Reliance Entertainment, especially its digital arm, adapted the data-driven decision-making approach from beginning of start of its global mobile gaming business.
Analytics has been adapted primarily for all the digital entertainment businesses, predominantly mobile gaming, OTT, and casual gaming aspects. We have full-fledged end-to-end analytical system operational and working to collect and process the data from worldwide. We collect and process 65 million game events (data points) from mobile devices every day.
The first major use case is Reliance Games, where we collect majority of data points and churn them to get the product and user insights. Since it is designed by us end-to-end, we call it Reliance Games Analytics (RGA). We track the game sessions of each device and user the moment the app gets download and opened the first time. Data gets processed in the analytical platform and we use the same for insights and decision making once it is available in visual analytics tool.
The second major area is OTT. Analytics uses here big time to understand the consumption as well as recommendations. The third is Image market place that we have launched recently. There we use analytics hugely for search and recommendations, along with content decisions.
We are thinking of extending analytics to all of our businesses, especially traditional businesses like films.
Exciting journey ahead
Reliance Entertainment journey on analytics and some of the adapted solutions. Very long and interesting journey so far! Media & Entertainment is not an easy industry for CIO. This is the medium where business is getting disrupted rapidly because of digital wave. Large-scale transformations are required to run the business ahead of competition. If you are behind in technology here, you are going to vanish.
For my business, which is primarily B2C, content quality and customer experience is directly proportional to revenues. One of the primary objectives of bringing digital transformations is consumer experience. Since we have a range of digital businesses with almost a 100-million user base across the globe, getting user insights on all of the products getting used is of prime importance. This is where analytics helps. We brought analytics into the picture long back; it is the same story as Cloud adaptation, designed end-to-end analytical solution at a very early stage compared to market status at that time. Being a very early adapter of this tech, there was struggle at the initial stage when we conceptualized this, as there was no end-to-end solution available as it is today. Since technology itself was evolving, there were vendors who came with solution in pieces. Also, being in gaming industry it is more difficult to get solutions. Hence, we decided to design the entire solution stack ourselves, starting from collection of data (data volume is huge), modification of code in game to achieve the storing of events in device itself till they connect to net, and use vendor solutions like platform, hardware like storage and visual tools into it. Over the period, we have developed an ecosystem around the platform, which we call the Player Management Platform (PMP), to automate the system, like push notifications, identifying potential spending customers. Now we are almost at the verge of upgrading this to live analytics, where we can see what is happening in the product and user behavior on live bases.
Future roadmap of the company for analytics in the coming year – Some of the key areas we are focusing on.
As stated above, in our analytics journey, we are primarily focusing on automation and bringing AI/ML components to our digital ecosystem. There is a big focus is on live analytics and building AI component around it.
We are working on simplifying the collection mechanism by adapting serverless computing and planning to use max compute kind of platforms instead of traditional HADOOP we built earlier to get more efficient and faster output. Although we had built a large data-driven analytical ecosystem for all sorts of actions and corrections in the product and user experience, there was a need to automate the actions, more insights, and train the system based on data patterns. To achieve this objective, ML and AI mythologies have been adapted. We are building large algorithms around the data system and training the system to be the best. The objective is to make the system perform at its best without manual intervention; the focus is on retention of users that will yield more revenues. We are working big time on AI at present. AI is going to be one of such transformational technologies; if implemented in right manner, it definitely yields results. Below is infographic (only analytical ecosystem) of what we are implementing now.
I want to put in three simple words what will be the task ahead: Live Analytics, ML, and AI. To summarize in one word, Automation.
Data security. Data security and privacy is of late a very talked-about subject. My view is simple; when you are dealing with data, you have to secure it – there are no two opinions about it. We at Reliance Entertainment are predominantly B2C business. When it comes to digital business, it is a global business and no doubt it is more of data-driven and there is lot of data we collect and play around with. Fortunately, for us, during designing the digital transformations, both at physical as well as logical levels, I have taken security as core to design. So, we have not faced issues when it comes to implementing new global guidelines like GDPR.
The core elements of data security are confidentiality, integrity, and availability. Also known as the CIA triad, this is a security model and guide for organizations to keep their sensitive data protected from unauthorized access and data exfiltration.
There are a few data security considerations we have on our radar:
- Where is your sensitive data located?
- Who has access to your data?
- Have you implemented continuous monitoring and real-time alerting on your data?
Considering and designing your data network and policy is necessary, especially when you are dealing with huge volume of data.
Building up the NLP and AI play at the company. I would say we as a technology are more into ML stage. I won’t say AI because AI is a much greater subject of discussion. More of statistical methods and NLPs/algorithms are getting developed to make data learn the patterns and take the decision based on the outcome of those patterns.
To elaborate a little more, as one of the use cases, we are working on ML/AI options for specific cases like gamer churn predictions.
Use-case. Predicting whether a specific class of paying gamers (e.g., Whales) (Ref Matrix question) would churn or not after a specific duration of time window.
Impacted Metrics: Gamer engagement.
Propensity to convert model:
Use-case. Predicting whether which non-paying users (NPU) has a better probability of being converted into a paying user (PU).
Impacted Metrics: Gamer acquisition.
For any of AI/ML ecosystem to be in place, you need to have strong analytics system in place. The second factor is data; you should have proper, authentic, and enough data. All of successful AI/ML solutions have to build around these two ecosystems. If you see below infographic for above use case of AI for us, you understand where I am coming from, we are building strong training and deployment modules around the analytical systems and data.