Automating your Digital Advertising with Feed Based Technology | Articles

Digital is continuously growing and complexifying. This is especially true for eCommerce that needs to manage digital advertising campaigns with the main objective of having a positive return on investments (ROI) at the end of the day.

Being able to reach the right people with the right messages, which in the eCommerce language basically means being able to touch the visitors with adapted messages regarding their level of engagement towards the brand and where they are situated in the conversion funnel is one of the main challenge.

1. eCommerce Growing Challenges

Automation Opportunity

Among all channels, Search Engine Advertising (SEA) is probably one of the most important for eCommerce as it usually generates highly qualitative traffic to the website and has one of the highest ROI among all the advertising channels an eCommerce could use. On the other side, the market pressure on this channel keeps on increasing as we are seeing more and more actors entering the auctions with the main results of increasing cost-per-click.

eCommerces have usually the most complex SEA account(s) structure(s) since they need to cover a very large range of products all classified under different families and universes. Moreover, one of their main challenge, or the agency responsible of their SEA, is to always be up to date with the products stocks. Why so ? Because they simply don’t want to pay for visitors looking for a specific product they don’t have in stock at the moment. In the worse case, it can lead the user to a 404 page that will be irritated and probably left with a negative image of your brand resulting in a negative return on investments.

Being aware of this challenge we have been looking for solutions to help us automatically update the campaigns, ad texts and keywords we are buying based on the products availabilities on the online webshop. This article will describe the tool we created internally in order to set-up this flexible SEA structure.

2. The Tool

2.1 Structure and automation

As explained in the previous part of the article, the main goal of this tool is to make sure that we only buy keywords that are linked to products currently available on the website. The technology we developed internally leverages all the available information in the Google Shopping product feeds that is available on the Google Merchant Center (GMC). This allows us to cover all the catalog with very granular search campaigns and keywords. Indeed, those feeds almost have all the required information such as:
  • Product Name - to define keyword
  • Product Description - to enhance and create variation of the keywords
  • Website URL - to direct visitors to the right landing page
  • Product availability - to make sure we only appear on relevant queries

Those elements are sufficient to define which keywords need to be bought and where people need to be directed. Unfortunately, at this stage we are still missing the account structure which is constituted of:


In order to be able to define a logical structure we tweaked the product feeds by using custom labels. We used three custom labels that are used to specify campaigns’ name, adgroups’ name and the language of the campaign.

  • Custom label 0 = ‘Main Category’ = campaign
  • Custom label 1 = “Sub-Category” = adgroup
  • Custom label 2= “FR” or “NL”

At this stage we are already able to:

  1. Automatically create a structure in terms of campaigns and adgroups
  2. Populate the adgroups with relevant keywords that are based on the product name and product description columns in the feeds
  3. Lead the people to the exact website page that displays the product

In order to have a complete SEA accounts we still need to define default ad texts and settings (at campaign and adgroup level). There we go, we now have all the required informations to automatically create a SEA account structure.

2.2 The processus

The next step is to set-up the process that will automatically update this SEA structure. To do so we simply created a custom javascript that runs every night and compare current state of campaigns / adgroups / keywords that are active in the account with the updated file that contains the list of all available products on the website. Here are the different steps that the application will go through in order to perform its tasks:

  • Access the shopping feeds through the Google Merchant Center API
  • Compare the shopping feeds’ files with the current list of campaigns, adgroups and keywords in the account
  • Adgroup level:
    • New family added in the feed ? a new adgroup is created in the account
    • Previous family still in the feed ? reactives the adgroup in the account
    • Previous family not in the feed anymore ? keeps the adgroup paused until new update
  • Keyword level:
    • New product added in the feed ? new keywords are added in the account
    • Previous product still in the feed ? reactivates the keywords in the account
    • Previous product not in the feed anymore ? keeps the keywords paused until new update
  • A log file containing all changes is compiled as a final step.

3. Conclusion and what's next?

In terms of performances, the volume generated through this process remains limited but is highly qualitative as we are buying very specific keywords such as product references. This new account is not replacing our classical account but is bringing extra values on additional keywords by leveraging the Long Tail. Finally, it also helps reinforce performances of our shopping campaigns as we can now appear with both shopping ads and textual ads on product related queries. Knowing that by appearing two (or even three with the organic results) times on the same SERP improves performances of all channels this has definitely been a winning move.

In terms of next steps, we now need to focus on increasing the volume of traffic generated through these campaigns. How? Mainly by enriching the process of keywords creation but also by buying able to enrich the feeds with extra information as those are the key feature of this tool.

Author: Julien De Visscher


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