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 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.

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.

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).

The future of video streaming will continue to evolve with advertising continuing to play a leading role in video monetization..

To remain competitive, video streamers need the ability to aggregate, harmonize, and activate their data so they can meet the demands of personalization, at scale. Those who are able to achieve this will edge out the competition and find success in the streaming era.