Thursday, 17 April 2014

DTH Analytics - A New Opportunity

Took a small break from blogging and I am back with an idea which could create an impact in the market and also it would turn out be a new business frontier in the Analytics industry.
Had spent some time on our new 3D LED Television and that’s where I got this idea on the DTH Analytics

DTH Analytics

With more and more people opting for DTH, the consumer base for the DTH providers is growing exponentially and it is something similar to the trend that we saw on the Telecom Mobile industry in the late 90’s and early 2000. Now almost each and every citizen has a mobile phone and we can expect to see the DTH usage by each and every citizen in 5 years from now.
The unique aspect of the DTH is the amount of data that it is capable of collecting/capturing from each subscriber. Now, it is possible to see the subscriber viewing behaviour, subscriber subscription to the Movies on Demand, Packages, Channels, Add-ons, Recorded, Scheduled, etc. It creates large chunk of data to be explored and it becomes a whole new opportunity for the Data Analytics business organizations to create a value-proposition for the customer and the business by bridging the gap of the targeted marketing

"Wouldn’t be cool to get a personalized Movie on Demand Price while viewing the TV"

"Wouldn’t be cool to get a recommendation about a low price package or value-based pack on the subscription price based upon the viewing behaviour of the subscriber"

"Wouldn’t be great to receive a discount voucher on the TV based upon the viewing patterns"

The answer to achieve cool things is yes, it is possible with the data and data would be available for the DTH subscribers. Two important questions to answer here.

a.       Offering all the customers a discount voucher on the payment or channel packages irrespective of the pre-determined probability of accepting the offer
b.      Offering only to those customers whose propensity to accept for a given offer is high. High probable customers

Marketing effort is always to optimize the marketing spent and maximize the revenues from any effort. I am trying to emphasize the second point assuming we have the data pertaining to the previous offers made. Even, if we do not have the offer related data then it is task to understand the behaviour ascertain the likely probability and offer. The ROI in any case would be higher as compared to the intuitive campaigning.

For example, we can create the following based upon the subscriber viewing behaviour, his scheduled recordings, his payment, frequencies, etc
1.       Tailor made messaging about the events, discounts, food festivals, etc.
a.       If the subscriber’s viewing pattern is more on fashion related channels and he/she has the scheduled recordings on fashion related episodes then may be offer a discount voucher at a nearby fashion store on the purchase
b.      Assuming that we have the earlier offer data, we can compute the probability but even if the data is not available then learn deep/mine data to compute the probability
2.       Tailor made selling propositions(packages, channel packs, etc) to the subscriber
3.       Tailor made advertisements to the user based upon the preferences
4.       Tailor made Movie on Demand or Video on Demand Packs based upon the Subscriber’s data

5.       Customized Channel Pack to the subscriber based upon the Subscriber’s payment history, Demographics, Viewing Patterns, etc

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