5 Predictions on how GTINs Will Drive the Retail PPC Evolution

On May 16th Google started to require online retailers to embed Global Trade Item Numbers (GTINs) into their Merchant Feed. Most teams are still getting used to the operational changes this has brought.  Let’s take a look beyond that. I’ll dare to make five predictions on how GTINs will drive PPC evolution:

  1. Feed Title optimization will become less important.
  2. Product prices will influence informed bid management decisions.
  3. How GTINs and the mobile buy button make a perfect match.
  4. Google will strongly engage in personalized advertising.
  5. GTINs will help to build actionable models for complex user journeys.


Exactly how can this small alphanumeric product ID change the evolution of PPC for online retail in so many ways?


How GTINs help to improve the customer journey in the short term

Put simply, Google’s goal is to connect visitors throughout the customer journey with relevant advertising. As part of this, Google has identified a new consumer behaviour that they have coined ‘Micro-Moments’ which describe different phases of a customer journey when using Search. A user journey is divided into three moments –  inspiration (“I need some ideas”), comparison (“Which product is best?”) and action (“I want to buy it”).

In the beginning of this process, Google will use GTINs mainly to improve the customer journey in the lower funnel.


Essentially, GTINs allow Google to:

  1. Identify the same product advertised across multiple retailers in the online space
  2. Compare prices of the same product
  3. Aggregate product ratings and reviews across all retailers
  4. Get precise product data from the Manufacturer Center to show clean product descriptions and specs in ads

You can see first results, a format called the “product card”, live on Google here.

Screen Shot 2016-06-07 at 15.59.10


Prediction 1: Feed titles and title optimization will become more and more obsolete for popular products.

An area that is likely to lose importance  in the future is title optimization for products that  require a GTIN. Currently the title, as well as serving as the ad text, is also used to match search queries with products in the Merchant Feed. The product  title currently has a significant impact on eligible impression volumes and clicks, but with Google having “perfect” titles in the Manufacturer Center, it may in the future know better what products match best with the intent expressed in a search query.

Prediction 2: The increasing market transparency will make product prices become part of bid management.

While the ability to compare market prices across all retailers is very useful for shoppers, it’s actually quite daunting for retailers. No single company was able to exploit this level of global market transparency before. The transparency levels Google provides to consumers may push retailers to further engage in price and discount battles.

For retailers it should by now become clear that with specific product searches, the impressions and clicks they get will depend heavily on the price of their product. That said, it would be good to see bidding systems of the future with built-in industry pricing benchmarks, in order to help advertisers make informed decisions. We expect retailers with prices below average to benefit from lower product ad CPCs. Product ads displaying higher prices will have to be cross-subsidised with higher CPCs to still show up. In addition to more context sensitive bid management engines, we should not be surprised to see online marketing teams influencing price-making decisions in the future more heavily.

Prediction 3: Google will strongly engage in personalised advertising to maximize advertising relevancy

In Google’s move to supporting the entire customer journey, it will make personalisation a core element in new features. Personalisation in retail includes mainly relevant product recommendations:


  • Ideas “We found these products that may be interesting for you”
  • Comparisons “Other shoppers also liked these laptops”
  • Complements “Also see these popular designer cases for your laptop”


It is easy to see that these personalisation capabilities directly match with Google’s ‘Micro Moments’. Personalisation is built on machine learning techniques like Market Basket Analysis. Using transactional data at large scale makes it possible to empirically answer questions like:


  • Which laptop did shoppers actually buy after clicking on a laptop ad X?
  • What other products did shoppers buy together with that laptop?


All these algorithms do a much better job to detect statistical patterns with unique GTINs used everywhere.

This opens a huge range of applications for Google, including:

  • More relevant (product) ads shown on the search partner network
  • Personalised product discovery pages on, let’s say, Google+
  • Audience building –  in Display, Google could allow advertisers to target audiences that have, say, recently bought a washing machine. That is a strategy that actually competitor Amazon is said to follow by offering its own Demand Side Platform (DSP)

In short, advertisers should not be surprised to see Google asking them to also transmit GTINs with their transaction data in the Google (Analytics) conversion tracking.

Prediction 4: Google combines GTINs and the mobile buy button to develop and test new personalization features

Not only will Google offer price comparison features as seen on the product card, but it will, at least temporarily, also turn into a market place on mobile devices if retailers choose to opt in to the “Purchases on Google” – also known as the “mobile buy button”. Let’s see how the pieces of the puzzles will fit together.


Purchases on Google” taken from the AdWords blog.

With retailers opting in to Purchases on Google, the company will not only know what product ads shoppers clicked, but also what they finally bought. Implementing GTINs is a step forward in eliminating one of the grey areas of the performance marketer’s map. That is, when used in conversion tracking, GTINs may bridge the gap between what shoppers searched, the ads that were clicked and the products that were finally bought. This enables Google to develop a whole new range of personalisation, bidding and conversion attribution applications.


Prediction 5: GTINs will help to build actionable models for complex user journeys

GTINs will help to understand complex, multi-channel customer journeys, including offline, better. A 90% of all retail purchases  still happen offline. But, according to a study undertaken by Deloitte Digital in 2015, a 64% of in-store sales are influenced by digital. As of now, Google offers Store Visits, a feature that enables to estimate how many people went to a bricks-and-mortar store after clicking an AdWords ad. Still, offline purchases can not be tracked back to the parts of the customer journey that started online.


There are still many more parts missing to connect in-store sales with preceding online research. However, GTINs enable us to correlate advertising clicks with transactional data more precisely.  

In the long run, this capability will bring marketers new options to unify customer journey data silos and allow them to build actionable conversion attribution models that will help to finally mitigate the Wanamaker problem: to know which half of the money you spend on advertising is wasted.



Don’t be surprised to see GTINs in a lot more contexts than just the Google Shopping feed. It serves as the key data to understand customer journeys in retail and to provide personalized experiences that help to create frictionless experiences across channels. Yes, that new level of market price transparency is daunting, but there is a clear interdependency on both sides. Google depends strongly on a healthy, varied and competitive ecosystem to drive click volumes and bids among advertisers and to be able to compete with Amazon.



Alexander Paluch

Alexander has several years of experience as an IT consultant and Product Owner in the e-commerce industry. Today he works as a Senior Product Manager for crealytics.

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