Digital Marketing + Artificial Intelligence

The digital marketing arena is buzzing with the integration of AI technology, making it the go-to companion for modern marketers. It’s easy to get overwhelmed by all the different tools and capabilities when it comes to AI though. Especially in relation to the Digital Marketing Ecosystem where AI integration opportunities are so expansive.

To make the opportunities a little easier to understand, I like to map them out across four different categories: Task Automation, Bid Optimization, Content Generation, and Statistical Modeling

Task Automation

AI is stepping in to tackle the mundane, leaving marketers with more room to focus on strategy. It’s like having an extra set of hands to take care of the day-to-day. Freeing up critical bandwidth to scout out new opportunities.

The most valuable task automation tools have struck the perfect balance between customization and intuitive navigation.  Extremely customizable tools empower users to automate super-specific workflows, streamlining repetitive tasks across multiple applications.  

In automating tasks that are specific to an individual’s role, however, it’s easy for the setup effort to outweigh the time-saving impact. This is why an intuitive UI is equally important when evaluating task automation tools. Those that make automation quick and easy, can be widely adopted across the org AND justify the initial setup time by meaningfully elevating productivity, if only for a single contributor.

Below are a few of my favorite task automation tools, grouped by use case:

Statistical Modeling

Advanced statistical modeling via AI is a massive “unlock” in terms of uncovering campaign performance and user behavior insight.  The analyses that used to require a data scientist to execute can now be run automatically via natural language instruction, using an LLM such as ChatGPT Plus or Google’s Bard (requires file upload capabilities).

Some of the analyses I perform most commonly are:

for predicting the impact of spend across platforms. 

for forecasting based on historical campaign data. 

for modeling scenarios where data may be incomplete.

for pattern recognition within large datasets. 

for understanding the nested structure of customer segments.

for understanding the relationship between two sets of variables.

In addition to the data science application, advanced modeling via AI, can also be integrated directly into your tracking and analytics software to extrapolate the data you do have (in real time) to fill whatever visibility gaps exist. This is especially valuable as privacy regulation continues to mar the industry and expand visibility gaps. 

The tool that does this best is Google Analytics 4, due to Google’s treasure trove of user data that can be leaned on for extrapolative modeling and identity resolution.  Google Analytics 4 is also the leading web analytics candidate due to its ability to create website visitor audience segments that can then be pushed directly to Google Ads for activation across ad campaigns.

A few of the other most sophisticated AI-fueled capabilities of GA4 are: 

Campaign Optimization

While there are many third-party tools popping up that tout capabilities for real-time campaign optimization via AI, none are as powerful or as precise as the tech that’s being integrated into the ad products themselves by walled garden behemoths.   

Platforms like Google and Meta are incentivized to integrate AI to improve campaign performance for advertisers so that ad budgets will be concentrated in their ecosystems. These companies have more user data, liquid capital, and development/computing resources than any 3rd party startup.  Rendering it a waste of time to scour the internet for supplementary ai bidding tools, and a waste of money to pay a CPM premium to leverage the tech. Marketers should instead focus their energy on understanding the mechanics underlying each platform’s most advanced “smart campaign” products, and strategically integrating them into your media mix. 

These products are honed to crunch incomprehensible amounts of user behavior and campaign reporting data in real-time to serve each user with the ad combination that’s most likely to achieve your specified objective. These tactics are typically pretty “black box,” and tend to force advertisers to relinquish control in favor of a more “set it and forget it” approach. 

Considering that best practices for the last decade have been to meticulously segment campaigns for additional control, this will feel counterintuitive and rather nerve-racking.  However, this paradigm shift is key to embracing the new frontier and strategically wielding a competitive advantage via AI.  

Advertisers should pivot their focus away from granular control of targeting/bid settings, and instead focus on feeding these new ad products with the content and data they need to be most successful.  Ensuring the proper integrations have been configured to passback as much first-party data as possible to the platforms. 
On the creative front, advertisers need to begin considering specific (persona-tailored) asset variations as their targeting lever.  By breaking down a creative concept to speak to different cohorts of consumers, you provide the algorithm with the different options it needs to match the most optimal asset to each user in order to achieve your desired action. 

 

Below is a summary of the most sophisticated AI-driven ad products across each media platform. 

Google Smart Bidding (SEM) / Shopping (PLA)

Utilizes machine learning algorithms to optimize bids for max conv value or conv rate. 

Google Display & Video Automated Targeting

Leverages AI to identify users who are likely to be:

  • Similar to an advertiser’s existing customers
  • Interested in advertiser’s specific products
  • Actively researching similar products

Google SEM Responsive Search Ads

Uses Google’s AI to dynamically assemble & auto-test different ad combos to determine the most effective for each search query.

Google Performance Max (PMax)

Access all Google Ads inventory to find untapped/incremental demand from new search queries. Combines Google’s AI tech across bidding, budget optimization, audiences, creative, attribution, & more.

Meta Dynamic Creative Optimization (DCO):

Uses AI to personalize ad creatives (images, copy, and offer) in real-time based on profile, behavior & context.

Meta Advantage+ Shopping Campaigns (ASC):

Leverages advanced AI  to eliminate the manual steps of ad creation, automating up to 150 creative combos at once.

TikTok Dynamic Creative Optimization (DCO):

Employs AI and advanced modeling to dynamically select different ad elements (images, captions, & CTAs) to deliver the most relevant version of ad to each viewer

TikTok Smart Bid / Budget Optimization:

AI algorithm intelligently allocates ad budget across various ad sets based on performance history and potential to achieve specific optimization goals.

YouTube Video Ad Targeting:

Utilizes Google Analytics pixel & Predictive Audiences to optimize ad delivery & encourage user actions

Content & Creative Generation

This is another area in which it’s very easy to get overwhelmed by the hundreds (and growing) of tools that are available for marketers today. The primary benefit marketers should aim to exploit across all generative AI tools is speed-to-market, especially in relation to concept variation/personalization. 

Advertisers should still be leaning on design teams for concept development.  But should then heavily leverage audience intelligence tools to identify specific consumer cohorts that behave similarly (think: cluster analysis). 

Investigating what compels one cohort vs. another. Then executing the singular concept in 5 – 10 different tailored variations that each revolve around the nuances specific to that subsegment.  This allows the AI-driven “smart tactic” ad products to maximize campaign engagement by serving hyper-relevant content.