Master personalization with digital marketing

The timeless wisdom of Stephen Covey's The 7 Habits of Highly Effective People emphasizes, “Start with the end in mind, with a vision and blueprint for the desired outcome.” has resonated with me for many years.

This principle is even more true in the realm of personalization in digital marketing. We've found that organizations often struggle to drive personalization efforts because they don't have a way to conceptualize the essence of personalization. Without a strategic framework for personalization in digital marketing, approaches can be limited to tactical uses and hinder scalability.

McKinsey's 4 D paradigm (Data, Decision, Design, Distribution) provides a structured approach to personalization at scale. Within this framework, conceptualizing a personalization strategy works seamlessly with the “decision-making” component. Curiously, despite the comprehensiveness of this framework, there is still a significant lack of practical guidance for carrying out the 'decision-making' aspects. In a subsequent article, McKinsey lamented the prevalence of “black box systems” or, worse, a lack of decision-making logic that ultimately fragments the customer experience.

While discussions about decision-making often drift toward the appeal of machine learning and AI-based solutions, it's important to address the basic essence. Certainly, there is scope for advanced technology to be leveraged in strategic decisions. However, its efficacy remains incomplete without a solid conceptual model. Digging into the complexities of machine learning can sometimes result in “hand waving” because the actual complexity can be overwhelming. My belief lies in the fact that robust conceptual models form the basis, the compass that guides effective decision-making.

With this perspective in mind, let's take a look at how a solid conceptual model can be built as a foundation for successful decision-making to drive personalization in your digital marketing strategy.

The essence of conceptual models in personalization in digital marketing

At the heart of every conceptual model are three key components that pave the way for effective decision-making in personalization: stages, offers, and conditions.

• Stage: Mapping the Customer Odyssey
  Stages represent stages in a customer journey, similar to chapters in a captivating novel. Each stage encapsulates a defining moment in the customer's interaction with your brand. Whether reflecting the stages of awareness, consideration, conversion, or even tailored to customer personas, stages provide a canvas on which personalized experiences can be expertly woven.
• Offer: Personalization in action
  At each stage, offers play a central role as personalized touchpoints. These touchpoints include customized content, recommendations, or interactions based on an individual's needs. Offers transform common interactions into personal ones, encouraging customers to explore, engage, and connect on a deeper level.
• Condition: Guiding Compass
  Conditions, a fundamental principle of personalization, guide the journey from stage to offer. Think of them as complex rules that govern which offers are shown to which customers. Conditions take into account factors such as customer demographics, behavior, purchase history, and context to ensure the right offer reaches the right customer at the right time.

Related article: 5 AI analytics trends for CX personalization

Visualization of personalization models

For this conceptual model to be correct, we need to be able to visualize it. Just as you can't build a house without a blueprint, you need a blueprint to visualize your personalization strategy. This helps discuss strategy, agree on offers and terms, and define the requirements necessary for developers to implement the decision-making approach.

Below is an example of an online retailer model.

The stages at the top are defined as the typical stages of the customer journey: awareness, consideration, purchase, service, and loyalty. Each stage has potential offers. Each offer has associated content that can be used to drive personalization, but the definition of that content is not required for the model definition and is therefore not shown.

Condition as a positive expression

I found it helpful to define all conditions as positive statements. Something that should evaluate to true. This avoids confusion and duplication. Instead, apply conditions to your offer as “requirements.” This must be true. or “restrictions” – this must be false. In the above model, an offer to “Share on Social” is defined at the “Purchase” stage. There is a requirement that you do not use the offer unless you have recently completed a purchase. Similarly, in the “Services” stage, there is a limited “Credit Card Sign Up” offer that ensures you don't already have a credit card.

Personalization overview

These concepts provide the language needed to discuss what personalization looks like in a digital marketing model. This is not limited to journey-based approaches, but applies to any offer organization that helps discuss strategy. Below is an example of a model for a healthcare company based on patient personas.

Instead of journey stages, there are personas at the top, which contain a collection of offers that can be applied to each persona.

It is important to note that the goal is not to create one model to rule them all. Applying multiple models in different contexts and possibly across different channels can be very effective.

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Here's an example of the same healthcare company model applied to the patient journey.

In this example, each stage represents a different milestone from the time a pregnant patient enrolls as a new patient through each trimester and postpartum. This gives you a concise conceptual model to think through your patient's needs and present your offer when it makes the most sense and is most likely to convert.

These visual models will help you discuss and agree on your approach. Once you've adjusted that, you need to define the best way to select offers.

Related article: 3 ways e-commerce brands can use AI for personalization

Trend: Uncovering the path to personalized offer selection

Although the decision-making process may seem simple: selecting the first offer that matches predefined criteria, this approach ensures that all offers hold equal value. is assumed. However, the reality is more nuanced. Not all offers are created equal, and different customers have different conversion propensities based on the offers they are presented with. The essence of optimizing results lies in delicately balancing two factors: the inherent value of an offer and its potential to lead to conversion for specific individuals. This delicate balance is what we call “trends,'' the dynamic forces that drive the art of offer selection.

Evaluation of all valid offers

Instead of settling on the first offer that meets all the criteria, our approach evaluates all valid offers and assigns a score based on a combination of their expected value and probability of conversion. A crucial criterion is the cumulative score, which guides us towards the offers with the most promising potential to expand the effectiveness of personalization.

The challenge of leveraging sexual proclivities

However, the path to leveraging propensity is not without its challenges. The key is to accurately define the probability of conversion. This task often leads organizations to adopt a more direct first match approach.

But the area with the most transformative potential lies within machine learning. Machine learning algorithms have the potential to create predictive models that calculate probabilities with unparalleled accuracy based on comprehensive data analysis. Fortunately, getting there requires large datasets that can be generated starting with a “first match” approach.

Related article: How AI and data analytics can drive your personalization strategy

Implementation considerations

Creating a conceptual model is key to a personalization strategy that can scale over time. This process also reveals the requirements that the development team needs to understand in order to turn the model into a working solution.

It's never really over

Also note that the model creation is not actually complete. Over time, your model will evolve as you learn more about your customers and add additional offers and conditions. Creating variations of your model and testing them against each other is a great way to ensure continuous improvement.

Implementing the model

How you implement these models will largely depend on the tools you've already invested in, as well as the other “D's” of personalization at scale: data, design, and distribution. There are many tools out there that address some, if not all, of them. If you don't have a platform that supports your personalization strategy, we recommend taking a “composable” approach to assessing your needs, as described in our previous article, “Navigating the Composable Long Tail.” Masu. Martech landscape. ”

Final thoughts on personalization in digital marketing

In the world of personalization in digital marketing, strategy is a compass to navigate the complexities of customer interactions. By adopting the basic concepts of stages, offers, and terms, we not only clarify the path forward, but also build a framework for impactful engagement.

This approach transforms personalization in digital marketing from just a transaction to a meaningful conversation. Stages outline the journey, offers create connections, and terms bring precision to the equation. Adopting this conceptual framework allows you to strategically and artistically capture the essence of personalization and create experiences that resonate.

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