Industry
Communications, Media and Information Services
Connect. Curate. Comprehend: Unlocking Value Beyond Boundaries
Solution
Data and Analytics
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Cognitive Business Operations
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Highlights
- For media and entertainment companies, contextual content translates into reduction in costs, increase in customer acquisitions, and increase the average revenue per user (ARPU).
- Timely access to data is vital to measuring the ROI on content.
- The complexities around ROI measurement necessitate dedicated applications to store, search, and retrieve granular details such as their rights usage, avails, as-run, generated ad revenue, consumer profiles, and so on.
In this article
Abstract
We live in the age of information overload.
When massive amounts of content are being created and consumed across channels and mediums, is content still king? It may still rule if it is contextual, relevant, and personalized.
Consumers are likelier to rate those brands highly that push personalized content and communication, which results in repeat purchase most of the times. For media and entertainment businesses, contextual content translates into higher customer acquisition and increased average revenue per user (ARPU). The industry’s traditional players also value content-level targeting and tailored advertising.
Numerous distribution channels and a highly fragmented customer base can make it challenging to accurately determine the content's return on investment (ROI). We discuss the challenges media companies face in creating a targeted content strategy and ensuring that it delivers the intended results.
The strategy for success
Content needs to deliver results, fast.
The media and entertainment (M&E) industry has witnessed a massive transformation in how content is created and consumed.
Traditional broadcast and cable companies need to invest more in data analytics and next-gen digital technologies such as artificial intelligence, machine learning, and sentiment analysis to curate more personalized content. On-ground observations on how the media players are engaging with their customers lead us to believe that companies that focus on personalization can easily increase their revenue by around 15%.
As media businesses spend more on content and acquiring consumers, the problem of stretched budgets and measuring the ROI on content generation comes into play.
However, a study published by the CMO Council reveals that many marketing executives worry that their content strategies lack focus. Around 43% rated their content as hit or miss without absolute consistency, and 49% cited budget as the top factor that shapes and impacts their content strategy.
In the M&E industry, rising costs due to diverse distribution mediums such as print, TV, internet, and social media, and serving them to a fragmented consumer base can further complicate their content-targeting initiatives.
Content production and acquisition take up the lion’s share of expenses. As businesses seek to reach consumers across various channels and devices to increase the ARPU, a few critical questions must be answered:
- How do we acquire the right content for our target audience?
- Are we optimizing the value of the obtained content effectively?
- How can we generate maximum ROI for the content developed?
- How can we reduce the cost of operations?
Technology contextualization for ROI measurement
Before digitization, the M&E landscape was much simpler.
And so was measuring the ROI of content. In today’s scenario, the complexity lies in targeting the audience with personalized and contextual content across every medium and platform. The technology that has created these complexities for M&E businesses may also have the solution. However, to achieve tangible results, it will be necessary to contextualize the use of these technologies to:
- Understand consumer needs and feedback for acquiring the right content
- Understand the value of the content through specific distribution mediums
- Deliver contextualized content, including ads, to consumers
- Run efficient operations to deliver targeted content across mediums
- Target content to the right consumer and correlate the consumer feedback
This is where data becomes critical.
Contextualizing the use of technology helps make the best use of data from all sources throughout the content supply chain (see Figure 1). It is not a sequential or parallel process. Each method is independent yet interdependent. It extends across all the players in the content ecosystem, from content owners, aggregators, publishers, advertisers, and consumers to every medium.
Figure 1: Data sources across the content supply chain
Having timely access to accurate data is crucial for effective ROI measurement. The data sets reflect consumer needs and feedback on content; correlating them can help measure the value of content. M&E companies can also use data and technology to reduce manual intervention and cost overheads across the value chain.
A data platform for a granular view of content
Measuring the return on the content delivered is not easy.
The complexities surrounding the various factors for ROI measurement necessitate dedicated applications (rights management, ad sales, and an underlying data platform) to store, search, and retrieve granular content details such as their rights usage, avails, as-run, generated ad revenue, consumer profiles, and so on. Timely retrieval helps the business plan the delivery of content, collect feedback, and provide insights to drive customer acquisition. A granular view of content can protect the business from legal risks and financial leaks.
Applying data extraction and analytics to ad sales functions (inventory, business partners, relationships, ratings) and consumer buying behavior, reveals an in-depth view of consumer patterns and personality. In addition to managing costs for the rights management functions (rights in, rights out, content sales, programming finance), it would help optimize audience targeting and support content customization and contextualized targeted ads, resulting in improved ARPU. Extending these individual applications across multiple mediums enables the cross-utilization of consumer intelligence, paving the way for ensemble interactions and bundled advertising with effective targeting.
For an M&E company, a robust data platform is the key to accurate data-driven decisions at all levels. It stores and manages all supplementary and complementary data related to the content and the consumer with inputs from internal and external partners and systems.
The data platform can also serve as a base for automating primary business and operational workflows internally and externally. For instance, to achieve programmatic ad serving, data sets covering the supply side (inventory, pricing, programming schedules) and demand side (target market, products or brands, budgets) must be integrated. When done at an enterprise level across mediums and services, such integration provides the foundation for delivering more cost-effective bundled advertising.
Data platforms also provide business intelligence across a range of areas – some use cases are listed in Tables 1 and 2.
Reduce content cost |
Example |
Prerequisites |
Leverage content effectively across rights dimensions |
Leveraging the merchandising rights of an internationally acclaimed children’s movie would fetch more revenue. A print campaign can be run to promote the merchandize, in addition to on-ground events. |
Available rights for each content and its components can be stored and retrieved giving the most granular view. |
Monitor content performance within and across mediums and services |
Tracking and analyzing the content performance data at a granular level—production company, genre, title, year of release, cast, and so on—can help monitor content acquisition spends |
Each schedule realizes the internal scheduling (patterns) data such as time slot, day, month, market intelligence, and revenue. |
Optimize content acquisition costs |
Bundling rights acquisition reduces costs across mediums. For instance, companies can negotiate merchandising rights for print by lowering the overall spending when the price of TV rights is high. |
An in-depth view of historical rights usage, unique content, channel, platform, region, and medium level and its stored components with deep insights and analysis for decision-making. |
Maximize revenue by optimizing content distribution |
Profiling users based on their mood, with inputs from connected devices (IoT-driven) and targeting content accordingly. For example, data from the smartwatch could tell that if the user is having a bad day and recommend a feel-good movie. |
A detailed and granular view of the historical demographics for the content, their social media behavior, changing mood patterns from connected devices data, and other complementary data for deep insights. |
Table 1: Content cost optimization use cases
Increasing the revenue (ads and subscriptions) |
Examples |
Prerequisites |
Measure content consumption |
Understand and analyze content consumption across mediums and services to increase targeting and contextualization across target demographics and generate new sources of revenue. |
Consumption, social media, and internal scheduling (patterns) data |
Optimize ad placement
|
Leverage audience insights to optimize ad placements in individual mediums and ensembles across mediums and services. |
Consumption patterns and optimization algorithms based on business KPIs |
Offer personalization and better recommendations |
Contextualize ad placements for every consumer. |
Consumer viewing patterns and internal scheduling (patterns) data |
Reduce customer churn |
Use effective targeting, personalization, and contextualization of content and ads combined with online and offline social media behavior-based recommendations. Data sets from connected devices such as smart watches and playlists can be leveraged to serve mood-based content. |
Social media behavioral data, connected devices consumption data, and core media services usage data |
Table 2: Revenue use cases
The impact of content
Deep impact will require deep insights.
Netflix has a much higher valuation compared to the traditional M&E businesses such as Disney or Lionsgate. The new age entertainment giant makes its own shows, buys from studios, and sells directly to customers. Others like Amazon are trying to break into the entertainment market, with the e-commerce behemoth acquiring MGM for $8.5 billion. Traditional media businesses are trying to compete by consolidating. As M&E companies strive to stay ahead in a changing business landscape, leveraging robust data platforms can provide deep insights into the content at its frame, short-form or long-form level. It will help collect, correlate, and make sense of data from several fragmented sources to create rich and targeted content that customers today demand. More importantly, as consumer patterns drive the M&E industry, data platforms will be essential for rationalizing costs, improving ROI, and scaling up the business.
