With the constantly-evolving way consumers interact and react to content, media, entertainment, and telco companies have faced increased pressure to change their business models. Big data is helping guide this new strategy. By leveraging big data analytics throughout the media, entertainment, and telco, companies are experiencing numerous benefits from better understanding their viewers.
Big Data in Media and Telco: 6 Applications and Use Cases now.
6 applications of big data in media, entertainment, and telco
When you know your customer inside and out, you can tailor content to their interests, price subscriptions appropriately, and air extremely relevant ads. As a result, you attract new customers and reduce customer churn. Win-win, right? The following are 6 applications and benefits that can result from using big data in media, entertainment, and telco.
- Predict audience interest
- Increasing audience acquisition
- Reducing audience churn
- Improving ad targeting
- Monetizing content
- Developing new products
Predicting audience interest
Live streaming, pay-per-view, and subscription-based viewing have dramatically transformed the media industry. Whereas traditional television only allowed viewers to change the channel or turn off the TV, modern entertainment allows viewers to choose what content they watch and how they watch it.
With the large number of programming choices now available to viewers, companies may have difficulty navigating how their viewing decisions relate to general audience interest. Big data helps companies understand correlations between customers’ TV watching decisions. Big data provides information on customer search history, ratings, and social media. Additionally, some media giants monitor how long users watch videos, how users respond to trailers, and even how users react to changes in the layout of new websites and applications.
Analyzing what makes certain content popular is crucial in making important business decisions. Big data can inform how media companies develop and market new programs and initiatives in accordance with their audience’s interest.
Increasing audience acquisition
We learned that media companies can leverage big data to better serve the customers they already have. But how can big data be leveraged to appeal to new customers? Take TI Media as an example.
TI Media is a magazine and digital publisher that reaches over 14 million adults in the UK per month. Its 40 brands span a variety of genres, including: entertainment, sports, luxury, and technology. TI Media experienced a drop in print readership and advertising revenue that did not improve when it migrated to an online model.
When TI Media finally began to integrate all data sources (print, digital, social media, video, apps, podcasts, in-person events) into one big data platform, the company was able to mine patterns in customer behavior. Today, TI Media sends 30 million targeted emails a month. Establishing an effective contact strategy led to a 5% higher response rate, and to partnerships with companies like Ownable to further close the revenue gap.
Reducing Audience Churn
Customer churn is a huge pain point in the entertainment industry. Understanding exactly why customers cancel a membership or subscription helps reduce customer churn and maintain customer retention.
Radio Television Belge de la Communauté Francophone applied big data to successfully increase audience retention. To increase engagement and stand out among cable, French, and German channels, the Belgian broadcasting network instituted a big data warehouse.
This platform compiled network, financial, HR, production, and distribution data to deliver more enhanced operational reporting. RTBF insists that big data affords them the ”ability to predict the unpredictable”, thereby minimizing churn and maximizing entertainment value.
Improving ad targeting
Advertising is a fundamental form of revenue for many media brands. Advertisers pay an enormous amount of money for product placement and ads between show segments. With big data, companies can create and highly-targeted ads.
GlobalWide Media used big data to enhance their advertising strategy. GlobalWide Media is a digital marketing agency that fulfills over 60 billion ad requests per day in Asia, North America, and Europe. These marketing campaigns lead to $3 billion in annual sales. GlobalWide Media adopted a big data platform to skillfully target certain subpopulations within its broad global community.
GlobalWide Media implemented a streamlined data warehouse to collect data from both internal and external platforms. Their new warehouse gathers valuable conversion data, speeds up business reporting, and allows GlobalWide Media to tailor their campaigns to better fit customer lifecycles. Big data enables GlobalWide Media to consistently deliver high-performing ads across its enormous consumer base.
Big data analysis can uncover correlations no one ever knew existed. Combining these insights with a 360 degree view of a customer can be very advantageous.
For instance, companies can cater ads to the geolocation of their customers. Weather conditions can spur purchases for insurance, cars, and even shampoo. Advertising around major events or holidays can lead to spikes in grocery store and other retail transactions as well.
Developing new products
When shows get pitched to a production company, the selection committee picks what they have a hunch will do well. With big data, designers, writers, and media executives have the power to create and choose products based on a mathematical probability of becoming popular.
Companies equipped with big data platforms can predict content success ahead of time instead of operating solely on gut feelings. Big data can guess storylines, actors, apps, offerings, and formats customers will enjoy before they know they enjoy it.
Big data challenges in media and entertainment
Clearly, big data is useful to media and entertainment brands. However, there are some challenges to actually making big data work its magic.
According to the Reuters Institute interpretation of InformationWeek’s Big Data survey, top barriers to successful use of big data were budget constraints (38% of respondents) and lack of business interest (13% of respondents). Other hurdles responders mentioned were lack of big data management tools and more important IT priorities. In addition, some respondents said there was a stark disconnect between collecting data and making it meaningful.
Companies cannot reap the benefits of big data without the tools and employees to decipher and maintain it. Big data technology is necessary to store and process all the data media and entertainment companies collect. To minimize cost, companies can augment datasets by tacking on public data sources like geolocation and government statistics.
Champions of big data should convince their leadership to fund new developer and data science roles. Building a team specifically for big data processing can reduce load on the IT department and can produce a tremendous return on investment in terms of revenue, customer retention, product ideas, and ways to monetize. As the big data department matures, business stakeholders can be empowered to create visualizations, dashboards, and reports themselves.
Big data technology and tools for media brands
There are many big data technologies out there. So how do you choose the best tool for your company?
First, it’s important to understand that data integration is a critical element of big data success. There are big data tools that aggregate, sort, and manage data for practical use. An entry-level big data platform should integrate both cloud and on-premises data sources.
Easy integration is also a key factor. Big data platforms may have connectors for multiple data sources, such as CRM, ERP, and website data. Others offer file management, data flow mapping, and data governance capabilities. Some can be open source, which makes it much easier for developers to tackle tough data problems they might face.
A few tools are focused on the analytics part of big data. Advanced big data platforms provide real-time analytics. Batch data pipelines in these systems can evaluate and validate hypotheses to help businesses make split second decisions. Some sophisticated tools incorporate machine learning as well, which can streamline data preparation across multi-cloud and hybrid environments.
Overall, big data software is typically split into the following categories: cloud versus on-premises tools, data integration platforms, and data governance tools.
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Cloud vs. on-premises tools
Big data software is essential to house growing data volumes. Companies must choose to either continue with legacy on-premises offerings or switch to more futuristic self-service integration tools on the cloud.
The cloud can handle increasingly large datasets, expedite the time it takes to initiate new projects, and can integrate with any number of data sources. Cloud-based tools are also cost-effective. Developers can work at their own pace and schedule deployments when they are most convenient to the company at large.
By the same token, cloud-based technology exposes media brands to security risk. However, big data cloud platforms have taken extensive measures to abide by regulations like GDPR and ensure privacy and security. For businesses who still remain hesitant to put all of their data on the cloud, hybrid cloud and on-premises options available as well.
Data integration tools
One data source is great, but what really adds value is joining multiple data points together. Data integration is a key component of a good big data program. Media companies have myriad data sources, like in-app surveys, online reviews, and social media that can be strung together to construct a full picture of the business and its customers.
Looking at the data holistically can influence new marketing campaigns, products, and even pricing models. A good big data integration solution must integrate on-premises and cloud data sources, such as: databases, SaaS products, Spark, and Hadoop.
Data governance tools
Preparing for a big data application is a project in and of itself. You might want to consider data governance resources to simplify development, certify data quality, and facilitate a collaborative working environment.products
Insights from big data are only as good as the quality of data used in an analysis. Data governance tools can enrich data with verified external sources (think credit scores, addresses, etc.) and can de-duplicate, standardize, and validate information. Moreover, data governance tools can enable anyone on a big data team to certify and reconcile data. Certification tasks can be assigned to subject matter experts in various departments, and can be tracked for audit purposes.
Data governance tools can also liberate data analysts’ time. Data analysts spend most of their day cleaning and transforming data. If data governance tools with built-in machine learning could comprehend what the analyst was trying to achieve, most cleansing activities could be automated. This would free up time for the analyst to inspect and scrutinize the data.
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Getting started with big data in media, entertainment, and telecommunications
With so many companies flourishing post-big data implementation, it is easy to make a case for big data in entertainment. Conclusions drawn from big data can lead to audience growth, reduced audience churn, upgraded advertising strategies, thoughtful pricing, and even new products. While big data has significant advantages, is close to meaningless if a company does not have the tools or staff to interpret it.
Purchasing a solution that offers data integration across cloud and on-premises environments is a fantastic place to start. The Talend Data Fabric is a single suite of applications that can integrate media and entertainment data sources using 900+ connectors and components. Talend Data Fabric maintains data integrity across multi-cloud and on-premises environments, and even offers built-in machine learning.
Try Talend Data Fabric today to start making your media, entertainment, or telecommunications company a customer’s one and only pick.Try Talend Data Fabric