How Data and AI are Evolving Media and Entertainment - World Tour New York | Salesforce

In the past, the Formula one experience could have been seen more romantic. With a picnic on the hill of a track, watching the heroes. The evolution from the old days has been incredible. Today, the F1 experience is very special.

F1 is the greatest sporting and entertainment spectacle on the planet. It touches all your senses. The smell of the tires, the sound of the car roaring past.

As soon as the light go out, you know you are watching something history.

May never see again.

Try to survive has been phenomenal. To capture new fans interested in the behind the scene experience. Knowing our drivers, it's exciting funds worldwide.

We'Ve over 500 million fans. Over a third of them are new. In the last four years, our database.

Is continuing to grow 30, 40% year on year.

The key for us is to make sure that we have the customer at.

The center and therefore our strategic partnership with Salesforce is imperative. We want to make sure that we're designing an experience for our fan like no other. But really less than 1% will actually go to a Grand Prix physically every season. So how do we engage the other 99% of fans? Fans have changed. We've seen a much younger and a much more female fan base.

They'll have their TV broadcast in front of them. They'll have a device showing all of the live content, another showing all of that rich data that comes through.

Everyone has a different relationship with Formula One, with Customer360 and that single source of truth. We're creating these digital experiences that really resonate with a fan at an individual level.

With AI data and CRM, we are now able to innovate like never before.

Innovation has been always the drive of our success. The DNA of Formula one.

From an AI perspective, we can truly talk to our fans on a personalized one to one basis.

Speak different language, have different narratives, understand.

Those different markets, be proactive, create these magical connections and bring them closer to the action than ever before. The key to doing that is data.

We've been collecting data across all of these touch points. We have so much data coming into us.

Salesforce are helping to visualize that data and we're using data cloud to create personalized experiences in a way that nobody's ever seen before. We like to think of ourselves as a fan company and as we grow our fan base and we grow our data, working with Salesforce, we're finding loads of new ways to bring those experiences to life.

That's really what it's all about, the joy of our fans. Embracing the unique experience in formal work, this is really magic for us.

Hi everyone. Can everyone hear me? Okay, great. Awesome. My name is Rajat Sharma and I'm part of the industry advisory group here at Salesforce. I have the good fortune of working with our clients in the media and industry ecosystem. First and foremost, I want to start with a thank you. Thank you for choosing to spend your time with us in this holiday season. Wishing you and all of your families a great season ahead. So today I will start at a high level around the implications of generative AI within the Salesforce ecosystem, as well as its implications on the media industry in particular. And then my colleague Chinmay will take you further and deep dive on how we use that for unified audiences. So, first and foremost, data and intelligence are really the new big two factors. Now your natural reaction to that would be, duh, that's always been the case. When has data and intelligence not been the most important factor? Well, one of the big changes that LLM brought into our ecosystem is, is in the name itself. Is the language part of that large language model? The level of interactivity that you are now able to give your business users with the data and all the information that's available within your business ecosystem is incredible. These are tools that historically were only available to the data science teams within your organizations, and now you can bring them in to the systems of work that your everyday business users are used to using. But that intelligence really relies on the fuel of data underneath it, especially in a business environment. That intelligence works in two cases. One, it is supported by proprietary information that is applicable to your enterprise, and then two, it is available where your users want it so that there's no learning curve to this process. So let's use this framework and explore the challenge of generative AI in media. Now, the data aggregation aspect seems the most natural problem place to start. But connecting your data is not easy. Most of the CDOs that we talk to today say, yes, we get LLMs, yes, we get GPTs, but if it is not supplemented by the data infrastructure that we need to fuel it, it's almost worthless to us. So connecting that data across the different data sources with their taxonomies, with their frequencies, that is one of the hardest challenges that most data CDOs are trying to solve today. And that's where Data Cloud comes in. Data Cloud not just works and brings that data into your CRM apps, but it works alongside your existing data infrastructure. If you've done the hard work in trying to aggregate part of your data into a data lake, data Cloud sits and builds on top of that infrastructure and brings that information to the apps that your users are used to using. On a day to day basis. You can harmonize that data, you can segment that data and the structure is built around unifying using your key epicenters of information, your customer Data and your B2B relationships data as the main unifier. Now, once you have all of that information in a unified ecosystem, the next challenge is intelligence. And in an enterprise ecosystem the biggest criteria for information or for intelligence is security. You need to have the security to be able to transact and interact with that data, knowing that your data and your questions and your prompts are secure. Now that's one of the biggest challenges with LLMs or intrinsic to LLMs. An LLM cannot point exactly where they took a piece of information, what they did with it, and exactly how it is surfaced in the responses that it is giving to you. That is exactly the reason why Salesforce invested so much in the security gateway layer so that you have the security to know that all the information that you're asking the LLM stays within your four walls. It brings in the demasking and the masking and the demasking layers steps in the middle of the process. But most importantly it keeps the user experience as exactly the same as we're all used to. The typing of the question is exactly the same as we are used to doing it in our GPT environment. I'm not going to do the hand raising who all have used GPT here because I feel like at this point everyone's pretty much used that. So this is a slide that you've all seen throughout the world tour hopefully. So I'm not going to dive into the exact specifications of each of these layers of intelligence. But the most important aspect of this is that the user experience stays exactly where it is and you have the security of knowing that the information that you are asking the LLM for stays within your four walls. Combined with the data cloud infrastructure and the Einstein Trust layer, you can very quickly see what the Salesforce differentiation is and what the Salesforce advantage is layer. On top of this, the app ecosystem as well as our integrations with the rest of the technology partners and you can see Salesforce's differentiation. So what does this all mean for media? We've talked about generative AI in a sort of a high level infrastructure, but what does it mean for media? Generative AI is already having a tremendous impact on media. It was hotly debated during some of the union strikes earlier this year for both writers and actors. The advertising industry has Started using this for creating multiple unique personalized copies at scale. It's been used for post production and pre production as well as doing creative credits. So there's incredible amount of generative AI data already within the media that you consume on a day to day basis. Given the sort of wide ranging aspect of this topic, let's break this down into two different categories. The first one will be the creative aspects of how to use Gen AI in media and the second one we'll look at more from a procedural perspective. Stuff that you and I probably spend on a day to day basis now starting from tracing the journey of the creative aspect of generative AI and media, it all really started last summer. Even a few months before ChatGPT came into our lives. There was Dall E and Dall? E was the first text to image generator that we'd seen. Sam Altman was asked earlier this year at Dreamforce as to what was the most surprising aspect of building an LLM. And this right here, building LLMs for creative tools, that was something that was more surprising even to the people who built it. Because creativity in LLMs was not something that they had started out as a use case to begin with. Turns out the things that lead to the hallucinations within LLMs also gives it a level of creativity which says something about the human mind as to where creativity comes from within ourselves. But increasingly since the release of Dall E, we've seen use specific AIs LLMs that depending on if you want to create music, if you want to create a movie script or even a video, you have the LLMs specifically for that. And that's really the direction that LLMs are likely to take as we keep going forward now, the impact on the content generation side of this is going to be tremendous. It is going to democratize access to tools that historically only a premium video creator or a movie studio had. This will result in a lot of content being available online and the platforms then that are curating this content will have a much more incredible role to play in the ecosystem. Now talking about the actual procedural aspect of generative AI, these are the things that as I mentioned, you and I end up doing on a day to day basis. This is analyzing huge sets of data or building pitch decks to communicate your messaging out to your clients, coming actually or even developing some apps that are specific to your application. These are the things that generative AI will have an incredible amount of impact on because now you will have the world's best intern, the world's best analyst, the world's best notetaker right at your fingertips. We spent an incredible amount of time on a day to day basis trying to figure out where the right information is within an organization or who to reach out to to get to that information. These are the things that Genai will help automate for you based on assuming that you have that data integration underneath that layer. Fortunately, Salesforce has had a tremendous amount of experience in generating and giving users access to artificial intelligence for more than a decade. Our Einstein tools today are the most used CRM AI in the world, making over a trillion predictions a week. So we're committed with the help of our AI lab to developing AI in an ethical and in the diligent manner that a technology with this wide ranging impact requires. So with that I'll hand it over to Chinmay to now walk you through the audience specific use cases. Thank you.

Thank you. Rajat, can you all hear me? Okay, thank you so much. Rajat walked you through the general applicability of AI. How do we look at it from a CRM perspective? And then what does it all mean when it comes to apps? Right, so apps are what you have been used to as marketing cloud and sales cloud and everything else and all the other clouds that are part of the Salesforce ecosystem. Now when you connect them all together with data cloud, then we make that customer 360 work for you. And what does that mean within the media and entertainment world? That means it's the number one CRM AI CRM for media and entertainment. So again what does that mean? So that means taking sales cloud and applying to media for you. So enabling streamlined ad sales, for example, taking service cloud and making that applicable to media. So that means ensuring that subscribers can make sure that they get the right support, customer support or whatnot from wherever they are. Similarly with mulesoft, how can we think about mulesoft helping media and entertainment? That means ensuring that we have readily available integrations to several systems upstream and downstream. So that means ad servers, ad trafficking systems, rights management systems, paywalls for subscribers, and many more when it comes to commerce as well. Right. So we ensure that each of the apps are applicable for the media and entertainment world so that your customer360 is applicable once you have all the data in and you can ensure that it fits the media world. And where does media cloud come in into all of this? So of course we have all the individual applications, but then what's the glue between them all to ensure that they fit the media and entertainment world? What about all the specific Processes that need to call each of the apps and make sure they work together. So that's where media cloud comes in. And the way we look at it is we want to support our media and entertainment customers vision to drive their hybrid monetization channel. So that means they can be getting their revenue from subscriptions and ad sales, or ad sales and digital commerce, or just content sales and licensing. Maybe so. So we want to support them where their revenue and monetization needs are by helping them deliver personalized audience experiences with intelligence and automation at the center of it all. So how do we do that and how do we make that happen? I'll walk you through this multi tiered cake. So starting at the bottom in gray would be all of your systems that we would integrate with mulesoft. So Mulesoft Direct, specifically made for industries and for media in this case. So we would interact with all of your systems and media cloud through MuleSoft and then going up the layer, everything that we mentioned, right? So number one, AI CRM for media and entertainment. Therefore we take sales cloud and make it sales cloud for media. Take service cloud and make it service cloud for media. All of it is built on our platform. And then comes the magic, which is the media data model. So this has hundreds of objects that we created specifically with media processes in mind so that you don't have to create those customized objects on your own and therefore maybe hit a ceiling and your org can get too brittle that you can't scale. So we take on the burden of standardizing those objects that you may need for your day to day operations against your monetization channel of choice. So take that data model across subscriptions, across ad sales, across content, sales, across commerce. And then we provide a whole host of automation tools. So the next layer up and these tools we have several use cases of our customers using just these automation tools and the data model to automate many of their process flows. Like they created their own contact center, for example, a media contact center, they have generated capabilities to ingest RFPs and have them in a way that they can actually query them and ensure that products are built on top. So many, many examples of that. And so all of that can also be built on at the next level. With media cloud modules, you have a product catalog that's specifically built for media where you could store all of your ad products or all of your content titles to make sure that everything is surrounded with that one thing in mind, bringing all of your data together. And then we also have an industry specific media specific configure price and quoting engine, media specific contract management engine that can be used to submit iOS online and do revisions and then capture that back so that it doesn't have to be an email trail every time. And then you have a B2B as well as a B2C order management module that can take everything that has been quoted so far and then ensure it goes downstream into the appropriate fulfillment channels. So for example, you take all of your quote line items or your line items in a media plan and then ensure that they go to the right ad server downstream or to the right DSP downstream for fulfillment for ad serving. Then at the top we have the application layer where we've built two end to end applications with UI that can take a user through, for example a salesperson at a publisher to go from creating from receiving an rfp, creating a media plan and executing the campaign and managing the campaign all at the application layer so that that person does not have to swivel chair into multiple systems for digital ad sales, for linear radio ad sales, for out of home or for print. And you have that management layer up on top in one place. Similarly with subscriber lifecycle management There is a B2B version where the customer service rep will see everything about a subscriber in one place with the churn score there recommendations for next best action summarizations with Genai as to what the case had been about in the past so that answers can readily be presented to the customer. And all of this can be easily plugged in with other add ons specific for the media world like loyalty, like billing, like Slack, like intelligence and unified audience profiles which we will zero in on a little bit. But what does this all mean for you? And why media cloud and why not just sales cloud? Right? Well, all of these media specific processes and capabilities and objects that we have built will ensure that it's a 33% reduction in custom development that we've seen in our customers. And it's also a 30% reduction in integration efforts so that you don't have to start from scratch every time because hopefully you're not the only one in the ecosystem using a particular ad server like Google Ad Manager. So we take on that burden to make those integrations happen so you don't have to do it every time. And speaking of audience engagement that you saw early on with Formula One, it's what we have, the number one AI and CRM that we have that enables a customer like Formula one to identify what their customers are engaging with and how they can ensure that the engagement continues and what all they can do to further ensure that the customer's lifetime value is increased. So we are a platform for that media monetization and automation. And there's AI everywhere, right? So we have Genai with segment creation and content creation that Rajat spoke about earlier. But we've been doing predictive AI with Einstein for a long time. So you have that lead scoring already. You have send time optimization for emails that can be sent. For example, with Genai, you can actually respond very quickly to an RFP that you receive. Summarizing, here's what we gather that you're looking for and we will get back to you soon. Just that acknowledgement, making it personalized will save a lot of time. So what does that mean to ensure that you can maximize your audience's engagement? That means developing those really deep audience relationships and understanding them through insights and AI, unifying these profiles that you get from various sources and bringing them in one place so you can activate them appropriately and you can scale those media moments with that data. So ensure that every interaction of your customer is captured and reported back so that you can action on that. And underlying it all that I mentioned is automation. So that's the key in speeding up anything that you may have. So AI with automating those processes will make sure that you can get those optimizations and efficiencies. So how do you make that happen? How do you build these unified audience profiles? Of course you have a lot of sources of data. So you have CRM data in many of our own Salesforce applications that you may have. And then you build, you bring that together along with your other data storage options. You can use MuleSoft to bring those together. And of course your campaign ad campaign data, for example, that you may have sitting in many other ad servers, right? Or your own data lakehouse. Bring all of that together, connect that, bring it together, harmonize it through data cloud, and then ensure that it fits into the data model that we have, into the objects that we have. And then you have that singular unified source of a profile for your customer. So then you can start ensuring that the magic happens, which is analyzing what insights are there as part of those audiences. How can you start providing personalized journeys and activating them for different recommendations, whether that's for subscriptions or different recommendations for purchases, or saying that, oh, maybe they're on the edge of churning and you can proactively recommend something based on what their subscription engagement has been, right? So that's where you can act appropriately and this gives our sports customers a unique view into their customer360 into their audiences to make sure that they can increase the lifetime value of their customers. So we have sports teams and clubs, leagues as well as stadium and event management customers who are leveraging Salesforce Customer360 to increase audience engagement. But they're not the only ones who are leveraging this. So we have trailblazers across the entire industry. For example, NBCUniversal is actually reinventing the fan experience by leveraging our Salesforce 360. So they've ensured that what they're using is for automation. And There has been 100% improvement in email open rates for them and 50% less time to create a campaign audience. Spotify has been using us for improved adopts automation. So again, automation in the picture. And they know about their target audience really well and we're helping them connect that with everything else that they need to do to reach that target audience. Uber has been using us to increase their productivity and efficiency across global sales teams as they are on their way to ensure their advertising revenue grows to a billion. And we're happy to be part of their journey. Multichoice is using us to maximize engagement with our data and they have seen a 45% decrease in cost per conversion. And they've also used us to streamline a lot of their paywall partners and efficiencies right there. And finally, Globo has actually leveraged us to streamline subscriber care, especially with the AI portion there that I mentioned, like Churn recommending, recommending the right next step for them to take and ensuring that the lifetime value is maximized. So that's 10% reduction in customer support costs that they've seen already. So will you be next? I hope so. Thank you very much for spending your time with us and please reach out if you have any questions.