Lightbox is a New Zealand streaming TV service committed to delivering the local market the best viewing experience possible. Lightbox offers subscribers unlimited access to brand new, globally trending shows, cult classics, New Zealand favourites, and a huge range of kids shows. The Video on Demand landscape is fast paced and highly competitive.

For Lightbox to compete against global content providers it is essential that data is at the core of all their strategic business decisions, to ensure they continue to offer exciting and relevant content.

Lightbox is a New Zealand-based company with big competition, so ensuring strategic content purchase decisions and remaining customer centric is crucial to their success.

Following an internal review, Lightbox engaged with data specialist Qrious to garner a deeper understanding into customer viewing behaviour. They wanted to understand what they needed to master to remain competitive for customer viewing time, and furthermore, how to sustain competitiveness. To access this data, Qrious brought on Matillion ETL for Amazon Redshift.

The specialised data expertise of Qrious allowed not only a fresh perspective on existing data but the ability to consolidate and overlay data from a number of different sources, using the transformative power of Matillion ETL.  Lightbox and Qrious identified the first challenge as one that is faced by many organisations – to consolidate all data sources across the business into a central data warehouse.

Using the code free pre-built components in Matillion ETL for Amazon Redshift, orchestration jobs were built to load the data. Secondly, it was imperative to develop a segmentation that would incorporate the audience’s taste groups based on the type of shows that Lightbox offers and also understand how customers are engaging with the content.

Amazon Redshift was implemented as the data storage facility. This provided the opportunity to bring three separate data storage systems into one centralised location.  To understand why some customers became inactive and ceased viewing, Qrious specialists took a less traditional view on churn propensity modelling and instead started by mapping out what “good” viewer behavior looked like. This then became the benchmark for all subscriber activity, and any behavior variation from this ideal standard was highlighted to be addressed. 

Two models were developed, the first being a Recency Frequency Monetary (RFM) model to understand how customers are using Lightbox, how they are engaging and how often. The second being a Taste Segmentation model which was developed to understand subscriber content preferences.  4 Taste Segments were then developed and provided in a report to categorise the subscribers based on the content they were waching.

Behind the scenes, Matillion ETL for Amazon Redshift enabled Qrious to quickly develop data loading and transformation jobs so that the data could be accessed and understood in a meaningful way. Data pipelines could be further simplified with Matillion ETL for Amazon Redshift through utilizing a number of included features. For example, the Scheduler can trigger jobs to load data at regular intervals, the Task Info Panel provides a detailed summary of job execution times to identify and rectify inefficiencies as well as Amazon Redshift Spectrum specific features. “Through the work we have done with Qrious 

Reporting efficiency. Having access to a centralised data store grants access to teams across the organisation, simplifying the reporting process, saving time and resource. Management and finance teams can pull data they need easily and in real-time. Analysts are able to carry out investigative work on areas such as customer information, engagement levels and churn analysis.

Self-service monthly updates. Lightbox are now able to run a regular report that investigates engagement levels and how subscribers are watching the content. These self-service reports are used to guide marketing activity. Based on different people’s viewing behaviour they will receive varying and relevant marketing communications. 

Subscriber Insights. Customer segmentation profiling is used in conjunction with the RFM model to understand what content different people are watching, informing recommendations to viewers and content purchase decisions. 

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