What if you could pinpoint your most engaged users, understand what makes them tick, and tailor your strategies to boost conversions and retention?
In this webinar, our very own Jude Felix, Data Scientist at Accrease will show you how to activate an engagement score and drive significant business impact. Discover how engagement scoring can turn your customer data into actionable insights that fuel growth and improve outcomes.
Here's what you can expect:
How Can Engagement Scoring Impact Your Business? Understand why engagement scoring matters and how it directly contributes to driving meaningful results for your business.
Introduction to Engagement Scoring: Learn what engagement scoring is, its role in analytics, and why it's crucial for effective decision-making.
Role of Adobe Analytics & Customer Journey Analytics: Compare key features and benefits of Adobe Analytics (AA) and Customer Journey Analytics (CJA), and understand how these tools help bring engagement scoring to life.
Data Requirements: Identify relevant data sources, understand the types of data needed, and address data quality and integration challenges.
Building Your Engagement Scoring Model: Learn the steps to define key metrics, collect data, develop, and validate an effective engagement scoring model.
Implementing and Activating the Model: Discover how to integrate your engagement scoring model into AA and CJA, and how to monitor and adjust it for maximum impact.
Takeaways
Bonus: As an added bonus, all attendees will receive a complimentary pre-counseling offer! This includes a free one-on-one consulting session with Jude to help you get started. This offer is for those who use Adobe Analytics and are ready to activate their data for engagement scoring.
What if you could pinpoint your most engaged users, understand what makes them tick, and tailor your strategies to boost conversions and retention? With Adobe Analytics (AA) and Adobe Customer Journey Analytics (CJA), you already have a wealth of data on user behaviour. The real challenge lies in distilling that data into clear, actionable insights.
An Engagement Scoring Model helps you do just that. By assigning scores based on user interactions, you’ll quickly identify your top-performing audience segments, understand which behaviours drive the most value, and prioritize efforts to maximize business outcomes.
In this guide, Jude Felix, Senior Consultant at Accrease will show you how to build an Engagement Scoring Model step by step. You’ll learn how to transform raw analytics data into a powerful tool for targeting, personalization, and optimization, giving you a clearer view of what drives success—and how to replicate it.
Jude Felix,Senior Consultant
What is an Engagement Scoring Model?
An Engagement Scoring Model is a framework that assigns a numerical score to users based on their interactions with your digital properties. By scoring user engagement, you can:
- Identify which user behaviors or interactions are most valuable.
- Segment users based on their engagement levels.
- Make data-driven decisions for targeting, personalization, and optimization.
The process for creating this model combines both gut-based intuition and data-driven analysis to determine which metrics matter most for your business goals. Below are the steps to make the model.
Steps to create the Engagement Scoring Model
Creating an effective engagement scoring model is a powerful way to understand and quantify user behavior. It helps businesses tailor strategies and drive more meaningful customer interactions. Following a structured approach, you can identify key engagement metrics, analyze their impact, and assign appropriate weights to create a robust scoring system.
In this process, we will walk through 7 key steps, starting with defining the data timeframe, selecting relevant metrics, and conducting correlation analysis. We’ll then benchmark metrics, assign weights, and create an engagement score formula. Finally, we validate the model to ensure it accurately reflects user engagement and drives real business results. This systematic approach helps you gauge engagement effectively and provides actionable insights to refine customer experience and strategy for better outcomes.
1. Decide the data time frame
The first step is to decide how much data you want to include in your model. The last 30 days is typically a good starting point, as they capture recent user behaviour without including too much stale data. However, depending on your business or campaign needs, you may choose a different timeframe (e.g., last 7 days or last 90 days).
2. Select key metrics
Selecting the right metrics is the most important step in building the engagement model. There are two main approaches to choosing these metrics:
The intuition-based approach: Based on your experience and knowledge of your customers, you can choose metrics that you believe are important for driving engagement. Some of the metrics might include:
- Page views
- Time spent on site
- Number of sessions
- Product views
- Purchases
- Revenue
The data-driven approach: If you want a more empirical approach, you can identify the metrics most correlated with achieving a specific goal, such as purchasing or signing up for a newsletter. You can do this through statistical correlation analysis, which we will discuss in the next step.
3. Perform Correlation Analysis
To validate and refine the chosen metrics, you can use statistical methods to find out which ones are most closely associated with user success or your defined goals. The goal here is to understand which metrics have the greatest impact on user engagement.
How do you conduct the correlation analysis in Excel?
Gather your data:
- Export relevant data from AA or CJA for your selected metrics over your chosen timeframe (e.g., the last 30 days).
Enable Analysis Toolpak in Excel:
- Go to File > Options > Add-ins.
- In the Manage box, select Excel Add-ins, and click Go.
- Check the box for Analysis Toolpak, and click OK.
Run the correlation analysis:
- Organize your data in columns, with each column representing a metric.
- Navigate to Data > Data Analysis and choose Correlation.
- Select the range of data and choose Output Range to display the correlation matrix.
Analyze the results:
- The correlation matrix will show you the correlation coefficients between each metric and the goal metric (e.g., purchases, signups).
- A correlation coefficient ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no correlation.
Figure 1 below shows the results of a correlation calculation in Excel based on a few selected metrics.
Figure 1 Correlation calculation in Excel.
4. Benchmark a Score Using the Correlation Coefficients
Once you have your correlation coefficients, the next step is to decide which metrics to include in your engagement model. To do this, you can benchmark a threshold score by calculating the average correlation coefficient across all metrics.
- Metrics with correlation coefficients equal to or above the average should be included in the engagement model.
- Metrics below the average threshold may not significantly contribute to engagement and can be excluded.
Based on the coefficients of correlation from the Figure 1 we can create a benchmark chart like in Figure 2. The metrics number 3, 5, 6, 7 and 8 are he metrics that is highly correlated.
Figure 2 Benchmark chart
5. Assign weights to the metrics
Now that you have selected the most relevant metrics, you need to assign them weights. These weights determine how much influence each metric will have on the overall engagement score.
Normalizing Correlation Coefficients to Assign Weights:
Take the correlation coefficients from the previous step and normalize them so that they add up to 100%. This can be done by dividing each metric’s correlation coefficient by the total sum of all selected metrics’ correlation coefficients and multiplying by 100.
For example, if you have three metrics with correlation coefficients of 0.8, 0.5, and 0.3, you can calculate the normalized weights as follows:
These normalized values become the weights for each metric, ensuring that the metrics most closely associated with user engagement have the greatest impact on the score.
6. Create the Engagement Score Formula
Now that you have both the metrics and their respective weights, you can calculate the engagement score for each user. The formula will look something like this:
For example, if you score based on Page Views, Time Spent on Site, and Purchases, figure 3 shows how this can be created in AA or CJA.
Figure 3 Calculation of engagement score
7. Validate the Engagement Model
After building the engagement scoring model, it’s crucial to validate it by applying the model to a sample of users and comparing the scores with actual business outcomes. As shown in figure 3, we can Do higher engagement scores correlate with higher conversions or more purchases?
If the scores don’t align with real-world outcomes, you may need to revisit the metrics or re-run the correlation analysis to fine-tune the model.
If the model accurately reflects user engagement and correlates with success, it’s ready to be deployed.
Why use an Engagement Scoring Model?
An engagement scoring model provides businesses a tangible way to measure and track user engagement over time. Here’s why it matters:
Identify high-value users: By scoring users, you can quickly identify your most engaged users, allowing you to focus your marketing efforts where they’ll have the greatest impact.
Target personalization: Use engagement scores to deliver personalized experiences based on the level of user engagement, creating a more tailored and relevant experience for your audience.
Optimize user journeys: By understanding which behaviors drive engagement, you can optimize your website, app, or marketing efforts to encourage those behaviours, improving overall customer satisfaction and conversion rates.
Conclusion
Building an engagement scoring model using Adobe Analytics or Customer Journey Analytics data is a powerful way to turn raw user interaction data into actionable insights. By following a data-driven approach backed by statistical analysis, you can create a model that accurately reflects user engagement and drives better business outcomes.
This method combines both intuition and rigorous analysis, ensuring that the metrics you choose are grounded in actual data and that the engagement scores you generate align with your business goals. Whether you’re focusing on retention, acquisition, or conversion optimization, an engagement scoring model provides the foundation for smarter, more effective marketing and product strategies.
Start building your model today to maximize your Adobe Analytics data.
Want to dive deeper? Join Jude Felix for a 20-minute Mini Masterclass: 'How to Activate Engagement Scoring to Drive Business Impact.' In this session, Jude will walk you through building and activating engagement scoring models using Adobe Analytics and Customer Journey Analytics (CJA). Don't miss out, sign up here: Mini Masterclass
Recently, there have been many discussions and articles on Artificial Intelligence (AI), which is an important topic, now changing how many industries operate. We had the chance to talk to Dana Icikzone, Senior Solution Consultant at Adobe, about this and discuss the release of their new AI Assistant within Adobe Experience Platform.
This blog post is built on our interview with Dana, by reading it you´ll learn more details about the AI Assistant within the Adobe Experience Platform: How different roles can benefit, the general business impact, and perhaps most importantly; the trust and privacy of it. You will also gain valuable perspectives, guidance, and recommendations on how to work with and fully utilize it, from an expert’s point of view.
This is just an introduction. If you are interested in diving deeper into this topic, we are constantly creating blog posts, Mini Masterclasses and content deep diving into the most recent AI prompts, trends and impacts. Follow our LinkedIn page and get noticed when we release something new. But first...
What is the AI Assistant?
To briefly introduce you to the AI Assistant - it is a conversational interface powered by generative AI models. It allows users to ask questions and receive answers based on a combination of base models, custom models, decision-making algorithms and business goals. Embedded within the experience platform, it operates across all applications, including the Real-Time Customer Data Platform (RT-CDP), Adobe Journey Optimizer (AJO), and Customer Journey Analytics (CJA).
The AI Assistant is designed to work seamlessly across various applications within the Adobe Experience Platform. This integration ensures that users can have consistent conversations and obtain relevant answers regardless of the specific application they are using. For instance, an RT-CDP user can still get insights based on CJA data, making workflows more efficient.
Some key technical features
The AI Assistant offers several key features that enhance its utility:
Conversational interface: Allows users to interact naturally and obtain quick answers.
Custom models: Tailored to specific customer needs, ensuring data privacy and relevance.
Role-based access control: Ensures that users can only access data they are authorized to view.
Operational insights: Provides actionable insights based on enterprise data.
Knowledge expansion: Helps users expand their understanding of the platform and their roles.
Verifiable layers: Ensures transparency by providing sources and explanations for all data.
How can the AI assistant benefit my role?
Many different departments and roles benefit from the AI Assistant, including, for example, IT teams, data analysts, and the marketing department. Dana highlighted how it can serve as a companion to developing expertise, managing routine tasks, and providing quick answers to workflow-related questions.
"It should be a companion helpig anyone to become kind of an extended expert," she said.
The IT department
As responsible for ensuring data is collected and being assessable, the IT department can utilize the AI Assistant for data exploration, management, insights, and discovery. For example, in automating routine tasks, thereby freeing up time for more strategic activities. The AI Assistant can answer operational questions, such as how often a segment is used or where a schema field is applied, making data management more efficient.
The data analysts
Data analysts are the professionals ensuring data is thoroughly analyzed and interpreted. They can use the AI Assistant to dive deeper into data sets, perform complex queries, and gain insights quickly. The AI Assistant helps in understanding data structures, troubleshooting specific scenarios, and optimizing workflows. It can also assist in finding and analyzing audiences, making data analysis more streamlined and effective.
The marketing department
Marketing teams ensure data is activated and utilized in strategic decisions, and they also benefit significantly from the AI Assistant. For example, by obtaining quick answers to workflow-related questions and troubleshooting issues. It can aid in campaign creation, audience discovery, and optimizing customer journeys. What the AI Assistant does is help filter out information available within the platform. This can support the decisions you make in terms of creating campaigns, making marketing operations both more efficient and effective.
Prompts and usage
Using the AI Assistant effectively involves specific prompting skills. Users can ask knowledge questions, operational insights questions, and troubleshooting queries. For instance, a digital analyst might ask, "How do I build a segment?" or "What is an identity map?" These prompts help users quickly access necessary information without sifting through extensive documentation.
Another practical example Dana shared is the AI assistant's ability to help find specific audiences within a platform. She explains the problem: "Imagine that you would have to go through every single audience, and there might be thousands of different audiences within a platform". Traditionally, managing your audiences would require a lot of time, creating new audiences, often duplicating existing ones. The AI assistant helps avoid these inefficiencies by providing quick access to the necessary information. For instance, it can help a data analyst quickly identify the most relevant audience for a marketing campaign, saving you a lot of time.
Business impact
The AI Assistant impacts businesses by enabling quick access to enterprise data, facilitating knowledge expansion, and automating tasks. This leads to increased productivity, faster campaign creation, and overall improved efficiency. Dana emphasized how "...being more productive, more efficient and faster as a resource, will enhance your operations." The integration of AI in business processes also helps bridge the gap between different roles, making teams more versatile and efficient. For instance, it can help data analysts understand marketing strategies, and vice versa, leading to more comprehensive and effective campaigns.
Increasing productivity: By automating routine tasks and providing quick access to information, the AI Assistant allows employees to focus on more strategic activities. This leads to increased productivity and faster decision-making processes.
Efficiency improvement: The AI Assistant helps in reducing the time and effort required to perform various tasks. Faster campaign creation and efficient data management contribute to overall easier, efficient and enhanced operations.
Enhancing knowledge and expertise: The AI Assistant aids in expanding the knowledge and expertise of employees by providing quick answers to complex questions. This helps in improving product proficiency and role expansion, making employees more versatile and valuable to the organization.
Trust and privacy
Trust and privacy matters are crucial in the implementation of AI as assistants. Dana emphasized that the AI Assistant on the Adobe Experience Platform is built with these considerations heavily in mind. It uses custom models specific to each customer, ensuring that data is never accessed outside the customer's environment. Role-based access controls further ensure that users can only access data they are permitted to see.
Privacy, security, and governance
The AI Assistant was built with privacy, security, and governance at the forefront. Users must be granted permission to interact with the AI Assistant, and role-based access control policies are strictly honored. This ensures that only authorized personnel can access specific data sets and information.
Customer data protection
The AI Assistant is also designed honoring customer data stewardship. Data is not used or shared across customers, and filters can be leveraged to scrub Personally Identifiable Information (PII). All data provided by the AI Assistant comes with verifiable layers, such as source and explanation, ensuring transparency and trust. Importantly, no third-party data is used to provide answers, which further safeguards customer information.
Dana highlighted, "The AI Assistant uses a combination of models, and one is the custom models that are customer-specific, and those models would never be used, or the data would never be accessed outside of that customer."
Verifiable layers
One of the spotlight features of the AI Assistant is the provision of verifiable layers. Users can always verify where the answer comes from, which is crucial for maintaining trust and accuracy. Dana noted, "There's always a source you can verify, where the answer comes from, which is really important within an AI system."
Future of AI
The future of AI serving as assistants is promising, with potential advancements in automating tasks, generating new segments, and even suggesting optimal strategies based on set goals. Dana believes that as technology evolves, it will continue to drive innovation and efficiency in business operations."I think generative AI is probably the biggest game changer for Adobe in the past decade and has an incredible potential for customer experience solutions” she explains.
AI Assistants are transforming how businesses operate by providing quick access to data, enhancing productivity, and ensuring trust and privacy. As Dana highlighted, the technology's impact on various roles and business processes will only grow, making it an indispensable tool in the modern business landscape.
Want to know more? Read more about the AI Assistant at Adobe and join the conversation: Adobe AI Assistant
We are constantly creating more blog posts, Mini Masterclasses, and content diving deep into AI. If you don't want to miss out follow our LinkedIn page here -> Accrease (Partner of the year)
