Discovering detailed preferences
For McLaughlin, providing offers to customers isn’t just about increasing sales. Instead, he sees it as a helpful service that introduces customers to a product or service that they are sure to love. “We call this ‘maximising serendipity,’ simply increasing the chances that we show our customers something that will be of interest to them,” he says.
Using Adobe ID to help identify customers across channels, Sky UK can learn a great deal about customers based on their app usage and TV-watching habits. The digital analytics team traditionally built rules to divide customers into different segments, but even with Adobe Experience Cloud, it was time-consuming to define truly detailed and personalised segments.
For example, few customers are fans of all sports. Generally customers have specific sports and teams that they follow closely. Trying to recommend the Sky Sports propositions using a sport or team that the customer doesn’t support tends to not be very effective. At the same time, marketers could spend weeks writing individual segment rules and still not account for all possible combinations of favourite sports and teams.
Rather than building out rules and segments, Automated Personalisation within Adobe Target uses machine learning to organically discover a customers’ sports preferences and deliver much more effective and appreciated product recommendations.
“Adobe Sensei is the only way for us to build, maintain and deliver hyper-focused offers across channels,” says McLaughlin. “It gives us the chance to surprise and delight customers with recommendations that are not only relevant, but wanted.”