Netflix has reached 130M subscribers worldwide with its SVoD service. Undoubtedly the use of analytics has been decisive in its success. The recommendation service, production based on predictive analytics, content purchasing based on analytics…
Operators have been slow to understand and implement analytic systems in their operations.
In addition, the TV service in Europe among the operators is traditionally looked upon as the poor service of the operators, because its margin is lower, its engagement among users is lower, and it has a strong dependence on the contents decided on by the channels themselves.
The use of analytics can enhance the service, and help compete against Netflix for the pay TV market.
The CSP has an enormous amount of customer data. In fact, alongside banking, it could be one of the sectors which can best categorize a household.
Particularly, with the television service it is possible to obtain a lot of data: on the one hand the Video On Demand consumption (available on the B/OSS systems), and also the consumption of linear channels (thanks to the applications in the decoders developed specifically for this). Combining this data with the rest of the operator’s data allows to profile each household and each individual user within it.
Moreover the operator knows the users geolocation. This allows for campaigns to be carried out in real time for example, recommending the right content at the right time. And other more complex scenarios.
However OTTs do not have that much information about their customers. It is possible that they don’t need it. Yet, they exploit the information they do have very well. Nonetheless the operator should use the information to improve the use and satisfaction of the TV service. Only by making the most of the data can they increase the use of the TV service and battle face to face against Netflix, HBO and other OTTs.
first : define use cases
Like any project which implies technology, success can only be guaranteed if it’s managed by a business case. Let’s take a look at some use case examples in which operators can enhance their TV service thanks to data.
example 1: profile of each customer
for each customer we can build a profile of interests for TV contents, to combine with the other data of their profile, not only to each customer but also to various users of the same service. This customer profile can be used by the actual operator to improve its campaigns, its customer service or prioritize its investments, to name a few. But it also opens up a wide range of monetizing possibilities of this data with public or private organizations.
example 2: marketing campaign improvement
One way to exploit the information obtained is by improving the efficiency of the marketing campaigns: we can offer each customer packages with relevant content which they haven’t yet contracted. We also get to improve customer experience at this stage: less offers but more suitable to each customer’s needs: Why send a customer an offer for Football channels if they have never watched a match on Television?
example 3: recommendation systems
We can build a content recommendation engine for each customer. When the customer switches on the televisión they will find recommended contents of their interest, not only in VoD, but also in the linear contents that are being broadcast or that soon will be. With the recording function available in most operators, we can create functions such as, automatic recording of events recommended for the customer.
example 4: recommendation in real time
We can analyze customer’s geolocation information and predict the hours they will be at home or even detect it in real time. At that time is when the operator would send them the recommended content offer and not at another time when the possibility of it being forgotten later is greater.
example 5: audience analysis
Traditional audience measurement systems have a large statistical error, the samples are very small and can include certain biases. However the operator can obtain the real audience of all its customers. In addition this information can be combined with the other profile variables.
This knowledge gives the operator an advantage when contracting contents that are relevant for their customers, reducing costs and maximising satisfaction with the available contents. Likewise they can reach agreements to jointly exploit the information with the channels, producers and other members of the value chain within the audiovisual field.
example 6: segmented advertising
Having the real audience data also enables monetizing. It is very valuable information for advertisers, who until now, have had to rely on traditional audience measurement systems. Now they can obtain real information about the kind of users that watch each channel, in what quantity and how they react to their ads.
It is also possible to include advertising for each user in the operators programming guides EPG. Years ago a television operator had a red button in the programming guide during football matches. When pressed, a pizza was ordered to a local chain restaurant and it was only required necessary to enter the user’s web data of the pizza chain. Just this case awakens numerous monetization possibilities in the TV service.
second step: retrieving information
To put this in motion, the operator needs to collect all the information available in the different areas of the company. Most operators have already started projects to advance in this step. However in many cases, they are not recovering all the available data nor are they always aware of the information that is available from certain fixed or mobile elements in the customers’ device.
With all the information, intensive work hours and intensive resource work begins; data cleaning, data anonymization, data merge and data analysis. These tasks represent 80% of the project time.
the important step: profiteering results
If the business case directs the project, the analytical phase and the generation of insights can not be left to last and must be the exploitation of the results obtained. It is essential that the results are incorporated into the company’s processes and systems. Customer profiling must be incorporated into the CRM to be visualised by the agents and allow them to interact more accurately with the customer. Appropriate marketing campaigns need to be generated within the campaign management system. And all these values must also be incorporated into the data warehouse to be taken into account in business intelligence.
Word of advice, do not allow the integration of insights into the systems and processes of the operator, remain for a later phase of the project. The ROI will not appear and there is a risk of it being cancelled.
Contact us to learn how Augura’s analytics TV solution, powered by Optare Solutions, can help you get more of your TV service with these and other use cases.