It was not long ago that everything in Google Ads was managed manually, providing advertisers with greater control over their campaigns. However, in today’s rapidly evolving digital landscape, leveraging artificial intelligence (AI) in your Google Ads strategy has become a necessity, not merely an option.
Google’s targeting, including keyword match types, continues to expand, pushing advertisers up the funnel. Without AI-enhanced bidding during real-time auctions, your ads may be displayed to a less qualified audience, resulting in diminished lead quality. AI offers numerous benefits, such as automation and the capability to reach a broader audience.
However, if not implemented correctly, AI can actually work against you rather than for you. You may lose control by not being able to limit your efforts to bottom-of-the-funnel targeting and ensure it is effective before experimenting with broader targeting options in separate campaigns, each with its own test budget over time. In this blog post we shall discuss what Google’s AI is in more detail, as well as tips on how to make Google’s AI work for you and regain control over your advertising again.
What is Google Ads AI / Machine Learning
Google Ads AI, also known as Google Ads Intelligence, is a suite of artificial intelligence-powered tools and features designed to enhance the effectiveness and efficiency of advertising campaigns on the Google Ads platform.
When used correctly, AI can simply the ad creation process, improve targeting, and increase the return on investment for advertisers of all sizes.
Google Ads AI, a suite of machine learning tools and algorithms, is designed to optimise and transform how businesses engage with their potential customers online. This innovative approach utilises artificial intelligence to analyse vast amounts of data, automate complex processes, and enhance decision-making in real-time, empowering advertisers to achieve better outcomes with less manual effort.
How does Google Ads AI Work?
Smart Bidding: At the heart of Google Ads AI lies its Smart Bidding feature. This tool uses machine learning to optimise your bids in real-time, aiming to maximise the return on investment. Smart Bidding strategies like Target CPA (Cost Per Acquisition), Target ROAS (Return On Ad Spend), and Maximise Conversions allow advertisers to tailor their bidding strategies to match their specific business goals. An AI-powered automated bidding system that optimises bids based on historical data and conversion goals.
Responsive Search Ads: AI also powers Google’s Responsive Search Ads, where multiple headlines and descriptions are tested and automatically arranged to display the most effective combinations to different users. This adaptability not only increases the relevance of ads but also enhances the engagement rates, driving better campaign performance. Machine learning technology that tests various combinations of headlines and descriptions to determine the most effective ad copy.
Audience Expansion: Google Ads AI can identify patterns and similarities in audience behaviour, enabling it to find new segments likely to be interested in your products or services. This expansion is not just about reaching more people but reaching the right people, thus increasing the efficiency of ad spend.
Predictive Analytics: Leveraging historical data, Google Ads AI predicts future customer actions, such as the likelihood of a click leading to a purchase. These insights allow for more strategic ad placements and content, ensuring that campaigns are more targeted and results-driven.
Automated Insights: One of the most significant advantages of AI is its ability to provide actionable insights automatically. These insights can highlight changes in consumer behaviour, campaign fluctuations, or opportunities for optimisation, guiding advertisers on where to focus their efforts for maximum impact.
Broad Match Type & Wider Exact and Phrase Match::
Exact match is no longer exact, and phrase is more like broad match than ever before. Broad Match is a powerful keyword targeting option within Google Ads, designed to reach a wider audience by displaying your ads for searches that include misspellings, synonyms, related searches, and other relevant variations of your targeted keywords. This flexibility is essential for advertisers looking to maximise exposure and capture a broad spectrum of potential customer queries.
Performance Max Campaigns: These campaigns use AI to analyse landing page content and assets to find the best converting queries across multiple Google platforms.
Conversational Experience: A chatbot-like interface that guides advertisers through the campaign creation process, from generating keywords to crafting ad copy and selecting visuals.
The Potential Benefits of AI Advertising
By leveraging artificial intelligence, Google Ads offers several advantages to advertisers:
Increased Efficiency: Automating routine tasks such as bid adjustments and ad testing frees up time for strategic thinking and creative work, making advertising campaigns more efficient.AI streamlines the campaign creation process, saving time and effort for marketers. Enhanced Targeting: Intelligent algorithms help identify high-intent keywords and relevant audience segments.
Enhanced Accuracy: AI’s ability to analyse data at scale minimises human error and ensures more accurate targeting, leading to better quality leads and higher conversion rates.
Scalability: With AI, campaigns can easily be scaled up or down based on performance data and business needs without the same proportional increase in workload.
Creative Optimisation: AI-generated assets and ad variations can lead to better ad performance and engagement.
Data-Driven Decisions: Machine learning models analyse vast amounts of data to make informed bidding and optimisation choices.
As Google continues to develop and refine its AI capabilities, advertisers can expect even more sophisticated tools and features to emerge, further revolutionising the digital advertising landscape.
Advantages of Broad Match
- Increased Traffic: Broad Match can significantly increase the volume of traffic to your ads by allowing for a wider array of search terms.
- Discovery of New Keywords: It facilitates the discovery of new, valuable keywords that might not have been considered initially, broadening your reach and competitive edge.
- Simplified Management: Reduces the need for an exhaustive list of keywords, simplifying campaign management while still capturing extensive relevant traffic.
The Potential Pitfalls of AI Advertising
Using Google Ads AI offers numerous advantages, such as increased efficiency and enhanced targeting capabilities. However, there are potential drawbacks when comparing AI-driven campaigns to traditional manual campaigns.
While Google Ads AI offers numerous benefits, it also comes with potential drawbacks when compared to manual campaigns.
While Google Ads AI can significantly enhance campaign performance and efficiency, it’s crucial for advertisers to balance the use of AI with manual oversight and intervention. This hybrid approach ensures that campaigns remain aligned with strategic business goals, adapt to new market conditions effectively, and maintain a level of creativity and personal touch that AI alone might not achieve. Here’s a closer look at some of the challenges and considerations:
Loss of Granular Control
Google Ads AI automates decision-making based on data and algorithms. While this can improve efficiency, it can also result in a loss of granular control over individual campaign elements. In manual campaigns, advertisers can make specific adjustments based on nuanced insights or emerging market trends that AI may not immediately respond to. One of the primary concerns with AI-driven campaigns is the reduced level of control advertisers have over specific aspects of their campaigns:
Wider Targeting Lead Quality
AI systems may not allow for the same level of precision in selecting and excluding keywords and audiences that manual campaigns offer.
Loss of Granular Control
There’s less control over exactly where and when ads appear across Google’s network.
Learning Curve and Initial Performance
Implementing AI-driven campaigns requires a shift in strategy and can lead to initial performance issues:
Data Collection Phase
AI systems need time to gather and analyse data, which can result in suboptimal performance during the initial learning period.
Dependency on Quality Inputs
The effectiveness of AI campaigns heavily relies on the quality of inputs provided by advertisers. If you don’t get your Google Ads conversion tracking setup correctly, including setting primary and secondary conversions- and ideally offline conversion tracking, you might not be providing an adequate training dataset.
Over-Reliance on Algorithmic Learning
AI systems require substantial historical data to learn and make accurate predictions. New campaigns or products without much historical data might not perform as well because the AI lacks sufficient information to optimise effectively. This can lead to inefficiencies or suboptimal spending in the early stages of a campaign.
Potential for Misalignment
There can be a misalignment between the immediate business objectives and the AI’s learning phase. For instance, an AI system might focus on gathering data and learning in the short term, which could detract from more direct performance goals like immediate sales or lead generation. Improperly defined campaign objectives can lead the AI to optimise for the wrong outcomes.
Adapting to Rapid Changes
AI models may not adapt quickly to sudden market changes or unique, one-off events. Manual interventions can be more responsive in such scenarios, allowing advertisers to pause campaigns or shift strategies more dynamically.
Broad Targeting Issues
AI-driven campaigns, especially those using broader targeting like Broad Match in keywords, may attract traffic that is less relevant. This can lead to lower conversion rates and higher costs per acquisition as the AI experiments with different targeting options to learn what works best.
Transparency and Understanding
Google Ads AI operates as a ‘black box’ where many of the internal workings and decision processes are not visible to the advertisers. This lack of transparency can be challenging for marketers who wish to understand exactly why certain decisions are made or why campaigns are performing in a particular way.
Limited Data:
Smaller audience sizes or unique product offerings may not provide enough data for AI to make optimal decisions.
Budget Management
Automated systems can sometimes make rapid, large-scale adjustments that spend budgets quickly, especially in competitive bidding environments. Manual campaigns allow for more predictable budget use, as changes are made deliberately by the campaign manager.
During this learning phase, advertisers might see higher costs or lower returns until the system optimises.
Bid Adjustments:
Automated bidding limits the ability to make real-time, manual bid adjustments based on immediate market changes or business needs.
Creativity Limitations
While AI can test and optimise ad combinations and placements, it often relies on predefined templates and machine learning models that might limit creative expression. Unique, innovative ideas that fall outside of these templates might not be fully realised or tested in an AI-driven environment. Poor-quality assets or insufficient variations can limit the AI’s ability to create effective ad combinations.
Limited Insights & Transparency Issues
It can be difficult to pinpoint why certain ads or keywords are performing well or poorly. AI-powered campaigns often operate as “black boxes,” making it challenging for advertisers to understand exactly how decisions are being made.
Attribution Challenges:
Understanding which specific elements of a campaign are driving conversions becomes more complex.
Contextual Nuances:
AI might miss subtle market or audience nuances that human marketers would recognise and act upon. The ability to interpret AI-generated insights and translate them into actionable strategies becomes crucial.
Skill Set Shift:
Adopting AI-powered campaigns requires a different skill set from traditional manual campaign management. Marketers need to shift from tactical, day-to-day management to more strategic, big-picture thinking. However, this depends on the reporting and measurement criteria that we shall discuss in this blog post.
While Google Ads AI offers significant advantages in terms of efficiency and scalability, it’s important for advertisers to review these potential drawbacks and put plans in place to make sure the AI is achieving your goals. A balanced approach, combining AI-driven automation with human oversight and strategic input, may yield the best results for many advertisers.
Match Type Targeting
While Broad Match offers extensive reach, it requires careful monitoring and integration with Google Ads AI to ensure that the traffic driven is relevant and likely to convert. Advertisers should regularly review the Automated Insights and performance data to tweak and optimise their keyword strategies, ensuring that Broad Match contributes positively to their overall campaign goals.
In essence, Broad Match is a robust tool in the Google Ads arsenal, ideal for those looking to expand their digital footprint and engage with a broader audience. By leveraging the full spectrum of Google Ads AI features, advertisers can harness the true potential of Broad Match, driving both reach and relevance in their digital advertising efforts.
Performance Max
Performance Max is a single campaign to run across all of Google’s advertising networks. Currently it is used more frequently in the ecommerce industry than within the lead gen/service industry. This is also the case with broad match keywords with the higher in the funnel search targeting.
pMax gives a lot of control to Google, and to use it you have to be sure that your Google Ads conversion tracking is setup comprehensively (ideally offline, in addition to online conversion racking)
Comparing AI By Industry: e-Commerce vs Lead Gen (Service Providers)
Getting broad targeting and AI to work with e-commerce advertisers that sell products online is easier than with lead generation (service providers).
E-commerce providers have traditionally had access to better data for closed-loop reporting, which allows them to view sales revenue within Google Ads. This capability makes it much easier for them to measure ROI and ensure that campaign budgets are allocated in a way that maximises sales and minimises costs, or achieves the target ROAS (Return On Ad Spend). For example, e-commerce companies can set a target ROAS, such as spending £1 to generate £7 in return (a ROAS of 700%), which maximises their potential. Previously, this was all managed with manual bidding, but in essence, AI optimises for the same goals.
For service providers, using AI to optimise for their precise objectives can be more challenging. This difficulty arises because e-commerce companies optimise for the final stage in their sales process—the actual purchase—whereas AI optimises service providers for the cost per lead, the first step in their sales process. This “input” is a less reliable indicator of customer potential and lead quality (most likely to convert into a customer). As a result, you are training Google’s AI to secure the most leads possible at the lowest cost using target CPA, without considering lead quality.
While this is not an issue with a small set of highly targeted keywords using match types to reach people exclusively at the bottom of the funnel, as targeting broadens, the AI might not prioritise the ideal lead profile (Ideal Customer Profile and Buyer Persona). This can result in more leads at a lower cost, but potentially with a decrease in quality. So, when comparing the lead-to-customer conversion rate, an increased number of leads might not positively impact your company’s bottom line.
Lead Value Optimisation for Service Providers
The good news is that it is becoming easier for service providers that use Google ads to generate leads that are closed offline.
Lead Value Optimisation (LVO) is a crucial strategy for service providers aiming to maximise the return on investment from their Google Ads campaigns. By employing Return on Ad Spend (ROAS) bidding strategies, service providers can dynamically adjust their bids based on the projected value of each lead. This method not only targets ads more effectively but also ensures that marketing budgets are allocated towards the most lucrative opportunities.
ROAS bidding uses historical conversion data and real-time insights to predict the future value of leads. For instance, if a particular type of lead, such as those signing up for high-value services, consistently converts into significant revenue, Google Ads will automatically increase bids for similar leads based on their expected conversion value. Conversely, for leads that tend to generate less revenue, the system will lower the bids, conserving your advertising spend.
This sophisticated approach allows service providers to fine-tune their advertising efforts, focusing more on quality leads rather than sheer volume. By optimising for lead value rather than just lead generation, businesses can enhance their overall profitability and ensure that their ad spend directly contributes to their bottom line.
Implementing Google Ads AI in Your Strategy
Implementing AI in Google Ads campaigns can significantly enhance the effectiveness of your advertising efforts through automation, advanced targeting, and real-time optimisation. Here’s a detailed guide on how to successfully integrate AI technologies into your Google Ads strategy:
1. Understand AI Capabilities in Google Ads
Before implementing AI, familiarise yourself with the AI features available in Google Ads, such as Smart Bidding, Responsive Search Ads (RSAs), and automated insights. Understanding each feature’s functionalities and how they can benefit your campaigns is crucial.
2. Define Clear Campaign Goals
AI works best when it has specific goals to optimise towards. Define clear, measurable objectives for your campaigns, such as increasing conversions, maximising clicks, or achieving a specific return on ad spend (ROAS). These goals will guide the AI in making data-driven decisions.
3. Gather and Organise Your Data
AI systems rely heavily on data to make informed decisions. Ensure that your Google Ads account is set up to track all necessary data points, such as conversions, click-through rates, and interaction data. Use Google Analytics and Google Tag Manager to enhance data collection and organisation.
4. Choose the Appropriate AI Tools
Select the AI tools that align with your campaign goals:
- Smart Bidding: Use Smart Bidding strategies like Target CPA, Target ROAS, or Maximise Conversions to let AI optimise your bids based on the likelihood of achieving the defined outcomes.
- Responsive Search Ads: Implement RSAs to allow AI to test different combinations of headlines and descriptions and serve the best-performing ads to your audience.
- Performance Max Campaigns: These use AI to optimise your campaigns across all Google platforms by analysing the performance data of your landing pages and other assets.
5. Set Up AI-Enhanced Campaigns
When setting up your campaigns, configure the settings to enable AI features. For Smart Bidding, for example, choose the strategy that best suits your goals, and set up conversion tracking to let AI assess and optimise bid strategies effectively.
6. Test and Learn
AI in Google Ads is highly effective at testing and learning from different ad elements. Set up A/B tests for various components of your ads, such as different bidding strategies or ad formats. This will allow you to understand which strategies work best and how AI can be further tuned to improve performance.
7. Monitor Performance and Adjust
Regularly review the performance of your AI-driven campaigns. AI can provide insights and suggested optimisations based on performance data. Use these insights to make informed decisions about adjustments to campaign settings, budgets, and strategies.
8. Scale Successful Campaigns
Once AI tools prove effective for specific campaigns, consider scaling these strategies to other parts of your advertising efforts. AI can manage increased budgets and more complex campaigns efficiently, allowing you to expand your advertising reach while maintaining effectiveness.
9. Stay Updated
AI technology and capabilities within Google Ads are continually evolving. Stay updated with the latest tools, features, and best practices by regularly reviewing Google’s updates and participating in digital marketing forums and training.
Implementing AI in Google Ads involves a blend of strategic planning, a thorough understanding of available AI tools, continuous testing, and data-driven optimisation. By effectively utilising AI, advertisers can enhance campaign performance, achieve better scalability, and optimise their ad budget more efficiently.
Strengthening Your Measurement Strategy with High-Quality Data
The foundation of any successful AI-driven campaign is high-quality, consented data. Google’s AI is only as good as the data you feed it, and first-party data is particularly valuable. This data, collected directly from your customers, provides insights that are specific to your audience, enabling more accurate targeting and better campaign outcomes.
Consent and Data Collection in the UK and EEA: For businesses operating in the UK and EEA, collecting and maintaining user consent is crucial. Google’s Consent Mode can help you create a strong framework for this, ensuring that you comply with GDPR regulations while still capturing essential data. This mode adjusts how your Google tags behave based on users’ consent status, helping you gather more actionable insights without compromising privacy.
Site-Wide Tagging: Implementing robust site-wide tagging with the Google tag is essential for capturing the most relevant data. This tag allows you to track user interactions across your website, providing comprehensive data that AI can use to optimise your campaigns. Ensure your tags are properly set up to capture the metrics that matter most to your business, such as conversion actions, user behaviours, and engagement metrics.
Aligning Conversions with Business Goals
AI’s true power in Google Ads comes from its ability to optimise for specific business outcomes. To fully leverage this, it’s important to align your conversion tracking with your business goals.
Assigning Conversion Values: Start by assigning values to your conversions based on what drives your business, whether that’s revenue, profit margins, or customer lifetime value. This allows AI to prioritise conversions that contribute most to your bottom line. For instance, if your goal is to maximise profit, assign higher values to conversions with higher profit margins.
Value-Based Smart Bidding: Once your conversions are valued appropriately, use value-based Smart Bidding across your campaigns. This AI-powered feature helps you bid more effectively on the conversions that matter most, optimising your budget allocation and maximising your ROI. Smart Bidding uses machine learning to adjust bids in real-time, considering a wide range of signals to predict conversion likelihood and value.
Reaching and Re-Engaging Customers with Customer Match
Customer Match is another powerful tool in your AI arsenal. It allows you to upload your first-party customer data to Google Ads, enabling you to reach and re-engage your existing customers across Google’s platforms.
- Customised Campaigns: Use Customer Match to create highly personalised campaigns that resonate with your audience. By targeting users based on their previous interactions with your brand, you can deliver more relevant ads, increase engagement, and drive higher conversion rates.
Prioritising Creative Inputs and AI-Powered Tools
In addition to data and bidding strategies, the creative elements of your ads play a crucial role in campaign success. Google’s AI-powered creative tools can help you develop a wide range of assets, ensuring your campaigns are visually appealing and effective.
Creative Strategy: Start with strong creative inputs that align with your brand and campaign objectives. High-quality visuals, compelling copy, and clear calls-to-action are essential.
AI-Powered Creative Tools: Once your creative assets are in place, use Google’s AI-powered tools to generate variations and optimise them for different formats and audiences. These tools can help you produce a diverse set of ads tailored to various placements, ensuring maximum reach and engagement.
