Where you have a 35% chance of success for a TV show, a Netflix original holds 93%. Why? The brand excels in utilising customer data to offer powerful recommendation algorithms for the right subscriber at the right time.
Most brands turn to Netflix as an ideal case study for acing personalisation through recommendations at scale, and they should.
During its early days, the Netflix Recommendation System transformed from a regression problem that predicted ratings to a page-generation problem, and then to maximising user experience. Soon after, the streaming platform began using several algorithms, such as personalised video ranking, Top-N Video Ranker to Trending Now Rankers — this was 2020. Everything that could be personalised was personalised. But the brand hasn’t stopped optimising.
More recently, Netflix developed a new ML algorithm based on reinforcement learning to create an optimal list of recommendations by considering a user’s finite time budget. Why? In a recommendation use case, often the factor of limited time to make a decision is ignored. To address the issue, it added this new dimension to its system.
Nate Burke, CEO at Diginius, says, in theory, the formula for achieving personalisation is simple. But, “in reality, success requires so much more than printing a name on an everyday household object.” Burke says brands must allocate powerful resources to understand customers and develop offerings that suit them.
A Data-centric Approach For The Win
Everything from brand experience and content to the functionality of the final product or service must be designed and developed by keeping each end-user in mind. The first step is to collect customer data points effectively.
For instance, Netflix collects several first-party data points from its subscribers’ data, from the user profile information to the time and date when the user watched a specific title, the device used to stream the content and watch patterns. The insights are fed into a feedback loop to improve the products or customer service.
Businesses operating across various sales channels might need help collecting data representatives of multiple customer segments. “Online customers are likely to be different from in-store ones. An offering personalised to the latter is only guaranteed to feel customised to the former, or vice versa,” says Burke.
Meanwhile, image recognition capabilities allow brands to recognise specific colours and visual patterns within product images. Using this technology, brands can offer personal recommendations to customers based on their tastes and preferences. Similarly, brands use AI/ ML technologies beyond the traditional use cases to get greater control over how they engage with customers online.
The crux of personalisation remains that customers forge stronger attachments with products or services that appear to be specially tailored for them. For brands, encouraging these profound connections will create a loyal customer base and earn more significant lifetime value.