If you want to create relevant customer experiences and ultimately build stronger and better relationships with your customers, then personalization is mandatory.
Personalization has been around for quite a while, from back in the days of segmented direct-mail campaigns to the more recent focus on personalization driven by Artificial Intelligence (automated personalization at scale).
It seems AI is the solution that will help organizations currently struggling to launch personalization by making everything self-tuned, self-optimized, and so forth. The theme of marketing events, of billboards in the San Francisco area, and of every sort of tech vendor is that it’s the answer for everything—just push the AI button and all is magically optimized.
Yet, as a consumer, it’s very rare that I experience a really great personalized digital experience. In fact, many marketers and agencies are not doing AI-based personalization at all—or even simple rules-based personalization, for that matter.
It seems that ”simple” might not be that simple after all, which can be frustrating for marketers, whose executives constantly hear how easy it is to have self-tuning omnichannel-connected personalized experiences.
We’ll take a look at that a bit later in this post, but, first, let’s take a look at the three different generations of personalization and demystify the value of each one.
1st generation of personalization
First generation personalization, which is still in place for the majority of marketing folks who are running personalization today, is based on explicit customer data like demographics and psychographics. Typically, it is implemented by creating a segment, most often for an email campaign. An example would be targeting all females in a certain location who haven’t been active for the past three months. It is put into play by sending a targeted email (same content for all recipients) with a ”Dear Hannah, …” salutation.
First generation personalization is no different from direct mailing done in the fifties—it has simply moved digital and has more data available for segmentation. Of course, it’s always more relevant to send a segmented email campaign with targeted content than a one-size-fits-all email campaign.
2nd generation of personalization
Second generation personalization is what we as organizations have been struggling with for the past 10 years. It’s mainly website personalization that targets a specific segment using a mix of data: geolocation, inbound marketing campaigns, pages that have been viewed, cookie or DMP data, and so forth. Some of the more advanced scenarios include real-time behavioral profiling that can be used to determine in-the-moment intent of the customer, and they’re typically very effective in terms of outcome.
Second generation personalization is ruled-based—we enable different rules, such as whether a visitor is interested in
service X, is from
country Y, and hasn’t converted on
goal Z, and then show them specific content. The benefits of rules-based personalization are that it’s very easy to start implementation, it doesn’t require much data, and in most cases it beats the default experience. The flip side is that it doesn’t scale unless you have a lot of resources—like A LOT of folks who love it and do it all day, all week as their main work. They constantly change rules and keep on top of experiences and newly created content. Rules-based personalization is great as an appetizer, because it helps show value for the organization by creating quick wins. But to properly scale it from there on requires an evolution to the third generation.
3rd generation of personalization
Third generation personalization is individualization. It focuses on the individual, looking at their context by using different data insights about them. Through that, it strives to offer the most relevant experience, which helps the visitor with the current task. This builds a closer relationship and ultimately increases your key conversions and repeat business.
Individualization is typically AI based (unless you have a fabric of employees creating rules all day long) and is powered by high-quality data. Since poor data and AI will in most cases provide mediocre experiences, having great data is the key to unlocking the potential of AI.
As marketers, we’ve been great at doing first-generation personalization, because that’s what we’ve grown up with. And although we’ve been evolving into the second generation and experimenting with that, we’ve become stalled, because it isn’t scalable without the many resources required to manage it. The third generation has been positioned as ”just push this button” and everything will be great.
Let’s take a look at the foundation of personalization and how to get the most value from it at scale with individualization. The proper foundation is data, data, and data—it’s about having good data that lets you understand
- progression in the customer journey
- in-the-moment intent
- who the person is
- the situation
Progression in the customer journey
If we start with the customer journey, a simple example might be awareness, research, decision, customer, and onboarding. Everyone who arrives on your digital channels can be placed in a stage, specific to where they are along the journey. For this, you’ll need to listen digitally to their various signals, which can help you understand in which stage the customer should be placed.
The first signal you should seek to decode is how they arrive. If they arrive from a Google PPC campaign focused on a new product launch, then they might be in the early stages. If they arrive from a link in an email that you just sent to new customers, you know they are a customer (so don’t try to sell them the same products again!). Other signals can come from the ways in which they are consuming content. If they’re looking at details on select products, they might be researching; if they’re just browsing high-level category pages, they might be in the awareness stage.
Goals/conversions and events can help you track progression through the journey. When someone visits one of your digital channels for the first time, they are anonymous, and your task is helping them with their task, since that builds trust and trust leads to further engagement. Goals can help you track micro-conversions (e.g., downloading a pdf, watching a video, reading a review, etc.) as well as more important conversions focused on contact acquisitions, which can ultimately lead to them becoming a new, and then a repeat, customer.
Your site might have different sections, such as
/careers/ etc. You probably also have a number of service/product categories. To understand your customers’ in-the-moment intent, take a look at what they’re doing at your site, what content they’re consuming. Are they looking for a specific product or service? Are they looking at thought leadership? What are the topics, what might they be looking for? Understanding how your customers behave and how they consume content can help determine their in-the-moment intent, and these factors serve as signals to provide the most relevant experience. The more content they consume combined with search insights in real time, the more accurate you’ll be able to derive their in-the-moment intent.
A good approach here is to start high-level by first understanding which main section of your site they are interested in and then working from there toward the details.
Who the person is
Most website visitors are anonymous, and we can’t tell if this is Ann or Lee. But we can try to understand a bit more about the person by looking at various personality traits. For example, a visitor might be classified as methodical if they are looking at very detailed content about a product/service, comparing different offerings, and so forth. The more you can classify by looking at real-time behavior, the better you can use this for relevant next steps.
Start by mapping those insights you can collect from anonymous digital behavior to known contact data to help you deliver the best visitor experience. Known contact data can be a gold-mine when connected to a CRM, CDP or other customer databases.
The situation is data that can help you understand the customer’s current environment, e.g., the device they are using and its capabilities, where they are, the time, the weather in their location, whether they are in transit, etc. Based on their situation, there may be specific experiences that would be most engaging for them.
Mapping out data that helps you understand your customers’ intent is a great starting point, from which you can use segmentation with rules-based personalization to drive the first quick wins. And from there, you have a better quality data foundation for AI-driven individualization.
So what are your next steps in terms of personalization?
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