How Sky Betting is using customer identity to bridge the owned and paid media gap

Sky Betting and Gaming UK says investing in Signal’s customer identity platform is allowing it to turn paid media into “display CRM” and finally join the dots between a customer’s journey across paid, owned and earned channels.

The UK betting and gaming provider is among the latest customers to sign up to Signal’s Customer Identity Solution following its official global launch this week. The platform is pitched as an agnostic offering that unites data from across the enterprise into a single customer identifier and management solution. This then acts as the engine to better targeting audiences through real-time media and marketing activity.

The platform’s key selling point is the ability to build a customer data foundation based on a deterministic identity graph. Persistent identities are created based on a variety of sources such as first-party data, browsing and behavioural signals.

Sky Betting started trialling the Signal platform last year and signed a commercial agreement in early 2017. Its head of data, Andrew Walton, told CMO the objective was to join up all customer and behavioural data across devices, applications and properties to have an addressable identity the team could use to better target content through paid and owned channels.

Sky is a huge entertainment and sports brand, and Sky Betting is the largest online betting provider in the UK in terms of customer volume. Being part-owned by the parent company, the Sky Betting business has access to Sky’s portfolio of online properties and sports and its user base. In addition, Sky Betting has its own sites, which Walton said have expanded massively in recent years thanks to growth in both the core gaming platform, as well as ‘free to play’ games.

“We have huge contact point with a large percentage of the UK gambling population both through our existing base or those betting with competitors,” he said.

A single customer identity

Using Signal, Walton said Sky Betting’s initial ambition is to better address prospects and existing customers digitally through paid digital media, pushing out more relevant offers based on their sporting preferences, behaviours and level of engagement in real time. If an individual is browsing information about a team, for example, marketing could push out welcome offers for that team, Walton said.

“It’s also about talking to existing customers. We’ve tended to use our online and social channels as acquisition channels. We didn’t talk to customers directly – we’re putting a lot of spend out there with media but not being clever with it,” he said. “It’s about starting to treat these channels – online, inventory, paid media, social – as display CRM and complement our traditional CRM channels.”

Sky Betting previously did some retargeting using front-end behavioural data signals. The problem was it didn’t take advantage of the first-party and complementary data sets the business has built around customers and consumers, Walton said. Sky Betting’s customer segments are based on sports preferences, frequency and preferred betting products, supported and augmented by a customer research team.

Signal allows the team to join up identity across devices, by looking at where customers are engaging with the brand, or logging into their account, and deterministically identify them. The key is engaging with customer at the moment of intent.

“We have segmented the customer base into segments, and have propensity, cross-sell, models and so on that we can load behind the scenes and into Signal and join that together with the front-end data and real-time activity,” Walton explained.

While Sky Betting is just at the start of activity, early trial campaigns using addressable advertising generated an up to 15 per cent increase in customers placing bets.

As an example of a campaign, Walton said Sky Betting may work to encourage a light user to bet on one extra game per week. Using Signal, the team can now not only show that customer inventory that encourages that in real time, they can also stop serving them creative in real time if it’s no longer necessary.

Getting the data foundations right

Just putting in a new technology platform isn’t the whole answer, however. Three years ago, Sky Betting began building out a Hadoop database cluster to improve on its traditional data warehouse environment. This allows for bigger computations and running models at scale, Walton said.

Structurally, the business also made data a shared services function, working across its two internal divisions: Betting and gaming. The team includes data scientists, devops, management, technical resources and the CRM team.

On top of this, Sky Betting has invested in MediaMath’s DSP in-house to buy media programmatically. The next focus for Walton is bringing on a dynamic creative optimisation platform, which can be integrated with Signal to help automate decisions around activity and again, personalise what customers see online.

“Then it’s about expanding this to other channels, such as social and in particular, Facebook,” Walton said. “We do have the ability to use client-side tags for Facebook campaigns, and flow attributes or load up custom lists to build custom audiences. We want to use our ability to have custom audiences based on deterministic hashed data and have Signal automate that process to real-time opt in and out of Facebook campaigns.”

The holy grail is that a high-frequency football customer would see consistency of message regardless of the channel and device, Walton said.

“We’re aiming for that over the next 6-12 months and to have that all routed through Signal,” he added.

Maturity of customer data management platforms

Signal A/NZ managing director, Warren Billington, said the Australian market has been leading usage of the vendor’s platform, with many applications of the technology led by work done locally. One of these has been work done by Signal with Coles and its FlyBuys program.

“The importance of customer identity and identity resolution is among the key challenges for marketers, but it’s also becoming a fundamental objective to solve for organisations,” Billington said. “It’s about how they identify customers at a level of reach and scale, wherever those customers are - whether they’re interacting across owned and operated environments, or wanting the ability to reach and target off network through paid and earned media.”

Competing technologies, such as marketing technology stack organisations, offers these capabilities as point-based solutions. “But the big challenge has been how to harness my first-party data and customer identity and apply that through to adtech,” Billington said. “Those areas have almost worked in perfect isolation.

“We see our technology as allowing marketers to leverage these customer identities… and build a bridge between martech and adtech environment, as well as through the entire enterprise, even to traditional channels such as call centres and retail.”

Billington agreed the customer data solution space was a “noisy space”, thanks to rise of customer data platforms, and moves by the likes of Adobe and IBM to solve the data unification challenge – Adobe through its common data language framework discussed at the recent Adobe Summit; and IBM through its Universal Behaviour Exchange.

“There are attempts being made to drive convergence between martech and adtech. But I’d still argue the ability to identify customers and activate against those will work fairly well within those technology one company is providing, but if you want to leverage that identity through other technologies, it’s much harder,” Billington said.

“Marketers don’t just want to buy Adobe technology, they want to buy best-of-breed. Ours is a more independent approach that’s giving marketers control over their first-party data, allowing them to build customer identity and leverage it as they choose.”

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