With the rise of machine learning, Google Ads and Google's advertising network are becoming increasingly predictive and intelligent.
With the rise of machine learning, Google Ads and Google's advertising network are becoming increasingly predictive and intelligent. To stay ahead of the curve, advertisers must be prepared to adapt to innovations in Audience targeting, advertising Automation, and multi-touch Attribution. So, to help you better understand what is possible with Google's automation, audience and attribution tools, we've put together a quick guide to what you can do to improve your advertising efforts.
The rise of automation in recent years has been a game changer for advertisers. For example, Google's Smart Bidding uses enhanced audiences as signals for improved bidding efficiency, to help drive conversions and improve conversion value. This targeting feature utilises machine learning in auction-time bidding to maximise results. Smart Bidding strategies include:
- Enhanced CPC – aids manual bidding in getting more conversions by automating manual bid adjustments in-line with expected conversion, i.e. by clicks that are most likely to lead to conversions.
- Target CPA (formerly known as Conversion Optimiser) – sets bids to assist in getting as many conversions as possible at the Target Cost per Acquisition that you set as a goal.
- Maximise Conversions – sets bids to achieve the highest amount of conversions for the budget you have set.
- Target ROAS – lets you bid based on a target return on ad spend (ROAS) that you set, helping you get more conversion value or revenue at this target ROAS.
We would advise advertisers start with Enhanced CPC and then move towards the most appropriate Smart Bidding strategy which best aligns with your advertising goals. For example, if you have limited conversion data in you Ad account, it is recommended that you use Enhanced CPC. If you have a specific cost per conversion in mind, then Target CPA may be for you. If your campaigns are greatly limited by budget, then Maximise Conversions could be the most appropriate bid strategy to more aggressively go after conversions for the budget available.
Your customers are on their digital devices at multiple times throughout the day, so there are many opportunities to engage with those who are valuable to your business. With an Advanced Audience strategy on Search, you can reach people who are most likely to take action using real-time and first-party data.
Combining automation tools with audiences is integral to maximising the success of your Ad campaigns, including the following:
- Remarketing Lists for Search Ads (RLSA) – increase high-intent returning visitor traffic by tailoring your search campaigns based on whether a user has previously visited a website, and the pages that user viewed.
- Similar Audiences – This is a useful targeting feature that is based on first-party data lists (remarketing mainly), that helps to expand the reach of your best-performing audiences by targeting new users who have similar characteristics to your current site visitors.
- Customer Match – With Customer Match, you can utilise data that customers have shared with you offline and online, by uploading email lists to your Search campaigns.
- Detailed Demographics – Detailed demographics are an expansion of the current demographics in Google Ads, allowing advertisers to target audiences by marital status, home ownership, education, and parental status.
- In–market – These Audiences allow us to target users who have been researching a certain service in the last 30 days, indicating that they are going to convert in the near future. There are many high-specific in-market audiences available to add to your Search campaigns – from Cars to Insurance, Clothing and Air Travel.
Audiences are now as important as keywords in driving the highest quality traffic to your site. The good news is that if you are already using a Smart Bidding strategy, just add the relevant audiences to your campaigns and Google will use all of this data to bid higher or lower on these audiences, based on their conversion potential.
An attribution model is a rule, or set of rules, that determines how credit for conversions is assigned to different touch points during conversion paths. As users interact with multiple touch points before they reach a final destination, including Video, Display, Organic, Social, Search, conversion paths should therefore be analysed using conversion data. This should then be followed by moving to the most appropriate Attribution Model based on time lag and conversion windows. So what are your options?
- Last-click – Although this attribution model is currently the default for all conversions on Google Ads, we would never recommend Last Click. By focusing only on the last interaction before the conversion occurs, Last Click oversimplifies the customer journey and gives an inaccurate picture of the conversion data.
- Data-driven – This isan algorithm-generated model, which reviews all touch points during the customer journey and then determines which channels were most effective and at which point they were most effective. This is the â€˜Holy Grail' of attribution models, but requires a minimum of 600 conversions in the past 30 days. We would always recommend using DDA if your campaigns meet the minimum requirements.
- Linear – With this model, each touchpoint in the conversion path shares equal credit for the sale. This is recommended if you have a new account or limited data within your existing account.
- Time decay – Looks at all interactions leading up to a conversion, however most of the credit is given to recent interactions leading up to a conversion. This is recommended if your product has a short conversion cycle with a 1.5 day time lag or less between first click and conversion.
- Position-based – Most (& equal) credit is given to the first and last interactions, effectively telling us what began the conversion and what closed it. This is recommended if your product requires more consideration and has a time lag of over 1.5 days between first click and conversion.
Want to know more about automation and machine learning? Have a look at our blog – Machine Learning: The Future of Digital Advertising?
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