The Future of Programmatic Advertising: ChatGPT and the Role of AI

Written By: Brennan Ball and Tim Gaughan

ChatGPT and AI in Programmatic

Sure, you have recently heard of ChatGPT, the AI chatbot capable of answering all of your queries in a human-like text chat response. It does all of this in a conversational setting with a call and response, as if someone was responding to you with large bodies of text in seconds. With the rise of ChatGPT, there still is a question about how can this tool be optimally utilized. Although the topic of AI has exploded from the advancement through ChatGPT, AI and machine learning have been used for quite some time now in the digital advertising world.?

In the programmatic space, AI is something widely utilized to keep campaigns optimized. Each DSP has created its machine learning algorithms to help optimize greatly toward a campaign’s success.

Bidding Optimization

One of the great ways AI is utilized in programmatic DSPs is the use of bidding optimization. Here, the AI uses pattern recognition to determine the value of each user, which allows the advertiser to bid on worthwhile auctions rather than inventory. In most marketing campaigns, the key to a successful campaign is to hit the right users with your ad. To do that, we need to be able to win a bid wherever that individual goes, and bidding optimization can help us do that. This will ensure that money is most effectively spent on individuals that will be most likely to convert toward your campaign's objective. Bid shading is an example of how bidding optimization can help improve your campaign's inefficient ad spend. Bid Shading is a technique buyers use to avoid overpaying which allows advertisers to reduce their offer to pay for a particular ad impression. This may lead to them winning the auction at a lower cost, while still achieving their desired goal of reaching the target audience.

KPI Optimization

Key Performance Indicator (KPI) optimization involves analyzing and enhancing the performance of specific metrics that are deemed important to a particular business or organization. The aim of KPI optimization is to improve the overall efficiency and effectiveness of the chosen KPIs by determining the most effective ways to track and measure their progress. This involves setting achievable targets, analyzing data, and making changes to processes, strategies, and systems in order to drive better results. KPI optimization can help organizations to identify areas of success, as well as areas in need of improvement, and make informed decisions that drive growth and progress. By continuously monitoring and optimizing KPIs, organizations can stay ahead of the competition and achieve their long-term goals. A retail grocery client recently reached out to Add3Connect looking to gather highly engaged prospecting audiences for a display campaign. Add3Connect utilized a DSP’s KPI optimization AI to focus a campaign toward higher click-through rates for this campaign. The campaign ended up resulting in an above average click through rates and a happy client. Another client in real estate was looking to gather conversions at a certain CPA, so when guiding the DSP towards a goal of CPA, it was able to optimize towards users that have a higher propensity to convert. This campaign resulted in achieving the client’s KPI goal and continues to perform well, continuously optimizing and learning as the audience keeps being fed into the pool.

Spend Prioritization

Ad spend prioritization is the process of allocating resources and budget to the most effective advertising campaigns, channels, and strategies. The aim of ad spend prioritization is to maximize the return on investment (ROI) from advertising spend by ensuring that resources are directed toward the most promising and effective initiatives. This process involves analyzing data, assessing the performance of different campaigns, and making informed decisions about where to allocate resources. Ad spend prioritization can help organizations improve the efficiency of their advertising efforts, reach their target audience more effectively, and ultimately drive better results. By regularly monitoring and adjusting ad spend priorities, organizations can stay ahead of the competition and achieve their marketing goals. Effective ad spend prioritization requires a deep understanding of the target audience, the advertising landscape, and the impact of different initiatives on the bottom line.

Lookalike Models

Lookalike modeling is a machine learning technique used in marketing and advertising to identify individuals who are similar to a target audience based on their behavior, characteristics, and attributes. Lookalike models use data from a "seed" audience, such as past customers or high-value prospects, to build a profile of the desired target audience. This profile is then used to identify similar individuals among a larger pool of potential customers.?

The goal of lookalike modeling is to reach new potential customers who are likely to be interested in the product or service being advertised. By using lookalike models, organizations can improve the efficiency of their advertising spend, reach new customers more effectively, and ultimately drive better results. Lookalike modeling requires a significant amount of data and a deep understanding of machine learning techniques, but it can be a powerful tool for organizations looking to improve their marketing efforts. Often this audience technique can be best used when a target audience is not so easily defined, varying in behaviors and demographics.?

Human Guidance is Still Needed

Although AI is great at optimizing objectives, it still needs direction. Human guidance is still necessary for AI to excel and perform. Oftentimes, AI will make decisions based on a small basis of data, and we must ensure that the AI is making the correct decisions toward the campaign's objectives. AI can only be as effective as the data it ingests, so ensuring it is absorbing and utilizing that data correctly is a large part of programmatic digital advertisers’ jobs. Add3Connect ensures that your programmatic campaigns will be most effectively achieving campaign goals, utilizing their professional experience as well as guidance towards AI technology. We are always looking towards the future, actively researching and adopting the latest AI-powered solutions to stay ahead of the game.

Want human power behind your programmatic ads? Connect with us today!

Sources:

Braccialini, C. (2022, July 25). Online advertising: Everything you need to know in 2022 https://blog.hubspot.com/marketing/online-advertising ?

Freifeld, L. (2020, December 17). Keys to KPI optimization. https://trainingmag.com/keys-to-kpi-optimization

Han, Y. (2019, April 30). Are Your Campaigns Really Using AI? 10 Questions to Ask Your DSP. https://blog.stackadapt.com/is-your-dsp-using-ai

Martins, J. (2022, November 15). What are Key Performance Indicators? https://asana.com/resources/key-performance-indicator-kpi

Nikolau, I. (2022, July 8). What Is Bid Shading? The Ultimate Guide (2022). https://snigel.com/blog/what-is-bid-shading

Sluis, S. (2019, March 15). Everything you need to know about bid shading. https://www.adexchanger.com/online-advertising/everything-you-need-to-know-about-bid-shading/?

Tableau Consulting. (2019, November). Look-alike modeling - perceptive analytics. Look-alike Modeling - Finding Similar Customers Through AI. https://www.perceptive-analytics.com/wp-content/uploads/2019/11/LookAlikes_Case_Study_Ver1.0.pdf

The Trade Desk (n.d.). DSP Features Performance. https://www.thetradedesk.com/us/our-platform/dsp-demand-side-platform/koa-ai-artificial-intelligence