New Zealand streaming TV service Lightbox offers subscribers unlimited access to new, globally trending shows, cult classics, New Zealand favourites, and a huge range of kids shows.
However, the video-on-demand (VOD) landscape is fast paced and highly competitive. For the company to compete against global content providers it was essential that data is at the core of all its strategic business decisions, to ensure it continues to offer exciting and relevant content.
The challenge
For Lightbox, ensuring strategic content purchase decisions and remaining customer centric is crucial to its success.
Following an internal review, Lightbox engaged with data specialist Qrious to garner deeper insights into customer viewing behaviour.
The company wanted to understand what it needed to master to remain competitive for customer viewing time, and furthermore, how to sustain competitiveness.
Qrious’ specialised data expertise allowed not only a fresh perspective on existing data but the ability to consolidate and overlay data from a number of different sources.
Together 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.
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.
Model solutions
To solve the data consolidation problem, AWS 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 mapped what “good” viewer behavior looked like. This then became the benchmark for all subscriber activity. 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 model was a taste segmentation model which was developed to understand subscriber content preferences. Four taste segments were then developed and provided in a report to categorise the subscribers based on the content they are watching.
The two models can work in combination to give an overall powerful understanding of what customers are watching, when and how.
The result
Hema Patel, general manager of Lightbox, says through the work it has done with Qrious it are moving towards becoming a truly data-driven business.
“…enabling us to ensure we are providing our subscribers with the best possible viewing experience and stay competitive with global content providers”.
The three outcomes were as follows:
Reporting efficiency
Housing the data in a centralised store grants access to teams across the organisation, simplifies the reporting process and saves time and resources.
This means management and finance teams can easily pull data they need 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.
