Introduction

You have decided to be (more) data-driven? rely less on the infamous “gut feeling”?

That’s great, but how will you do it? What should you address first?

You cannot go from “gut feeling” to “data-driven” for all facets of your business in one day.

Therefore, in a way quite similar as companies do with a “digitalization” process, you need to go through a Data Transformation Process.

At Boxalino Winning Interactions, we are there to help, not only with our technologies, which will be the key pillar of your success, but also with a concrete methodology you can apply practically, step-by-step and area-by-area throughout your activities.

In this post, we are presenting what this methodology is and what it means in the concrete example of Google Shopping optimization.

Step 1: The Detailed Tracking of what appears on the screen

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

But in to become really data-driven, it is required to adopt a more radical tracking approach: “track everything that appears on the screen of the visitors devices”.

For example, this means that if a visitor comes to a Product Detail Page (PDP) and sees a delivery of 2 weeks because the product is not currently on stock, such information should be tracked at that moment and in a structured way (which means a clear name and easy to analyze values like a color code ‘orange’ and the exact delivery date shown to the visitor: ‘22.08.2022’). Also, it should only be tracked if it did appear on the screen of the device of the visitor (and for how long) and not simply be tracked because it was part of the content of the page.

Such radical tracking approach is important for the following reasons:

1. In order to evaluate what factors have an impact on your KPIs (conversion rate, average order value, etc.), you need to assess it on the basis of what your visitors have seen (and even how long they looked at it which might indicate that it was a point of importance for them) and not only that it was part of the page.

2. One could argue that the fact a visitor saw this information (which depends on the stock status at that moment) can be calculated considering the product URL, the stock at the beginning of the day and the number of orders done that day before the time of the page view,.
   But it is much more effective to directly “track what the visitor has seen”, removing therefore both the effort of making such calculation and removing the risk that this information might not be fully reliable due to the complexity of the calculation and other data issues.
   Also, you will want to track many elements on many pages and you will not have the resources to do so if each event requires a complicated calculation to be analyzed accurately.

3. You might change over time the way that you are displaying the product delivery (going from a color code to another, adding some icons or changing them, indicating the exact date, or the duration instead). All these visual differences should alter what is tracked because it did affects what the visitors saw.

In short, if you track what visitors see precisely when they see it, you track final and definitive data you can analyze directly and efficiently without doubting the validity of the data.

Use Case: Google Shopping Optimization

In the case of Google Shopping Optimization, the tracking of what is happening before the user clicks on the organic or advertised product in Google is provided by Google directly.

Make sure to integrate these 3 data transfers provided by Google to BigQuery to make them available for analysis: Google Ads, Google Analytics 4 and Google Merchant Center as documented here.

The tracking to be done is now the one which is happening on your web-site, typically on these pages:

1. The Product Detail Page

2. (all other pages the visitors might visit after landing on the Product Detail Page)

3. The Basket

4. The Checkout

As part of our “area-by-area” methodology, we recommend you to do first basic tracking for the pages of 2, 3 and 4 (as per JS Tracker API standard events, without the parts requiring ‘bx-attributes’).

You can (and should) address them as well in the future, but understanding exactly what is happening on the Product Detail Page is the most important task for your Google Shopping Optimization.

Therefore, for this case, focus your tracking effort on the point 1: The Product Detail Page. Here are the typical visual elements which should be tracked:

1. Main Image (and image sliders)

2. Prices (main and other displayed pricing information, like original price and discount)

3. Delivery (stocks, delivery time, possible delivery options, …)

4. Ratings and reviews (number of stars, number of reviews, display of the reviews, …)

5. Variant selection (if several options are possible)

6. Add to basket, add to wish-list and other options (pinning, …)

7. Loyalty program information

8. Description: text, expansion of text, description sections, table(s) with information, …

9. Recommendations : similar products, cross-selling products, bundles

10. Advantages: free gifts, …

11. Related content

12. …

As a focus, consider that everything appearing in the first page scroll (on a desktop screen) 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 (so you have at least an idea if people scroll so deep at all).

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 :

  • 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).