Data-driven M&A: Unlocking value and driving growth for media companies

  • Insight
  • 8 minute read
  • August 13, 2024

The media sector’s evolution over the last 30-plus years reads like one of the stories it produces for audiences. And the last decade has been particularly dramatic. With M&A as one enabler, the first phase of revenue acceleration involved vertical consolidation of content distribution channels and streamlining costs based on scale. The second phase was a race for content to dominate the streaming market.

As traditional strategic distribution business models wane, streaming and user-generated content (UGC) platforms have emerged, pushing the technology, media and telecommunications (TMT) industry to find new ways to shore up revenues. This push is existential. While 45% of CEOs PwC surveyed don’t believe their company will be viable in 10 years, among media CEOs it’s 57%. The ability to access and leverage data has become a game-changer in this landscape and can serve as a fundamental value driver.

Tapping into this value isn’t easy. In fact, 46% of TMT companies say data monetization is one of their most significant challenges to transforming in the next three to five years. M&A could expand these capabilities and help you reinvent your business model, and you’d be in good company: 84% of TMT executives are already getting into adjacent or new market segments that require new capabilities.

The value of data

Savvy media companies use data from customer preferences and online behavior histories to personalize experiences and selling strategies. This approach can enhance their own products and services and drive monetization possibilities. It also creates opportunities for third parties or B2B providers to tap into their customer base or aggregate data via collaborative tech like data clean rooms. However, it’s wise to keep in mind that data sharing comes with privacy and compliance considerations.

Privacy and data security concerns complicate access to individual identifiers, prompting media companies and advertisers to source third-party data and use artificial intelligence (AI) to augment their limited access. Additionally, cookie deprecation and ever-expanding global privacy laws further reduce data access — making first-party data even more valuable. This is especially critical for media companies. Data on viewing habits and content preferences can help inform the creation of more engaging content. For example, streaming platforms can analyze how genres, actors and directors are performing among different audience segments and use those insights to make production –– and acquisition –– decisions.

Turning data access into monetization strategies

Personalized recommendation systems are the most visible, direct way that media companies harness customer data. For Netflix, approximately 80% of the content users watch is launched via these recommendations, which may save millions of dollars that might have otherwise been allocated to reduce customer churn. Amazon's recommendation engine reportedly can boost sales significantly –– and by measurable metrics.

Ad-based platforms enabling third-party access to customer data can also deliver value. For example, targeted ads displayed on Facebook achieve an average click-through rate (CTR) of 0.9%, significantly surpassing the industry average of 0.1%.

B2B data monetization for companies generally can involve selling data as a standalone product or yield even more value by bundling it with a complementary product or offering it as a service. Businesses can also engage in data enrichment.

B2B data monetization opportunity Example
Data as a product (DaaP) Selling a bundled data set providing insights about consumer preferences that could be used to make predictions about customer behavior or to inform product and service design
DaaP + complementary product  Selling a bundled data set coupled with access to more built-for-purpose solutions for analysis and delivery, like recommendation engines and analytics dashboards
Data as a service  Offering a subscription-based, third-party service that can cleanse and customize specific data sets to suit the buyer’s needs
Data enrichment Adding additional relevant consumer data –– such as demographic or behavioral data –– to a company’s existing data sets to deepen insights that could be used to improve ad targeting or to personalize customer experiences

M&A challenges and tactical approaches

As media companies look for ways to win in the face of declining traditional models, subscription fatigue, and financial and digital disruption overall, data –– especially first-party data –– can open up new revenue streams. And M&A can provide a targeted path to expand access to first-party data. Although key players are leveraging data to deliver key business outcomes, data-driven deals face the same challenge as any M&A: the market knows they often don’t deliver the expected value to the acquiring company.

Addressing the following challenges can help obtain greater value from a deal, from negotiation to closing to integration:

Technology integration

Challenge

Technology platforms are often layered and involve legacy infrastructure and support – all of which house important customer data (e.g., preferences, content consumption patterns, ad engagement, spend). Before you can harness the overall power of data, integrate your technology across business units to provide a holistic view. Understand that this can be a complex, multi-year effort.

Tactical approaches to consider

Build an end-to-end data architecture that supports the business model you select and includes data and ad tech platforms so you can offer:

Centralize data so it’s accessible and you can more quickly execute against what the data is “saying.” Make sure your data integration process includes a unified approach to content meta data –– which can be critical for searchability and recommendation engines. Also make sure the data is accessible for business use, but also consider governance and usage rights to help avoid legal troubles. It's all about balancing utility with responsibility.

Build a common AI foundation between platforms, data, trained models and serving layer. This makes sure AI use cases that leverage data are effective, reliable and scalable.

This may require no or low-code applications, such as personalization engines, marketing automation platforms and loyalty programs. Choose a provider and solution that aligns with your overall strategy, use-case needs and existing tech.

Business model development

Challenge

Data monetization models are still emerging and demand out-of-the-box thinking. Defining the right model to monetize data –– with respect to content, advertising, business relationships and product development –– can be daunting. Remember, nearly half of TMT leaders say data monetization is one of the most significant challenges impacting their business strategy.

Tactical approaches to consider

Tailor pricing models to customer needs to capture more value. For instance, processed data that has been tuned for accuracy against user queries and vetted for intellectual property has value for end-users in functions like legal, marketing and research. In this case, the value-add should be monetized and a ‘per user’ pricing model may make sense. Unprocessed data, however, doesn’t necessarily have to be of lower value. For example, data science teams in product functions may be willing to pay higher for unprocessed or raw data as they may be using it as an input for their proprietary data model with unique tuning parameters and filters. In this case, a ‘per size’ pricing model may make more sense.

With new innovations in GenAI, data aggregation, summarization and enrichment are easier. This means new players can enter the competitive market, as well as buyers, depending on where they play in the value chain. For instance, a news media company that provides raw data to political risk assessment companies may find it more valuable to offer a political risk score directly to customers instead of going through an intermediary.

With advertising projected to account for 55% of total growth in the entertainment and media industries over the next five years, media companies should ramp up their advertising models by integrating product discovery with purchase and consumption, creating their own walled gardens. Live and immersive entertainment venues and districts can also capitalize on geospatial solutions to enhance the user experience and drive incremental revenues.

With newly acquired data, a gaming company or studio might be able to better customize in-game experiences and storylines. Emerging tech can also enhance the ability to auto-generate in-game ads and product placements.

Data can help companies create customized offers, targeted pricing, focused promotions, etc. Customer acquisition and cost-to-serve expenses should decrease. Customer satisfaction with the experience should increase –– and so should the average revenue per user (ARPU), benefiting return on investment (ROI). Also consider whether the data can be used in proprietary solutions, provided as a service or as part of access to a broader platform. Beyond supplying data within your current industry channels, you could potentially collaborate with a data marketplace that allows your organization to share and monetize data in new ways.

In an age of data breaches and unauthorized access, usage rights are critical to building trustworthy systems that unlock the value of data. Business users, product owners and channel partners should abide by the rules you contractually set, including but not limited to security, privacy, legal, data retention and lineage. Newly acquired data should be categorized and classified.

Organizational planning

Challenge

For media companies, data monetization can lead to ownership and governance challenges. Structuring the business to assign proper ownership across technology, digital product, content, sales or marketing and finance teams is critical to success and requires education and cross-team collaboration.

Tactical approaches to consider

Evaluate your organization’s operational and functional readiness to leverage data as a competitive advantage with these critical tasks in mind:

Analyze core competencies each function should provide (e.g., the sales function’s ability to sell data-driven strategy, HR function’s ability to find and develop the talent needed to drive the strategy).

Assess your organization’s culture relative to data and determine whether any cultural changes should be made to support the organization’s success with data-driven strategy, and if so, what changes are needed.

Given a strong data portfolio, how might costs decrease across functions and capabilities? How might your value chain be simplified and some activities, particularly manual ones, be automated or reduced? With simplification, how might the customer experience be redesigned to result in both higher ROI and higher customer satisfaction?

Privacy considerations

Challenge

Adhering to changing data privacy laws (and differences by region) will likely be a challenge for the foreseeable future. The acquisition of any company comes with some level of data risk, and media organizations looking to acquire and use customer data should be able to answer the following:

  • Is the acquired organization or acquired data operating in or collecting personal data in regions outside the buyer’s current operations?
  • Does the data collected by the acquired organization introduce new regulatory requirements or risk (e.g., healthcare or financial data)?
  • What is the acquired organization’s current level of compliance and capability to comply with regulatory requirements? And, for data acquired via business relationships, what is the level of compliance of the organization with respect to data privacy, security, etc.?
  • Are there contractual permissions, obligations and restrictions related to the acquired organization’s collection and use of customer personal data?
  • What is the buyer’s risk tolerance for potential targets that might be operating in gray areas of permissible data use?

Tactical approaches to consider

These solutions can capture value from data while complying with privacy requirements. This technology enables data collaboration between two or more parties through neutral, privacy-safe environments that facilitate insights, activation, enrichment and measurement. Building the right clean room and data collaboration strategy will likely require diligent provider selection, adherence to compliance and information security requirements, and the identification and implementation of the right technology solutions.

Several privacy regulations require companies to provide customers with opt-in and opt-out choices for the use of their personal data, specifically for targeted marketing purposes. These apply to the use of online trackers, sharing or selling of data to third parties and internal mastering of customer profiles. Many solutions exist to support companies with collecting and applying customer preferences across their ecosystem.

Data savviness is the key to strategic differentiation

Looking ahead, data will likely play an even more prominent role in the sector value story. It’s critical to get tactical integration approaches right –– and quickly. Consumers expect personalization. And 82% of consumers are willing to share their personal data in exchange for a more personalized experience. Media players with access to first-party data about consumer preferences will likely have the advantage. And for those with multiple products and platforms this opportunity is even more compelling, as data across touchpoints creates a clearer picture of the customer. Data savviness, especially combined with the use of AI and analytics, is already becoming a critical strategic differentiator and advantage. The winners in this story will likely be companies that quickly excel and outmaneuver their competitors, while also adapting to regulatory changes.

Contact us

Lori Bistis

Principal, Deals Transformation, Boston, PwC US

Kim David Greenwood

Principal, Strategy&, San Francisco, PwC US

Colin Carroll

Principal, Customer Transformation, PwC US

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