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What are the 4 levels of ecommerce data analysis and how to use them?

2 min read


Data analytics technologies are rapidly evolving.
For e-commerce leaders in consumer brands, this enables a major uplift in productivity and profitability.
Consumer brands that sell online are now using tools
that utilize AI, Machine Learning and Workflow Integration to speed up processes, improve decision support
and execute at even greater speed and scale.

Let’s look at how the science has evolved and why digital shelf analytics vendors should continue to invest in platform development that will sustain their customers’ competitive advantage.

What are the 4 levels of data analytics?


Descriptive Analytics tell you what happened.

Simple scorecard-type dashboards that track the performance of your SKUs in online stores can show you what happened yesterday that positively or negatively affected your online sales. 

Was your product in stock? What was the price? How many ratings and reviews does it have? How did it perform in organic search? 

Descriptive analytics allow you to see what happened in your digital store.


Diagnostic Analytics tell you why something happened.

Let’s take an example. Let’s say sales of your product is slow in Retailer X although traffic to the product landing page is high. Traffic to the same product’s page on Retailer Y is similar yet sales are higher. Why?

Drilling down into the data might reveal that the content on Retailer X’s product page is poor and is having a negative effect on conversion. Alternatively, you might find that the product is not yet available to ship from Retailer X but is immediately available from Retailer Y. 

Now you know the root cause of the problem.


Predictive Analytics tell you what might happen next.

Predictive analytics will help you make decisions today that will influence what happens tomorrow based on similar events in the past. Let’s say your seasonal product has a limited life cycle and you want to reduce waste by manufacturing just enough to meet demand. 

Now you can forecast sales and make important distribution decisions.


Prescriptive Analytics tell you what to do next.

Today’s next level analytics tools use Artificial Intelligence, Machine Learning and Workflow Integration to empower consumer brands to make better decisions while reducing human effort. 

In some instances, these powerful algorithms will also execute actions, measure the effect and continually improve.

This gives brands:

  • a zero touch solution for growing e-commerce sales and 
  • a magic formula for increasing market share.

What’s not to love about that?


Prescriptive Analytics for the Digital Shelf with Workflow Integration




How have data analytics tools evolved in e-commerce?

Since the early 2000’s, harvesting and processing data from multiple online sources isn’t a tough job, but Digital Shelf Analytics tools have progressed quite a way since then. Today, we are implementing advanced, automated decision-making tools such as eStoreCheck and AMZ Navigator that enable brands to make ever-sharper decisions from massive and complex data sets. 

Our next level e-Commerce Automation Suite of tools uses workflow integrations to reduce manual effort and increase productivity. Today's e-commerce teams need actionable insights that enable them to work at speed and scale:

  • How quickly does it point me to sales opportunities?
  • Can it help me get more things done in less time through targeted work?
  • Can it give me a magic formula for growing market share?

If you’d like to find out more about our next level e-commerce decision automation tools, just get in touch today.

You can also download our full White Paper The Power of Prescriptive Data Analytics for CPG Brands




Jan Krzysztofik
Jan Krzysztofik
Head of Demand Generation

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