As consumers get more comfortable with AI and understand its ability to improve their lives, companies are making investments to improve key supporting technology, such as translation, algorithms and discovery.
In IBB Consulting’s work with media companies, we’ve identified three key areas where opportunity exists to integrate AI and machine learning to improve the customer experience, boost revenues, increase productivity and more. From chatbots and content creation to new levels of personalization, the following areas are opportunities to integrate AI into everything from consumer-facing properties to internal functions.
- Chatbots For The Customer Experience & Engagement Win
AI-driven chatbots can already interact with people on the web or on mobile to help find information, answer questions and sell services. We’re early in the game when it comes to how they will be deployed, with the goal being to ultimately get to a place where communicating with a chatbot feels no different than chatting with a real person.
Most customer requests and issues are basic and can be handled by programming chatbots to understand questions and trigger words, then provide answers by querying specific data sources. From a customer care perspective, chatbots and virtual assistants can be a cost-effective way to meet the needs of customers any time of day, with no wait. If necessary, chatbots can be programmed to hand off to a human agent should the conversation become too complicated. Over time, this will not be necessary as machine learning drives continuous improvements and the chatbot’s understanding of how to address an issue it once could not.
Chatbots can also provide new customer experiences. For example, The Food Network built a Facebook chatbot that recommends meals to interested customers by searching its database of more than 60,000 recipes. HBO built a customized website for “Westworld” that included a chatbot named Aeden that interacted with visitors and helped them explore the park.
This is just the beginning of what’s possible. Content owners can create destinations that feature chatbots modeled off of favorite characters. Fans can chat about their favorite topics, ask questions about the show, or really, just about anything. AI-driven chatbots will reduce OPEX as they become more effective and create better engagement with consumers by giving them another way to interact with their favorite content and characters. The added layer of engagement will then drive more tune-in to popular content and create more impression opportunities for lesser-known content.
- Content That Creates Itself
AI can also serve the role of content creator. It’s not going to start writing TV scripts, but today, AI is smart enough to pull data from multiple sources to generate financial reports, sports commentaries and brief event summaries, it can aid in research or even basic content creation. However, while AI can report facts, it cannot add emotional responses or opinions. It also struggles to create detailed storylines. As machine learning continues to evolve, media companies can lean on the technology to produce other kinds of content, such as series and movie reviews that pull from, and aggregate in a cohesive story, what consumers are saying on social media.
AI can also support video production efforts. Fuisz Video is an interactive video and technology company that allows marketers to simultaneously shoot videos from multiple perspectives. Integrated AI then automatically produces a seamlessly edited clip that is ready for distribution or finishing touches. In an age when getting content online as fast as possible is a key to driving social sharing and large audiences, media companies need to capitalize on every advantage available to them.
AI can also be used to accomplish the opposite task. Instead of providing a single edit, AI can create a range of edit choices. Maybe different cuts need to feature different actors more prevalently than others. Or different edits to fill different time requirements may be needed. This functionality will be critical as programmers aim to get hyper-surgical in targeting of audiences across different social networks and platforms.
- Getting More Intelligent About Personalization
Today, when users interact with companies, they can sometimes be made to feel like just another number. They’re asked to enter account information and PINs. The rep they speak with typically does not have time to comb through their history or profile before a conversation starts. This couldn’t be more at odds with the kind of experience that customers value most.
Today, personalization is a major differentiator as media properties compete for customers. Viewers are favoring tailored recommendations, like Spotify’s Discover Weekly. Netflix’s recommendation algorithm has been estimated to save the company $1B annually by keeping users engaged and reducing churn.
AI can be used to customize service interfaces or web homepage experiences, hiding content that would not appeal to a certain customer, while putting a spotlight on precisely what will keep them watching, listening or clicking for long periods of time. AI can also be trained over time to offer recommendations that are personalized, but not too personal (aka creepy). IRIS.TV has already started curating video libraries for media companies to align with each viewers’ preferences and behaviors, while Vidora is helping news companies customize communication to reduce churn.
Media companies should consider building more robust recommendation engines that leverage deep learning to create more personalized experiences. Having the capability in-house vs using a vendor solution provides content providers with more flexibility and gives them the option to use the technology in other parts of the business. As the technology continues to learn it will become an increasingly valuable asset for target marketing and other monetization opportunities.
Kicking off AI efforts in a smart way
AI’s possibilities and support requirements can be overwhelming, but investments in machine learning and application of AI across the media business is a critical step toward staying ahead of the competition. After all, digital native businesses are building from the foundation with AI capabilities in mind.
Media companies should identify and prioritize AI business opportunities that either solve a current problem or transform the business in a visionary new direction. Developing rapid proof of concepts using proven vendors and technologies can return immediate insights. With so many AI companies emerging, potential acquisitions should be evaluated.
We’ve only seen the beginning of what’s possible with AI. While there is a long road ahead, moving forward with a strategy is the only way to address the challenges and opportunities media companies will face along the way.
