually make manual analysis hard and challenging to scale.
There are a number of tools that can help you automate excel. Supermetrics, for example, is a good choice. So is PowerBI or Tableau — although you will still be required to manually refresh data. You are also then investing in a piece of third-party software. So, whether or not you already have access to these tools for another business purpose will impact the effectiveness of this approach. But each of these solutions can provide good dashboards and exploratory analysis.
With that said, if storing and classifying your long-term Amazon customer data in .csv files fills you with dread or your organization is just too big to take this approach, third-party analytics tools can:
- Centralize and simplify the review of data trends
- Provide both long-term and short-term views
- Augment the detail of your analysis beyond what you could achieve manually
Step 4: Look for integration across Amazon data channels
Amazon data can provide some deep insights into customer behavior and buying trajectories. But the data is siloed across multiple reporting functions — and the pieces are hard to put together. Tools are best when they let you pull data from these multiple channels. Leading-edge solutions deploy machine learning and AI to adaptively learn from data trends and seek out the most useful data reports for you.
You want to look for automation software that can pull information from across the Amazon ecosystem (ABA, product pages, MWS, the DSP). Amazon MWS is particularly critical if you want to gain a transaction-level view of different products — rather than simple high-level information. But you also want a tool that can provide you with context based on data from other sales channels you use.
Step 5: Prioritize transparency and data access
All of this data analysis is about finding patterns. By understanding trends, you can repeat success and mitigate failure. With enough data, you can make accurate predictions and take action to stay ahead of the curve. The less time your team needs to spend crunching numbers, the more time that can be spent on using data-driven insights to strategize and act to create outcomes that matter.
You want a tool that tells you why it’s making certain decisions and lets you export data. This helps with integration across your entire system, but is also signs of a quality process.
By being confident in what sources are driving your decisions, you can review the relationship between your marketing efforts and sales outcomes. With daily updates and long-term trends, you can make cross-department decisions on internal adjustments on the most profitable days, products and campaigns.
Step 6: Always think about outcomes
A good analytics tool will help you:
- Shortcut steps to taking actions
- Contextualize information
- Provide insight
- Tell you why something is happening
- Offer suggestions on how it can be improved.
Here are some of the most important outcomes your analytics tool should be able to deliver, making the best use of your Amazon data.
Create detailed customer personas
Pulling information from across the Amazon ecosystem builds a far more detailed picture of who’s buying your products, where they’re from and which products they buy. The challenge is sorting this information to create a live picture of detailed customer personas. An analytics tool should do this for you.
Convert purchasing patterns to ‘buying trajectories’
Amazon delivers data on purchase volumes, and Amazon MWS provides granularized information on the different products that specific individuals buy, as well as the order in which they are purchased. When cross-referenced with persona categories, you get ‘buying trajectories”. An analytics tool should create clear projections, allowing you to target ads and create bundles.
Work out how much each customer is worth to you
Over the long-term, analytics tools can crunch the numbers on repeat purchases and match that information with persona data and PPC data. Over time, you develop an increasingly detailed estimate of how many products different buyer personas are likely to buy. Deploying machine learning and AI, predictions can be made with stunning accuracy — delivering robust customer lifetime value (CLV) calculations.
Improve search terms and keywords
The problem with standard keyword research tools is in the detail. Examining your current Amazon PPC product performance and scoping out new search terms is only half the battle. Analytics tools and machine learning can do the heavy lifting required to spot opportunities by comparing your product categories with how different search terms perform. When combined with persona categories, geo-location analysis, basket comparison and purchase patterns, you get a powerfully detailed picture. A good analytics tool will present this information in an easy to understand dashboard.
Step 7: Is there a plan for the future?
You don’t want to invest in software that’s only going to work for your business today. You want a tool that will help you grow, and scale with that growth — continuing to add value. When thinking about business objectives, you should go beyond your immediate concerns, and look at how you might want to use that tool in the future. What do you predict your next two or three business objectives will be?
You also want to look for a supplier that offers support, and is focused on the future. Amazon is a continually shifting ecosystem. Does the company you're partnering with understand that, and are they committed to continually improving their products and services to align with new data as it becomes available.
Consulting services are also a valuable addition. Data specialists often have insights into how their product can be best deployed, helping you hone your strategy. Although any SaaS product should have a self-service option, these kinds of advanced consulting capabilities help future-proof that relationship. What’s more, analytics can reveal very specific business issues that professional services can help you resolve.
The right tool for the job
The key question when choosing an analytics tool boils down to how it will help you succeed against your competition in an increasingly competitive landscape?
Your analytics tool must give you:
- Insight into customer search behavior and trends
- Access to the right data
- Increase in overall market share
- Increased ROAS/decreased ACoS and greater sales.
It cannot be stressed enough that the ultimate power of any analytics tool depends on your ability to act. The cleverest insights won’t impact your bottom line if they simply sit on the shelf — faster and more actionable results are fundamental when choosing the right analytics tool. Keep that in mind. If you want advice tailored to your specific needs, get in touch — we’d be happy to help.