30 second summary:
- Personalization has become a critical differentiator for brands that are hungry to deliver a relevant customer experience.
- Brands have typically relied on rule-based personalization and manually defined the if / then logic to deliver experiences to specific audience segments.
- However, a rules-based approach can quickly become very complex and ultimately untenable the more segments, experiences and variations come into play.
- Machine learning makes personalization easier to scale decision making and increase efficiency when human processing power is limited.
- The symbiosis of rule-based and machine-learning-based personalization offers brands the best approach to control critical aspects of the customer journey while delivering customized experiences
Rising consumer expectations and competitive pressures have created a new reality for marketers: Personalization is no longer a luxury, but a fundamental standard of service in today’s digital economy.
In order to provide relevant experience, companies have typically followed an approach known as rule-based personalization. The If / Then logic is used to adapt the customer journey according to a series of manually programmed targeting rules.
However, for brands looking to scale their personalization efforts, it is not always efficient or manageable to rely on a completely manual approach to get the best experience possible. Because of this, many brands are leaning towards machine learning algorithms to aid the decision-making process.
Both approaches offer different advantages – which is why companies should work with these solutions instead of discarding them for each other.
The beauty and limits of rule-based personalization
How does rule-based personalization work? Suppose a visitor lands on a brand’s homepage for the first time. If so, a welcome message will be displayed in the hero banner on the website. If the visitor is new and located in Ireland, a welcome message with Ireland-specific content will be displayed on the homepage hero banner.
These conditions, which can range from simple to complex, are all set by humans, not machines. This is a key factor in the success of rules-based personalization initiatives as marketers bring in deep industry and branding knowledge that AI may struggle with.
The task of developing such rules ensures that the segmented and contextualized experiences a brand delivers are based on intuitive insights and real experiences.
However, this can easily become a tedious, data-intensive task that involves numerous test deployments with detailed measurements of each tested variation for each audience segment to determine optimal programmatic targeting rules.
No matter how mathematical a marketer is, ultimately there is always a limit to the number of segments that can be managed before it all gets too complex.
With an overwhelming number of combinations and permutations, choosing a winning variant becomes nigh on impossible in the face of an ever-changing customer base. This is where personalization based on machine learning comes into play.
When should personalization based on machine learning be integrated?
Machine learning enables brands to automate the collection and interpretation of customer insights, with algorithms or decision modules determining what variation is served to a customer based on performance. While this approach requires less human input than traditional rules-based personalization, the intent is to expand, not replace, the marketer.
Rather than faithfully employing a “Winner Takes All Approach,” where a single winning variant is implemented across the visitor pool after statistical significance is reached, machine learning can be used to analyze the performance of each variation in each traffic segment in real time and analyze the most relevant content to choose from of target groups.
This makes personalization based on machine learning more personalized, as one variant may not be suitable for all visitors. When you deliver experiences in this way, the experience is always compromised for some of the visitors.
Crucially, machine learning optimization saves a lot of time and resources when running A / B tests, which greatly improves productivity and the bottom line. Go on vacation or back to school.
Rather than running an A / B test and trying to optimize the customer experience on the fly, machine learning algorithms allow predicting positive outcomes for each individual, thereby maximizing sales over the duration of the entire campaign.
Marketers should run short-lived experiments like this one, compare the optimization mechanisms to their control group, and then validate their results.
The best from both worlds
For all the benefits, algorithmic decision-making is not inherently superior to rule-based targeting.
Remember, marketers bring to the table the unparalleled insight and judgment always required to set the strategy and logic for these campaigns. It is the implementation of rule-based and machine-learning-based personalization side by side that achieves the best results.
Brands today face a number of requirements: Turning first-time visitors into customers, winning back customers and gaining loyal customers who offer the brand a high value for life.
You need to accomplish these goals while taking into account the inevitable changes in the preferences and interests of these customers.
While marketers and merchandisers always play a crucial role in determining a brand’s vision, identity, and product choices, numerous tactical decisions can be made including: B. Which motives should be used, in which order products should be marketed and which emails should be sent to customers, which can be controlled by AI engines with far superior results compared to people who make such decisions without the support of machines.
These systems expand human capabilities and create new opportunities for retailers by increasing the most important aspect of the trade: relevance. The more relevant and engaging an offer or range of products is to a particular person, the more likely they are to buy, enjoy their purchase, and be more committed to the brand.
Rules-based personalization will continue to serve as an indispensable tool, giving marketers the ability to control which audiences are offered a particular experience. In many cases, it remains the most logical approach to contextualizing parts of the customer journey.
However, as brands want to scale personalization, machine learning becomes essential. So the question for brands is not which path to take. Instead, why isn’t it both?