Part I: AI for Media Intelligence: The Good, the Bad, and the Ugly

Eric Koefoot, President and CEO, PublicRelay

Everybody is talking about how artificial intelligence (AI) is changing the world and how it is the future of just about everything. Even communications professionals are abuzz with their desire to jump on the AI bandwagon for their media analytics.

It’s true; AI can be pretty impressive. It is already recommending products to consumers, catching credit card fraud, and targeting advertising with uncanny accuracy and effectiveness. Even doctors are starting to get assistance from AI in diagnosing disease through analysis of symptoms and lab results.

But in our lust for the latest technology cure, we must refrain from assuming that AI will fix everything right away. For a number of applications, AI struggles mightily to match the insights that the human mind can provide with ease.

Some of the aspects of AI that need improvement will certainly continue to get better as training data sets improve, computing power increases, and algorithms get smarter. But according to many leading experts in the field, other applications of AI will likely struggle for years – if not decades – to come. Many call this the ‘classification challenge’ within supervised machine learning.

As a digital media professional with over a decade of experience building one of the premier media intelligence solutions available, I believe that while AI can assist media monitoring efforts, effective use of AI for rich analytics is much farther from the end-zone than some would lead you to believe. I will further elaborate on this in a three-part blog series.

Part I-AI for Media IntelligencePart I: What AI Does Well – The Good

Most experts would agree that AI can be great at handling scenarios that involve a yes/no answer, particularly when there is an ongoing, robust feedback loop to tell the system when its prediction is right or wrong.

Let’s look at three examples of AI usage that illustrate this strength:

1. Determining fraudulent and legitimate charges – When a credit card is stolen, it creates a perfect learning opportunity for AI. A bank customer reports their card stolen and identifies which charges were legitimate and which were fraudulent. Any uncontested charges are implicitly used as confirmation of valid transactions, thereby reinforcing the characteristics of legitimate transactions. If you take the same information from millions of cardholders, AI can use those data points to predict with uncanny accuracy changes in a purchasing patterns that are likely fraudulent.

AI also uses “geofenced” data to protect your credit card account – knowing the locations (geographies) where you normally spend. In addition, AI “learns” how you (and thousands of other similar voyagers) travel, using historical patterns of purchases – hotels, restaurants, etc. to approve or flag as suspicious any out-of-town spending. Why does that work? Because AI has perfect data from a feedback loop with thousands or millions of data points and is being taught the right answer every day. Even if you move to a new city, AI can use a real-time feedback loop to generate new data and adjust its predictions with no human input required other than your purchase history in your new hometown.

Credit card transaction validation is a very effective use of a yes/no feedback loop that drives powerful AI learning and effectiveness.

2. Online advertising and product recommendations – When you see ads on the internet, most of the advertisers are using you to help test thousands of variants of different advertising attributes such as the type and size of ad, time of day of delivery, pricing deals, and even the words shown to you. They might even target specific product ads based on what you have shopped for in the past. (Who hasn’t been chased by ads across the Internet because of a pair of shoes you purchased one time on an e-commerce site?)

How are they doing this? The advertising companies are improving their targeting by using AI reinforced with perfect information. When you click on an ad and go to an e-commerce site, you either buy, or you don’t. YES vote. NO vote. AI will constantly learn which advertising attributes work and cause people to click. With millions of interactions to learn from – all tagged with reliable, fact-based results – computers can learn very quickly what works best in just about any situation.

In a similar scenario, Amazon, the e-commerce giant, uses AI to drive product recommendations. For example, when buying a shirt on Amazon, you see a set of product photos (slacks, belts, etc.) with the headline, “People who bought this also bought these:” What the AI technology at Amazon and similar online companies does is look for patterns in people’s purchasing behavior to suggest additional items that follow that same pattern. Of course, if you then click and buy the recommended product, that’s one more ‘plus’ vote for that AI recommendation.

Advertising customization and targeting and Amazon online shopping are more good examples where AI is learning from actual transactions. You either clicked on the ad and bought the recommended product, or you didn’t. It’s a yes/no answer providing perfect feedback.

3. Spam Email Identification – Identifying when an email is legitimate or spam is one of the best mass-applications of classification in the supervised machine learning field. Often called ‘Ham or Spam’, AI uses patterns of words, characters, and senders to identify likely spam. Yet the system can only improve if humans flag emails as spam – or go to their spam folder to retrieve legitimate emails and flag them as ‘Ham’ (not spam).

Early spam identification systems used the feedback of the masses to apply standardized, mass email filters to individual users. In recent years, some spam filters have begun to allow for additional customized spam determination based on individual user preferences and feedback. This approach becomes especially helpful as some people flag legitimate e-commerce offerings a spam – offers that they perhaps opted into months or years before but no longer want to receive. Other users will desire to keep those very same emails coming to their normal inbox.

Unfortunately, not all applications of AI have this ideal loop. If you’re assuming AI is going to solve your media analytics challenge soon, that is a stretch. AI struggles with interpreting complex situations with either small data sets or indeterminate answers that evolve over time. Part two of my series will provide more information about where AI fails, especially in media intelligence.


PublicRelay delivers a world-class media intelligence solution to big brands worldwide by leveraging both technology and highly trained analysts. It is a leader on the path to superior AI analytics through supervised machine learning. Contact PublicRelay to learn more.