What is rule-based personalization, and why you should care

Over the last several years, delivering personalized customer experience has shifted from a smart personalization strategy to a mission-critical requirement. In fact, 72% of customers say that they only pay attention to content and messages that are customized to their specific interests, and 63% of customers say that they will abandon a brand that offers impersonal (generic, boring, one-size-fits-all) communication.

And we are not just referring to individuals who may be looking for everything from cosmetic products to resort hotel vacation packages. A growing number of B2B customers, which are typically represented by a group of 6 to 10 decision makers with sales cycles usually lasting several months, also expect a personalized experience, which is a trend known as B2Me personalization.

And so, the pivotal question that brands must answer is: How can we optimize our website to consistently deliver personalized customer experience in a way that is not just engaging and effective, but also scalable and profitable? The answer is by taking full advantage of rule-based personalization.

In essence, rule-based personalization enables brands and ecommerce retailers to create conditions that, once met, automatically provide customers with personalized, hyper-relevant content and messaging based on their purchase history, demographics, in-the-moment behavior, and other factors. Unlike dynamic personalization driven by machine learning algorithms, rule-based personalization operates on a set of specific criteria.

The engine that powers rule-based personalization is a customized database of “if/then” logical commands, which automatically determine what each customer — and more specifically, member of each customer segment — should see and experience based on known values and/or assumed values.

For example, a rule might dictate that new visitors to an ecommerce website should be greeted with a pop-up offering a discount on their first purchase.

These logical commands are rooted in conditions and actions:

• Conditions are local statements that determine if a certain fact is true or false
• Actions are logical steps that are implemented when a condition is true

Naturally, brands must define their conditions and actions before they can implement rule-based personalization. It is also possible to create combinations of multiple conditions and actions.

There are two main types of rule-based personalization: Explicit personalization and implicit personalization.

1. Explicit personalization
Rule-based personalization that uses known values is referred to as explicit personalization (sometimes referred to as historical personalization).

Data sources could include:

• Location (IP geotargeting)
• Online and offline campaigns triggered
• Referrer (channel)
• Date and time
• Search keywords (this data source is especially valuable when driven by AI-powered on-site search)
• Device (e.g., desktop, smartphone, tablet)
• Asset downloaded (e.g., ebook, white paper, checklist)
• Action taken (e.g., requesting a pricelist, scheduling a demo)
• Integrations with other systems (e.g. CRM, apps, commerce, PoS, PIM, ERP, social media login, etc.)

2. Implicit personalization

Rule-based personalization that uses assumed values is referred to as implicit personalization (sometimes referred to as intent-based personalization, contextual personalization, and adaptive personalization).

The key difference between implicit personalization and explicit personalization, is that the latter seeks to understand who a customer is, while the former seeks to grasp why a customer has visited.

Data sources could include:

• Web pages visited during the current sessions
• Content accessed during the current session (e.g., watching a video, viewing an infographic, reading a blog post, etc.)

Both explicit personalization and implicit personalization data sources combine to help brands determine why, what, when, and even how (e.g., hero banner vs. pop-up notification) each customer should be engaged and nurtured to move forward on the customer journey map that covers:

• Awareness: customers are fully or partially unaware of their options
• Research: customers are conducting research to determine their next steps
• Evaluation: customers are comparing potential solutions
• Purchase: customers are engaging in a transaction

It is also important to note that rule-based personalization using explicit and implicit data sources can help brands connect with customers after the transaction. This not only helps ensure customer success and satisfaction, but it can be exceptionally profitable when brands cultivate customers into loyal partners and energized ambassadors.

Enterprise-grade digital experience platforms feature an out-of-the-box library of both types of rules (and associated data sources) for multiple common use cases, which can be configured accordingly. They also support customized rules to support specific business goals.

Here are some simple examples of the different kinds of rule-based personalization:

• Type
• Condition (statements that are determined as true)
• Action
• Explicit (based on a known value that is not specific to a customer)
• A customer is in the U.S. based on their IP address
• Display the hero banner for U.S. target audiences
• Explicit (based on a known value that is specific to a customer)
• A customer has purchased a certain product in the past
• Display messaging and graphics that inform the customer of additional products that are related to their past purchase
• Implicit (based on assumed values)
• A customer has accessed the “customer testimonials” page of the website during the current session
• Launch a pop-up notification inviting the customer to access case studies that highlight quantitative and qualitative results
• Implicit (leveraging AI)
• A customer (who is anonymous) visits the website in the last week of October
• Harvest all available internal and external data sources to deliver content and graphics that have generated the highest conversion rates in the last week of October

These examples look at scenarios that provide or display something to customers who meet the condition. However, it is possible to hide content or elements as well.

For example, if the true condition is set as “a customer is in the U.S. based on their IP address,” then the associated action (and subsequent rule) could be “hide the hero banner for international audiences” or “hide the element that promotes product models that are only available in Europe.”

These examples also highlight that rule-based personalization does not rely on having conversations with customers to glean insights and clues about their preferences and intentions. This aspect is vital considering that 85% of customers will simply not communicate with a brand through any means (chat, email, phone, in-person) until they have spent at least some time “checking out” their website, and often across multiple visits using different devices.

Now that we have explored how rule-based personalization works on a basic level, and how it can be configured with some common explicit personalization data sources and implicit personalization data sources, let’s highlight the benefits of rule-based personalization.

Benefits to rules-based personalization include:

• Improved customer segmentation and digital relevancy mapping

This helps brands craft, curate, and deliver the most effective and relevant content and experiences (including display and layout) for all types of customers.

• Better performing calls-to-action (CTA)

Research has found that personalized CTAs perform a whopping 202% better than generic one-size-fits-all CTAs.

• More targeted and relevant product recommendations

Brands that use AI and machine learning-based “recommendation engines” to deliver personalized content often see a major increase in engagement.

• Increased time-on-site

KissMetrics.com points out: “Time-on-site is the best metric for determining how useful visitors find your entire website, instead of just focusing on one page.” In addition, time-on-site can have a positive impact on SEO rankings.

• Increased sales

Brands that excel at personalization earn 40% more revenue from those activities vs. brands that do not excel in this area. In addition, personalization typically drives 10-15% revenue lift.

• Increased customer loyalty and lifetime customer value

Fifteen percent of a brand’s most loyal customers account for 55-70% of its total sales, and referral leads convert 30% better than leads generated from other marketing channels and have a 16% higher lifetime value.

• Better omnichannel experience 

Customers who switch between devices can be welcomed back to the website as familiar friends vs. expected to start the journey all over as new strangers — which is something that some customers will simply not do.

• Optimized email campaigns

Brands can use advanced content management technology to gather everything they know about their customers, in order to personalize every aspect of their email campaigns (content, layout, frequency, dates, etc.).

• Optimized marketing strategy

By analyzing engagement values (initial settings and performance), brands can enhance efficiency across and between channels. They can also evaluate relevance (low engagement value typically indicates a lack of relevance and a need to make improvements), and ensure that digital marketing objectives remain in alignment with business objectives.

• Data-driven insights

Rule-based personalization generates valuable insights into user behavior and preferences, enabling organizations to refine their personalization strategies and optimize content delivery further.

• Continuous optimization

Rule-based personalization software enables organizations to continuously optimize and refine personalization strategies based on real-time feedback and performance metrics, ensuring that personalized experiences remain relevant and effective over time.

• Increased efficiency

Rule-based personalization streamlines the content delivery process by leveraging automation for the selection and presentation of relevant content, saving time and resources for organizations.

To wrap things up, let us look at some rule-based personalization best practices, including some that can lead to quick wins that boost momentum, buy-in, and excitement across the organization:

• While lower-level pages are often used to profile customers and gather insights, rule-based personalization is often much more effective on higher-level pages.
• Go beyond just providing mobile customers with a responsive site, and instead leverage explicit data (such as location) to craft rules that deliver an even more relevant and impressive customer experience.
• Create multiple conditions (which as we mentioned earlier are the scenarios that trigger actions and invoke rules), in order to increase the chances that a customer will be engaged vs. overlooked. In this way, if a customer does not satisfy the first condition, then they are tested against the second condition — and so on — until they trigger a rule that provides them with the right content and messaging that they are more likely to find relevant, agreeable, and interesting.
• While using rules to personalize content is vital, do not overlook the impact of personalizing how a component or element is displayed. It only takes .05 seconds (50 milliseconds) for customers to form a first impression of a website. Great design can help ensure that it is not the last!
• Speaking of first impressions: While all customers are important and valuable, it can be highly beneficial to create rules that give first-time website visitors a special welcome.
• Use rules to connect and retarget inbound experiences. For example, a customer who arrives on a website after clicking through an email can be presented with a hero banner or video that is connected to that specific campaign or promotion. Research has found that 63% of customers are positively influenced by personalized product recommendations on home or landing pages.
• Track and test to see how personalized content performs compared to default content when displayed to a specific audience. Performance in this context means the difference in percent between the trailing value per visit (TVV) of the personalized experience vs. the TVV of the default experience for customers who meet the

Brands just getting started with rule-based personalization may feel overwhelmed — especially if, like many organizations at the initial states of their personalization journey, they are struggling with:

• A fragmented mix of external and internal data sources
• Plenty of bureaucratic red tape regarding content creation and approval processes
• Confusion and conflict as not everyone understands how personalization works

These common obstacles can lead to what is known as the “personalization plateau”, which happens when initial personalization efforts and enthusiasm to drive and deliver personalization — and reap the enormous benefits described earlier — stall and sputter.

The best advice here is to take things slow and start small. Roll out a couple of personalization scenarios that require relatively minor changes to see what works and what doesn’t with certain audience segments, and A/B testing, and build from there.

All brands know the essential importance of delivering personalized customer experience that makes each person they connect with feel important, special, and valued. However, the web has completely changed the paradigm and raised the bar. Without the right tools, it is simply infeasible for even the most obsessively customer-centric brand to deliver personalization through every possible website touchpoint.

Thankfully — for both successful brands and satisfied customers alike — rule-based personalization fills the gap in a way that is engaging, effective, scalable and profitable. It can make all the difference between cultivating a long-term relationship vs. regretting a missed connection. Sitecore offers robust tools and capabilities to implement and manage rule-based personalization effectively. Learn more about Sitecore Personalize today.