The Data Daily

What The Intelligent Enterprise Means For Media Companies

Last updated: 04-28-2019

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What The Intelligent Enterprise Means For Media Companies

The digital landscape is changing. What media companies once took for granted – channel loyalty, captive audiences, and scaling on the back of a portfolio full of tangible products – has fallen by the wayside. In place of Thursday night programming blocks and CDs, we now have streaming services and custom content experiences. Behind it all lies artificial intelligence designed to catapult business into the upper echelons of intelligent enterprise.

The question is what all of this means for media companies looking to stay on the cutting edge of innovation while still speaking to their core audiences. How can brands expand and take advantage of AI technology while minimizing risk and maximizing efficiency?

At its core, the concept of intelligent enterprise is about achieving goals using data assets. That’s the simple explanation, but there is so much more to becoming a smart, more global business. For media companies, the intelligent enterprise is a way of solving the challenges that come with digital disruption, namely how to:

In a world where connectivity is paramount, media companies can use AI to bridge the gap between provider and consumer as well as between consumers, constructing a far-reaching community that experiences, buys, and shares more.

User experience, or UX, has become the guiding light of almost every industry in countries around the world. A great sales funnel attracts new leads, nurtures those leads, and then converts those newly warm leads into customers. None of that happens unless you can reach consumers on their level and give them an incredible experience as they traverse your brand’s buyer journey.

With AI, you get more options for customization than ever before. Intelligent enterprises in the media industry use technology to:

Look around – you’ve likely already seen AI changing how you interface with your favorite media outlets. Every time you ask Siri to play your kid’s favorite song or Amazon recommends a product based on previous purchases, you’re seeing AI algorithms at work. Those are simple examples of complex systems, but there’s so much more to these technological possibilities yet to be explored.

Take sentiment analysis, for example. This process uses computations to dig deeper into audience opinion and understand not just what’s on the surface but what customers really want. In our introductory piece on machine learning and the media, we used the following example:

While the actual words in each sentence are almost identical, which would’ve fooled search engines of old, the meanings are different. For brands looking to use customer feedback to inform marketing campaigns, understanding the difference between “I love my athletic coach’s shoes” and “I love athletic shoes in Coach” could lead to smarter targeting and more economic ad spend.

Whereas old technology basically skimmed content searching for keywords, new iterations of AI are learning how to understand intricate word patterns and putting keywords into context. Of course, there are applications far beyond the written word.

Sentiment analysis is how streaming companies are refining artist-led music channels; when listeners want to hear music like the Rolling Stones, AI doesn’t just throw together selections from a few classic rock albums and call it a day, it breaks down the patterns behind the notes, considers genre and feel, looks into what other users who like the Rolling Stones also select and then uses all that data to build a playlist you’re scientifically inclined to love.

AI customer service bots can even use sentiment analysis to measure speech rate and vocal stress to determine how a caller feels about a given subject. Those insights are practically priceless and could be used by media companies to get a more objective reading from phone-based focus groups after a new product launch.

Even as artificial intelligence propels the media industry forward, there’s a need to maintain a human aspect to interactions and services. The key to creating this balance is to ensure that products, services, and partners are all working together to achieve a common goal. AI must be in tune with the human face of your company, and both of those elements should lead the way for collaborators.

Having a chatbot is a savvy way to curb overhead while still being available 24/7 for customer inquiries; having a chatbot that doesn’t talk like a chatbot or spit out generic replies is a thousand times better. Until that technology is available and affordable, brands should limit computer-generated responses to simple questions (“What’s the name of the new Bruno Mars album”) and leave more complex interactions to the humans.

It all boils down to brand impact. What your marketing campaigns, product launches, and customer interactions leave behind will inform how your brand performs in the future. Becoming an intelligent enterprise demonstrates that your company cares about how it’s perceived and how much customers enjoy brand offerings. Customers are smarter and more educated than ever, and they know when they’re being heard and when they’re being ignored. With AI, someone’s always listening. That someone might as well be you.

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