Personalization across your digital assets can take many forms. It can range from simple campaigns like a “We were missing you” message to all returning visitors who are returning after a while, to more advanced techniques like individualized product recommendations based on a business’s unique requirements. These two examples and all forms of personalization in between can deliver significant value to your visitors and customers. One type of personalization is not inherently more valuable than any other — the key is in matching the strategy to the goal of ensuring that you are getting the most out of your personalization efforts.
At the heart of any personalization strategy are rules and machine-learning algorithms. While machine-learning personalization is growing in usage and popularity, rule-based personalization remains a powerful tool to provide experiences to groups of people with similar characteristics. The personalization spectrum varies from rules that govern broad segments, to individualized experiences driven by machine learning.
Whether you’re just getting started with personalization, or you’re looking to take your personalization strategy to the next level, you need to fully understand the use cases for both rules and machine learning. This article will explore various use cases to provide you with a solid foundation to help you reach your personalization goals. This article focuses on Rule-Based Personalization.
What is it?
Rule-based personalization allows B2B business owners and marketers to deliver experiences to specific groups or segments of clients based on manual creation and manipulation of business rules.
As the definition above suggests, segments build the foundation of rule-based personalization. Segments allow you to categorize a subset of your website visitors or app users according to their attributes and behaviors and then use rules to customize the experience for each segment.
The simplest way to think about rule-based personalization is in the form of IF/THEN statements:
If a prospect comes from Singapore, THEN show her a message about a webinar hosted by your business in the area, IF a visitor is a member of your loyalty program, THEN provide a message welcoming him back to your website.
As long as you can collect data for it, you can create a segment against it. The segments you create will depend entirely on your business and your specific goals. For example, a travel company may want to speak differently to its website visitors from different regions of the country, but a software company may not find any value in segmenting visitors by geolocation. That software company may want to focus on the industry for its visitors instead.
Segments can be created based on any identifiable attribute or behavior. To help you understand the different types of segments that can be created, let’s dive into attributes and behaviors.
Attributes describe any intrinsic characteristic of a visitor. They can be derived from two key sources:
These attributes are detected as soon as a person lands on your site. Examples include geolocation, industry or company (based on reverse IP address lookup), time of day, and source (such as search, email, social, paid ad, etc.). These attributes are particularly valuable in helping you personalize an experience for first-time visitors that have not yet interacted with your site, although they can be used in many other situations as well.
These attributes are not detected from the web, but rather are pulled in from a database-driven system such as a CRM, email marketing solution or a data warehouse. They can include anything that you store in one of those systems, such as whether the visitor is a prospect or customer, a high-value customer, in a particular category, etc. Note that in order to tie a website visitor to data in another system, you need some kind of identifier (such as an email address) for that visitor.
Behaviors describe any action taken on your website or app. As visitors engage with your digital property, you can alter their experiences based on actions they have taken in the past or in their current sessions (or both). Behaviors can be broken into three main buckets:
Simple behavioral analytics such as a number of visits to your site, average time on site, visit recency, etc.
Page Visit Behavior
Data about specific page views for an individual, such as which pages he or she has visited and the number and frequency of visits per page.
This considers in-page context (e.g. category, tags, brand, style, etc.) and level of engagement for an individual based on mouse movement, scrolling, inactivity and time spent per page to provide the most accurate indication of affinities, interests and true intent.
Broad vs. Narrow Segments
A segment created using just one or two of these behaviors and attributes would be considered a broad segment. For example, online apparel retailers can personalize geolocation, showing everyone in certain regions the clothing and footwear appropriate for their climates. A B2B site could recognize a visitor’s business type to orient the content they see around industry-specific case studies.
Segments that begin to combine multiple behaviors and attributes are narrow segments. They often leverage nested “AND” or “OR” logic to create very specific groups of visitors. For instance, a clothing merchant could recognize a repeat visitor from New York who originated from a specific ad campaign type and point him to the “sun-lovers” sell merchandise that he has engaged with in the past. The B2B tech company site could recognize prospects from the financial services industry that have already spent a certain amount of time exploring particular product pages, such as network servers, and promoting a relevant white paper.
Marketers need to weigh whether the effort to design an experience for a very small group of people is worthwhile. In some cases, this would be appropriate. For example, if you are implementing an account-based marketing (ABM) strategy, you want to create highly tailored communications to reach specific accounts. However, in other situations, machine-learning personalization may be a better fit. This will be explained in more depth in the next chapter.
Once your segments are created, you can design specific experiences to deliver to those segments via rules. There is a vast spectrum of rule-based experiences you can provide, ranging from the very simple to the very complex. Callouts, info bars, and pop-up messages are typically easy campaigns to deploy with rules. They are a great place to begin your personalization journey and generate some quick wins.