How are AI-Engines Revolutionizing Paid Search Ad Campaigns?

As more businesses have access to required technologies, automated machine learning is becoming a more and more common alternative for deciding what price to bid on paid search ad campaigns. A bid that is too low will prevent you from connecting with leads, while a bid that is too high will reduce your return on investment. In addition to being the same as what everybody else is using, Google’s automated bidding lacks the data it requires to maximize your ROI. Knowing consumer trends, purchasing patterns, seasonality, demographics, customer lifetime value, and other information is also necessary to accomplish that goal.

Features of a successful Automated Bidding Model for Paid Search Ad Campaigns:

• Use statistical inference based on prior bids to calculate each ad’s price elasticity.
• Consider the actual value anticipated from each individual ad click based on prior clicks
• Iterate based on fresh information
• Instead of making the mistaken assumption that past performance would always be a reliable indicator of future performance, quickly respond to changes in the bidding environment or the effectiveness of visits.

Things to keep in mind while using AI-powered Automated Bidding Strategy:

• Models will draw incorrect conclusions if they are unaware of what is happening on your website. For instance, your model might start placing lower bids on those keywords if you test a fresh landing page and it lowers your ROI. The model can still be stuck bidding low on the keywords even after switching to a better landing page because there isn’t enough fresh information to raise the bids.
• Models that too rely on statistical significance run the risk of testing a lost approach for an excessive amount of time, while models that don’t take statistical significance into account may miss out on good possibilities while supporting flukes.
• Check your model for feedback loops. For instance, you wouldn’t want your model to make a higher bid on an advertisement with a high conversion rate if the sole factor contributing to the high conversion rate is the high bids. Conflicts of this sorts should be avoided.

Steps for Automated Bidding Paid Search Ad Campaigns:

• Define your goals and KPIs: Before implementing any automated bidding strategy, it’s essential to identify your goals and key performance indicators (KPIs) for your search ad campaign. These could be different for each campaign, but common KPIs include cost-per-click (CPC), cost-per-conversion (CPA), return on ad spend (ROAS), and impression share.
• Collect data: To train your machine learning algorithms, you need to collect historical performance data for your campaign. You can get this data from your ad platform, such as Google Ads, Bing Ads, or Facebook Ads.
• Choose a bidding strategy: There are several automated bidding strategies you can choose from, including:
  • Target CPA (Cost-Per-Acquisition): This strategy sets bids to help get as many conversions as possible at the target CPA you set.
  • Target ROAS (Return on Ad Spend): This strategy sets bids to help get as much revenue as possible at the target ROAS you set.
  • Enhanced CPC (Cost-Per-Click): This strategy automatically adjusts your manual bids to help you get more conversions.
  • Maximise Clicks: This strategy automatically sets bids to help you get the most clicks within your budget.
  • Maximise Conversions: This strategy automatically sets bids to help you get the most conversions within your budget.
• Choose an AI tool: There are several AI tools you can use to implement automated bidding. Platforms like MarineOne, Skai and Adobe Advertising Cloud provide AI-powered Ad platforms.
• Monitor and optimize: Once you implement an AI-powered bidding strategy, it’s essential to monitor its performance regularly and make adjustments as needed. This includes analyzing your data, reviewing your KPIs, and testing different bidding strategies to find what works best for your campaign.