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As the video streaming industry continues to evolve, competition is growing more intense. In the past, growing subscription numbers was seen as the path to success. This made acquiring and retaining subscribers priority number one. Now, despite the fact that subscribers remain the primary source of revenue for the majority of players, they are no longer seen as a viable way to grow the business. Market saturation has forced video streaming companies to pivot their growth strategy, resulting in many of them looking toward ad sales as a way to meet their targets.

Table of Contents:

• Supercharge your ad sales with AI and data
• What is ad sales in streaming?
• The challenge of ad sales in streaming
• Legacy TV players and digital natives have their own unique challenges
• Generative AI to the rescue
• Use AI to better understand your advertiser and enhance external sales
• A robust data layer is the foundation for your AI operation
• A future of innovation and information-based success

Ranking of ARPU from Following Revenue Streams

1. Advertising
2. Commerce (e.g., ticket sales, apparel, content-related items)
3. Subscriptions
4. Live experiences
5. Licensing and rights management
6. Siloed data

_{Source: Ranking based on a count of responses of “most valuable” ?from respondents with more than one revenue stream.}

Ad sales in video streaming is the process of selling advertising space within streaming content or on streaming platforms, apps, or services.

Money generated from ad sales can help fund more innovative content that increases audience engagement and gives organizations a competitive edge. Furthermore, unlike subscription revenue, increased ad engagement translates to higher average revenue per user (ARPU).

Advertising in streaming also provides valuable insights about user preferences through their ad completion rates and interactions. For example, if a company notices a high completion rate for ads featuring behind-the-scenes footage for a popular show, it indicates that users find such content engaging, and it might be a sign that companies should incorporate more of that into their library. This data allows streaming companies to better target ads, offer more personalized content, and optimize the overall experience. It attracts advertisers who are willing to pay more for the promise of an effective campaign.

Despite clear upsides, scaling advertising in streaming is easier said than done. Multiple device and ad formats, lack of industry standardization in measurement and verification, and manual workflows present challenges to video streamers looking to expand their ad sales operations.

Both legacy TV and digital natives are still running a significant portion of their ad sales operations manually. Sales executives are spending time on manual work like writing repetitive emails, customizing standard pitch materials, creating campaign wrap-up reports, and reporting campaign performance. These day-to-day tasks are tedious, and they significantly slow the pace of business. This puts streaming companies at a competitive disadvantage compared to other forms of online advertising (like search, display, and video) in which platforms have faster, easier transactions

As industry ad spend shifts toward performance-driven versus brand awareness, streaming publishers need to invest in technology to automate sales and analytics operations in order to improve performance and lower costs.

Both legacy TV streamers and digital natives face issues with measuring and tracking ad performance. Things like fragmented customer journeys, cookie limitations, privacy concerns, and siloed data make it hard to track users across devices, accurately attribute conversions, and understand the effectiveness of an ad campaign. What’s more, a lack of standardization and defined metrics make it hard for advertising clients to compare the success of a campaign on one platform to another. This results in a lot of variability around things like verification, currency in the ecosystem, or even what a successful ad campaign looks like.

Even more consequential is the competition from retail media networks. These networks have grown tremendously in the past few years and are on track to outpace linear advertising. This is largely due to their ability to measure attribution. These networks own all the data. They can not only see that an ad has been clicked on, they can also see whether or not the product was actually purchased. This ability to attribute puts them a step ahead of streaming publishers who, unlike retail media networks, do not own the point-of-sale data. Because these publishers don’t actually sell the products they advertise, they must rely on third-party attribution or analytics partners to prove ad effectiveness.

Legacy TV players and digital natives also face challenges that are specific to their particular business and operational models.

Legacy TV players have difficulty automating operations due to workflows differing across streaming and linear TV formats. Most leading publishers continue to invest in consolidating inventory across linear and connected TV (CTV), attempting to create products that smoothly transition between the two. However, because the industry hasn’t come up with a standard way of doing this, it remains an ongoing work in progress.

The primary challenge facing digital native publishers is that unlike legacy players, they don’t have long-standing connections with the agencies who purchase advertising space on behalf of advertisers. This is a significant disadvantage as these companies are the leading purchasers of TV inventory. As a result, digital natives are seeking to differentiate themselves through increased analytics, improved targeting, and unique audience reach.

As streaming publishers enter this new advertising era, they need to re-envision their way of working. AI can help them harmonize the data in their siloed systems, paving the transition to automated processes. It can also help track metrics and provide the analytics needed to help clients trust that their investment is being maximized.

An AI-powered platform with built-in generative AI capabilities ingests structured and unstructured data to provide teams with recommendations that enable them to boost efficiency, reduce errors, and drive personalization, at scale. This translates to the ability to help ad sales teams with everything from lead generation and relationship-building to writing personal client emails, summarizing sales calls, and coming up with talking points.

Generative AI’s conversational interface is its greatest asset. Sales teams can use natural language to communicate with CRM dashboards. And because an AI-powered platform also holds the totality of the data at an organization’s disposal, it can quickly surface answers to questions such as who is advertising across an organization, how successful have their ad campaigns been, what pricing models have worked, and more. These answers appear in seconds—without days of research across multiple systems, phone calls across teams, and hours of legwork.

Generative AI also makes it possible for teams to put together marketing pitches, media plans, wrap-up reports, and analytics more efficiently and effectively, and it enables them to personalize communications, at scale.

Its ability to work in the background means that generative AI can automatically log and capture information like emails sent and received, meetings attended, and calendar events to ensure that any relevant information is added to the appropriate record. This data can then be used to track client engagement, analyze trends, and make informed decisions, all of which go a long way toward nurturing client relationships.

Even further, it can use natural language processing (NLP) and machine learning to log conversations, identify key topics, and pick out mentions of competitors, specified client interests, and other important information. The ability to capture and take in this type of unstructured data and then use it to drive more informed business decisions is what makes generative AI so powerful. It unlocks possibilities, enabling teams to gain much deeper insight into client needs, preferences, and goals, and it informs more strategic overall decision-making.

Generative AI capabilities can also greatly improve advertiser support services, providing things like responsive digital portals, easy-to-search databases, and self-service tools, all of which increase productivity, boost efficiency, and meet advertiser expectations. For example, if an advertiser reaches out with specific questions, an agent can use the system to easily surface relevant articles and create a response that includes the information at the click of a button. If the advertiser needs quick answers, a chat bot or self-service option can be made available, drawing from that same article/information database.

Strong client relationships are essential to a successful ad sales operation. An AI-powered platform can help teams nurture these relationships by organizing, understanding, and activating essential information like:

• The advertising spend for a particular advertiser
• The type of campaigns they are running
• Information about other advertising done with the organization (or parent organization)
• The number of campaigns and the nature of those campaigns
• The brand messages they are communicating and whether they align with their platform and audience

The unified view ensures that all relevant information pertaining to a particular advertiser is included in reports, insights, or analyses. This is especially helpful if a client has, or has had, multiple campaigns running across different arms of an organization. They can use this past data to create custom insights and actions, such as:

• Building out specific media plans based on client requirements and platform capabilities
• Suggesting optimizations to ensure they are fulfilling the guarantees made to the client
• Aggregating all the data in a singular client profile, allowing all stakeholders to benefit from each others’ work and easily see and understand what’s going on now and in the past

A team attempting to pitch an advertiser with a very specific campaign target can input the advertiser’s goals into the system and get immediate insights into the best audience to target. They can then build a plan accordingly. Once that ad is running, the system can also handle real-time reporting and/or in-flight optimization (with human approval).

Having an effective pricing strategy is also extremely important for video streamers.It affects everything from revenue generation to market positioning to competitive advantage. It also greatly influences the strength of the advertiser-streaming publisher relationship. An AI-powered platform can help streaming publishers optimize pricing by analyzing market dynamics and customer behavior and then pairing that information with historical and real-time data such as demand, competition, and engagement. This ensures teams are coming to the table with the best possible pricing strategy.

Historically, the ability to easily access and understand large and complicated data sets has been limited. AI provides real-time analytics, negating the need to wait on an outside data science team—and making immediate decisions possible.

AI is only as effective as the data it has to draw from. Any company thinking about leveraging AI must also ensure that their data is organized, accessible, and actionable. This makes a development platform with the ability to support data integration alongside AI a key part of the overall solution.

Investments in AI-driven data tools will grow even more significant as we progress into the future. The elimination of cookies means that advertising-focused data layers will become even more reliant on first-party customer data. This loss of cookies will also significantly impact how identities are matched in data from outside sources, lowering the quality of that data. An investment in data integration not only increases operational efficiency, it gives streamers an advantage over the competition.