In the previous post, we introduced the architectural challenges behind supporting multiple chart types within a scalable dashboard solution. We established that different visualisation libraries require data in different structural formats. This mismatch makes the transformation layer a critical part of the architecture.

In this post, we focus on how the system decides which transformation logic to apply for a given chart type. Two design patterns make this possible: Strategy and Factory. Together, they allow us to select and execute the correct transformation algorithm at runtime without introducing tight coupling or complex conditionals.

 

Strategy Pattern: Chart-Specific Data Transformations

The Strategy pattern allows us to define a family of algorithms (transformations), encapsulate each one, and make them interchangeable. In our chart module, each chart type has its own transformation strategy responsible for converting raw report data into a format suitable for the rendering library.

 

Structure

The implementation revolves around a contract interface that defines two key methods:

// Interface defining the strategy contract 
export interface IChartDataTransformer 
{  
  /**  
  * Determines if this transformer supports the given chart type.  
  * Each transformer checks if it can handle the visualization type.  
  */  
  supports(chartType: string): boolean;  
 
  /**  
  * Transforms raw report data into chart-specific format.  
  * Each transformer implements its own transformation algorithm.  
  */  
  transform(reportData: any, chartType?: string, report?: any): any; 
}

Each chart transformer implements this interface:

@Injectable({ providedIn: 'root' })
export class BarChartTransformer implements IChartDataTransformer {
  supports(chartType: string): boolean {
    // Returns true for bar chart type identifiers
  }

  transform(reportData: any): any[] {
    // Analyzes dimensions and metrics
    // Normalizes data structure
    // Applies color mapping
    // Enriches with metadata
    // Returns chart library-compatible format
  }
}

 

What Each Strategy Handles

Each transformation strategy encapsulates logic specific to a chart type. This includes:

• Dimensional Analysis: Understanding how many dimensions and metrics are present, which affects grouping and series construction.

• Data Normalisation: Converting the consistent API response into the structure expected by the specific visualisation library (e.g., row-based data for ag-Grid vs. series-based arrays for Highcharts).

• Metadata Enrichment: Adding display labels, tooltip formatting, and contextual information derived from report metadata.

_{*Quick note: At first glance, this example may appear to violate the Single Responsibility Principle. However, in practice, the transformer acts as an orchestration layer, delegating specific responsibilities to dedicated services. The example is simplified to keep the focus on the pattern rather than internal service composition.}

 

Benefits

• Each chart type can evolve independently without impacting others.

• Adding support for a new chart only requires implementing the interface—no changes to existing code.

• Clear separation of concerns between transformation and rendering logic.

• High testability: each strategy can be unit-tested in isolation using mock datasets.

 

Factory Pattern: Selecting the Right Transformer

While strategies encapsulate transformation logic, we still need a mechanism to select the correct strategy at runtime. This is where the Factory pattern comes into play.

The factory acts as a lookup mechanism that returns the appropriate transformer based on the chart type, without consumers needing to know about concrete implementations.

 

Structure

We use an injection token to collect all chart transformers, and a factory that receives them via dependency injection:

// Injection token for collecting all chart transformers
export const CHART_TRANSFORMERS = new InjectionToken<IChartDataTransformer[]>(
  'CHART_TRANSFORMERS'
);

@Injectable({ providedIn: 'root' })
export class ChartDataTransformerFactory {
  constructor(@Inject(CHART_TRANSFORMERS) private transformers: IChartDataTransformer[]) {}

  getTransformer(chartType: string): IChartDataTransformer | null {
    // Searches for transformer where supports() returns true
    // This is the common method enforced by the interface defined earlier.
    return this.transformers.find(t => t.supports(chartType)) || null;
    // Returns null if no transformer found
  }
}

The factory is used by the main chart data transformer (which participates in the pipeline we’ll see in the next post):

@Injectable({ providedIn: 'root' })
export class ChartDataTransformer implements ReportTransformer {
  constructor(private chartFactory: ChartDataTransformerFactory) {}

  isApplicable(reportData: any): boolean {
    // Checks for visualization type presence
  }

  apply(reportData: any): any[] {
    // Gets transformer from factory
    const transformer = this.chartFactory.getTransformer(..);
    if (!transformer) {
      // Throws error if no transformer found
    }
    // Delegates to transformer's transform method
    // Remember, this is part of the contract defined earlier in strategy pattern.
    return transformer.transform(..);
  }
}

 

How It Fits Together

The factory leverages Angular’s dependency injection to receive all registered transformers via the custom token. It acts as a registry and lookup: it doesn’t create transformers itself—they’re registered elsewhere (we’ll cover that in a future post). The factory is completely decoupled from specific transformer implementations.

When no transformer is found for a given chart type, the system throws a descriptive error rather than returning null. This fail-fast approach ensures bugs are caught early and production errors are clearly identifiable.

 

Benefits

• Decouples transformer selection from usage—consumers don’t need to know which transformer to use

• Centralised logic for transformer selection—single point of control

• Easy to extend with new chart types—just register a new transformer

• Type-safe transformer selection through TypeScript interfaces

• Strategies are unaware of each other; the factory is unaware of strategy implementation details

 

Conclusion

Pairing Strategy with a simple Factory keeps the chart transformation layer flexible and disciplined. Each chart type encapsulates its transformation logic behind a common interface, while the factory selects the appropriate strategy at runtime. This approach creates a fail-fast system that detects misconfigurations early and allows new visualisations to be added without modifying existing code.

---

Ravindra Soman

Senior Full Stack Data Engineer

Ravindra is a Senior Full Stack Data Engineer specialising in frontend architecture and complex data visualisation systems. In his spare time, he likes to cook new dishes for his family, read fiction and travel the world.

• LinkedIn

The post Architecting Scalable Chart Modules in Streamhub Analytics: Part II – Strategy & Factory Patterns appeared first on Streamhub.co.uk.

Problem Space & Design Goals

In modern web applications, data visualisation is a critical component that requires flexibility, maintainability, and extensibility. When building a charting system, supporting multiple chart types across heterogeneous rendering requirements quickly becomes an architectural challenge.

In our case, the system needed to support 15+ chart types that users could dynamically add to dashboards. Each chart could be powered by different charting libraries, and although the API responses were consistent, each library expected the input data in its own specific format. What initially seemed like a UI concern soon revealed itself to be a deeper issue involving data transformation, extensibility, and long-term maintainability.

This post, focuses on the challenges we faced and explains the architectural direction we chose. In the next posts we’ll see how we addressed them with a small set of design patterns.

---

 

Technical Context 

Our Analytics product is built using Angular as the primary frontend framework. For visualisation, we support multiple libraries depending on the widget type, including AG Grid for tabular data and Highcharts for basic chart-based visualisations, along with a few custom-rendered charts.

While the API responses remain consistent, each library imposes its own expectations on how data should be structured. This divergence in data contracts across libraries became a central factor in shaping the overall architecture.

---

 

 

The Core Mismatch: Same Data, Different Library

Expectations

One of the key challenges was that different visualisation components required the same dataset to be shaped differently.

For example, consider a dataset fetched from a consistent API:

[

    {"device": "android", "views": 12,000},

    { "device": "iphone", "views": 15,000 }

]

A data-grid widget powered by ag-Grid expects a flat row-based structure:

rowData = [

    {"device": "android", "views": 12,000},

    { "device": "iphone", "views": 15,000 }

];

However, a bar chart rendered using Highcharts expects a categorized structure:

series = [{

    name: "views",

    data: [12000, 15000]

}];

categories = ["android", "iphone"];

The underlying API data remains consistent, but each library requires a distinct transformation. As the number of supported chart types and libraries grows, this transformation layer becomes a central architectural concern rather than a simple formatting step.

 

 

Why Naive Approaches Break Down

A typical early implementation often resembles this:

switch (chartType) {

    case 'bar': return transformToHighchartsBar(data);

    case 'grid': return transformToAgGridRows(data);

    case 'line': return transformToHighchartsLine(data);

}

In practice, this switch statement and the corresponding transformation logic lived inside a single class responsible for orchestrating chart rendering. Over time, this class accumulated multiple responsibilities:

• Deciding which chart type to render

• Transforming raw API data

• Handling library-specific formatting

This violated the Single Responsibility Principle and tightly coupled transformation logic with rendering decisions. Every new chart type or library variation requires modifying existing logic, violating the Open/Closed Principle and increasing the risk of regressions.

---

 

Core Challenges

 

1. Data Transformation Complexity

Although the API responses are consistent, each visualisation library enforces its own structural contract. Supporting multiple libraries means handling:

• Structural reshaping (row-based vs series-based vs hierarchical)

• Aggregation or grouping logic specific to chart types

• Consistent normalization so that transformations remain predictable

The transformation layer must therefore be reusable and decoupled from the rendering library’s internal expectations.

---

 

2. Extensibility and Maintainability

As new chart types or visualisation libraries are introduced, the system should extend without requiring modifications to existing transformation logic. Without a modular design, each addition risks breaking unrelated visualisations.

Extensibility is therefore not just desirable—it is essential for long-term maintainability.

---

 

3. Shared State Interactions

Charts rarely operate in isolation. They react to shared dashboard inputs such as filters, metrics, and time ranges. Without clear architectural boundaries, state changes can trigger cascading re-computations that are difficult to trace and debug.

A scalable solution must ensure that state orchestration does not leak into transformation logic.

---

 

4. Separation of Concerns

These challenges highlighted the need for a clear separation of responsibilities across layers. Mixing decision-making, data shaping, and rendering logic within a single class created tight coupling and reduced clarity about where specific responsibilities belonged.

A more layered approach was necessary to ensure each concern could evolve independently.

---

 

From Naive to Scalable: Direction of Evolution

These challenges made it clear that the existing structure could not scale. The architectural evolution looked like this:                     

 

Naive Approach

In the naive approach, both decision-making and data shaping were embedded in a single class. This made the system rigid and prone to errors as new chart types were introduced.

               

 

Target Direction

 

 

The target direction separates responsibilities into distinct layers, allowing each concern to evolve independently while reducing coupling and technical debt.

---

 

Design Goals and Evaluation Criteria

Before settling on an architectural approach, we defined a set of guiding criteria:

• Extensibility: Supporting a new chart type should not require changing existing transformations

• Reusability: Common data shaping logic should be shareable across chart variants

• Library Agnosticism: Switching or adding charting libraries should not affect core transformation logic

• Testability: Data transformation steps should be independently unit-testable

• Predictability: Shared dashboard state should not cause uncontrolled recomputations

These constraints ruled out monolithic conditional logic and pushed us toward a more modular, pattern-oriented architecture.

---

 

Architectural Direction (High-Level)

Based on these goals, we converged on three guiding ideas:

1. A strategy-driven transformation layer to encapsulate chart-specific data shaping
2. A factory-based resolution mechanism to select appropriate transformations dynamically
3. An adapter layer to isolate charting library dependencies from core logic

This direction allows the system to support new chart types and libraries incrementally, without rewriting existing components.

In next post, we will see how we implemented right transformation logic for each chart type.

Ravindra Soman

Senior Full Stack Data Engineer

Ravindra is a Senior Full Stack Data Engineer specialising in frontend architecture and complex data visualisation systems. In his spare time, he likes to cook new dishes for his family, read fiction and travel the world.

• LinkedIn

The post Architecting Scalable Chart Modules in Streamhub Analytics: Part I appeared first on Streamhub.co.uk.

In today’s TV, video and media environment consumers have unlimited choices and act in thousands of different ways. The audience measurement industry has struggled to keep up, unsurprisingly in fairness given that understanding and reaching audiences effectively is more complex than ever. 

Now though, the experts have cracked it and cross-platform measurement has emerged as a vital tool for the media industry, integrating data from various sources such as linear TV, BVOD, streaming and others. This comprehensive approach offers numerous benefits, making cross-platform measurement indispensable for the industry’s future for the following reasons.

Audience De-duplication

Audience de-duplication accounts for viewers who consume content across multiple platforms. This process ensures that media companies understand the true size and demographics of their audience. By accurately defining the audience demographic between TV and BVOD, agencies can avoid overestimating their campaign reach and deliver more precise and relevant content to distinct audience segments.

Accurate Audience Targeting

Advertisers can target their desired audiences more accurately.This also allows for quicker reach of target demographics and more efficient use of marketing budgets. Accurate audience targeting results in higher ROI for advertisers, as campaigns are tailored to engage the most relevant viewers, reducing wasted ad spend and improving campaign effectiveness.

Redefining TV and OTT Inventory

By understanding how audiences interact with content across different platforms, media companies can strategically allocate advertising inventory to maximise engagement and revenue. This redefinition ensures that both linear and served advertising are used to their fullest potential, providing advertisers with optimal exposure.

By achieving these, a clear understanding of audience behaviour and effective targeting leads to increased confidence in TV/AV advertising. However, a number of challenges also exist in achieving it.

Lack of Standardised Measurement Framework

One of the primary challenges is the lack of a standardised framework. Without industry-wide standards, integrating and comparing data from different platforms is complex and less reliable. Establishing a universal measurement standard is crucial for accurate cross-platform analysis.

Need for Multiple Expertise and Knowledge

Effective measurement requires a combination of skills in broadcast media, digital analytics, and data science. This need for diverse expertise can be a barrier, as it necessitates significant investment in talent and technology. Media companies must invest in training and resources to build these capabilities.

Industry-Wide Collaboration and Development

Achieving effective demands collaboration across the media industry. Broadcasters, advertisers, and technology providers must work together to develop and implement measurement standards and technologies. Industry-wide cooperation is essential for creating a unified approach that benefits all stakeholders.

Thus, there are still plenty of challenges that need to be overcome in order to achieve cross-platform measurement. The following is a selective list of pioneering work in various countries:

Australia – OzTAM

In Australia, Television Audience Measurement (OzTAM) is the cornerstone of viewership analysis. OzTAM has incorporated cross-platform measurement to offer a more accurate picture of audience behaviour, aiding broadcasters and advertisers in making data-driven decisions. This integration ensures that Australian media companies stay competitive in a rapidly changing market.

UK – Barb / CFlight

The UK leads in cross-platform measurement with initiatives driven by Barb, including CFlight. Barb provides detailed audience data across various platforms, while CFlight delivers deduplicated reach and frequency metrics for TV and BVOD ads campaigns. These tools have significantly enhanced audience targeting and campaign effectiveness in the UK.

Japan 

Japan has been relatively slow to adopt cross-platform measurement, but some broadcasters made progress last year by implementing a POC with Streamhub. It demonstrated the potential benefits of cross-platform measurement, indicating the need for broader implementation and adoption to keep pace with global standards.

The speed of these initiatives varies from country to country, but Streamhub will continue to engage with cross-platform measurement for the advancement of the media industry. This year, a new POC is planned in Europe, aiming to provide cross-platform solutions globally.

As data specialists, Streamhub offers transparent metrics and fair analysis. We will continue to take on various challenges to contribute to the growth of the TV and media industry. If you are daunted by the prospect of undertaking cross-platform measurement, please feel free to contact us.

The post The Next Generation of Cross-Platform Measurement: Global Trends and Challenges appeared first on Streamhub.co.uk.

Our solution – Streamhub Analytics

Streamhub Analytics is a unified reporting platform for content and advertising with multi-data source analysis, including OTT data, viewing logs, online panel and user data. The platform consists of three components: Default Reports / My Reports / My Dashboard. It is a SaaS platform specialised for the media industry with high level of customization.

Default Reports – Various reports specific to content, advertising, and QoS * Enterprise plans offer the option to add a customised default dashboard

My Reports – Create custom content and advertising reports with any combination of metrics and dimensions and with a variety of chart types

My Dashboard – Create your own dashboards by combining default reports and original reports created in My Reports

What’s new?

Added flexibility to the dashboards to compare different reporting periods. Users can create reports with two different date ranges to easily compare the before and after. When selecting My Reports and creating My Dashboards, you can turn OFF the switcher in the calendar to enable this feature and carry the dates set in the reports.Users can use this feature to quickly compare performance in key metrics over different time periods side by side. Users can turn off this feature anytime to generate reports for custom time periods set by date filter. 

Measuring the effectiveness of platforms and content for marketing

It is essential to understand what content your existing audience is viewing on your platform.

Compare the entire platform or specific content on a monthly (or daily/weekly) basis to visualize viewing trends. Additional filters can be added to focus on specific series/contents for a more detailed analysis. It also helps to gain insight into what content existing audiences are viewing, on what devices and from where. Moreover, it can be effectively utilized to build and target audience segments based on these behaviours and insights for optimising the marketing and new user acquisition campaigns.

You can also develop insights into the platform’s potential investments, such as which content to focus on marketing or how content should be created in the future.

Visualisation of ad effectiveness and enhanced reporting to advertisers

How should advertisers maximize monetization by reaching a large audience through content? Clarifying advertising effectiveness is a critical element for platforms and advertisers, as well as for advertising agencies.

As with content analysis, identifying ads with longest average viewing times and high audience interest can enhance reporting to advertisers. Filtering by advertiser or ad creative and comparing monthly (or daily/weekly) trends provides a hook to derive new insights. My Report allows customization of reports thus giving increased flexibility by creating reports in alignment with advertisers’ requests and comparing them on a month-to-month basis.

In addition, the analytical data may be utilized in advertising sales to help obtain new advertisers.

Improve the monetization of your video business with Streamhub

Discovering the demand for video content and advertising and diving deep into audience insights is the key to profitability. Streamhub is an unified data platform that utilizes viewing and advertising data to increase audience interest and monetization. For more information on this feature and product, please contact us.

The post Feature showcase: Report compare mode appeared first on Streamhub.co.uk.

We conducted our first front-end focused tech webinar titled ‘RxJS Essentials for Angular Developers’ on October 8th. It was focused on the fundamentals of RxJS library and its benefits organised by our Angular experts Ravindra, a Senior Full stack data engineer at Streamhub, and Luis, our Senior FE engineer.

Here is the video and technical summary bellow.

We started the discussion by providing a high level overview of the Angular framework. This was followed by a succinct explanation about the need for reactive programming and how RxJS library helps developers in achieving that objective. Also, the audience for a deep-dive into each fundamental concept of RxJS.

1. Observable

An observable is a unicast object which passes stream of data from data source to the observer.

2. Observer

An observer, is an object with set of callbacks, which consumes the data passed by an observable.

3. Subscription

Subscription is an object which contains reference to the subscription to an observable.

4. Subject

Subject is a special type of observable which supports multicasting.

5. Scheduler

Scheduler helps in defining an execution context to control when observer will be notified about new data.

6. Operators

Operators are a set of functions that help in easy composition of complex asynchronous code. All these concepts were bolstered by many coding examples and some interesting real life anecdotes. These examples were especially designed to help developers use correct concepts in different situations. e.g. use of observable vs. use of subject, using different operators to make code easier to write and manage.

To summarise, this webinar gave its audience a brief but in-depth overview of RxJS and reactive programming. We believe these concepts will surely help developers in designing complex solutions in a more elegant and efficient way.

Finally, we are looking for engineers to join our team at Streamhub!
Here is currently available positions. If you are interested, or you know anyone who would be, feel free to get in touch.

The post Tech Webinar: RxJS Essentials for Angular Developers appeared first on Streamhub.co.uk.

We made an exciting trip to Riga for EGTA’s 2023 Marketing & Sales meeting last week where we had the opportunity to understand the latest challenges in the TV advertising business across various European markets.

We were invited to present our views on TV’s quick shift to Total Video and the progress we made with our cross-platform measurement project called X-Flight to rapidly deliver unified reach metrics. 

It seemed timely as the event started with Mindaugas Rakauskas, CEO of TV3 Group Latvia, explaining that following the fragmentation of audiences, ad sales had transitioned from siloed sales to mixed total video inventory packages. This was followed by accounts of similar shifts and the accompanying challenges across various markets. 

Below we highlight the key topics that resonated most with our own mission to empower the Total Video ad ecosystem. 

1. Are all impressions equal? 

The debate here focuses on the differences in the values of an impression based on factors such as device, screensize, location, content, nature of the viewing experience alongside more traditional things such as duration and placement. On top of this we also believe that auditability and transparency of measurement will determine the value of an impression, an individual or a household. Such factors should be considered for inventories sitting behind walled-garden. 

2. Useful reach is more important than incremental reach 

The discussion here was around the fact that all walled garden players claim their reach is vastly incremental reach to TV but unproven. True incremental reach needs to be useful measurable reach. 

3. Addressable linear TV opportunity

The industry survey by Altman Solomon confirmed that the number one opportunity for broadcasters is  addressable linear TV advertising. This assumes that publishers will offer a larger variety of audiences beyond sociodemographics, better matching the needs of the advertisers both national and local. 

Sky’s AdSmart is a pioneering example of both the technological capabilities and commercial success where year-on-year Sky is seeing tremendous growth in sales and customer numbers. Another exciting, nascent technology is the HbbTV standard that is making gradual progress with strong promises for addressability and ad replacement capabilities. 

4. Data interoperability (liquidity)

Interoperability of data is posing challenges at different levels and stages of the media ecosystem. Arguably, the biggest disconnect is between the activation dataset and the post-campaign reporting. Resolving this could better bridge outcome-based and reach-based measurement, enabling more effective planning and yield management. Clean rooms are providing part of the solution but we feel that problems also need to be resolved in a more macro-level in relation to standardisation and shared cross-industry datasets. Such a measure would further empower initiatives such as the eGRP offering presented by Prima from the Czech republic. 

5. “Access to accurate measurement data” is the undisputed top challenge and priority

Lastly, we wanted to focus on the European broadcasters’ call for “better measurement”. The survey mentioned the “overwhelming reliance on broader standardisation measures for resolution” and that “distributors need accurate measurement that properly blends ratings and streaming data”. 

In the UK, CFlight, the cross-platform measurement standard initiated and developed by Sky and RSMB, has been adopted for use by agencies and broadcasters since 2022. The recent announcement by BARB to take over its governance and development is a tremendous step for the UK TV industry to lead the exploration in the new era of converged measurement. 

The announcement of our Streamhub XFlight project jointly developed with RSMB was also in direct response to this growing need for broadcasters to offer a holistic, transparent and standardised method of cross-platform measurement for any market that needs it. With the 3 month POC coming to an end, next steps are to push the MVP into the market to drive the necessary consensus, but more importantly, to prove that XFlight can work across multiple markets just with a finite level of customisation. 

If XFlight’s MVP is a success, this will mean that the capability to offer a standardised, modern and efficient measurement platform will become relatively simple to adopt and roll out into multiple markets. Furthermore, XFlight benefits from the capacity to also integrate any further census or multiple panel datasets to enhance the core measurement, and even link things directly for activation. 

To summarise, we found that the top challenges confronted by broadcasters in Europe and around the world are universal. Whilst the approaches to resolving the issues have local nuances, the crux of the problem and the direction of travel for the solution is the same. TV has a great opportunity ahead but needs to modernise faster than ever to maximise the strength of TV whilst bridging its core business to all things digital.

The post The debate around cross-platform measurement – EGTA M&S Meeting Report appeared first on Streamhub.co.uk.

The strategic partnership will address growing demand for hybrid measurement across linear and streamed TV with a unique cross-platform metric

RSMB and Streamhub have announced a ground-breaking partnership that is set to create a unique offer in the cross-platform measurement of viewing linear and streamed TV. 

The rapid evolution of streaming and connected TVs is creating new opportunities for consumers, content producers and advertisers alike. This has led to a need for measurement methodologies to adapt to these new demands. One manifestation of this is the need for hybrid measurement, for example the integration of traditional TV panel data with the first party data of TV streamers). The new partnership between RSMB and Streamhub will provide solutions in this growing area of TV and video measurement.

The partnership will provide clients with RSMB’s currency grade data modelling capabilities matched with Streamhub’s multi dataset analytics and activation platform.

The initial focus of the partnership will be to build out incremental reach calculations for clients. This will allow reporting of the reach delivered by streaming services over and above the reach of linear TV through the high quality integration of currency metrics with first party data. The partnership will initially focus on Japan where Streamhub will be representing RSMB’s capability in the market. However, key modules will be available in all markets internationally.

Chris Mundy, CEO of RSMB commented: “RSMB has a successful track record in managing data integrations and calculation of currency metrics. Partnering with Streamhub allows us to operationalise these measurement solutions more quickly, in more markets.” 

Akihiro Tsuchiya, CEO of Streamhub, said: “We are excited to see our audience analytics product move into cross-platform measurement, bringing together our two strengths: managing complex streaming data and handling currency-grade panel data to service both TV and streaming advertising opportunities.”

The post RSMB and Streamhub announce next generation cross-platform measurement partnership appeared first on Streamhub.co.uk.

What’s better than watching an amazing catalogue of content for a small monthly subscription? Doing the same for free… Enter Free Ad-supported TV (FAST) channels, where you can watch compelling programming in a TV-like playlist in exchange for ads, but with easier access, choice and value to the consumer. With high adoption rates across American and European markets, it is gathering momentum as a new way to drive continuous audience engagement and high-value ad revenues.

The ROI

According to a recent Deloitte report on digital media trends, 47% of Americans now watch free ad-supported streaming TV services. Estimates cited in TV[R]EV’s research take OTT ad spend in 2025 to about US$25 billion, with FASTs being central to this growth. Big players like NBCUniversal, ViacomCBS, Amazon etc, all want a piece of the action.

The success of FASTs can be attributed to a number of factors:

• Free
• Large content libraries
• User experience akin to a traditional TV
• Device agnostic and pre-installed on many smart TV platforms

And most importantly, they carry more relevant yet lower ad-loads than TV.

 The graph above shows the number of ad impressions through FAST channels on Wurl reaching 12 times since launching in 2020. Not only that, but FAST channels performed better over time with higher viewership numbers and better ad fill rates, leading to more revenues.

Contingent upon many factors, including channel placement, ad fill rate, average CPM, content quality, duration, and refresh rates, FAST channels will drive ad revenue from $2.1 billion in 2021 to $4.1 billion in 2023. Such a return is more than enough reason for the excitement in the market to counter the decreasing revenues in linear TV.

A key differentiator, moreover, with the FAST linear viewing experience is its capability to support a wide range of audience and advertiser verticals, as shown in the following data compiled by Pluto and Tubi.

What Next?

FAST is still in its early days, with Pluto TV, the most well-known FAST channel provider having launched in 2014. What isn’t new are the typical issues that OTT providers have around measurement, user management and targeted advertising – in other words, the data ecosystem and its management.

At present, much of this data comes from disparate platforms and ad servers rather than neutral 3rd parties leading to issues of transparency and incomplete datasets, be it for audience analysis or targeting and post-campaign reporting.

Therefore an effective data strategy that links up all the different sources to unify and validate the reporting will be key. Streamhub is working with multi-service media companies including FAST channels to develop such linked measurement and reporting data platforms that can work across multiple markets.

As such, Asian markets can look to learn from early adopters in the West to build smarter collaborative models from the start, and enter the FAST lane.

To learn more on the latest in OTT/CTV and data developments, check out our blog (https://streamhub.co.uk/news/) or email bizdev@streamhub.co.uk.

Anurag Gangras

QA and Support Analyst

Anurag is an exerienced QA tester and marketing Analyst, with a passion for all things video. By night he is also a talented video content creator. 

• LinkedIn

The post Life in the FAST lane appeared first on Streamhub.co.uk.

More services = need for better measurement

Last week Streamhub launched a new suite of analytics and post-campaign reporting for Live streaming and cloud DVR services that combines it with VOD reporting to provide deduplicated and incremental reach measurement across devices. 

Live streaming reports are not new but this marks a major strategic step forward for the commercial broadcasters who can now provide fully consolidated reporting across VOD and Live streaming for both ads and programmes. 

The Challenges

Live streaming provides incremental inventory and reach to the TV channels through its dynamic ad insertion proposition. The move towards real-time dynamic ad insertion(DAI) represents a significant technical challenge, where client-side reporting is essential to provide accurate reporting compared to server-side log-based reporting. 

As streaming services and data sources proliferate the provision of a user-friendly analytics and reporting experience was of high importance, especially being a service  used by both sell-side and buy-side.

Moreover, there was a need to provide data orchestration as-a-service to clean up and automate all the different viewing logs from the different broadcasters and to standardise this data against the online digital currency panel to provide cross-service and cross-device reach and frequency metrics. 

The market implications

Prior to 2022, the legislation forbade broadcasters from providing a 24/7 live online stream of licensed linear broadcast channels in Japan. Now, however, broadcasters have finally executed their ambitions to make Live & DVR content a core part of their respective OTT offerings. 

For viewers:

Not only are they gaining access to timely Live and DVR content to enjoy, but there will also be a far more personalised advertising and content experience than could be achieved through traditional linear TV. 

For Ad sales: 

Broadcasters now have a wider pool of targetable inventory to sell, and importantly, more contextual audience data to use for activation – leading to more choice for the Advertisers and to lead to optimised yields across all inventory. 

For Editorial and Marketing: 

Furthermore, marketing and editorial teams are also benefiting greatly from the added level of contextual insights for Live and DVR consumption which can be analysed alongside VOD and linear TV audiences. This kind of apples-to-apples parity in reporting opens the door for a more nuanced understanding of the habits and behaviours of different audience segments, which if utilised well, will ultimately power a more engaged relationship between audiences and the streaming services they use every day.

How is Streamhub supporting?

Streamhub is providing the following: 

• Data orchestration as a service
• Panel fusion methodology across Live + VOD
• Multi-source Analytics and reporting platform 
• Presenting multiple viewing data types in a single platform
• Activation platform for addressable advertising

Better measurement strategy

Maximising monetisation and audiences through connected services is a global agenda for all broadcasters to counter the halt in the growth of traditional TV advertising – especially in markets where linear TV targeting is unavailable. The Japanese market is no exception to this and the ultimate goal to protect and increase CPMs is a key mission with the introduction of the Live / cloud DVR services as the Linear TV CPMs are unusually low compared to other key markets. 

The Japanese market may be latecomers to the game, but by taking the time to consider the rollout carefully, they have been able to implement an advanced data and tech strategy where the Live streaming, DAI system and Analytics stack are shared between them to keep costs manageable but above all, as there is consensus that there is no point competing with the tech piping. 

Next steps

Through Streamhub’s shared reporting and data stack, broadcasters can now focus on developing new addressable propositions across all their connected devices and start working on incremental reach methodologies to include a diverse set of linear TV data including set-top-box data and panel data into its ad proposition.

Thanks for reading to the end – we hope this article has been insightful. For more information about Streamhub’s data services and products, head to our contact page or drop an email to bizdev@streamhub.co.uk. 

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