Mounting consumer expectations and competitive pressures have created a new reality for marketers: Personalization is no longer a luxury but has become a basic standard of service in today’s digital economy.
To serve relevant experiences, companies have typically adhered to an approach known as rule-based personalization, which utilizes IF/Then logic to tailor the customer journey according to a set of manually programmed targeting rules.
But for brands seeking to scale their personalization efforts, relying on an entirely manual approach to determine the most optimal experience isn’t always efficient or manageable. That’s why many brands are gravitating towards machine learning algorithms to assist in the decision-making process.
Both approaches offer distinct advantages – which is why organizations should work with these solutions in tandem, rather than jettisoning one for the other.
The beauty and limitations of rule-based personalization
How does rule-based personalization work? Say a visitor lands on a brand’s homepage for the first time. If this is the case, then the site will showcase a welcome message in the hero banner. Layering in an additional audience condition, if the visitor is new and is located in Ireland, then the homepage hero banner will feature a welcome message with Ireland-specific content.
These conditions, which can range from simple to complex, are all set by humans, not machines. This is a key factor behind the success of rule-based personalization initiatives, as marketers bring to bear deep industry and brand knowledge that AI may struggle with. Tasked with devising such rules ensures that the segmented and contextualized experiences a brand delivers are based on intuitive insights and real-world experience.
However, this can easily become a tedious, data-heavy task, involving numerous test deployments with granular measurements of every tested variation against each audience segment in order to determine optimal programmatic targeting rules. Ultimately, no matter how mathematically inclined a marketer may be, there will always be a limit to how many segments can be managed before it all becomes too complex. With an overwhelming number of combinations and permutations, selecting a winning variation in the face of a constantly changing customer base becomes nearly impossible. This is where machine learning-based personalization comes in.
When to incorporate machine learning-based personalization
Through machine learning, brands can automate the collection and interpretation of customer insights, with algorithms or decision-making engines determining which variation a customer will be served based on performance. While this approach involves less human input than traditional rule-based personalization, the intention is to augment the marketer, not replace them.
Instead of faithfully deploying a “winner takes all approach,” whereby a single winning variation is implemented across the entire visitor pool upon reaching statistical significance, machine learning can be used to analyze the performance of each variation across every traffic segment in real time to serve the most relevant content to select audience groups. This makes machine learning-based personalization more, well, personalized, as one variation cannot be suitable for all visitors – deploying experiences in this way will always compromise the experience for a portion of visitors.
Crucially, optimization via machine learning saves significant time and resources when it comes to A/B testing, making it a substantial boon to productivity and the bottom line. Take a holiday or back-to-school promotion. Instead of running an A/B test and trying to optimize the customer experience on the fly, machine learning algorithms make it possible to predict positive outcomes for each individual and thus maximize revenue over the duration of the entire campaign. I implore marketers to run short-lived experiments such as this, comparing the optimization mechanisms against their control group and then validating their results.