What to expect from your Smart Bidding Campaigns
Many platforms (including Google Ads) allow for bid adjustments, which enable budgets to favour specific campaign variables. However, the process of analyzing campaign performance to optimize campaigns can often be tedious.
Utilizing AI to Simplify Ad Operations
Artificial intelligence models are modernizing and improving operations across a wide range of industries and verticals. A considerable opportunity exists today for the marriage of data science and advertising campaign management.
Some leading buying platforms offer built-in AI solutions designed to help campaign managers analyze data quickly to inform strategic bid optimizations. One example would be Auto Bidding, found in Google’s Display & Video 360 platform.
Auto Bidding vs Fixed Bidding
Auto-bidding integrates an AI based approach to understand line item-level targeting and identify the user segments and placements that are most likely to drive conversions. Like all intelligent learning models, Google’s Auto Bidding program is fuelled by your campaign’s real-time data to quickly refine its optimization algorithm. Some of the factors it might take into consideration include a user’s demo, geo, browsing affinities, device preferences, etc.
With Fixed Bidding, you can manually set a “fixed” bid for all the users that fall within these targeting parameters. Alternatively, if you were to use Auto Bidding, the machine learning algorithm would analyze avast array of data points about your engaged users that will assign weighting on an individualized impression basis. This level of granularity is virtually unachievable using manual bid adjustments and is just one way in which AI based bidding objectives can help drive campaign efficiency.
Below are some sample auction insights that Auto Bidding might take into consideration:
- Users age 25-29 are displaying identical delivery costs to users age 30-34. However, users age 30+ might take multiple conversion actions upon reaching the landing page, and thus CPC bids should favour this age group accordingly.
- From a geo standpoint, users located in the countryside are more engaged with home renovation messaging than users in larger cities. As a result, Google should prioritize delivery of home renovation creatives to users in smaller geos.
- A day-part analysis reveals optimal times when target users are most likely to engage with your ads, or even how many impressions they’ll need to receive before they take a conversion action. The results indicate that ad delivery should be restricted to weekdays only and bids increased between 10am and Noon.
Although a campaign manager might reach these same conclusions after pulling a series of custom reports, Google’s Auto Bidding model recognizes these patterns almost instantaneously and much less time and budget are lost as a result.
Auto Bidding is not a solution to every problem. You must make sure you have sufficient data for the AI model to learn from, in order to facilitate quality predictions throughout the life of your campaign.
If your conversion volume is low or retargeting pool is too small, then Auto Bidding AI models will struggle to learn, leading to lags in output quality and efficiency. On the other hand, if you have healthy conversion data, substantial engagement, and a scalable retargeting audience, then you’re perfectly positioned to take advantage of the benefits of automated bidding.
Just remember that optimization and refinement can take time. Learning periods can frequently run between two and four weeks, depending on campaign complexities.
Fuelling Greater Insights
While Auto Bidding does the heavy lifting, ie. constant analysis and reallocation of bids and budgets across line items, campaign managers can dedicate more brain power to client relationships and sharing insightful takeaways.
For agencies, reporting against campaign KPIs (CPA targets, web traffic volumes, leads, etc) is critically important in order for clients to gauge success in tracking toward macro marketing goals. However, the ability to provide deeper insights around audience breakdown, interaction patterns, and creative preference, are more easily facilitated with AI based bidding objectives in place.
We have covered some of the ways in which intelligent bidding models can maximize efficiencies for campaign managers and fuel greater learnings for brands. Paired with automated, real-time reporting dashboards, brands looking to breathe new life into their digital advertising efforts have a multitude of AI-powered options to explore.
Contact the data science and media buying experts at Hotspex Media today to learn more.