We've designed Seelk Studio as a simple tool for you to analyse your sales data, nonetheless we give you the possibility to track as many Amazon products (ASINs) as you wish. 

What are datapoints?

Seelk Studio enables you to group your Amazon products using Custom Attributes (read our article on Custom Attributes). This means that you can have several hundred Amazon products grouped in a sub-group of a Custom Attribute. This is not only a clever way for you to analyse your Amazon sales (read our article about Contribution to Growth) when comparing date ranges, but also means faster load performance as the sale data has already been pre-computed. 

However, in many cases you want to analyse your sales at ASIN level - this is normal (also encouraged 😄!) - Seelk Studio enables you to have the same Contribution to Growth calculation when looking at individual ASINs in a data table.

In many cases, Seelk Studio scales very well at ASIN-level and you can quickly output sales data for several hundreds of products linked to your account.

However, in some cases, when you select a larger date range, such as year to date at weekly aggregates and across several thousands of ASINs - the BI is thus dynamically calculating several 10,000s of data points and you may experience performance issues. 

A further point is that if you have ASINs which are on multiple Amazon marketplaces (ex. France, Germany and the United Kingdom), and you have not filtered on that specific marketplace, Seelk Studio needs to compute sales data for each of those marketplaces. All these different computations are referred to as data points. 

Sales data performance speed 

The long and the short of it is that when you have a large account with several thousands of ASINs and you are loading data at ASIN-level across a large date range, you may experience data performance issues on an average computer (ex. Intel i5 with 8GB RAM) and the loadtime might take more time than expected.

Below is a quick breakdown of the estimated loading time required for different thresholds of datapoints:

5000 datapoints ~15s
10000 datapoints ~19s
15000 datapoints ~20s
20000 datapoints ~22s
30000 datapoints ~29s
40000 datapoints ~34s
50000 datapoints ~50s
60000 datapoints ~55s
100000 datapoints ~90s

We are steadily working on improvements to Business Intelligence as major priority in our product development and aim to deliver a system that can handle several tens of thousand data points in the very near future.

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