13 Oct AI advertising with Google
In this article, we cover the AI-driven features within Google’s advertising platform that you could be using to automate and optimise your campaigns.
AI is the latest trend in the media and advertising industry with most brands and marketers wanting to get in on the action. But the reality is that you may already be using AI and don’t even realise it.
Dynamic Search Ads – Hyper-targeting your audience
The customer journey is more complex than ever. According to a study by Google, 90% of consumers who own multiple devices switch between screens to complete a task and typically consult more than 10 sources before making a purchase.
This presents challenges for marketers who have to tailor their ads at scale to provide customers with information that is relevant to their moment based on a number of factors including time, device, location, interest, and intent.
Out of the 3.5 billion searches that happen daily on Google, 15% of them are new. This means that you won’t be capturing them within your keyword list and are missing out on potential customers who are at the buying stage. With Google’s Dynamic Search Ads , you can aim to capture these customers. The tool dynamically customises keywords based on the actual search terms used by the customer, allowing you to take advantage of these new searches.
Ad Customisers – Cutomisation at scale
This is another new feature that allows further customisation at scale to every context: keyword, time, device & user location. To activate this feature you first need to create a data feed in your AdWords account under ‘Shared library – Business data’. Once you have done that, you’re ready to set up customisers in any text ad within your Search or Search with Display Select campaign. A customiser can show text from the data that you’ve uploaded, or text from a function within your ad. For example name of the product, cost of the product, store location, etc.
Implementing Ad Customisers increased overall CTR by 32% for the brand Extra Space Storage and increased the conversion rate by 8.7% for Rosetta Stone. Source Google.
AdWords Smart Bidding – Opening up big possibilities
Bidding Automation is an advanced machine learning tool that drives informed bidding decisions by taking into consideration factors such as the customer’s operating system, language settings, location, time and browser.
AdWords Smart Bidding is an option available at the campaigns settings level however if you would like to test Smart Bidding for different strategies such as enhanced CPC or CPA within an existing campaign, you can create an A/B test by determining a portion of the traffic that you want to go to through the A/B test experiment.
Another useful feature is the simulation tool that allows you to determine the most profitable CPA target against a given budget. The best part is that it will also predict at what point your ROI reaches its optimal level, where adding any more budget will start to decrease your ROI.
Multichannel Attribution – Looking past the last click model
Technology has changed everything. People now have a huge selection of devices, channels, and content to choose from, across multiple platforms. This creates a lot of fragmented data which can be hard to track and action for marketers. Legacy, linear paths to purchase were relatively simple to measure. Today’s paths are not.
Speakers at the Google Masterclass, stressed the importance of moving on from the last click model and adopting multi-channel funnels, such as the ones that are available in Google Analytics, to present a 360 view of the path to purchase.
Google announced that they will be launching Google Attribution (free version) and Google Attribution 360 (paid version) to all users in early 2018. Attribution will consolidate data from all of the Google advertising channels and track customer touchpoints across all of its apps and products.
The deterministic cross-device graph is powered by Google’s largest web properties, with over 1bn 30-day active logged in users and algorithms that stitch the path fragments for an individual’s entire journey, enabling the model to understand the full customer journey.