Recosense Labs Inc

Category: Data Engineering

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Analytics Artificial Intelligence Data Engineering Finance

How an Investment Banker Can Benefit From AI, Data Science?

Investment banking is part of the banking industry, and its primary business is sourcing and managing capital on behalf of other companies and businesses. Investment bankers are actively engaged in the planning and launch of Initial Public Offerings (IPOs), Mergers and Acquisitions (M&A) and various other big-ticket deals in the business and corporate world.  

 

For investment bankers, there is a lot at stake. In the first nine months of the current year, global mergers and acquisitions have topped $4.5 trillion from over 40,000 deals. All these deals and exchange of money were made possible by relentless effort and data-crunching by investment bankers.        

 

In the banking and finance sectors, data is essential to the business. But when you consider investment banking, data is all the more critical. It is what makes the job of decision-makers in this industry all the more complex.

For example, if a company is seeking an M&A deal, it will appoint an investment banker whose responsibilities will include creating appropriate financial reports, seeking eligible partners, preparing blueprints, investment ideas and the financial details of the deal. They have to analyse huge amounts of data and draw insight from it. 

 

This brings data science and other tools of Artificial Intelligence into the picture. 

 

Are Investment Bankers Ready for Data Science and Digital Tools?

Investment bankers can use Artificial Intelligence to work smartly. AI is a combination of data science techniques, machine learning advantages and data analytics insights. But for them to use Artificial Intelligence, there can be a steep learning curve to overcome. For example, anyone who wants to make sense of big data must have a background in computers, mathematics or statistics. Some knowledge of computer programming is one of the desired skills, if not downright mandatory.

 

However, once investment bankers know their way around data science and digital tools, they will witness a boost in their productivity and efficiency. 

 

How Data Science Is Working Smart Even for Investment Bankers

How Data Science Is Working Smart Even for Investment Bankers

 

Investment banking is one of those sectors that consume and generate a great deal of data. Data science tools and technologies are, therefore, crucial for the smooth functioning of this sector. 

 

For example, data science involves creating appropriate solutions to analyse and draw meaningful inferences from unstructured data. One of the objectives of data science is to create structured data that can be used for business analytics. And, as we know, analytics is an extremely useful set of data insights that help decision-makers fine-tune their strategies and offerings.

 

The entire process of machine learning, data science and data analytics is equally important to investment bankers who have to delve deep into a sea of data and come up with a handful of pearls that we see as the details of the final deal. Machine learning tries to mimic and automate human intelligence. To do so, it uses available data and the data that it generates in the course of its normal functioning.

 

If investment bankers use AI tools, they can cut down significantly on manual and repetitive tasks, including data analysis that consumes much of their time, energy and effort.

 

Examples of Current AI Applications in Investment Banking

AI applications save costs for the global banking industry to the tune of $522 billion annually. They are deployed to generate leads and revenues, offer regular products and services and prevent fraud and theft.

 

Investment banking, too, has started using AI solutions to optimize its processes. 

 

Katana is a predictive analytics tool from ING Financial Markets Global Credit Trading, London. It uses historical and real-time trade data to provide statistical forecasts. Pilot tests show that it offers 90% faster price quotations. It reduces the cost of trading, and traders can quote their best prices more frequently.      

 

Kortical is a London-based company that offers predictive analytics through their AI platform, The Kore. It allows users to upload data directly from an Excel sheet and add “parameters” to create a machine learning model required to clean and analyse their data.

A general picture so currency notes

 

Investment Banking and Metadata Management

Investment bankers need to work much like data scientists to create clear, engaging and compelling business and financial models and marketing pitches. If you look closely, you will realise that a data scientist can perform the role of an investment banker with little to no orientation. Conversely, an investment banker requires relevant coding skills that can be used for data analysis and data analytics.  

 

For example, like all bankers, investment bankers have huge amounts of data that need to be transported from one platform to another within and outside the system. This consumes a great deal of time and effort. In this context, metadata management that administers and describes other data assumes significance, and an investment banker needs to be conversant with it.

 

Metadata management creates processes and policies to ensure that available information can be easily accessed, analysed, shared and integrated. It helps to find, use, preserve and reuse data. An investment banker without any knowledge of metadata management may not be able to access and use data that may be available to him within a few clicks.  

 

Summing up

Investment banking is a global, high-value, and highly competitive area of banking. Like all other sectors, it depends heavily on data analysis and data analytics, not merely for the competitive edge but also for routine functions.

 

However, with the use of AI for solutions, investment bankers can cut down on repetitive and manual tasks and use their time and energy in high-value endeavours.

 

AI applications in investment banking are pivoted to three main pillars of Artificial Intelligence — data science, machine learning and data analytics. Investment bankers with some skills in these areas can use AI to their advantage. 

 

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Artificial Intelligence Data Engineering Finance

Phases of an AI Data Audit – Assessing Opportunity in the Enterprise

Artificial Intelligence has proved to be a game-changer for many industries and has evolved exponentially over the few years. It is already incorporated into various sectors like smart homes, Ecommerce, driverless cars, and computing systems using machine learning and data mining Recently it has made its mark in artificial intelligence in accounting and auditing as well.

 

What Are the Different Types of the Audit?

  • An external audit is a financial audit wherein the accuracy of financial statements of an entity is established by an auditor.
  • Internal audit is the operational audit for units, departments and business functions.
  • A compliance audit is to evaluate if the organisation complies with internal or regulatory standards.

 

Auditing Steps without AI 

Auditing without the introduction of Artificial Intelligence was a cumbersome process that included the following steps:

  • Step 1 – Appointment of the auditor and ensuring that the assignment form commences.
  • Step 2 – Risk assessment and assessment of the company’s situation from various sources.
  • Step 3 – Audit approach incorporating and analysing key audit risks – as per high, low or medium, and how these risks affect the planned approach of the audit.
  • Step 4 – Administration of an appropriate staffing plan
  • Step 5 – Audit team briefing including planned audit approach, the key risk areas, how to address them, and clarifying the role of each member.
  • Step 6 – Client service by making useful recommendations
  • Step 7 – Client communication regarding any changes in the nature/scope of the assignment.

 

Auditing Steps with AI 

In audit with AI, there are four steps:

  1. The audit process (quality review and reporting)
  2. Risk assessment (identifying risks using data)
  3. Audit delivery (identify previously unseen patterns)
  4. The first line (continuous monitoring and alert systems)

Auditing Steps with AI

AI Auditing Considerations

Auditors can use machine learning in auditing systems to:

  • Automate manual tasks like documentation.
  • Study financial records by parsing data.
  • Monitor all kinds of data – structured and unstructured.
  • Identify anomalies caught by manual auditing.
  • Reviewing and analysing historical transaction data.
  • Make predictions about future risks and events based on the above.
  • Auditors have to manually evaluate several documents, so instead of securitising each one, they depend on sampling – This doesn’t achieve the goal, as every aspect isn’t thoroughly checked. Machine learning in audit goes beyond sampling, reviewing all available information automatically to bring high-risk documents to human attention.
  • Auditors have a checklist or follow the procedures, whereas data-driven AI automates all the checks allowing auditors to focus on more substantial tasks suited to human cognition.

 

Phases of a Data-Driven AI Data Audit

 

Phase 1 – Reducing Audit Risk through Artificial Intelligence and Audit Automation

Noncompliance can have serious consequences, so there is an urgency to audit client irregularities and detect fraud. Today auditing firms are now deploying Artificial Intelligence software to:

  • Expand their scope and identify high-risk transactions.
  • Detect anomalies and fraud in financial reporting.
  • Optimize resources and increase margins.
  • Protect the reputation and brand image of the client and their audit firm.

Phase 2- Optimizing Audit and Assurance

Audit firms are incorporating machine learning, robotic process automation, data analytics, and artificial intelligence to identify hidden patterns of fraud, automate repetitive manual tasks, and locate situations where compliance has been circumvented. Data-driven AI increases speed and accuracy, reduces costs and ensures efficient deployment of auditors. It includes:

  • Assessing engagement risks and negotiating deals
  • Planning audits
  • Performing audit fieldwork
  • Identifying exceptional behaviour
  • Generating reports and analysis
  • Considers automation risks

 

Phase 3 – Determine Objectives for the Data Audit.

Data audits can help organisations tackle major concerns like data security, customer data accuracy, legal compliance, data storage, etc. AI-enabled audits are not only beneficial for the business but are vital in enhancing customer experience.

 

Phase 4 – Determine the Cross-Functional Data Audit Team.

It is next to impossible to manually conduct 200 audits a month, but data-driven AI that uses a high-frequency process audit and involves all layers of management, from team leads to executives, can conduct repeat checks of high-risk processes, prevent defects, foster continuous improvement and provides the best results.

 

Phase 5 – Determine Departments, Functions, and Processes to Focus on

AI-enabled auditing focuses on identifying and improving opportunities in all business processes for compliance. Some of the main areas where AI auditing is deployed are:

  1. Cash Handling
  2. Credit Usage
  3. Vendor Billing
  4. HR Compliance
  5. Budget Control
  6. Process Improvement
  7. Customer Service
  8. Vendor Comparisons
  9. Cost Savings

Advantages with AI in Auditing

  1. Fewer controls – Audit automation brings about efficiency in auditing and makes manual processes like data collection, analysis and making predictions easier to handle and manage using ML.
  2. Risk-based assurance – AI can identify anomalies based on risk and not rules and immediately flags transactional data that deviates from the standard set.
  3. Secondary check is cross-checked with the available data and correlates it with numerous variables to check accuracy.
  4. Frees up time for both the organisation and the client as large amounts of data can be processed with little manual effort that frees up the time of humans and lets them focus on other areas of the audit to provide a better financial picture.
  5. Ethics are promoted as AI can be tested to ensure that the data is assessed with the original objective in mind, making it more ethical, legal and responsible.
  6. Predictive value – AI can review and assess historical data and make predictions of any future risks based on the data.
  7. Cybersecurity – AI offers many companies a competitive advantage in protecting the privacy of data in accounting and auditing firms.
  8. Improvised role of the modern-day auditor as AI serves as an effective tool for auditors allowing them to work better with the help of technology.
  9. Reduces the risk of fraud through a better auditing system and recommends changes to the company in the most efficient way possible.
  10. The ultimate goal of AI is to provide high-quality audits to enhance the confidence of businesses, industries and organisations in the financial markets.

Challenges of Using AI in Auditing

Although AI offers great innovations and opportunities in auditing, it also comes with associated risks like:

  • Audit automation risks of embedding human biases in the algorithms. Amazon had to quit machine learning in audit when it discovered the tool was biased against women.
  • Risk
    s of inadequate testing and oversight, embedding human logic errors, and possible financial or reputational damage.
  • Human intervention is needed to complete an audit.

Wrapping It up

AI is evolving and transforming the audit sector by processing large amounts of data and providing results based on compliance, law, legalities, trends and secondary data. It creates an efficient way for big and small organisations to quit the manual compliance findings and use AI to report data, identify errors and report risk to clients.

 

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Analytics Data Engineering

Predictive Analytics: Transforming Data into Future Insights

In this digital age, technological advancements all around the world are striking. This resulted in a massive progression in the world economy, as well as in the different countries. Many companies have been getting recognition and prosperity over the years because of these advancements. They are most likely planning in advance for the business or company growth by looking into their possession data.

Organizations in different sectors are now at a different level, The competition and the survival of these companies are maintained through modern and more complex methods. You will no longer find big enterprises relying on current events and simply going with the industry’s flow. Organizations are using past events and data to gain insight into what would happen in the future. 

You might be wondering if such a thing is truly possible. Is it really possible to predict the future by looking into mere numbers and data acquired in the past? It sounds crazy, but that’s just how the current state of our technology is.

This article will introduce one way to make it possible: Predictive analytics!

What is Predictive Analytics? 

Predictive analytics uses, analyzes, and assesses historical and recent data to predict future events or outcomes. It incorporates different statistical techniques and machines, algorithms, and technological instruments like AI transformation to identify what would be the future movements of one data, market, trends, and other valuable and predictable variables. 

Through this advanced analytics, many industries that are keen for future events in their respective fields are being saved and continuously progressing. Predictive analytics shows the likelihood of one or more occurrences based on past data. It can represent relationships between variables or conditions and also identifies possible risks or opportunities in the near future. 

Why is Predictive Analytics Important? 

There are benefits you can gain in using predictive analytics. It is not an exaggeration to say that businesses, companies, organizations, and even individuals use predictive analytics to ensure growth and gains. It makes the operation and plans certain and safe by predicting the future. It allows individuals and organizations to use the acquired information of possible future to their advantage. Because predictive analytics uses past data to gain future insights, it will become possible for you to plan ahead, implement actions, and make the best possible decisions towards success. 

Benefits of Using Predictive analytics

Benefits of Predictive ANalysis

1. Predict future performance, events, outcomes 

Predictive analytics is used to predict the future through assessing historical data using statistical methods, machines, and other advancements. You will become certain when you have insights into possible outcomes. You allow yourself and your business to think far from the future because of the acquired future insights. 

One great real-life application is when retail companies can use predictive analytics to gain foresight of the supply and demand in the market. Assessing acquired data from the previous performance of the company can also generate useful insights to forecast revenues, possible losses, and future performance of sales and marketing accurately.

2. Stand in a more advantageous competitive position

If you know the future, you can do something, prepare, and have the upper hand. If you are handling a business, using predictive analytics to predict the market movements, demands, and customers will put you ahead of your business competitors. A technology-inclined company can also use predictive analytics to look into possible trends, problems, and opportunities. 

3. Minimize risk 

You will know and realize the potential threats or risks to your business that could put you in dire situations. Paired with other different analytics and business intelligence, you can avoid serious threats that can potentially ruin or harm you. It minimizes risks you can get by planning and managing plans and solutions to solve the problems. Predictive analytics also detects frauds through observing and assessing pattern behaviors and movements that could give you time to stay away from serious problems and vulnerabilities. 

Investors and traders also use predictive analytics in identifying good stocks or companies to invest in. They usually use the previous market performance of the company in the last few months or years to assess the movement of the market in the future. This minimizes the risk of losing your invested money and helps you generate a risk management plan. 

4. Prepare better decisions 

Having the opportunity to gain future insights or movements can drastically improve the chances of growth. Planning and thinking of ways to be competitively advantageous are critically important in an organization or business. The farther you can predict, the more prepared you can be. 

Just like how you assess past data to gain future insights, you can now come up with new assessments and procedures of your business, organization, or company to gain future advantages. Business process automation, operation, and management departments are using past events and data to gain insight into what would happen in the future.

5. Boost growth 

Being able to see the market or movement months and years from now can give you more opportunities to take advantage of. Predictive analytics not just prepares you for knowing the worst situations but also the best events. Boosting and ensuring growth is one of the benefits predictive analytics can give you. You can start optimizing your operations, improving each department or sector, and ensuring your group’s effectiveness and efficiency. 

Key Takeaways 

Predictive analytics gives you the advantage of predicting future movements and gain insights through analyzing and assessing past data. Now that you know that there is a way to not purely based on assumptions, you can definitely maximize the benefits of using predictive analytics. In this time where high-technological advancements are possible, you can become more advanced than anyone and any company as early as now. 

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Data Engineering Media

OTT Beyond Data: Trends, Challenges, and Opportunities

As the competition in the OTT market heats up, fresh insights and perspectives on consumer’s behavioral data are emerging. A complete element of OTT revolves around data usage to better comprehend audience preferences and predict future content choices. It emphasizes the power of technology and the influence that OTT players have over their subscribers’ data.

 

OTT providers are aware of what a subscriber streams, on what device, how frequently, at what time, for how long, and at what cost. These businesses could leverage this data to gain deep, actionable insights into their audience’s preferences.

 

OTT Data: What Do Industry Experts have to Say?

Puneet Misra, Zee’s President of Content (Linear and Digital) and International Business, said that data needs to be presented in a way that inspires people’s creativity during a conversation with Sameer Nair, CEO of Applause Entertainment. According to Nair, while OTT is unique in terms of the technology backing it and the way people consume content, it is comparable to linear as they distribute content ultimately.

 

Misra feels that creators must consider it a new opportunity since the audience is now the major focus. He stated that tailoring one piece of content to various audiences is the real complexity creators must realize.

 

Misra highlighted the four Cs they are working on during the discussion: consumer, content creation, content discovery, and content collaborations. Their teams are working hard to ensure that they get much deeper into the viewer environment. He emphasized the need to figure out how to acquire data insights and serve them to creative artists for great streaming content creation.

 

Nair, on the other side, believes that, despite their uniqueness, viewers exist in groups. While you may have individualistic viewing behavior, you ultimately function inside a greater mass of behavior approach. With the current size of the OTT market and the rate at which competitors are expanding, there is a greater demand for content creation than for content acquisition.

 

Top 2 Trends Shaping OTT: 2021 and Beyond

Here is a rundown of OTT industry advancements and top two OTT trends to look out for this year and beyond:

 

1. Widespread 5G Technology Adoption

The arrival of 5G, the next generation of cellular network technology, is projected to increase the volume and quality of content consumption to unprecedented levels. The key benefits of 5G would be apparent in high-definition video streaming, sports coverage, event broadcasting, and other experiences requiring reduced latency.

 

Additionally, 5G technology will enable high-bandwidth content such as immersive applications, Virtual Reality, Augmented Reality, and 360-degree live-streaming.

 

2. New Phase of Enhanced Viewer Engagement With Content

With so many streaming services to pick from, consumers want a unique experience that keeps them engaged with a service. The transition to an active audience has transformed the streaming scene into a two-way content and interactivity flow between the audience and service providers.

 

Streaming services leverage viewer data and offer a personalized experience by delivering relatable and relevant content that entails the subscriber to continue with the service. It is the beginning of the OTT 2.0 phase.

 

OTT Service in 2021-2022: Challenges and Opportunities

OTT is becoming a congested space today, with an increasing number of competitors, at the supply end of the sector. To compete more effectively, OTT providers are turning their focus away from technology and towards content.

 

Challenge – How to Crush the Competition

All reputed OTT platforms are investing extensively in the production of creative, original content. Along with it, regional content production is gaining traction as OTT providers use data democratization to enter lower-tier market segments.

 

Moreover, to keep viewers hooked for a long, several OTT players capitalize on the popularity of famous TV shows while regularly unveiling originals. Other established video streaming business players are already motivated by original/exclusive content.

 

Opportunity – Rising Demand for Omnichannel Experiences

Consumers in the digital age can access smart gadgets and amazingly fast internet connectivity, which drives the desire for omnichannel media content access experiences. 

 

In today’s scenario, OTT-apps enable access to on-demand content, in both audio and video format, and on nearly any compatible device.  Although millennial OTT users prefer streaming content on smartphones, tablets, and laptops, over 60 people will utilize television in the next five years. Whether it’s dedicated streaming gadgets or smart TVs and smartphones, digital-age customers expect to be able to stream media content anywhere, at any time, and across multiple devices.

 

The Bottom Line

Viewer interests and preferences can be better understood with the help of subscriber data. Even while the potency of harvesting subscriber data is undeniable, it’s crucial to acknowledge the impact and contribution of the human aspect in gauging consumer choices and preferences.

 

Also, content-driven distinctiveness relies heavily on artistic characteristics. Many other disciplines are needed to supplement subscriber data analytics for OTT companies. The composition of insights from multiple sources on top of analytics will help OTT players identify content themes that appeal strongly to the viewers.

 

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Artificial Intelligence Data Engineering Media

How AI Can Help Streaming Platforms Fight High Churn Rates

Studies of consumer behavior have demonstrated that the pandemic has led to a massive increase in screen time and entertainment viewing as lockdowns force consumers to spend more time indoors and online.

Subscriptions to online streaming platforms jumped a whopping 26% globally within a single year, crossing the 1 billion mark. This was just as box-office revenues plummeted by over USD 30 Billion in the same period. This surge was led not just by new-age streaming platforms such as Netflix and Amazon, but even legacy content producers such as Disney and HBO reaped windfall gains.

Netflix

However, there’s a dark cloud hovering over these big numbers – how much of these gains can streaming companies retain? More importantly, will these impressive increases in customer acquisition numbers still hold once the effects of COVID-19 have subsided?

It is a poorly kept secret that consumer retention is a problem that streaming platforms grapple with, especially with the barrage of online content now available to consumers. But there’s a solution in sight – AI-driven personalization.

The Big Churn – The Other Side of Increased Screentime

Churn rate is a measure of how many customers a business loses in a given period relative to the new customers acquired. A certain amount of churn is natural as no business can retain all of its customers all the time. But when your churn rate begins to shoot through the roof, it is time to sit up and take notice of this high churn rates.

The numbers for the streaming content industry indeed seem to fall in the latter category. According to data, the high churn rates for streaming content channels stood at a whopping 41% in 2020.

The forced isolation brought about by the pandemic encouraged consumers to spend more time looking at their screens. But it has also induced restlessness, anxiety, and ever-shorter attention spans, leaving streaming companies little margin for error.

The Importance of Customer Retention – A Story in Numbers

Streaming companies are witnessing massive gains in subscriptions, accompanied by significantly high churn rates. So why should they worry about customer retention?

The answer is that the two metrics have differing values for businesses. While customer acquisition numbers may grab more eyeballs, customer retention brings in revenues. Here are some numbers that make this picture more clear:

  • Acquiring new customers is more expensive than retaining existing ones. A long-held rule of thumb in business has been that it costs 5-times more to acquire a new customer than to retain an old one.
  • Customer retention adds more incremental business value than customer acquisition. According to data, even a small increase in the customer retention rate of about 5% leads to anywhere between 25-95% increase in profits.
  • Finally, most business revenues are generated by existing customers than by new arrivals.

Personalization – The Solution to High Churn Rates

Netflix is the leading streaming content provider in the world. It also has the lowest churn rate – at about 2.5% for the fourth quarter of 2020. How does the streaming giant do this?

Netflix

The answer is by using AI and data engineering to make sense of the large amounts of consumer data. The result is a high level of personalization that translates directly into lower customer churn and better user engagement.

Netflix gathers data on the minutest aspects of user behavior, such as which day of the week they watched a particular series, at what point did they rewind, how many times they viewed the same content, etc.

All this data is fed into AI models to produce the next chart-topping, binge-watching, fan-favorite TV show. This is an approach that keeps customer engagement at its heart to win the battle against high churn rates.

Customer-centricity for streaming platforms essentially translate to three deliverables:

1. Hyper-Personalized Content

Viewing habits of consumers have undergone a sea-change thanks to the pandemic. For instance, there has been a significant increase in the consumption of health and fitness-related content as viewers confined to their homes seek to improvise on their health and workout routines.

Similarly, there has been an increased demand for reruns of old shows as the anxiety and uncertainty produced by the pandemic drive viewers to seek the comfort of familiar characters, stories, and places.

2. Integrating the Web for Better Content Discovery

Content now needs to be easily searchable on the web. Platforms must analyze and leverage insights from user search data to build the next best products and services.

Moreover, the content also needs to be available to users with single-click checkout designs as web users typically have little patience with tardy checkout procedures extending over several screens. At the same time, streaming platforms need to implement more rigorous anti-piracy controls to control illegal content viewing.

3. Better Customer Service

Increased viewing times naturally translate into a greater volume of customer issues that need resolution. In addition, greater anxiety and shorter attention spans induced by the pandemic mean platforms have lesser room for error when it comes to issue and query resolution if they want to improve their customer retention metrics.

Personalization

 

Using AI and Data Engineering for Personalization

AI offers breakthrough solutions to streaming platforms battling high churn rates in offering greater customer value and a better viewing experience.

By analyzing data to identify trends in consumer behavior, AI-driven frameworks can offer solutions based on the three-pronged approach of identifying consumer needs, quick prototyping, and balanced scaling. Here’s how such an approach would work in practice:

  • Using AI for Data Analysis: AI can quickly distill large volumes of consumer data such as programming preferences and search histories into actionable insights.
  • Leveraging Data Engineering for Scalability: On-cloud infrastructure allows AI-generated insights to be fed into algorithms to allow for the scaling up of data.
  • Transparent Design for Easy Accessibility: The insights can be packaged and presented in an accessible design to allow end-users and strategic decision-makers to make definitive business interventions.

To Sum Up

Personalization is the answer to customer retention challenges faced by streaming platforms. The technology for personalization, however, can be expensive, and its implementation can divert platforms from their core focus – content creation.

For personalization to work best, it is imperative that streaming platforms team up with boutique AI-solutions providers that specialize in providing services to the media and entertainment industry.

RecoSense specializes in using Machine Learning (ML) and Natural Language Processing (NLP) to understand user behavior and create profiles that can be leveraged to deliver personalized content across platforms.

Categories
Analytics Artificial Intelligence Data Engineering

Maximize ROI With AI-Driven Content Acquisition Strategy

A couple of years ago, a single hit show could carry an entire streaming platform on its back. However, as the market begins to saturate, OTT or streaming service providers are teaming up with content producers to earn a precious resource – their audience’s interests. As a result, they are tapping into the potential of data, more specifically consumer data, to ensure that every bit of content added to their portfolio fits into the larger picture of profitability. Here, we will be reviewing the role of Artificial Intelligence (AI) in driving ROI during content acquisition strategy.

How can Artificial Intelligence Enhance Content Acquisition Strategy?

 

AI automation can enhance BI and analytics for M&E streaming platforms in the following ways:

1. Incorporating Data in Content Acquisition

When entertainment followed a linear schedule, the content acquisition strategy was purely intuitive. Even if there was some amount of data involved, it was highly niche and case-specific. Such an approach is also a reflection of the fact that entertainment was a one-way street – a broadcast rather than a dialog between audiences and networks.

Data in Content Acquisition

However, disruptors like Netflix changed the entire playfield by freeing up the market with on-demand services. Now, network executives no longer enjoyed autonomy over what one gets to view, while audience opinions began to be heard.

By establishing channels to collect viewer data, streaming platforms could make informed data-driven decisions on what their audiences like or dislike. Accordingly, these inputs began shaping the content acquisition strategy. The additional layer of AI automation can make the heaps of structured and unstructured data more meaningful and boost ROI.

2. Monitoring Performance of Content

A successful and holistic content strategy must measure the performance of existing content – on and off the platform. Such a feat is possible through AI automation-led data analytics that comprehensively weighs the pros and cons of the content hosted (or being considered) by the platform. It also involves real-time insights derived from market analysis to learn about content and content producers that are trending or popular and how they fare against your existing collection.

For instance, Amazon Prime took The Middle off the air in India as it failed to generate enough views to register a profit. However, as discussed, the role of AI is not limited to getting rid of dead weight.

Customer engagement metrics

It can also push for data-driven decisions based on real-time insights to navigate the content acquisition strategy dynamically.

For example, streaming platforms that acquired the rights for shows like The Golden Girls and F.R.I.E.N.D.S during the pandemic could successfully capitalize on the audience’s need for comfort and nostalgia in such troubling times.

Similarly, AI could help decipher audience sentiments for when a show gets discontinued or canceled. Consider shows like Sense8, The Mindy Project, or Twin Peaks that had to be revived in response to the backlash from viewers as a classic illustration of this effect.

3. Predictive Modeling for Content Strategy

Businesses can also utilize AI automation to conduct predictive analysis for future content acquisition strategies. 

For this effect, networks can use BI and analytics to identify the popular shows performing excellently over their streaming platforms. They can then map it against similar shows using a multivariate predictive analysis, thereby shortlisting a prospective list of content they can acquire in due time.

Furthermore, one can also factor in the historical performance of the show when it was on the air and anticipate the viewership that it will garner post-acquisition. This exercise will set the benchmark for measuring content performance.

4. Enhancing Value of Existing Content

Whether it is repurposing content or partnering with content producers, streaming platforms must focus on one thing and one thing alone – to offer value.

AI can enhance the strategic and monetary value of content by making the platform more customer-friendly. 

Content performance metrics

For example, it can improve the visibility of content by enhancing the search and discover features. Say a user is looking for a feel-good, action movie of British spies, then your search engines must lead them to Kingsman: The Secret Service. And even if you do not have the rights to the movie, you can recommend related content based on several factors.

Similarly, you can curate a catalog of recommendations that are more suited for the viewer’s tastes as per their interests or content consumption history. Such measures can add value to your existing content and reduce audience churn to maximize ROI.

Can RecoSense help Boost ROI through AI-Powered Content Acquisition Strategy?

Measuring the success of a content piece can be easy – one can simply analyze the number and volumes of views, user habits, and audience demographics. However, what is crucial and complex is understanding the rationale behind the “why” a certain media strikes a chord with the viewers.

Fortunately, RecoSense harnesses the power of AI-driven data to answer such a compelling question. 

RecoSense AI analytics

It offers you an in-depth profile of your customers and their preferences. At the same time, it demystifies different content types and prioritizes them as per requirements. Combining these data sets can lend direction to the content acquisition strategy of streaming platforms and maximize ROI. 

In essence, it is the application of all the channels of AI deployment and subsequent benefits that we have discussed above.

Categories
Data Engineering

Data Streaming Technology for High Volume Data Feeds

Data streaming has become the core of enterprise data architecture amid all the data generated from non-traditional sources like security logs, web applications, and IoT sensors. The digital universe was estimated to comprise 44 zettabytes of data in 2020 and is likely to reach approximately 463 exabytes daily by 2025 worldwide.

 

To keep up with this explosive growth of data, companies are pivoting towards real-time data streams. In this article, we’ll take a closer look at data streaming, its benefits, how it works, examples, and ways to create a data stream.

 

What Is Data Streaming?

Streaming data is defined as data that is continuously generated by multiple data sources such as server log files, real-time advertisements, e-commerce purchases, geospatial services, and other instrumentation in data centers.

 

This data is simultaneously sent in the data records in small sizes, usually in kilobytes, and sequentially and incrementally processed on a record-by-record basis.

 

The processed data is used for various insights and analytics, such as correlations, filtering, and sampling. The insights from data streaming provide real-time visibility into different aspects of business and client activity, allowing companies to respond promptly to emerging situations.

 

How Data Streaming Works?

It is a architecture comprises a framework of various software components that process large volumes of information from multiple sources. Since streaming is a challenge rarely solved with a single ETL tool or database, there has to be an architecture that comprises several building blocks. Here are the four key components of streaming architecture:

 

1. Stream Processor

The first component is the software solution that fetches data from various sources and converts it into a standard message format. Some examples of this big data technology include RabbitMQ, Apache ActiveMQ, and Apache Kafka data.

 

2. Real-time ETL tools

Data streams from a stream processor need to be aggregated, transformed, and structured before it is analyzed with SQL-based analytics tools. That is why ETL tools are required to receive user questions, fetch events and apply queries to provide results, such as an API call, a visualization, an action or alert, and even a new data stream.

 

3. Data Analytics

The third component is analyzing data or streaming data analytics for prompt actions.

 

4. Streaming Data Storage

The last stage is storing streaming event data. There are several low-cost storage technologies, including a data lake.

 

Examples of Data Streaming

The top three examples of streaming data are:

 

  1. Vehicles with sensors as well as industrial equipment and other machinery are data sources that send vital information to a streaming app. The app is used to monitor performance, identify potential defects, and automatically order spare parts to avoid downtime.
  2. Financial institutions use big data streaming tools to monitor changes in stock prices in real-time, calculate value-at-risk, and automatically rebalance portfolios depending upon price change.
  3. Another streaming data example is media publishers, who use it to deliver a relevant and better user experience. These companies stream clickstream records from their online properties, aggregate and enrich user data, and optimize content placement on its website.

 

The Benefits of Data Streaming

 

1. Detects Patterns

With real-time data streaming, you can detect patterns over time with continuous data processing and analysis. This is difficult to achieve in batch processing since it breaks data or events into different batches.

 

2. Scalable

Exploding data volumes can break a batch processing system, forcing companies to allocate more resources or modify its architecture. Modern stream architecture is hyper-scalable and can handle gigabytes of data per second with a single stream processor.

 

Therefore, stream technology helps to efficiently deal with growing data size without making infrastructural changes.

 

3. Visualize Data

Streaming analytics makes it easy to display updates in real-time and see what is happening in each passing second. It also provides valuable business insights and sends alerts about any serious issue.

 

4. Increases Competitiveness

Streaming architecture gives a competitive edge over companies still based on batch processing analysis since it provides real-time analytics.

 

5. Boosts Security

Stream technology is the best way to detect and prevent fraud since it immediately detects aberrations and notifies users to restrict the damage.

 

Batch Processing vs. Real-Time Streaming

Modern organizations are using real-time data streams due to the complexity and flow of data. Today, data is sent continuously in different volumes and formats from multiple locations such as cloud, hybrid cloud, or on-premises. Thus, it is important to know what type of architecture is best suited for your organization.

 

There are some critical differences between batch data processing and real-time data processing. In the legacy batch processing methods, data is collected in batches before it is processed, stored, and analyzed. On the other hand, streaming data flows continuously and processes it in real-time.

 

Batch processing is lengthy and is meant for the volume of data that is not time-sensitive. Stream processing is quick and is used for information that’s required immediately.

 

Turning Batch Into Data Streaming 

Since the nature of your data sources plays a big role in deciding whether you’d like to use batch or streaming processing, you should know how to convert your batch data into real-time.

 

If you are currently working on legacy data sources, such as mainframes, you can use various tools to automate data access and integration processes to convert your batch data into streaming data.

 

As the volume of data increases, many platforms have also emerged to provide the right infrastructure required to create streaming data applications. Some big data streaming tools include Amazon Kinesis Streams, Apache Kafka, Apache Flume, Apache Storm, Amazon Kinesis Firehose, and Apache Spark Streaming.