Artificial Intelligence(AI) and Machine Learning(ML) are the two key buzzwords these days in the Startup World. And why not, it’s optimizing our efficiency in everything we do. So, how can ad tech be left behind?
Ad Tech,to be frank, is sort of a messed up market. It has fallen short on many promises it has done. The good and bad thing about ad tech is that there is an abundance of data. Yes, we have all this information to better understand our customers, but most marketers don’t know how to leverage the data and move forward with it.
Very few in the ad tech world have the analytical skills to effectively evaluate big data. This lack of training can cause damaging effects to brands and companies because many are misinterpreting and misusing important data.
Here is how ad tech partners with AI to get lower CPC prices, higher click-through rates (CTR) and conversions, and an even greater ROI.
As of now any ad tech company is confused about any 3 key decisions which are ad network selection, ad placement on the website and analytics.
Here we see how use of Artificial Intelligence can help Ad Tech industry find better answer to these tricky answered questions.
AI In Ads Positioning
Instead of developers haggling in front of their PCs to determine which ad position will determine best revenue for your website, AI simplifies the same thing by employing machine learning algorithms to study historical data to find the relevant ads for targeted user group.
AI algorithms use heatmaps to study the where the vistors on the website are actually going and thus,putting the relevant ads on those positions to focus on the user.
The heatmaps have never been used much in the Ad tech before. AI is surely gonna change that while studying each and every map in detail to find the best ad positions for marketers.
The AI also keeps in mind UX of the website helping the publishers to optimize them in the best manner possible.
AI In Ad Network Selection
There are many Ad Networks who provide different kind of ads to the website owner and he is required to sort the ads according to the website.
This whole process is called ads mediation and is very relevant for higher revenues of the website. The AI technology helps here by both minimizing the human effort and doing it more efficiently while making sure that only relevant ads reach to the final user. These relevant ads are defined through the keywords associated with a website In the AI based approach , ads will be place through data, facts & intelligence where data will be user or website’s past history. UX, website content, geo & timing will be counted as facts here.
The best part of employing AI for ads optimization is that it reduces human effort by employing a data oriented approach reduces human error and puts the ads only after data says “Yes” on two parameters which are ad position and ad network selection.
And all of this will only get better when machine learning algorithms are employed to find the best ad-user match.
It is regularly stated that all ad tech companies don’t take their data analytics very seriously!
Analytics has always been an issue for any ad tech and in the end publishers aren’t very satisfied. However, The AI based approach will propel reporting and analytics feature of ad tech companies to new levels.
“We have been able to guide some publishers to help them answer questions like which kind of content their audience likes and where shall we put the CTA button to convert their one time users into loyal users helping them increase their daily traffic by a good margin” says Abhinav Agarwal, founder of AdStair, India’s first AI automated ad tech company.
If ad tech companies’ analytics start impacting the content and website’s UI/UX ,the upcoming times are really exciting for ad tech and its parties i.e publishers, ad tech platforms and of course the people who run the real show, USERS.
(Disclaimer: This is a guest post submitted on Techstory by the mentioned authors. All the contents and images in the article have been provided to Techstory by the authors of the article. Techstory is not responsible or liable for any content in this article.)
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