Source: Sky U.K.’s McLaughlin Taps Digital Data For Omnichannel Customer View

Established businesses often come under fire for being slow at transformation, but a complete—or omnichannel—customer view should help speed up the process and benefit the bottom line. That’s the view of Rob McLaughlin, who joined Sky U.K. from DigitasLBi less than a year ago to head up the digital analytics function within the Insight and Decision Science division. His job is to build up the media and entertainment company’s customer intelligence capabilities.

In conversation with recently, he explained the framework underpinning the approach and the outcomes of combining well-organised offline customer data with digital. It’s a challenge familiar to many in marketing and technology today, so we began by asking him about the role he plays.

McLaughlin: The Insight and Decision Science division was previously Sky IQ—a joint venture between Sky and Experian—but it’s now fully integrated into the business. We’re the guardians of all forms of customer data, from when it’s created in the acquisition phase in the early stages of the customer relationship, through to engagement and retention with that customer.

My role as head of digital analytics is a new one, and I was recruited specifically to fulfil the objective of exploiting digital data to bring value to the business. The approach I have to achieve this is through creating an omnichannel view of our customers. Why do we want to do that? So that we can drive improvements in customer experience but also drive business benefits, whether that’s working out how to service our customers, what content to buy, or what products and propositions to develop.

I have stakeholders across the business, from advertising and marketing for acquisition purposes, through to upgrade and cross-selling, customer experience, brand and in-life servicing, to help and support for our customers. And then, further too, across the content programming to understand what our customers are watching and what there is a demand for. We have a very structured engagement model, and the division is horizontal to ensure all stakeholders have access to this customer data capability. Why is an “omnichannel customer intelligence” so important to you?
My team’s work is driven purely by the belief that, in order to understand the customer, we need to see their behaviour at all touch points with our organisation. If we only take a limited number of the engagement points, we believe the approach would be fundamentally flawed.

We understand the need for optimisation and making individual channels such as websites and apps work more efficiently, but, when it comes to understanding customers and how best to service, support, or to sell to them, we can’t have silos of behaviour hiding anywhere. That’s why we have to get the full view of their interactions.

It may sound trite, but our approach is to be customer rather than channel-focused. Digital for us is a set of channels that includes web, app, and interactive TV—they’re super important, and we want to use the data from those channels—but it’s only powerful if it’s customer-centric, and that means natively bringing it alongside the offline data. When I say natively, I mean conceiving and creating digital data, which, at source, is designed to fit with its offline relatives.

The prize we seek is to optimise the customer relationship in addition to optimising channels per se. We can certainly make ourselves far more relevant to the bottom line of the business that way rather than just being very good at optimising websites or apps. Real margin can only be found at the customer level. What’s your framework for gaining the full picture of the customer?
Some of the established approaches to customer intelligence—what I call “old-school”—are often overlooked. These have typically been developed for complex customer and business needs in the offline world, but they’re common to digital channels too. As such, they have the potential to deliver against the nirvana of omnichannel customer intelligence and on the customer view.

If you take customer identification, for example, customers are creating plentiful data in all channels. Every interaction and associated meta fragments amount to many petabytes of information, but, without a key to tie these pieces of data to a customer, they’re meaningless and don’t contribute towards any form of customer intelligence. It’s, therefore, essential to be obsessed with the customer identifiability. Identification is not a new concept, we have simply seen an increased need for adherence to customer identifiers/keys as digital channels have proliferated and the lines between anonymous, known, identified, and verified have blurred.

Data complexity is a natural result of the large number of channels customers interact with. Unearthing and acknowledging the commonality across these channels in customer triggers, actions, and outcomes have helped us create a common currency for customer journeys when constructing our omnichannel model. It effectively means customers can be understood across diverse channels but in the same terms of reference, and it prevents us from categorising the different channels around just an action or as a location. Fundamentally, most interactions, whichever on or offline channel they occur within, rely on the same back-office systems, and these have been a great source of commonality to track back to.

There’s often a big demand from within businesses for data and decisions in real-time—but you have to accept that not all data will be created and available in real-time, and not all decisions will be made in-channel in real, or even near-time either. If you, instead, set a universal macro-tempo for the omnichannel customer view, you avoid forcing all systems and processes to perform at speeds that are not native to them and don’t create misalignment in data latencies. Frankly, I have found the business requirements for real-time data and/or decisioning to be shallow, and we treat each requirement on a case-by-case basis, ensuring investment is limited to the necessary applications.

These are some of the ways we’ve built our framework and built the intelligence. It’s been a big step for us. Do you have an example of how the approach has already benefited the business?
We’ve uncovered areas where we can more effectively use digital channels to service customers, which is better for them but also of lower incremental cost to Sky. We have done this on an individual customer level, allowing for their behavioural history and preferences to drive our recommendations and actions in both on and offline channels. This allows us to sensitively target services or propositions to customers for whom they are most relevant. And, then, in cross-marketing attribution, we’ve uncovered opportunities to drive more effective courses of actions, such as how our customers choose to buy our product, whether through our contact centre or the web, app or TV. We can now select the course of actions we give them to drive them to purchase in the channel that is both easiest for them, but also most efficient for us. What’s the focus for you now?
We need to drive omnichannel intelligence deep into our day-to-day operations so that, as an organisation, we can answer the big, infrequent questions such as what products to develop and what content to buy next, but also those smaller, very high-frequency decisions for the customer such as which offer to present to a customer or what content to recommend to them. That’s our long-term aspiration, and it’s something we expect to be working on for the foreseeable future.


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