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Enriched Bidding: how to leverage AI to better optimize automated campaigns

Luca Ricci

As online advertising becomes more automated, the KPIs we ask AI and machine learning algorithms to optimize and the data we share with these algorithms will become one of the most important competitive advantages of businesses’ online advertising strategy.

In light of the innovations and automations of the Google and Meta platforms, ByTek has devised Enriched Bidding, an advanced digital marketing strategy to maximize the value of first-party data in automated campaigns. 

The premise is partly technological and partly strategic: as things stand today, advertising platforms do not allow campaigns to be based on business-specific metrics such as profit margin or customer lifetime value.

This leads to equal offers for all users, regardless of their specific value, with a consequent negative impact on campaign performance.


Optimizing campaigns on the most valuable users

What makes a bidding action “smart”?

Not all customers bring the same value to a company: some conversions are not important to business goals, while others have higher value and must be optimized accordingly. 

Classically configured advertising platforms – based on CPC, CPA, CPL – do not take this fundamental assumption into account: for them, it is as if all customers are equal. This is because traditional conversion values do not align with business goals. The result? Underperforming campaigns.

In essence, one should allocate the marketing budget to reach the most valuable customers, maximizing conversion value and ROI.

Today, there are already platforms that enable so-called smart bidding: machine learning and artificial intelligence are the main tools used in smart bidding to determine the right ad bid for each ad.

First, machine learning is used to analyze data from previous campaigns, such as click-through rate, cost per click, and conversion, to identify patterns and trends that can be used to predict user behavior. This information is then used to determine the likelihood of success for each ad offer.

In addition, artificial intelligence is applied to optimize ad bidding in real time based on contextual information such as user location, device used, time of day, and competition.

The smart bidding system also uses machine learning to constantly adjust the ad bid based on the performance of previous campaigns and the advertiser’s specific goals.


AI and ML for campaign management truly aligned with business goals

The method structured by Bytek is a further step toward truly optimized campaigns aimed at achieving real business goals. 

When people talk about Enriched Bidding, they mean a set of actions on data that allows them to apply a personalized bidding strategy for each user, maximizing campaign results. 

It is based on four main methodologies:

  • the “Margin-based Bidding”,
  • the “Funnel-based Bidding.”
  • the “Lifetime Value-based Bidding”
  • the “Predictive Lifetime Value-based Bidding”

These methods can be combined to achieve a comprehensive approach targeted to the specific needs of the enterprise.

To implement Enriched Bidding, some basic steps need to be followed:

  1. Select KPIs based on concrete business objectives
  2. Collect and prepare – with full respect for privacy compliance – data on margins, user behavior, product availability and transactions;
  3. Create an artificial intelligence model that takes all these factors into account and link it to advertising platforms.

This enables immediate visualization of campaign performance based on true business KPIs, without having to perform additional analysis to understand the effectiveness of campaigns.

In addition, this allows advertisers to focus on the strategic aspects of campaigns, such as creative and implementation of new features in the product, instead of the technical bid aspects.

This approach represents a major shift in the way online advertising campaigns are managed, putting control in the hands of companies rather than leaving it in the uninterested hands of the advertising platforms themselves-and allowing them to tailor tools to their specific needs, maximizing ROI and achieving better results in automated campaigns.