In continuation of the Part-I of this article, let’s continue to dig a little deeper into AI for advertising. So, as you are already aware of the introduction of Ad platforms, we will start discussing the same into the details.
Utilities, markets, and networks will be unable to declare how their AI will perform anytime soon. But that’s the point: Artificial intelligence even determines how your ad budget is spent, who observes your advertising thoroughly, and how profitable your overall campaigns are.
This indicates that if you run paid advertising, you should understand the terminology of artificial intelligence and ask the appropriate questions about how the AI practiced by ad platforms can influence your spending.
A basic example of this is:
Advertising on Facebook, explicitly ad frequency, and a score of relevance. These are the two crucial pieces of data that Facebook’s algorithms utilize to manage how much you pay and how your ads are presented, without human engagement.
You might believe it’s good to display your ad more frequently. But it’s not. Conventional advertising study has revealed that, within a brand purchasing timeline, optimum ad span is at least three exposures. Conventional advertising schools state that as many times as possible, you have to “immerse” the audience with the same ad.
This is the reason user feedback is taken into consideration by Facebook’s algorithms. If you present your ad too often, and users rate it poorly, your score of significance would fall. In most situations, the longer the duration, the lower the score of significance. An excellent score of significance implies that your ad is more likely to be displayed to a target audience than the other ads you compete with. That indicates better performance and lower costs.
Performance and spend optimization:
Quality optimization is one of the most vital advertising technologies for AI. Commercially available solutions utilize machine learning algorithms to assess how the ads function across various platforms and then present feedback on how performance can be enhanced.
In some cases, these platforms might utilize AI for advertising to smartly automate tasks that you know you should take depending on the best practices, saving you vital time. In other cases, performance concerns might be highlighted that you didn’t even recognize you had.
We are aware of one commercially available tool in the most high-level cases that automatically optimize spendings and manages ad performance, making decisions on how best to achieve your advertising KPIs on its own. In another case, there is at least one platform that automatically designates ad dollars across all audiences and channels. Hence people can focus on strategic jobs of greater value rather than manual guesswork on what works and what doesn’t.
There are AI-driven systems that will produce advertisements for you in whole or in part, based on what works best for your goals. Few of the social media ad networks already have this feature, which utilizes numerous smart algorithms to advise ads that you should run depending on the links that you are promoting. However, there are also third-party applications that really utilize intelligent algorithms to write ad copies for you. These systems leverage the generation of natural language (NLG) and processing of natural language (NLP), two AI-driven technologies, to write ad copies that function well or better than human-written copies — in a portion of the time and on a scale.
Your targeting ad is as significant as, if not more significant than, creating and copying your ad. You have a genuinely sound set of consumer data to target audiences thanks to platforms like Amazon, Google, Facebook, and LinkedIn. But it’s not simple to do this manually. Here, AI for advertising can help. We are at least aware of one AI system that looks at your past audiences and ad performance, examines it against your KPIs, and includes real-time performance data, then recognizes new audiences that are likely to purchase from you.
Advantages of AI in advertising:
- AI drastically enhances advertising performance because of more useful personalization depending on customer interests, preferences, location, demographics, and context.
- AI allows advertisers to explain consumer behavior to help you decide the most suitable message for its time and place. It optimizes ads to be displayed only to appropriate users, producing better results and user experience, as lesser ads are shown to people who aren’t interested in them.
- AI allows a more granular review than rules-based systems for channel selection, segmentation, and messaging optimization. AI can review billions of data points every day so that it can identify statistically meaningful trends in how consumers act and what strategies will be most productive.
- The lesson costs by working on that data automatically and quickly. It also reduces target profiling and time cost of planning. AI improves the privacy of user data; hence more data streams can be managed and matched. Additionally, AI enhances the level of automation in advertising.
So what do you do next?
Now, you have a better knowledge of AI’s potential, its use cases, and a few examples of real companies that utilize it in advertising.
This article and our blog are excellent places to begin.
But reading isn’t enough:
It’s undoubtedly crucial that anyone running ads move from theory to practice as swiftly as possible if they want to acquire a competitive advantage with AI and avoid getting left behind.
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Related: How to use AI for small business?