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:Β 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, youβll 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. Youβll learn how to transform raw analytics data into a powerful tool for targeting, personalization, and optimization, giving you a clearer view of what drives successβand 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. Weβll 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 metricβs correlation coefficient by the total sum of all selected metricsβ 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, itβs 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 donβt 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, itβs 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. Hereβs 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 theyβll 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 youβre 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
Understand the differences between Customer Journey Analytics and Adobe Analytics with our Senior Analytics Consultant Martine JΓΈrgensen
Customer Journey Analytics vs Adobe Analytics Data analysis is crucial for businesses to make informed decisions, and Customer Journey Analytics and Adobe Analytics are two prominent tools that aid in achieving this goal. Although both tools serve the purpose of providing valuable insights, there are key differences between them. In this blog post, we will explore these differences, discuss the benefits of implementing Customer Journey Analytics, examine successful case studies, and dive into the user profiles of both tools.
But first β What is Customer Journey Analytics? Customer Journey Analytics (CJA) has become a hot topic lately β and understandably. It is a powerful tool for businesses to track, analyze, and optimize customer interactions online and offline. It visualizes the entire journey from awareness to advocacy, helping identify pain points and preferences. By creating detailed customer personas, businesses tailor marketing and products accordingly. Businesses can use these insights to enhance marketing effectiveness, optimize resources, and create a seamless customer experience. It looks a lot like Adobe Analytics in relation to the UI and offers many of the same functionalities β so whatβs all the hype about? Although it looks a lot like Adobe Analytics in terms of UI and functionality, there are fundamental differences between the two tools that we will discuss later.
What is Adobe Analytics? Adobe Analytics is the more widely used web analytics tool that focuses primarily on tracking and analyzing website performance. It provides businesses with valuable insights into website traffic, user engagement, conversion rates, and other website-related metrics. Users can create reports with tables and data visualizations in a workspace to analyze and distribute insights.
Adobe Analytics offers a wide range of features, including real-time tracking, segmentation, data visualization, and reporting. It enables businesses to understand how users are interacting with their websites, identify the most effective marketing channels, and optimize website experiences to drive conversions.
What are the Key Differences Between Customer Journey Analytics and Adobe Analytics? As mentioned above, Customer Journey Analytics and Adobe Analytics differ in various aspects. Below are the most essential differences:
The data approach One fundamental difference is their approach to the breadth of data β meaning the variety of data sources. Customer Journey Analytics focuses on capturing and analyzing the entire customer journey, from the initial touchpoint to conversion and beyond. This comprehensive approach allows businesses to gain insights into the various touchpoints and interactions that lead to a conversion, providing a holistic view of the customer's experience. However, Adobe Analytics mainly concentrates on measuring website traffic and engagement metrics, providing valuable information on user behavior within the digital space.
Connect to any data source on the Adobe Experience Platform (AEP) for cross-channel analysis. It is essentially a analysis workspace sitting on top of the AEP whereas Adobe Analytics is an analysis workspace on top of an Adobe Analytics implementation β specifically designed for collecting data within the digital realm. In CJA, instead of report suites in the workspace panels, you will see βdata viewsβ. This is your βviewβ into the data connection that you or other admin users have created containing relevant data sources stitched together. Data views are like virtual report suites. Here you can work with the data, create derived fields, classify values etc.
The Architecture Thus, the architecture looks different for CJA and Adobe Analytics. CJA leverages the technologies of AEP. Here, data collection can come from various sources such as through Adobe's SDK, other Adobe solutions, third-party tools and more. The data is received in the AEP through streaming or batch files. Then, data is organized into a unified set of schemas and cataloged in the Experience Data Model (XDM) which enables a consistent view of the data. To get data from AEP to CJA, one must create a data connection in CJA. When CJA accesses the data lake in the AEP, it essentially pulls a copy into CJA. Then data from the data connection can be curated into a single data view. So CJA can be seen as an extension of the AEP β an analytics interface builds on the AEP.
An illustration of Customer Journey Analytics architecture
When it comes from Adobe Analytics, data is collected from web or app and is sent directly to an Adobe Analytics server where it is mapped into dimensions and events. This difference is vital to understand as it will also be important from a reporting perspective.
An illustration of Adobe Analytics architecture
Customization Another key difference is the level of customization. CJA offers a highly configurable platform, allowing businesses to tailor the analysis to their specific needs. This level of customization empowers organizations to create bespoke analytics solutions that align with their unique business objectives and KPIs. For instance, the derived fields feature in CJA. This can be compared to processing rules in Adobe Analytics but offers even more customization. With derived fields, the user can clean up data more easily, classify data and create more complex data manipulations. This can all be applied retroactively to the data which means that it will be applied to all the collected data and not just data collected after applying the logic.
In contrast, Adobe Analytics provides a comprehensive suite of pre-built features and reports, making it easier for users to start analyzing data without extensive customization. This out-of-the-box approach can be beneficial for organizations looking for quick and standardized analytics solutions.
Advanced Segmentation Additionally, Customer Journey Analytics offers advanced segmentation capabilities, enabling businesses to target specific customer groups based on their behavior and preferences. This granular level of segmentation allows companies to personalize their marketing efforts and create targeted campaigns that resonate with different customer segments. On the other hand, Adobe Analytics, although capable of segmenting data, places more emphasis on general website trends rather than individualized segmentation. This broader focus can be useful for organizations looking to understand overall website performance and trends across different user segments.
Benefits of Implementing Customer Journey Analytics Implementing Customer Journey Analytics brings several benefits to businesses. Firstly, it enables the organization to utilize the powerful technologies within the AEP. It provides a holistic view of the customer journey, enabling companies to identify pain points, bottlenecks, and areas of improvement across online/offline data sources. This knowledge empowers businesses to optimize their marketing campaigns, website experiences, and customer engagement strategies.
Furthermore, Customer Journey Analytics enables businesses to gain insights into highly engaged and potential prospects. By understanding the behavior, interests, and preferences of these prospects, companies can segment them and target them with tailored messaging and offerings. Advanced segments based on these 360-degree views of the user journey can be created and sent to the Adobe Experience Cloud to activate on these segments using other Adobe Products such as Journey Optimizer to leverage the value of the integrated tools. It is also possible to send these segments to other parties such as Google Ads, Meta etc.
Moreover, Customer Journey Analytics can also help businesses in predicting future trends and customer behavior. By analyzing historical data and patterns, companies can anticipate potential shifts in customer preferences and market demands. This proactive approach allows businesses to stay ahead of the competition and adapt their strategies, accordingly, ensuring long-term success.
Additionally, Customer Journey Analytics can be instrumental in improving customer retention and loyalty. By tracking customer interactions across various touchpoints, businesses can identify loyal customers and understand the factors that contribute to their satisfaction. This information can be used to create loyalty programs, personalized offers, and exceptional customer service experiences, fostering long-lasting relationships with customers.
Who Would be the User of Adobe Analytics and CJA? Adobe Analytics and Customer Journey Analytics have distinct user profiles. Adobe Analytics is commonly used by marketing professionals, web analysts, and digital marketers. Its intuitive interface and pre-built features make it accessible to users with varying levels of technical expertise.
Customer Journey Analytics, on the other hand, offers more advanced technical features. The level of customizable may appeal to users who require in-depth customer journey insights and tailored analysis. Nonetheless, given its user interface closely resembling Adobe Analytics, Customer Journey Analytics can offer value even to users who don't necessarily need highly customized reports but prioritize comprehensive insights into the customer journey. It retains familiar drag-and-drop functionalities and right-click options within the Analysis Workspace. Thus, transitioning from Adobe Analytics to Customer Journey Analytics wouldn't present a significant adjustment for users accustomed to working in the Analysis Workspace.
Before deciding which analytics tool to use, consider the following factors: 1. Data approach: Consider the variety of data sources that you need to analyze. If you need to capture and analyze the entire customer journey, Customer Journey Analytics might be the better choice. If you only need to analyze website traffic and engagement metrics, Adobe Analytics might be sufficient.
2. Architecture: Consider the technical requirements and resources needed for each tool. Customer Journey Analytics requires a connection to Adobe Experience Platform (AEP) and leverages its technologies, while Adobe Analytics is an analysis workspace built on top of an Adobe Analytics implementation. it's worth noting that while Adobe Analytics is optimized primarily for web and app tracking, it is indeed feasible to transmit data from alternative sources to Adobe Analytics. Yet, this process may not be as streamlined as it is when using CJA.
3. Customization: Consider the level of customization needed for your analysis. If you need more flexibility and control over your data, Customer Journey Analytics might be the better choice. If you only need to analyze website and/or app-related behavior and don't require as many on-the-fly data processing and customization abilities as CJA offers, Adobe Analytics might suffice.
4. User profiles: Consider the profiles of the users who will be working with the tool. Customer Journey Analytics might be more suitable for business analysts, data scientists, or marketing professionals who need a comprehensive view of the entire customer journey. Adobe Analytics might be more suitable for web analysts or digital marketers who simply need to analyze website traffic and engagement metrics.
5. Budget: Consider the cost of each tool and how it fits into your budget. Customer Journey Analytics is a more advanced and comprehensive tool, which comes with a higher price tag when migrating or setting it up. Adobe Analytics is a more affordable option, but may not provide the same level of insight and detail on every customer touchpoint as Customer Journey Analytics. Nonetheless, there are a lot of actionable insights that can be derived from using Adobe Analytics.
By considering these factors, you can make an informed decision on which tool to use for your data analysis needs.
Summary In conclusion, Customer Journey Analytics and Adobe Analytics are both powerful tools in the data analysis realm, but they have key differences. Customer Journey Analytics focuses on the entire customer journey, offers advanced customization and segmentation, and enables businesses to gain insights about highly engaged prospects. Adobe Analytics, on the other hand, primarily concentrates on website performance analysis, provides pre-built features, and targets a wider user based.
By understanding the strengths and characteristics of each tool, you can make informed decisions about which one best aligns with your specific requirements and goals. Whether it's optimizing the customer journey or analyzing website metrics, leveraging the right analytics tool can pave the way to data-driven success.
Are you just starting or looking to optimize your current operations?
At Accrease, we bring data to life. Most companies track their customer's behavior on the website but don't understand the data they collect. We help ensure to gather relevant data, make sense of the data, and present it back to you in a simplified manner.
Overall, we help you make decisions based on data so that you can improve your business.
