Artificial intelligence is all over the world, not least of all taking over agencies as a means of supplying actionable insights. Machine learning tools teach an AI what it should and shouldn’t do, iteratively producing better results with each cycle.
Today’s machine learning ad tools can both create ads and then use insights generated from their creation to adapt their next attempt. Many businesses have already embraced the new technology of AI and machine learning to develop ads and check their efficacy. Below, 11 contributors to Forbes Agency Council look at the lesser-used ways businesses can leverage machine learning advertising tools for their benefit.
Members share some underutilized strategies for leveraging machine learning in ad campaigns.
Photos courtesy of the individual members
1. Programmatically Understanding Your Customer
Take advantage of your customer data. Leverage machine learning and AI to identify who your customers are, then watch how frequently they come in and what they buy. Then leverage that information to put your customers into segments. Discount and provide offers based on those segments and use those segments to create look-alike models to find more of your best customers programmatically. – William Rodriguez, Silvercrest
2. Optimizing Campaigns
Using machine learning to help optimize ad campaigns while decreasing the amount of decision-making businesses have to face can often be overlooked. Machine learning can help determine keywords and copy that resonate with various target audiences. – Jordan Edelson, Appetizer Mobile LLC
3. Evaluating Ad Viewing Experience
It’s not just about reaching a user, but rather what experience you deliver once you do. Use AI to gauge a more progressive metric — viewing experience. According to a GroupM study, premium placements produce stronger response rates across the board with average uplifts of 10.5% for brand awareness, 19.2% for ad recall, 9.7% for brand perception and 10.3% for recommendation intent. – Dvir Doron, Cedato
4. Getting A Gauge On Sentiment
Understanding the sentiment expressed in both outbound marketing copy and inbound comments from engagers is my favorite application of ML. It’s useful to know when the addition of the word “awesome” takes my ad copy from “matter-of-fact” to “appreciative.” Creators and their bots can craft the right content for a variety of contexts. The unstructured data chaos of comments becomes actionable. – Serenity Thompson, A23 Advisors
5. Understanding Behavior Patterns
Machine learning can be an excellent way to identify causes of behavior change. Most ML ad tools can optimize ads based on data at hand. But if we train the algorithms based on specific behavior-based variables, we can understand more about why some ads work better, we can use these behavior traits to create multiplicative results. We recently experimented with calls to action and improved fourfold. – Hamid Ghanadan, LINUS
6. Optimizing Your Audience
With machine learning ad tools, it’s possible to run dynamic audience optimization to find the best working audiences for your campaigns with the lowest key performance indicators. This works by using a very highly-frequented split-testing method, which used to require a large investment of time for manual implementation. – Markus Hetzenegger, NYBA Media GmbH
7. Finding The Right Influencer
Influencer marketing can sometimes include time-consuming processes, such as the selection of the actual influencers. To help, machine learning can find the right influencers. More advanced AI tracks influencers in real time across the images and the copy they post, and their engagements to predict how their content will perform on an individual campaign to then determine the best influencers. – Maria Sipka, Linqia
8. Improving Real-Time Marketing
You can use ML to analyze and provide a sense of the consumer data to better inform marketing campaigns. We worked with New Balance and VMLY&R to create exception-spotting technology with a camera that streamed video into an AI system, analyzing people’s clothing and identifying the anomalies and exceptions to the “norm.” Leveraging AI enabled us to remove subject bias and engage more effectively with consumers. – Dustin Callif, Tool of North America
9. Offering Proactive Insights To Ad Creative
Businesses and brands overlook how assistive AI machine learning platforms can harness the first-party data from their ad channels to inform the development of new creative for tailored to each customer channel. The insights can proactively optimize ads, before they are even deployed. Most ML platforms focus on post release optimization, personalization and automation. – Vance Reavie, Junction AI Inc
10. Finding Niche Super-Tribes
The latest paid advertising platforms leveraging AI can assist you in finding niche audiences that are untapped. In order to find these “niche audiences,” it is very important to have a handle on your data sets so that you are able to determine the value of the audience to see if your consumer audience persona may need to be adjusted. A qualified data scientist is the perfect resource to help you. – Natacha Gaymer-Jones, SWOON MEDIA
11. Dealing With Human Resources Scarcity
If you are a small business or growing startup, chances are you have a limited number of team members you can sustain. In this case, machine learning ad tools may be really helpful. You can gather customer behavior insights and use this data to improve some areas. With the help of certain machine learning tools, you can boost sales or optimize customer service without having to hire more people. – Solomon Thimothy, OneIMS
