In today's digital marketing landscape, few mediums are as focused on direct response and conversion-driving as Google Ads. As a dominant player in the digital world, Google has even been subject to regulatory scrutiny due to its chokehold on the internet and digital marketers worldwide. However, businesses of all sizes continue to rely on Google Ads because of its ability to deliver results. From driving in-market website traffic to converting potential customers and providing opportunities for remarketing, Google Ads is a vital tool for digital marketing. Additionally, the platform is a leader in the advancement of artificial intelligence (AI) and machine learning. In this article, we will explore a particular tactic available in Google Ads that can enable smaller advertisers to use AI and machine learning in ways they may not be able to otherwise. Specifically, we will discuss the benefits of using shared budgets and portfolio bidding strategies, which can help smaller advertisers achieve better results by optimizing their ad spend.
Pay-per-click (PPC) advertising is an excellent way to generate website traffic, and with advancements in AI and machine learning, it now offers various smart bidding algorithms designed to optimize for and drive specific types of results. Google's bid strategies, such as Target ROAS (tROAS) and Target CPA (tCPA), take automated bid strategies to the next level by allowing advertisers to plug in performance targets and letting machine learning optimize to reach those objectives. This approach can be a powerful tool to elevate your digital marketing efforts in the right circumstances. However, AI and machine learning require a minimum level of scale to function correctly. Although anyone can access and implement these bidding strategies, only campaigns with enough historical data will be able to utilize them fully. This limitation presents a roadblock to advertisers with a limited budget, scale, or both. Google recommends a minimum of 15 conversions per month before implementing advanced bidding strategies. Some experts claim that a higher number is necessary. Regardless, it is essential to note that the fewer the historical conversions, the more challenging it is for Google's algorithms to optimize to drive that action efficiently. Conversely, campaigns with a large number of monthly conversions are more likely to rely on Google smart bidding to do the heavy lifting in their campaigns. Consistently driving the required number of conversions per month to utilize smart bidding can become quite expensive. Luckily, Google offers a workaround that can help some advertisers utilize its machine learning, which otherwise would not have enough scale to do so: portfolio bid strategies and shared budgets.
The keyword in the above statement is "some." Unfortunately, some advertisers will just be too small to utilize smart bidding. However, advertisers who do not have enough campaign-level conversions to use any of Google's bidding algorithms can pool their campaigns together and use portfolio bidding under a shared budget. These tools enable smaller advertisers to consolidate their campaigns and optimize their ad spend to harness the power of machine learning and AI.
By default, Google's bid strategies are applied at the campaign level, which can be challenging for smaller advertisers with limited conversion data. In these cases, a portfolio bid strategy and shared budget can be implemented to optimize ad spend across multiple campaigns. For example, suppose an advertiser has three campaigns that are too small to use smart bidding individually. In that case, they can group them together under one portfolio with a shared budget. If Campaign A generates eight conversions, Campaign B generates ten conversions, and Campaign C generates six conversions, they will be seen as twenty-four conversions in total. Google's machine learning algorithms will then optimize bids across all three campaigns to maximize the overall conversion rate. While each campaign remains independent from a reporting and structural standpoint, grouping them together allows the advertiser to leverage the power of AI and machine learning.
We have helped numerous clients apply portfolio bidding strategies and shared budgets who would not otherwise be able to access smart bidding strategies due to the lack of historical data. However, while portfolio bidding can be an effective solution for smaller advertisers with limited data, it may not be the best option in all cases. Advertisers who have a clear understanding of which campaign generates the most valuable conversions may want to prioritize their budget allocation towards that campaign. In such situations, portfolio bidding may reduce the advertiser's control over budget allocation and negatively affect the performance of their high-value campaign. Additionally, it's crucial to ensure that the conversion actions between each campaign included in the portfolio bidding strategy are the same to ensure optimal results. For example, grouping campaigns with different conversion goals, such as purchases and form-fill leads, in the same bidding strategy would not make sense and could lead to suboptimal results.
In conclusion, Google Ads offers various smart bidding algorithms that can help advertisers optimize their campaigns for specific goals. However, these algorithms require a minimum level of historical data to function effectively. For smaller advertisers with limited budgets or scale, portfolio bid strategies and shared budgets can be a useful workaround to leverage the power of machine learning and AI. It is essential to ensure that the campaigns grouped together under a portfolio bidding strategy are similar in nature and have equal prioritization. With proper implementation, portfolio bidding can help smaller advertisers achieve better results and improve the efficiency of their digital marketing efforts.