While the use of predictive analytics varies from company to company, industry to industry, its outcomes are nearly similar: It helps harness the power of data to have better business outcomes.
Media and Entertainment (M&E) companies – without exception – are into an increasingly competitive phase where consumers are swamped with content. This is a dominant factor driving the need to be smarter, efficient and agile in serving the content and right-sizing to the need. However, the M&E revolution in India started late as compared to many of the developed nations. There is a significant gap in adoption of disruptive business models as well as advance technologies to cater to the consumers. Now that the companies have started witnessing the benefits of new technologies, and the need for digital transformation, they are keen to be set up against global benchmarks. It is interesting that because a lot of things can’t be done by humans at a speed that the business demands, technology is the only alternative. In M&E industry, content becomes stale faster. To stay relevant to the consumers and cater to their dynamically changing needs it is important to automate, transform and advance.
Bridging Gaps with Predictive Analytics
Bennett Coleman & Company Limited (BCCL) or Times Group, as known popularly, is a 185-year old organisation. With over 100 print editions, multiple broadcast platforms ranging from news to movies to entertainment, many pure-play internet-based platforms and B2B platforms, innovation is that one force that has kept the organisation moving.
“From a technology adoption standpoint, each of the business units has its own front-end delivery model to connect with the consumers. However, the backend engine is unified. The robustness of this backend ensures the content doesn’t go stale. Digital technologies have helped us in a big way to ensure the freshness of all forms of contents,” says Rajeev Batra, CIO, Times Group.
However ubiquitous the backend technology may be, each business entity has its own set of challenges that require a different skill set to solve them. In Times Group, the use of predictive analytics is quite diverse. Let’s see some of the interesting use cases.
Saving the cost of news print: “The raw material import/procurement (newsprint), because of the price volatility and consumption variation, is largely based on the data that we churn out deploying analytics engine. The use of historical data, combined with machine learning and big data analytics-based algorithms, helps us in predicting (quite accurately) what the newsprint pricing will be with an error rate of only 5 to 15%,” explains Rajeev. This has helped the group save both cost and wastage of newsprint, which is perhaps the costliest commodity.
Bettering reader engagement: Times Group has put predictive analytics to create a very innovative and unique use case. “We have deployed newsroom analytics. The huge digital screens in the newsroom show which author’s story is trending on top in terms of readership, social sharing etc. Based on the real time feedback, authors are able to incorporate changes to stay relevant,” says Rajeev. This use of analytics has resulted into an increase in stickiness of the content, reader engagement and has also created a better brand strength.
Predicting print copies: Another small but significant use of predictive analytics is in ascertaining the number of print copies of newspapers on any particular day. It is based on the analysis of historical data. It saves wastage of newsprint, which is an expensive commodity. “Because of the use of predictive analytics, the paper wastage is brought down to less than 1% from 4% earlier,” informs Rajeev.
Another area, where predictive analytics can work wonders, is advertising. There is a lot unpredictability and seasonality, which makes it complex. With proper segmentation and complete consumer profiling, hyper-targeted ads can do wonders. Harnessing the power of analytics means better targeting to reach to right people with right ads.
A Sneak Peek into the Future
Predictive analytics, as the science is today, generates future insights with quite a significant degree of precision. It is, and will be, a dominant feature of any futuristic IT architecture. However, these mechanisms of predictive analytics require availability of a lot of historical data to be analysed through machine learning and deep learning mechanisms.
There are still a lot of areas left in Times Group where predictive analytics can help immensely. But that’s all for the future. “With better processing of both structured and unstructured data, which is coming in from both internal systems and internet and social networks, we can harness it to bring in more predictability in terms of revenue generation of reader engagement and even IT operations,” says Rajeev. “The technology availability today is key to run business. Areas such as advanced persistent threats (APTs), applying analytics for zero-day attacks are great areas for future exploration of deployment of predictive analytics,” he adds.
Organisations like Facebook collect and processes 1000s of TB of data daily. Google processes billions of requests daily. It proves the rise of data volumes is going to be insane as we go into the future. Advance or predictive analytics is too big to be ignored. Predictive analytics, if applied rightly, can help overcome the biggest challenge in the industry – understanding consumer behaviour. “It will help the industry, including us, cater to customer preferences with utmost precision. That, in turn, will help build consumer loyalty, and also alternate revenue models,” concludes Rajeev.