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November 18, 2024

Building an Engagement Scoring Model for Analytics Data: A step-by-step guide

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: 

- Total correlation = 0.8 + 0.5 + 0.3 = 1.6 

- Normalized weight for metric 1 = (0.8 / 1.6) * 100 = 50% 

- Normalized weight for metric 2 = (0.5 / 1.6) * 100 = 31.25%                       

- Normalized weight for metric 3 = (0.3 / 1.6) * 100 = 18.75% 

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: 

𝐸𝑛𝑔𝑎𝑔𝑒𝑚𝑒𝑛𝑡𝑆𝑐𝑜𝑟𝑒 = (𝑀𝑒𝑡𝑟𝑖𝑐𝑠1 × 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑𝑊𝑒𝑖𝑔ℎ𝑡1) + (𝑀𝑒𝑡𝑟𝑖𝑐𝑠2 × 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑𝑊𝑒𝑖𝑔ℎ𝑡2) + (𝑀𝑒𝑡𝑟𝑖𝑐𝑠3 × 𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑠𝑒𝑑𝑊𝑒𝑖𝑔ℎ𝑡3) + …

 [A1] 

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

Published by Jude Felix in Insights
Tags: Accrease, Adobe, Adobe Analytics, Customer Experience, Customer Journey Analytics, Engagement Scoring, User engagement
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October 23, 2024

Use Adobe Target as a CDP: A step-by-step guide to unlocking personalization

If you work in digital marketing, you cannot avoid having been exposed to conversations about how a CDP fixes most, if not all, of your problems. For companies who have bought into the Adobe stack, you'll likely be looking at Adobe RTCDP. 

A CDP isn't an impulse purchase (if such a thing exists in Marketing) but is often a long process. However, this shouldn't put you in a waiting position. If you have Adobe Target, you can start your CDP journey today and harvest some low-hanging fruits. If you need to consider a CDP, then using Target can help you build a business case for why purchasing one is valid. 

Kasper

Kasper Andersen, Partner

In this article, you will learn how Adobe Target can work like a Customer Data Platform (CDP), its good and bad parts, and how it can help you move towards using a complete CDP. This guide will show easy steps to help you get started.

Can Adobe Target Work as a CDP?

Adobe Target has many features similar to those of a real CDP. This means it can be a good choice for businesses that want to use customer data to make things more personal without paying for a full CDP. However, there are some differences between Adobe Target and a real CDP. 

A traditional CDP collects data from many sources, such as websites, emails, and in-store purchases, and combines it with ID stitching to create a complete picture of each customer. Adobe Target, however, doesn't have the same advanced ID stitching and is primarily intended for testing and improving experiences. Adobe Target can collect and use customer data, but it only has some tools a real CDP has to gather data from many sources. 

Still, Adobe Target does have some CDP-like functions when used with Adobe Experience Cloud. Customer Attributes can bring in offline data like purchase history and CRM data. For example, Adobe Target can use information from a CRM to suggest products that customers might like. This can help create a consistent experience across all channels. 

Adobe Target is good at combining customer data to create groups and personalize messages, just like a CDP. However, personalization will be on a group level rather than the 1:1 personalization a CDP can deliver.

How Adobe Target Collects Data

Adobe Target has some tools that help collect and organize customer data. When a visitor visits your site, Target creates a Profile based on the cookie ID and 'stores' information in the Profile that you tell it to. 

This is done using mbox profile parameters. 

This is great for returning visitors, as you can now personalize and promote messages based on the visitor's previous behavior. 

Personalization goes wrong when the same visitor accesses the site from a new device. A new device means a new cookie and a new Target profile.

Target has a fix for that. 

If your visitors are logging in, you can pass a DeclaredID to Adobe's Visitor ID Service or through a mbox parameter named mbox3rdPartyId. 

When mbox3rdPartyId (or the Visitor ID Service) is set, it stitches the cookie IDs across devices to ensure that the Profile stays the same across devices. 

Now, it begins getting interesting as Target begins behaving like a CDP. 

One key feature available when setting the DeclaredID is using Customer Attributes. This lets Adobe Target collect data from CRM, Call centers, and other sources all in one place. For example, a business can use Adobe Target to target customers who are evaluated as having a high churn risk and use that information to show a relevant message to reduce the churn. 

Adobe Target can also collect data in real time, which helps keep customer profiles up to date. If a customer leaves items in their shopping cart, Adobe Target can offer a discount immediately to help close the sale. 

Benefits of Using Adobe Target as a CDP

- Personalized Experiences: Adobe Target allows grouping more significant segments and personalization of them based on their common attributes. 

- Real-Time Responses: Adobe Target can react to customer actions instantly. For example, if a user is looking at a specific product, Adobe Target can immediately show them more related products. 

- Works with Adobe Tools: Adobe Target works well with other tools like Adobe Analytics. This helps businesses get more information about their customers and use it to improve their marketing.

Challenges of Using Adobe Target as a CDP

Not as Powerful as a Full CDP: Adobe Target has tools different from those of a real CDP, which does not allow one to get a complete view of each customer. 

Data Silos: Adobe Target is limited to how much offline data can be onboarded. 

ID Stitching: Adobe Target has fundamental ID stitching, which cannot be compared to a real CDP. 

Step-by-Step Guide: Setting Up Adobe Target as a CDP

Overall, there are three steps you need to perform to begin to do cross-device personalization with CRM data.

  1. Pass DeclaredID to Adobe
  2. Configure Customer Attributes (import CRM or other data sources)
  3. Create an Audience off of your newly imported CRM data

Pass DeclaredID to Adobe

As mentioned, this can be done using Visitor ID Service and the mbox parameter mbox3rdPartyId. I'll only walk you through the simplest of the two, the mbox3rdPartyId approach.

Ideally, your visitor's DeclaredID is already exposed in your data layer when users log in. If not, your IT department might have to expose it for you.

I'm also assuming you're using Adobe Tags (aka. Adobe Launch) as your Tag Manager solution and already know how it works regarding libraries and publishing flows. Here goes:

1. Create a data element for the DeclaredID (if it doesn't already exist) and map it to your data layer element. For me, it looks like this:

    2. You may already have an existing rule that triggers Target on all pages. You can edit this or create a new one.

    3. In your actions of the rule, add or click on the existing Adobe Target - Add Params to All Requests.

    4. Add a new parameter mapped to the data element in step 1. Make sure the parameter name is spelled correctly. Otherwise, it won't function as expected.

    5. Publish your new changes.

    Technically, you're done now.

    By deploying the mbox3rdPartyId, you can now do cross-device personalization, as Target will now ID sti...or rather, cookie stitch visitors with a DeclaredID. Of course, the visitor must log in on both devices for Target to stitch.

    Your segmentation is still limited to behavior collected by Target, so let's move on to ensure you can segment on offline/CRM data.

    Configure Customer Attributes

    Customer Attributes allows you to import offline data and tie it to the DeclaredID. Any offline data you can map to the DeclaredID can be uploaded into Customer Attributes, and by doing so, it becomes available to create Audiences in Adobe Target.

    Data points examples:

    • - Membership level/type
    • - Membership points
    • - Next best offers
    • - Next best action
    • - Churn risk
    • - Customer Lifetime Value
    • - Lead score
    • - etc.

    Before you start, ensure you know what data you want to upload and that the data can be mapped to the DeclaredID set on your site. Once you have that, you can get started with configuring the Customer Attributes:

    1. Navigate to People

    2. Navigate to Customer Attributes in the top menu.

    3. Click +New

    4. Creating the new Customer Attribute is pretty straightforward. The most important field is the Alias ID. When taking the mbox3rdPartyId approach, you can give it any name. If you've taken the Visitor ID Service, it is important that the alias matches the alias you've set in the implementation.

    5. Upload your file with the data and confirm the schema. Ensure that the first column contains the DeclaredID.

    6. Finally, activate the Customer Attribute for Target.

    Depending on your Target license, you might be limited to activating only three columns at a time.

    More importantly, you can automate the file upload. You could do this via your Marketing Automation tool (e.g., Adobe Campaign, Marketo, or something else).

    The benefit here is that you—in most cases—already have your CRM data integrated into these solutions. So, instead of creating a new integration, you benefit from having your Marketing Automation spit out a daily, weekly, and monthly file that Customer Attributes then pick up and load to ensure your data is kept up to date.

    Create an Audience with your new onboarded data

    If you've been able to follow along this far, then it is time to be excited, as this is where the fun begins.

    When creating a new Audience, you should select Visitor Profile. Here, your Customer Attribute will be auto-populated with the data columns that you activated.

    It is simply going crazy building Audiences and using them in your activities.

    Let's recap what you've achieved so far:

    - You're setting a DeclaredID for your visitors who log in. This allows you to A/B test and personalize cross-device.

    You've enabled and activated a Customer Attribute that imports offline data, allowing you to build segments on this data — a classic use case for why to invest in a CDP.

        Recap of the benefits of using Adobe Target as a CDP

        - Works with Adobe Tools: Adobe Target works well with Adobe Analytics and other products.

        - Real-Time Personalization: It uses real-time data to give personalized content right away.

        - Prepare for a CDP: Investing in a CDP involves getting teams together. You can slowly kickstart that process and even build a business case for why an investment in a CDP makes sense for your business.

        - Eager Businesses: It's suitable for companies who wants to move NOW and cannot wait for when budget is available.

          - Keep in mind that Adobe Target is not a CDP. However, it can be a great place to start for most companies considering a CDP but are struggling to build a proper business case or if you want to take your personalization program to the next level.

          Conclusion: Is Adobe Target a Good CDP for You?

          Adobe Target is a good option if you want to start creating personalized experiences for your customers without a complete CDP. It has tools for using customer data, creating audience groups, and providing real-time customized content. Adobe Target also works well with other Adobe tools, which makes it a good choice for many businesses.

          If you are starting with personalization or already using Adobe products, Adobe Target can help you get many of the benefits of a CDP without the high cost. It's beneficial for smaller businesses that want to use customer data to improve the customer journey.

          If you want to learn more about Adobe Target as a CDP or how other Adobe tools can help you use your customer data better, contact us! We'd love to hear from you. Leave a comment or share this post if you found it helpful!

            Published by Kasper Andersen in Articles, Insights
            Tags: Adobe Target, CDP
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            October 7, 2024

            Enabling scroll depth tracking in Adobe Analytics with the new WebSDK Plugin 

            Tracking how much of a webpage users actually view is crucial to understanding engagement with your content. Whether you’re analyzing long-form articles, product pages, or campaign landing pages, scroll depth provides direct insight into how far users are going before leaving the page.

            For years, the getPercentPageViewed plugin has helped marketers track scroll depth in Adobe Analytics. However, with the adoption of Adobe's new WebSDK, the old plugin is no longer compatible, leaving organizations that rely on modern architectures with a gap in their analytics setup.

            We’ve developed a new scroll depth plugin specifically for Adobe’s WebSDK, offering a more robust, flexible, and scalable solution for tracking user engagement. In this post, we’ll explain the problem with the legacy plugin, demonstrate how to visualize scroll depth data, and guide you through the implementation of the new WebSDK-based solution

            How to use scroll depth data in Adobe Analytics to identify drop-off points

            One of the most valuable insights scroll depth tracking offers is identifying where users drop off on your pages. For landing pages and key CTA pages, this is critical, as it reveals whether visitors are engaging with your content long enough to reach the main calls to action.

            Pinpointing drop-off points

            Scroll depth data allows you to see exactly how far down the page users scroll before they exit or lose interest. If a significant portion of your visitors drop off before reaching the key messaging or CTA, this indicates a potential issue in your content flow, page design, or CTA placement. By identifying these drop-off points, you can take action to reduce friction and keep users engaged.

            Consider screen size: percentages vs. absolute values

            When analyzing scroll depth, it’s important to take screen size into account. Users on mobile devices may experience your page differently from desktop users, as content is displayed in a more compressed format. For this reason, it’s often helpful to measure scroll depth both in percentage terms (how far down the page they’ve scrolled) and absolute values(such as pixels or viewport units).

            • Percentage-based tracking: This helps normalize data across devices, making it easier to see the relative point where users drop off, regardless of screen size. For example, if 50% of mobile and desktop users are dropping off at around the same percentage mark, this provides a clear signal about engagement issues.
            • Absolute value tracking: This gives you more granular control over understanding specific user behaviors, especially when dealing with longer pages or dynamic content that may load differently across devices.

            Mobile vs. non-mobile device analysis

            Analyzing scroll depth data separately for mobile and non-mobile (desktop or tablet) devices is crucial for understanding the full picture. Given the different user behaviors and screen sizes, you may find that mobile users drop off sooner due to the nature of scrolling on smaller screens, while desktop users may scroll further before disengaging.

            By segmenting your data, you can:

            • Mobile devices: Adjust content and CTA placement to ensure critical information is visible early on smaller screens, where users typically scroll faster.
            • Non-mobile devices: For desktops or larger screens, you may have more flexibility in content layout, allowing for CTAs to be placed further down the page, provided engagement levels remain high.

            Example: improving CTA engagement on a landing Page

            Imagine you’re running a campaign with a dedicated landing page designed to collect sign-ups. After reviewing the scroll depth data segmented by device type, you notice that the majority of mobile users drop off at 20%, while desktop users continue scrolling to 37%, but your sign-up form and CTA button are placed at 70%.

            By moving the form higher, say around the 15% mark for mobile users and 35% for desktop users, you ensure that more visitors see the CTA before they drop off. This significantly increases the likelihood of conversions across both device types. Scroll depth tracking helps you make these data-driven adjustments, optimizing the performance of your landing pages for all users.

            How to Implement the New Scroll Depth Plugin in Adobe WebSDK

            Now that we've covered how scroll depth data helps identify drop-off points and optimize your landing pages, let’s look at how to implement the new plugin in Adobe WebSDK.

            Step 1: Add the following code snippet at the end of the "send event" action in your page view rule:

            //clear previous page data

_satellite.cookie.remove('initialPercent');

_satellite.cookie.remove('highestPercent');

// Function to set a cookie with an optional expiration in minutes

function setCookie(name, value, minutes) {

    var expires = "";

    if (minutes) {

        var date = new Date();

        date.setTime(date.getTime() + (minutes * 60 * 1000));

        expires = "; expires=" + date.toUTCString();

    }

    document.cookie = name + "=" + (value || "") + expires + "; path=/";  

}

// Function to calculate the percentage of the page viewed

function getScrollPercent() {

    const scrollTop = window.pageYOffset || document.documentElement.scrollTop;

    const scrollHeight = document.documentElement.scrollHeight - document.documentElement.clientHeight;

    return Math.min(Math.round((scrollTop / scrollHeight) * 100), 100);

}

// Function to calculate the initial viewport percentage

function getInitialViewportPercent() {

    const viewportHeight = window.innerHeight || document.documentElement.clientHeight;

    const scrollHeight = document.documentElement.scrollHeight;

    return Math.min(Math.round((viewportHeight / scrollHeight) * 100), 100);

}

// Function to track page view data

function trackPageViewData() {

    // Set the initial percent viewed on page load

    if (!_satellite.cookie.get('initialPercent')) {

        // Calculate the initial viewport percentage

        const initialPercent = getInitialViewportPercent();

        _satellite.cookie.set('initialPercent',initialPercent);

        _satellite.cookie.set('highestPercent',initialPercent); // initially set with initial percent value, once user scrolls the page it gets updated.

    }

    // Get the current scroll percent

    const currentPercent = getScrollPercent();

    // Update the highest percent viewed

    const storedHighestPercent = parseFloat(_satellite.cookie.get('highestPercent') || 0);

    if (currentPercent > storedHighestPercent) {

       _satellite.cookie.set('highestPercent',currentPercent);

    }

}

// Initialize page view tracking

trackPageViewData();

// Initialize page view tracking

window.addEventListener('scroll', trackPageViewData); // Track on scroll events

window.addEventListener('click', trackPageViewData); // Track on click events

            Step 2: Create a data element that reads the initialPercent and highestPercent values from cookies and builds a string:

            var initialPercent = _satellite.cookie.get('initialPercent');
var highestPercent = _satellite.cookie.get('highestPercent');
if(initialPercent!= null && initialPercent!= 'undefined' && highestPercent != null && highestPercent != "undefined"){
  return "initialPercent="+initialPercent + " | "+ "highestPercent="+ highestPercent;
}
else{
  return "";
}

            Step 3: Map the data element to the desired field in the XDM object in the same page view tracking rule.

            Step 4: Build your changes in your development and make sure the eVar is captured with the desired value.

            Step 5: Create rule-based classifications to separate initial percent and highest percent values into separate dimensions and then create sub classifications to bucket them if needed.

            Considerations for scroll depth tracking

            Scroll depth tracking offers valuable insights into how users engage with your content, but there are a few important considerations to be aware of when implementing this feature in Adobe WebSDK. 

            Scroll data isn’t captured for users who bounce

            Currently, our scroll depth plugin does not fire if a user bounces, meaning scroll depth data won’t be captured if a visitor leaves the page without interacting further. This could lead to missed insights, especially on high bounce-rate pages.

            Testing before implementing to ensure a smooth rollout

            Before implementing the scroll depth tracking plugin in a live environment, it's crucial to conduct thorough testing to ensure it works as expected and doesn't introduce any issues that could affect performance or data accuracy.

            Maximizing insights with no additional cost

            Scroll depth tracking provides invaluable insights into how users engage with your content and where they drop off, especially for landing pages and key CTA sections. However, it’s important to be mindful of factors like server calls, bounce data, and fast-scrolling behavior when implementing this functionality.

            If you are looking to capture real-time scroll data or need help optimizing your scroll depth tracking, we’re happy to help. Contact us for guidance on the best approach for your business and to ensure you’re getting the most out of Adobe Analytics while keeping costs and performance in check.

            Santosh would be happy to help! Reach out, get started and ask your questions to: [email protected]

            Published by Santosh Kumar in Articles, Insights
            Tags: Adobe, getPercentPageViewed, Scroll depth tracking, WEBSDK
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            August 29, 2024

            The role and impact of the AI Assistant 

            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)

            Published by Vide Blomdahl in Articles, Insights
            Tags: Accrease, Adobe, AdobeExperiencePlattform, AEP, AI, AI Assistant
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            May 15, 2024

            Adobe Analytics vs. Customer Journey Analytics

            Understand the differences between Customer Journey Analytics and Adobe Analytics with our Senior Analytics Consultant Martine Jørgensen

            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 – What is Customer Journey Analytics?
            Customer Journey Analytics (CJA) has become a hot topic lately – 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 – so what’s 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 – 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 – specifically designed for collecting data within the digital realm. In CJA, instead of report suites in the workspace panels, you will see “data views”. This is your ‘view’ 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 – 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?

            Martine would love to help you out!

            Contact info

            -> [email protected]

            Who are we?

            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.

            Published by Martine Jørgensen in Insights
            Tags: Adobe Analytics, Adobe Experience Cloud, Adobe Experience Platform, Customer Journey Analytics
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            April 9, 2024

            7 Key takeaways from Adobe Summit 2024

            The 2024 Adobe Summit has come and done. It was not just a tech event, but a great mix of ideas, innovations, and strategies aimed at transforming how marketers engage with consumers and how brands deliver personalized experiences. Spanning various themes such as AI, personalization, omnichannel solutions and breaking down data silos, the summit provided a comprehensive overview of the current trends shaping the digital landscape.

            Let us delve down our 7 key takeaways from the summit.

            1. AI bridges creativity with productivity

            Adobe's AI ecosystem, highlighted by products like Adobe Firefly, now significantly enhances productivity across the entire content creation process. With features expanded to include audio, video, 3D, and custom models, Firefly empowers content creators to produce diverse and engaging content while reducing manual tasks and ensuring brand consistency. Moreover, the introduction of AI assistants across Adobe products streamlines tasks such as audience segmentation and content approvals, driving efficiency and productivity. With these innovations, brands can accelerate content production and delivery without compromising on quality or creativity. This is really a significant leap forward within content production workflows.

            2. Personalization at scale is achievable

            Achieving personalization at scale is no longer a pipe dream but a concrete actualization. Through AI integration and tools like Firefly services/APIs, brands can produce personalized content efficiently and cost-effectively. The enhancements in Adobe Experience Manager (AEM) enable variant generation, allowing brands to tailor content to specific audience segments at scale. This focus on personalization not only enhances customer engagement but also strengthens brand loyalty across multiple channels.

            3. Breaking down data silos is essential

            Seamless personalization relies on breaking down data silos and centralizing data to facilitate connected experiences for consumers. Adobe's Real-Time Customer Data Platform (RT-CDP) with Federated Audience Composition allows companies to blend data from various enterprise platforms (e.g. Azure, Snowflake) without duplicating efforts. The solution enables companies to leverage data where it exists while enhancing Adobe Experience Platform (AEP) capabilities. This unified approach to data management enhances customer profiles and facilitates targeted, contextually relevant experiences across channels.

            4. The importance of the content supply chain

            Efficient management of the content supply chain is essential to meet the increasing demand for personalized experiences. Adobe's Content Supply Chain solutions orchestrate the creation, distribution, and sharing of content efficiently. Combined with the right operating model, these tools deliver improved quality, velocity, and efficiency in content creation and deployment, driving enhanced customer engagement and brand loyalty.

            5. Adobe Journey Optimizer (AJO): B2B edition

            With the upcoming launch of Adobe Journey Optimizer (AJO): B2B Edition in summer 2024, marketers will gain the ability to execute campaigns targeting specific buyer groups and tailor messaging based on the roles within those groups.

            This marks a significant advancement in customer journey orchestration. Serving as an extension of Marketo Engage, this tool empowers marketers to efficiently manage account and buying group journeys on a large scale

            These groups, enable marketers and sales teams to identify the members of a particular buying group, track the group's lifecycle status, note the group's interests in solutions, and assess the overall engagement and completeness of the group.

            6. Seamless Customer Experiences

            Adobe's emphasis on omnichannel experiences underscores the importance of engaging customers across various touchpoints in real-time. The introduction of GenStudio as a central hub for end-to-end campaigns integrates Creative and Experience Cloud capabilities, facilitating seamless content creation and activation across channels. Additionally, Adobe's Federated Audience Composition enriches customer profiles with data from enterprise platforms, ensuring consistency and relevance in omnichannel interactions.

            7. Enhancing Customer Journey Analytics (CJA) with AEP integration

            Another highlight from Adobe Summit was the focus on Adobe Experience Platform (AEP) with integrated Customer Data Platform (CDP) and Customer Journey Analytics (CJA) alongside Adobe Journey Optimizer (AJO) integration. Particularly within CJA, there is an emphasis on enhancing analytics capabilities like Content Analytics, Audience Analytics, seamless insights experience between CJA and AJO, and support for summary data. These features provide marketers with a deeper understanding of customer behaviour, content performance, and audience engagement across touchpoints. The CJA-AJO integration allows marketers to analyse customer journeys seamlessly and optimize experiences based on real-time insights. This integration improves content usage measurement from AEM assets and enhances audience tracking from RT-CDP, empowering marketers with precise analytics for data-driven decisions and marketing strategies.

            Should you seek more detailed information on the key takeaways covered in this blog post or have any questions, do not hesitate to contact us.

            Published by Lotte Garnry in Insights
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            March 4, 2024

            Future of Adobe Campaign Standard and where AJO and ACv8 plays in

            In the marketing automation space, Adobe has four similar offerings: Adobe Journey Optimizer (AJO), Adobe Campaign Classic (ACC), Adobe Campaign Standard (ACS) and Adobe Campaign v8 (AC v8).

            If you are wondering why so many options and what the difference between them is, you are not alone.

            However, when you understand them, you realize that they make a lot of sense and each one has its own space in the Adobe stack. Sure, there are overlaps, but there are also important differences we need to address to get the full picture.

            Jonas Guldberg Pabst, Principal Consultant

            I hope this post will help you better navigate these options and help you choose the best offering for you and your company.

            If you are looking for the a comparison do not want to read all the details and conclusions, go directly to the comparison by clicking here.

            Platform Question

            When talking about the Adobe Campaign platforms, there are currently three in the list there is originated from the same system, but with years of development between them.

            The AJO platform is the newest automation offering from Adobe, which is built on top of Experience Platform with the ability to combine online and offline data at a granular event level creating a real-time customer profile.

            When I am saying there currently are three platforms, the oldest ACC platform is being decommissioned within 2027* and the ACS platform is being decommissioned in 2026*.

            This is to make room for the new kid in the stack called Adobe Campaign v8 also referred to as - Adobe Campaign Managed Cloud.

            The new AC v8 is a good combination of the two older Adobe Campaign platforms, with an injection of features from AJO.

            So we have the strength and speed from ACC, the marketer-friendly web UI from AJO (with added features) and the 360 profile view known from ACS.

            AC v8 can be controlled from a web UI, which is created in an extended design from the current Experience Cloud UI, used in Experience Platform and Journey Optimizer, through a powerful user-friendly drag-and-drop interface.

            You are also able to connect from the console as you have done until now in ACC, where all you do will be automatically reflected in the web UI, including the folder structure which is a totally neat feature.

            AC v8 is placed as a standalone product with all the strengths and weaknesses that come with this and it's a more siloed, but powerful database structure.

            AJO is a full web solution which is intertwined with AEP and can leverage all the cool functionality that AEP has to offer. Taking advantage of the rtCDP and the streaming segments we can create journeys that can be joined when data from an action is added.

            There is a large number of sources that can add data through the edge network, which all will be combined into the profile.

            AJO is placed on top of AEP as a service and will share its data directly and will also leverage the power of the more simple rtCDP and the segmentation features.

            So, which platform should we invest our money in, when departing from ACC/ACS?

            That question is after my belief, the most important question we need to ask ourselves and is something that we need to have an answer on, within the first half of 2024, or we will be left in the dark age when ACC and ACS are gone.

            Put the tech on the sideline

            To start the conversation on the subject, I will take us a bit back from the tech and look at the platforms from a cleaner perspective, where we can define our business rules before settling on the tech and investing in a platform that does not quite fit all of our future needs or has to much to offer.

            So, we need to talk about how we would like to place the marketing automation platform in the solution architecture, and if it should be a headless or more controlling factor of the automation in terms of how we collect and use the data.

            The headless will be AC v8 would be running primarily on offline data and sending batch-based experiences. Where AJO will be leveraging offline and online data in batch and real-time communication.

            In a few non-tech separations you should go with AJO for the real-time trigger experience and pick AC v8 for advanced offline batch communication through planned experiences.

            Working with data

            We can say that the biggest difference between the two is that AJO is a service on top of the Adobe Experience Platform (AEP), where AC 8 can be acquired as a standalone platform, just like ACC or ACS was placed in the Adobe automation stack.

            In AJO we are leveraging on the AEP XDM data model which works with having a flat profile model with many fields and few relational schemas, whereas we in AC v8 are working with a postgres relational database where the profiles can be linked to data in a nested model rather than the flat model as we see in AJO.

            From a non-tech perspective, this will mean that in AJO, we are publishing the same data on several profiles whereas in AC v8 we are linking the profiles to a schema containing all the data needed e.g. company. In AJO we have faster access to the profile data and the real-time or event updates there will be combined into the 360 customer profile.

            Cool features

            Both AJO and AC v8 will deliver the coolest features to help you deliver a heck of a customer experience, personalized at many data points for delivering the best customer experience.

            Each of the platforms will have a bit different view on how we can deliver that experience, even if they are quite similar when looking at them from a big perspective, there are small but larger differences.

            AJO will keep a good focus on having a finger on being able to trigger experiences based on online and offline behaviour, where at the same time they can create custom actions sending event data for internal usage in another system.

            All of the above can trigger communication in real-time using both offline and online data arriving in batches or through real-time events.

            AC v8 will use its power to have a relational data model, create complex user journeys with advanced workflow elements, leverage powerful built-in web apps, ability to send attachments in emails and export modulated data from the platform.

            AC v8 can trigger communication through API or make use of the data placed in the database from various custom sources.

            Comparison table

            When we have looked a little under the hood on the two platforms, we have made this table with a few simple one-liners about the two platforms.

            Journey Optimizer (AJO)Campaign v8 (AC v8)
            Ability to combine online and offline data at a granular event level creating a real-time customer profile  Offline data, limited view of online data to trigger communications
            Real-time segmentation and activation based on big data architectureBatch segmentation and activation reliant on traditional SQL database
            Powerful segmentation capabilities across online and offline data inputsStrong segmentation capabilities using offline data. If including online behaviour, there is a delay in absorbing online data.
            High personalisation capabilities – online content into offline (e.g showing the last product viewed but didn't purchase in emails) and vice versaPersonalization is possible with what data is imported into the daily or delta loads. Limited online or real-time behavior-driven personalization is possible
            High-scale sending capabilities (millions in minutes)More limited sending capabilities (20 million per hour)
            Offer decisions across all channels.Offer decision capabilities limited to email, direct mail and SMS
            Ability to talk to all customers at once through 1:1 behavior-driven communicationsBatch communications and limited web-triggered communications

            If you can see yourself and your organization in one of the below bullets, AJO is not the right choice for you.

            - Will only use on-premise data

            - No plan to use a Customer Data Platform (CDP)

            - Looking for a "lift & shift" of existing campaigns

            - Teams and processes are not aligned in achieving the organization's goals at a bigger and no need for an extensive automation setup

            Make the decision

            It is known, that it can be hard to pick the next platform and have your organization with you in understanding the need for a “new” platform, but this decision must be, as mentioned earlier, made within the first half of 2024.

            If you need help making the decision and have the advantage of seeing the platforms in action, you can contact Accrease to get you started.

            What about the development

            If you are interested in the history of the Campaign platform, and how it developed from ACC into AC v8 and AJO, I will roll out a few details about it here below.

            The original product was called Neolane and was owned by a French company called Neolane Inc. until it was purchased by Adobe in 2013 for $600 million.

            The Neolane product was released in 2001 and has from 2001 till 2013 gone through 4 major releases with new revolutionary features for a cross-channel platform of that era.

            It was first released by Adobe as Campaign Classic in 2013, and the original product has kept that name up until today where its in final years as version 7.

            During the years, Adobe released its “little brother” in 2017, called Campaign Standard which was released as a cloud version of Campaign Classic there was more marketer-friendly than the big brother ACC was.

            Campaign Standard did gain a lot of market reputation and many customers made the jump from Campaign Classic to Campaign Standard due to the lightweight possibilities that marketers saw in the new platform.

            With Campaign Standard came a new email editor, the introduction of Fragments and a simple but advanced editor fully based on drag-and-drop functionalities.

            Later in 2019 AEP was announced and unveiled at Summit to everyone's excitement, a full-blooded CDP, created from Adobe's own hands (and not bought as earlier products).

            With the release of AEP, we also got Journey Orchestration, which was a marketing tool that could leverage the power from AEP and trigger communication through various automation tools eg. ACC and ACS.

            In 2021 AJO was announced to the public and will be a tool placed on top of AEP which could leverage the AEP CDP power and put it into automation with its built-in actions for email, SMS and push.

            In the same year as AJO was announced Adobe also unveiled that they will continue the ACC lane and build a new version of the ACC platform that will be better implemented in the Experience Cloud and have the power of Snowflake from their new partnership with Microsoft.

            In 2024 the new web user interface was released, which was a revolution since marketers now easily can create experiences in an advanced automation tool like ACC without the technical console and old-looking platform.

            * Dates are as currently mentioned by Adobe, but they can change in the years to come.

            Published by Jonas Guldberg Pabst in Insights
            Tags: AEP, Customer Experience, Marketing Automation
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            February 22, 2024

            Top Picks for the Adobe Summit ’24 from Our Experts

            Summit US is around the corner, so like everyone else, we here at Accrease prepare for which sessions are relevant for us and what announcements we will see to ensure we can include these when working with our clients. 

            If you have yet to hear, then 'rumours' are that there won't be a Summit in London this year. This only means that there will be more focus on the US one and potentially more EMEA-based clients attending the US one.

            Currently, 249 sessions are available if you're attending in person and 28 online. As in previous years, I expect recordings to be available for everyone post-summit. So regardless of whether you'll be attending in person or expecting to watch the recordings afterwards, I've asked our Specialists which sessions they would recommend to help you prioritise in the large selection of sessions. So, let's jump straight into the recommendations:

            Fulton Yancy

            He has experience in digital analytics since 1996 and spent the last decade getting his hands dirty digging for insights in Adobe Analytics. Remember to check out his blog on www.webbanalys.se.

            Session: 
            2024 Adobe Analytics Rockstars: Top Tips and Tricks [S104]

            Reason: 
            Meeting the rockstars of Adobe Analytics is always a great way to boost inspiration and learn new ways to work with Adobe Analytics. This has always been my all-time favourite session at Summit as - regardless of my experience in the tool - I always walk away having learned something new. You should definitely take advantage of this session!

            Jonas Nielsen

            Former Consulting Manager at Adobe and Founder of Accrease. Jonas is a Swiss army knife for the Adobe stack, particularly AEP, and often finds himself by a whiteboard drawing the client's architecture around AEP.

            Session: 
            How Adobe Real-Time CDP Impacts Full Funnel Marketing [VS513]

            Reason: 
            I usually focus on sessions that give a deeper understanding of the architecture behind the solutions. I tried looking for a relevant one this year but found no one. Instead, I personally find this session really interesting. 

            Often, a CDP is only thought of as something for bought media strategies, but to take full advantage of a CDP, it needs to be thought into the entire marketing funnel.

            Sharath Kumar

            Former Consultant at Adobe - Well experienced in Adobe Campaign and migrations to/deployments of Adobe Journey Optimizer.

            Sessions: 
            Adobe Journey Optimizer Roadmap and Innovations [S801] & 
            Making the Leap with Generative AI to Scale Personalised Experiences [S911]

            Reason: 
            At Accrease, we collaborate directly with the product teams for the different solutions we support. And especially when it comes to AJO, as the solution is evolving extremely fast with new releases every month. They can only give us some insights, so the roadmap sessions at Summit are my favourite ones. Understanding the product's direction will allow us to ensure the implementations we plan take any new features into account.

            I find optimising the flows and scaling the personalisation using AI fascinating, so my second recommendation is precisely this. Many clients we work with don't have huge teams, so the efficiency and scaling of personalisation come down to working smarter, and I have no doubt AI is a big part of the solution for this.

            Samuel Rajkumar

            Marketo specialist unlike any other. Experienced in building digital strategies using technologies like CDP and combining them with Marketing Automation.

            Sessions: 
            Adobe on Adobe: Marketo Engage and Best Practices for Global Marketing Ops [VS215] & 
            GenAI-Powered B2B Marketing: Crafting a Roadmap to Excellence [S205]

            Reason: 
            I always find it inspiring to hear from clients about what they have achieved using Adobe Stack. What I find even more inspiring is the Adobe on Adobe sessions. I recall they started doing these years ago, and I love seeing how Adobe are using their own solutions - simply because they know all the ins and outs of its products to ensure it fully gains the most from them.

            This is an excellent opportunity to gain insights directly from industry experts who have successfully set up a Global Marketing Ops.

            My second recommendation is similar to Sharath's. Regardless of whether you have a large team, AI will be part of your workflow one way or the other. I don't necessarily believe that AI will be able to fully automate personalisation or something similar in the near future. But I 100% believe AI will make you and your team more efficient.

            Jonas Guldberg Pabst

            Campaign specialist, who was raised - not by wolves - but alongside Neolane, which later became Adobe Campaign. A companion of his ever since.

            Sessions: 
            Unveiling Adobe Campaign's New User Interface and Innovative Features [S803] & 
            Scaling Customer Journey Capabilities for Global Digital Experiences [S809]

            Reason:
            Adobe has announced that they will unveil their new Campaign v8 web user interface (UI) as part of Summit. These two sessions introduce the new interface and how to leverage the power to build customer experiences. As for the new UI, you may have noticed that the documentation has been updated with a few previews.

            The new interface is a revolution for marketers and is a good blend of the more sophisticated UI from Campaign Classic and the simple marketer-friendly UI from Campaign Standard.

            It will embrace new powerful features and help you elevate campaigns, build audiences, and unleash the true potential of cross-channel experiences through a user-friendly drag-and-drop interface.

            This is super exciting news. We here at Accrease look forward to introducing all the new features it gives our customers.

            Kasper Andersen

            Former Manager at Adobe for Proof of Concepts in EMEA & JAPAC. He has been working with Adobe Analytics since it was known as SiteCatalyst; however, his favourite solution is and always will be Adobe Target.

            Session: 
            Top Tips to Maximise Value with Adobe Target [VS817] & 
            How Adobe.com uses AI-powered Recommendations for Personalization at Scale [S814]

            Reason:
            With Target as my favourite solution, how can I recommend anything besides Target sessions? 

            The top tips session is my clear winner - if I could watch only one session, it would be this one. Ryan Roberts is a former colleague and friend, and he's been doing similar sessions in previous years and always received high ratings from the attendees. I love his sessions.
            During my time with Adobe, I ran a similar session at Summit in London. Needless to say, it was with great sparring and input from Ryan.

            Just like Samuel, I enjoy watching the Adobe on Adobe sessions. Combine that with AI, and you have my second favourite session. Not many Adobe Target customers have Target Premium, which allows for using the Recommendation add-on. Most think that it is only relevant if you're an e-commerce site. However, that certainly isn't the case. I'm working with B2B companies that have successfully promoted training videos and FAQ articles.

            There you go. These were the recommendations. Check back after Summit, as we'll be providing a summary of the biggest and most important takeaways.

            Published by Kasper Andersen in Insights
            Tags: Adobe Summit, AEP, Customer Experience, Marketing Automation, personalization
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            December 5, 2023

            Adobe Experience Platform Web SDK: Everything you need to know

            If you have been around for as long as I have, you may remember the old SiteCatalyst/Adobe Analytics H. code that got replaced with AppMeasurement.js to increase performance and make it more up-to-date. The same happened for mbox.js, which was replaced by at.js. Well, it's time to replace EVERYTHING with the new Platform Web SDK.

            Kasper

            Kasper Andersen, COO & Partner

            What is Adobe Experience Platform Web SDK?

            In the past, each Adobe product functioned with its own JavaScript library, server endpoint, database, and visitor identity management system. This led to a scattered and often confusing array of instructions, documentation, and installation processes. However, with the introduction of the Adobe Experience Platform Web SDK, these disparate elements have been unified into a single JavaScript library, bringing together identity, audience, analytics, and personalization capabilities under one roof.

            The Web SDK communicates with Adobe's Experience Platform Edge Network, a network of servers designed to handle and respond to the data and requests the SDK sends. This unified approach drastically simplifies debugging and makes managing data across different Adobe products more efficient.

            The Web SDK also introduces semantic data modelling, allowing users to name and structure their data more meaningfully and intuitively. This feature significantly simplifies the process of keeping track of data mapping.

            All these features and advancements make the Adobe Experience Platform Web SDK a significant leap forward in providing a streamlined, user-friendly, and efficient way of implementing Adobe's marketing technologies on a website.

            👉 Click here to check out how Saxo improved their page load time with 46% by migrating to Web SDK

            Benefits of Web SDK over AppMeasurement.js

            The Adobe Experience Platform Web SDK significantly improves over the previous standalone JavaScript library, appmeasurement.js. The primary benefits of the Web SDK can be broken down into the following categories:

            1. Unified Library:

            One of the most notable advantages of the Web SDK over appmeasurement.js is unifying various Adobe product libraries into a single JavaScript library. Previously, appmeasurement.js was just one of many libraries, including at.js, visitor.js, and dil.js, each associated with a different Adobe product. With the Web SDK, these are consolidated, simplifying the implementation process and reducing the possibility of bugs and compatibility issues.

            2. Simplified Debugging:

            Debugging is made simpler with the Web SDK. Instead of tracking data going to and from different servers with individual JavaScript libraries, the Web SDK enables data related to identity, audience, analytics, and personalization capabilities to occur within the same request to a single Adobe endpoint. This feature allows for more precise tracking and debugging of data.

            3. Open Source and Transparent:

            Unlike appmeasurement.js, the Web SDK is open source. This transparency allows developers to follow along with changes, submit their issues or improvements, and understand the workings of the library in more detail. Furthermore, minified and un-minified libraries provide a more transparent debugging experience.

            4. Asynchronous Loading:

            The Web SDK provides asynchronous loading, which can reduce the time it takes to deliver valuable content to users. This feature significantly improves over the traditional synchronous loading of libraries like appmeasurement.js, which could slow down website performance.

            5. Semantic Data Modeling:

            The Web SDK allows for semantic data modeling, enabling users to use more intuitive and meaningful names for their data fields. This approach contrasts with the system used in appmeasurement.js, which often requires users to keep track of less intuitive names like "eVar21" or "prop42".

            6. Improved Performance:

            The Web SDK consolidates libraries rewritten from the ground up to be smaller, leaner, and faster—the reduced network traffic and latency lead to improved website performance.

            7. Future-Proof:

            The Adobe team is continuously working on new features and improvements for the Web SDK, ensuring it stays relevant and beneficial in the long term.

            In conclusion, the shift from appmeasurement.js to Adobe Experience Platform Web SDK represents a substantial step forward in simplifying the implementation and management of Adobe's marketing technologies on a website.

            Why migrate to Web SDK?

            There are two primary factors to consider when deciding whether to migrate. The first is the evolution of Adobe Solutions and the new Adobe Experience Platform. The second one is the external technical landscape with the death of third-party cookies and Apple's ITP.

            Evolution of Adobe Solutions

            Just as with the previous releases of new JS libraries, the development of the early Alloy.js (code name for Web SDK) came from ensuring that the JS files were using the most up-to-date technologies and, at the same time, trying to reduce the redundancy of functions that were shared across the different legacy JS files.

            In parallel, Adobe worked on the Experience Platform (AEP), which is the foundation for solutions, Adobe Customer Journey Analytics (CJA), Adobe Journey Optimizer (AJO) and Adobe Real-Time Customer Data Platform (RTCDP).

            All these solutions depend on the Web SDK as the data collection method. So, if you are considering any of the solutions within the AEP stack, it will require that you migrate. From a project perspective, you're in a much better position to show quick ROI for the investment in these solutions if you're already using the Web SDK instead of having to migrate as part of a deployment of, e.g. RTCDP.

            Over time, the vision is that AEP will replace the current solutions like Adobe Analytics and Adobe Target.

            External technologies

            Working in digital marketing is more challenging than it used to be, as external technologies constantly complicate our lives. The death of 3rd party cookies has been underway for several years now, and Apple is continually pushing updates to their ITP to increase privacy for their users. This has started many conversations around cookieless tracking and server-side tracking.

            The Web SDK is a way to better future-proof your data collection while respecting users' privacy. With the support of Event Forwarding to destinations server-side and the possibility of keeping cookie IDs based on a server-side cookie, you're in much better shape to tackle the challenges that will and will certainly come.

            New features to benefit from with Web SDK

            1. Ability to use first-party IDs to generate longer-lasting ECIDs.
            2. A tighter integration between Adobe Analytics and Adobe Target does not rely on stitching separate network calls.
            3. Faster sharing of audiences from Adobe Real-Time CDP to Adobe Target.
            4. Real-time data transformation and mapping in Datastreams using Data Prep.

            What is required by my team to migrate to Web SDK?

            The migration to Adobe Experience Platform Web SDK is relatively straightforward and requires minimal changes to existing implementations. No updates are needed to your existing data layer, meaning you don't have to involve your IT team.

            We recommend a few prerequisites - they aren't required, but they will benefit you in the long run. Depending on which solutions you're using within the Adobe Experience Cloud, there are different things to consider. We'll cover the most common solutions.

            Adobe Analytics

            You need to ask yourself whether you're getting value from the current implementation (i.e. are you acting on the data you're collecting?). If not, get rid of it so you don't have to take the time to migrate it.

            1. Ensure your Solution Design Reference (SDR) is up to date. There's no point in migrating variables if they are not used anymore. If you don't have an SDR, now is the time to create one.
            2. While updating your SDR, you should also clean up your variables in Adobe Analytics - disable them if they are irrelevant or not used anymore.

            Adobe Target

            1. Review your mbox and profile parameters and ensure they are still required.
            2. Update your SDR so that you have a column that lists your Target parameters.

            Most of the migration is done within the UI of Experience Platform UI. From a high-level perspective, the steps required are:

            1. Creating and defining an XDM Schema
            2. Configure a Datastream
            3. Install the Web SDK Extension in Adobe Tags (former Adobe Launch)
            4. Create data element for XDM variables
            5. Duplicate existing rules and update them to make use of Web SDK Extension
            6. Add the necessary services (e.g. Adobe Analytics) to your Datastream
            7. Test and Validate
            8. Clean up by removing legacy extensions and rules

            If you involve a partner like Accrease, we can lift 95% of the tasks without you or your IT team's involvement. The only time you or your IT team would have to be involved would be in step 7, validating and coordinating the release of the migration.

            We don't use Adobe Launch as our Tag Manager Solution

            Even if you're not using Adobe Launch, you can still migrate to Adobe Web SDK, which is still recommended due to the benefits mentioned above. With that said, the process may be smoother with Adobe Launch.

            At Accrease, we are deep specialists within Adobe Technology, so if you're using another Tag Manager, there may be better teams to do the actual Tag Manager work. However, we are happy to spar and guide you in deciding whether to migrate and what to be aware of.

            We've already done several migrations with other clients if you're using Adobe Launch.

            Remember, Adobe Launch is free if you're using Adobe Technology. Migrating to a free Tag Manager Solution could reduce your current cost.

            What happens if I don't migrate?

            Nothing. Adobe is not forcing anyone to migrate - at some point, we expect that you eventually will have to migrate, but nothing indicates this as of the date for writing this.

            If you're like most other companies, you're part of conversations where things like load speed and server-side are being discussed. If so, migrating will address some of the concerns typically raised during those conversations.

            If you're considering investing in Adobe Experience Platform together with Real-Time CDP, Customer Journey Analytics or Adobe Journey Optimizer, then the Web SDK will be a requirement, and you will be better positioned for success if you're already using Web SDK.

            Don't forget to check out our recording of how to migrate to Web SDK 👇

            Published by Kasper Andersen in Insights
            Tags: AEP, Web SDK
            Featured Image

            June 19, 2023

            Zero-Party Data: The Game Changer in Customer Relationships You Can’t Afford to Ignore

            Discover the power of zero-party data in transforming customer relationships. This blog explores the definition of zero-party data, its benefits, and how businesses can leverage it to create personalized experiences. Learn how targeted campaigns, deeper customer insights, and increased trust can revolutionize your marketing strategy.

            Read more

            Published by Kasper Andersen in Insights
            Tags: Customer Experience, personalization, zero-party data
            Older Posts ›
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