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Source → Results tell you what is the result, but not why you have it and what to do to improve it
For example: My return on ad spend is very law low for one of my key brands and therefore the Google algorithm doesn’t allocate much budget to it. What should I do?Behaviors → Results are hard to put into context and it is quickly hard to know if you are addressing a situation which can be improved
For example: My mobile conversion rate is much lower than my desktop conversion rate and I invested months of work to try to improve it until realizing this is the case for most e-shops
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Data
These are the best kind (at least for data scientists). Basically it means that the change you want to test requires no change in your processes (IT or otherwise) and can be done behind the scene.
This is the case for example of any algorithmic or rule-based change in the sorting / selection of products in product listing or product recommendations. The change simply modifies what product will appear in which context and to whom.
The typical Data change consists of an data processing in BigQuery which results in pushing data to the Boxalino Lab (which are then automatically uploaded in Boxalino Real-Time Platform) and then to configure the Boxalino Admin to make usage of these new data, typically in an A/B Test.Process
we are changing/improving the management of our e-shop based on new analytics
example: we are improving our stock management process based on the information of the most viewed product with non optimal delivery timesVisual
we are changing what the user sees on the web-site
example: We are making a visual change on the page to show similar recommendations higher on the pageHybrid
This is a very common case typically when both Data and Process or Data and Visual are both needed together in a change, or even all 3 together
example: create margin groups to change the campaigns of Google Ads and change the source
A data change is required, but also a structing of your Campaigns which is a change in your processes
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