Many companies invest large sums on digital advertisements, and AI is well-positioned to optimize ad investments. In particular, machine learning can be used in optimization of programmatic display ads.
For example, AppNexus and PulsePoint both provide programmatic ad solutions, offering ad purchasing and sales based on machine learning. Other tools analyze ad spending and results across channels; not only your own, but also that of your competitors.
This is ad intelligence for the benefit of both buyers and sellers.
Machine learning can be used in optimization of programmatic display ads.
The digital advertising ecosystem is complex and changes quickly. Advertisers have a difficult time getting transparency on their own ads, not to mention what their competitors are doing.
Many layers prevent transparency, especially in the programmatic markets, where an ad impression can pass through a number of providers—from publisher to advertiser—before being filled.
Having insight into which advertisers are buying on which publisher sites and through which networks and exchanges helps everyone make better decisions.
Advertisers have a difficult time getting transparency on their own ads, not to mention what their competitors are doing.
Pathmatics is a company in this space that offers what can be described as a search engine for ads. They use machine learning to gather insights into ad spending and impressions, which can help companies respond to competitor activities or improve results in future campaigns.
I had a conversation with Kenneth Roberts, Head of Marketing at Pathmatics. He explained:
For buyers of advertising—brands and their agencies—timely information on their competitors’ advertising can make a major impact on the effectiveness of their own marketing and advertising performance. A brand, for example, can know if a competitor has launched a new marketing campaign or started spending for impressions on a new site (potentially a new target market). The brand marketers can compare themselves within their category and determine if they are being outspent. Advertisers can find sites that they are not advertising on—and the most efficient channel partner to buy through. They can also monitor brand safety and detect wasteful placements to cut or reallocate spend.
Pathmatics and similar companies can report on information such as desktop display, pre-roll video, mobile web, native, and social advertising.
Having insight into which advertisers are buying on which publisher sites and through which networks and exchanges helps everyone make better decisions.
For example, the Pathmatics pipeline for detecting ads, estimating impressions, and calculating potential spending uses machine learning models that are constantly updated with market data on prices, ad formats, site traffic, and indexed digital ad data.
It is easy to see how ad intelligence can be a marketing advantage over your competitors.
You can even let AI-based software automation tools manage and optimize campaigns autonomously—from purchasing ads to analyzing results. In essence, you can get a virtual digital marketer that manages and executes self-driven and self-optimizing digital marketing campaigns.
Let AI-based software automation tools manage and optimize campaigns autonomously
This is exactly what Albert Technologies does. You provide the creatives and goals, and their software tool (an AI agent they call Albert) uses machine learning to initiate and optimize ad spending across different media channels and devices over time.
It measures the results and adapts the ad investments automatically as the ROI changes.
Or Shani, the CEO and founder of Albert Technologies explains, “Albert is autonomous, or self-driven, meaning that he can create digital marketing interactions unassisted, using the results of multivariate tests and deep-level analysis to make better decisions moving forward.” He also notes, “Albert can run thousands and thousands of tests across hundreds and hundreds of variables in a very brief window of time.”
Whereas traditional solutions simply analyze data and then wait for a human to make a decision about it, AI tools like Albert can analyze the data, determine the best course of action, execute it, and then continually optimize itself in real time based on what it learns.
While Pathmatics and Albert are general tools for working on multiple channels, you may want to consider specific platforms only as well.
If you are interested in Google Adwords more specifically, AdScale offers algorithmic bid and budget management based on machine learning to help optimize advertising performance. StrikeSocial does something similar, but on YouTube.
It’s one thing to optimize the campaign at large, but another to optimize the ad creative itself. Dynamic creative optimization is the process of adapting the creative to the person seeing it in real time.
Dynamic creative optimization is the process of adapting the creative to the person seeing it in real time.
A huge amount of data can be mined to predict which creative will trigger someone to convert.
Some tools address ad optimization from this angle, for example by predicting the creative quality of your different ads, the optimal number of ads you should use simultaneously, and the best creative refresh cadence to avoid ad fatigue.
Have a look at Refuel4 or Spongecell if you are interested in this. Additionally, Adobe is now starting to add AI capabilities to Photoshop and their marketing cloud products as well.
More and more specialized tools are becoming available, which promises an interesting future for marketers. Only time will tell how much AI will be able to automate graphic design in the future.
