Sky's James Alexander talks about the importance of advanced data and analytics in improving pay-TV customer service

The Pay-TV Innovation Forum is a new global research programme for senior pay-TV executives, developed by NAGRA and designed to explore and catalyse innovation across the pay-TV industry, at a time of unprecedented change. As part of the programme, we are publishing a series of interviews with leading pay-TV industry executives from around the world to explore their views, perspectives and experiences of innovation in the pay-TV industry.

Pay-TV executives recognise the importance of customer and audience data, but agree that very few companies historically have had the scale and resources to harness it. However, there are strong signs that the pay-TV industry is likely to see significant developments in advanced data and analytics: The 2017 Pay-TV Innovation Forum survey found that strong customer and market insight capabilities as well as big data and analytics solutions were among the top innovation priorities for pay-TV businesses in ensuring they are fit-for-the-future and well-positioned to innovate successfully.

In this interview, James Alexander, Decisioning Director at Sky, talks about the importance of advanced data and analytics in improving customer service and shares his advice for pay-TV providers who are trying to implement advanced data and analytics.


What are your key priorities in terms of data and analytics at Sky?

My focus is driving the use of data across the organisation to tailor customer interactions, to improve their experience and generate incremental value to the business. I firmly believe this is critical to Sky’s continued success. To achieve this Sky is going through a series of ‘mini‘ transformations: Around our data infrastructure and migration to the cloud; rebuilding key operational systems to enable tailored interactions; increasing our analytics focus from insight towards providing actionable, smart customer treatments; and business operations change to take advantage of the new capabilities.

Which types of data are the most valuable?

Our focus is on first party data for now. Historical customer transaction and online interaction data are at the centre of most of our current activity. Viewing data holds a lot of promise but something we are at an earlier stage with. Social and third-party data is interesting, but we have more than enough opportunity to keep us busy without really adding them into the mix. We also try to think expansively about the data gaps we have – for example, caller emotion and how that might impact how we tailor the interaction – and how we prove the business case to start capturing that data on an ongoing basis.

How important are data and analytics to the wider pay-TV industry?

In the long run, data and how it is used will be critical to success. Do this right, and you will create opportunities to deliver better customer experiences, to run your business more efficiently and to improve performance of your business operations. Traditional pay-TV providers have historically used data analytics for business intelligence and a bit of ad hoc analysis. In contrast, data-native companies, such as Amazon, Netflix, or even Now TV, have data embedded in their organisations, products, and interactions with customers. Today, along with many other industries, traditional pay-TV providers are on a journey towards that data-native end of the spectrum.

How would you describe your vision for data-driven customer interactions in pay-TV businesses?

I think a lot of people in the industry share a very similar vision. It’s about having an accurate and comprehensive picture of an existing customer or prospect, with relevant data being available on a robust and timely basis. It has to involve a deep integration between data, analytical models or rules running on the data, and dynamic tailoring of customer interactions – through products and systems that have been built with data front of mind. Lastly, you will have an organisation who embraces this opportunity, thinks data first, thinks ‘test, learn and iterate’ and does so very efficiently. All of this is driving towards customers who consciously, or subconsciously, benefit and appreciate improved experiences and journeys.

What do you think the timeline is for traditional pay-TV providers to become data-native?

I think most companies are making progress towards building enabling capabilities. But it’s also about transforming mindsets and ensuring that the whole organisation – rather than only the insight team and the C-Suite – understands the possibilities in this new data-centric world. This takes just as long, if not longer, as building capability.

Based on your experience, do you have any advice for pay-TV providers who are trying to implement advanced data and analytics in their businesses?

Overall I would recommend focusing on making smaller, pragmatic gains towards the target end-state rather than getting too tied up in delivering the perfect, all-encompassing solution at once. Within this there are a few important things providers should consider:

  • Strong senior sponsorship.
  • Table stakes is having a certain minimum level of core capabilities: Data that is readily available and of sufficient quality and range to enable pay-TV providers to tailor customer interactions; and the ability to execute these interactions at a customer level.  If this requires investment, go back to the previous point – strong senior sponsorship.
  • Try not to get overly hung up on developing the perfect system, for example, one that deals with every possible aspect and variant of a customer interaction, or is harmonious and consistent in real time across multiple interaction channels. Building something simple but reasonably effective, with one eye on how it could evolve towards the ultimate goal, will allow you to deliver value much quicker.
  • Choose your focus area carefully and demonstrate value quickly. It is important to understand what your business is already doing to solve the problem you’re trying to solve in an automated and data-enhanced way. Sometimes it might be that the existing manual and human-led solutions, albeit inefficient, have already harvested most of the potential value and there is not much sense in developing a solution on top of what’s already in place. You may be able to deliver more value in other areas and in that way increase buy in for the overall transformation.
  • Don’t forget about the human. A lot of our complex interactions are call-based – and here our job is to help and enhance the agent’s conversation rather than to remove them from the picture. This means being flexible and getting agent’s input on what works for them.
  • Don’t get too distracted by real-time, machine learning, AI, etc. – at least initially. These all have a place and can add real value, but the basics can get you a long way.