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In this post, we are presenting what this methodology is and what it means for a concrete example: Google Shopping optimization.

Step 1: The Detailed

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Tracking of what appears on the

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screen

In the old days, what was meant with tracking was to keep a log of the page URLs visited by each visitor. Arguably, you might already do better than that today (for example, you might already use options like the Google Analytics Enhanced Ecommerce tracking).

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As a focus, consider that everything appearing in the first page scroll (desktop) should be tracked with full details, and that most of what appears on the second and third page scroll should be tracked with good details and that at least some elements should be tracked in each page scroll below.

Step 2: The Analytics of the Source, the Result & the Journey

Here we describe how a the analytics should be built a simple principle:

• The source = the analytics table with the data related to the use-case

• The results = the business KPI as metrics

• The Journey = Everything happening in the journey and defining the difference between the source (proxy) and the result (what was bought) as dimensions for segmentations

In the case of the Google Shopping Optimization:

• The source = the spending of advertisement each day on each product in each Google Ads campaign

• The results = the transactions, revenue and margin generated by the source

• The Journey

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  • The difference between the product click and the products sold

  • The different values displayed on the landing page for each parameter tracked with always the number of display / sessions and the results from people who saw this / clicked on it

Step 3: The Data-Driven Hypothesis: Collect & Conclude

Here the idea is to share the analytics internally and to collect a poll / form to answer two types of questions:

• what look like the biggest issues

• what could be the most important changes

Step 4: Targeted Testing: Data, Process & Visual

Here we speak of the set-up of a targeted testing a change which could be of the following types

• Data
  we are changing/extending the calculated data to operate a change
  example: create margin groups to change the campaigns of Google Ads and change the source
  

• 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 times
  

• Visual
  we are chaning 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 page
  

About Targeted Testing:

• Testing
  If possible, we do the change as a test (if possible an A/B Test) to have a direct causal understanding of the effect of the change
  

• Targeted
  we are doing the testing in a targeted way if possible, which might mean “personalized” either individually or in a customer segment but can also mean we are implementing the change a segment of the product sortiment

Step 5: The Learnings & the Prioritization

Here we discussed how to interpret the learnings (results of the tests)

as well as how to prioritize a large collection of data-driven optimization hypothesis by doing a prioritized spread-sheet with the ICE scoring (Impact, Confidence and Ease).