4 min read
It's hard to believe that, just 20 years ago, many advertisers planned and purchased campaigns a year in advance. Today, advertising is a split-second affair, fueled by advertising exchanges that continually broker the sale of inventory in auctions that take place in milliseconds.
Advertisers and their agencies are under pressure to change the way ads are delivered. General messages presented to broad audiences are no longer effective; messages need to be more targeted. This provides great opportunities for advertising exchanges to apply machine learning to help marketers optimize ad runs and save money.
With access to an enormous amount of rich data and machine learning tools, advertising exchanges can better address challenges around prediction and forecasting. A key metric that matters to marketers is the performance of their ad campaigns, or cost per acquisition (CPA). In order to improve the effectiveness of ad campaigns and reduce the cost of online advertising, ad exchanges need to improve their click-throughrate (CTR) prediction. The placement of the ad, size, and content of the advertisement are critical factors for setting the right expectations around pricing and yield for ad campaigns.
Another challenging area is to target viewers based on behavior as well as other factors, like time of day, topic of interest, and demographic profile. For example, simply knowing the age and gender of the person goes a long way toward optimizing the performance of an ad campaign and improving the user experience. And if you can apply artificial intelligence (AI) to combine indicators like location, friends, networks, social media sentiment, memes, and customer ratings, you enable advertisers to optimize their spending by choosing the best ad space for their messages.
Now that content personalization is becoming a standard consumer expectation, AI is starting to be used in many useful ways.
AI can also be effective in preventing ad fraud. You might have heard stories about click fraud or the deceptive practice of using automated agents to repeatedly click on ads to waste advertiser money. The dangers of click fraud have long been known in the advertising industry, and they remain significant in 2018. Click fraud was estimated to be a $6.5 billion problem last year. The recommended approach to fighting click fraud is to exercise some level of diligence and use AI to continuously look for fraud patterns.
For AI to work, you need a high-performing data platform and AI tools to train and understand your data set. MapR provides the data platform and a suite of data science tools to enable advertising exchanges to distill insights from their data and tailor their AI applications for specific use.
While AI can help tremendously in crunching through petabytes of data stored in a data platform, it's worth noting that it doesn't replace all human involvement. You still need to monitor the ad campaign's effectiveness. AI should amplify your capabilities and complement your intuition, so that you have more time and deeper insights to help clients launch new campaigns and expand.
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