“Begin with the end in mind, with a vision and blueprint of the desired result,” as emphasized in the timeless wisdom of Stephen Covey’s “The 7 Habits of Highly Effective People,” has resonated with me for years.

This principle holds even truer in the realm of personalization in digital marketing. It struck me that organizations often struggle to ignite their personalization initiatives because they lack a means to conceptualize its essence. Absent a strategic framework for personalization in digital marketing, the approach can remain confined to tactical applications, hindering scalability.

McKinsey’s paradigm of the 4 D’s — Data, Decisioning, Design and Distribution — provides a structured approach to personalization at scale. Within this framework, conceptualizing your personalization strategy aligns seamlessly with the “Decisioning” component. Curiously, despite the framework’s comprehensiveness, there remains a notable absence of practical guidance on executing the “Decisioning” aspect. In a subsequent piece, McKinsey lamented the prevalence of “black-box systems” or, worse yet, the absence of decisioning logic, which ultimately fragments the customer experience.

While discussions on decisioning often drift toward the allure of machine learning and AI-based solutions, it’s important to address the foundational essence. True, there’s a place for advanced technologies in decisioning strategy; however, their potency remains incomplete without a sturdy conceptual model. Delving into the intricacies of machine learning can sometimes lead to “hand waving,” as the practical complexity can be overwhelming. My conviction lies in the fact that a robust conceptual model forms the bedrock — a compass guiding effective decisioning.

With this perspective in mind, let’s explore how a solid conceptual model lays the groundwork for successful decisioning to drive your personalization in digital marketing strategies.

The Essence of Conceptual Models in Personalization in Digital Marketing

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

• Stages: Mapping the Customer Odyssey
  Stages represent the phases of the customer journey, akin to chapters in a captivating novel. Each stage encapsulates a distinct moment in the customer’s interaction with your brand. Whether mirroring stages of awareness, consideration, conversion, or even tailored to customer personas, stages provide the canvas upon which personalized experiences are artfully woven.
• Offers: Personalization in Action
  Within each stage, offers take center stage as personalized touchpoints. These touchpoints encompass tailored content, recommendations or interactions that resonate with individual needs. Offers transform generic interactions into personal dialogues, inviting customers to explore, engage and connect on a deeper level.
• Conditions: The Guiding Compass
  Conditions, the guiding principles of personalization, steer the journey from stage to offer. Think of them as the intricate rules that govern which offers are presented to which customers. Conditions consider a multitude of factors, including customer attributes, behaviors, purchase history, and context, ensuring that the right offer reaches the right customer at the right time.

Related Article: 5 AI Analytics Trends for CX Personalization

Visualizing the Personalization Model

To get this conceptual model correct, we need to be able to visualize it. Just like we wouldn’t build a house without a blueprint, we need a blueprint to visualize our personalization strategy. This helps us to discuss the strategy, agree on the offers and conditions and define the requirements that our developers will need to implement a decision-making approach.

Here is an example of a model for an online retailer:

Across the top, our stages are defined as the typical stages of a customer journey: Awareness, Consideration, Purchase, Service and Loyalty. Within each stage are potential offers. Each offer would have associated content that can be used to drive personalization but defining that content is not necessary for defining the model, so is not depicted.

Conditions as Positive Statements

I’ve found it helpful to define all conditions as positive statements. Things that should evaluate to true. This avoids confusion or duplication. Instead, I apply the conditions to offers as either “Requirements” — it must be true; or “Restrictions” — it must be false. In the model above, we have an offer defined in the “Purchase” stage to “Share on Social.” It has a requirement that we wouldn’t use that offer unless they have recently completed a purchase. Similarly, in the “Service” stage we have a “Sign Up for a Credit Card” Offer with a restriction that ensures they don’t already have a credit card.

What Personalization Looks Like

These concepts give us the language needed to discuss what a personalization in digtial marketing model can look like. This is not limited to Journey based approaches, but any organization of offers that helps us discuss our strategy. Here is an example of a model for a healthcare company based on the Persona of a patient.

Instead of Journey Stages, we have personas across the top, with collections of offers that are applicable to each persona. 

It’s important to note that the goal is not to create one model to rule them all. Having multiple models that are applied in different contexts and maybe even different channels can be highly effective.

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Here’s an example of a model for that same healthcare company applied to a patient journey:

In this example, the stages represent the different milestones of a pregnant patient from signing up as a new patient, through each of the trimesters and post-delivery. This gives us a concise conceptual model to think through our patient’s needs and present them with offers when they make the most sense and have the highest likelihood for conversion.

These visual models help us discuss and agree on the approach. Once we align on that, next we need to define the best way of selecting an offer.

Related Article: 3 Ways Ecommerce Brands Can Use AI for Personalization

Propensity: Illuminating the Path to Personalized Offer Selection

While the process of decision-making might appear straightforward — selecting the first offer that aligns with predefined conditions — this approach assumes that all offers hold equal value. Yet, the reality is far more nuanced. Not all offers are created equal, and different customers exhibit varying inclinations to convert based on the offers presented to them. The essence of optimizing outcomes lies in delicately balancing these two factors: the inherent value of an offer and its likelihood to lead to conversion for a specific individual. This delicate equilibrium is what we refer to as “Propensity,” a dynamic force driving the art of offer selection.

Assessing All Valid Offers

In lieu of settling for the initial offer that satisfies all conditions, our approach involves assessing all valid offers and assigning them scores based on a composite of their anticipated value and their probability of converting. The pivotal criterion becomes the cumulative score, guiding us toward the offer with the most promising potential to magnify the impact of personalization.

Challenges of Harnessing Propensity

However, the road to harnessing propensity is not without challenges. The crux lies in accurately defining the probability of conversion — a task that often prompts organizations to adopt a more straightforward first-match approach.

Yet, the realm with the most transformative potential lies within machine learning. Driven by comprehensive data analysis, machine learning algorithms hold the promise of crafting predictive models that calculate probabilities with unparalleled precision. The good news is that getting there requires a large data set which can be generated by starting with the “first match” approach.

Related Article: How AI and Data Analytics Drive Personalization Strategies

Implementation Considerations

Having a conceptual model is key to a personalization strategy that you’ll be able to scale over time. Going through this process also uncovers the requirements that a development team will need to understand to turn your model into a working solution. 

Never Really Done

Also keep in mind that you’re never really done with the model. Over time, as you learn more about your customers, add additional offers and conditions, your model will evolve. Creating variations of models and testing them against each other is a great way to ensure continuous improvement.

Implementing Models

As to how to implement these models depends a lot on what tools you’ve already invested in, as well as the other “D’s” of personalization at scale: Data, Design and Distribution. There are plenty of tools that address some if not all the capabilities you will need to be successful. If you don’t have platforms that support your personalization strategy, I recommend taking a “composable” approach to evaluating your needs as described in my last article, “Navigating the Long tail of Composable in the Martech Landscape.”

Final thoughts on Personalization in Digital Marketing

In the world of personalization in digital marketing, strategy is the compass that navigates us through the intricate landscape of customer interactions. By embracing the foundational concepts of stages, offers, and conditions, we not only illuminate the path ahead but also craft a framework for impactful engagement.

This approach transforms personalization in digital marketing from a mere transaction into a meaningful dialogue. Stages delineate the journey, offers create connection, and conditions bring precision to the equation. As we adopt this conceptual framework, we empower ourselves to create experiences that resonate, capturing the essence of personalization in a strategic yet artful manner.

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