When migrating a client to Adobe’s Web SDK, we encountered a specific challenge that required creative problem-solving, and we believe it’s worth sharing.
If you’ve ever had to deal with GDPR or cookie consent in a setup involving Audience Manager (AAM), you’ll know how tricky it can be to keep your tech compliant while still sending the right data. In this article, you will learn how we tackled it.
Sampsa Suoninen, Principal Consultant
The problem: Web SDK doesn’t support conditional data forwarding
In our project, we needed to use AAM, but only if the user had given consent for marketing. That sounds simple, but there’s a catch.
In the old setup (using AppMeasurement), we could use a flag to control when data should be forwarded to AAM. Easy.
But Web SDK doesn’t have that option. Once it’s configured to send data to AAM, it just... does. Even if the user hasn’t given marketing consent.
This meant we needed a way to dynamically control where the data goes, depending on the user’s consent, without writing custom code or compromising on compliance.
The workaround: use Adobe tags + multiple datastreams
Our solution was to use Adobe Tags (formerly Launch) and create two versions of every datastream:
- One with AAM enabled (for users who say yes to marketing)
- One without AAM (for users who don’t)
So instead of 3 Datastreams (for dev, staging, and production), we created 6 in total, doubling up to cover both consent scenarios.
Then we used Data Elements inside Tags to switch between them based on consent. Here’s how.
The setup: step-by-step
This solution doesn’t require any custom coding and can be managed directly in Adobe Tags.
1. Create static data elements for each datastream
Make a separate static Data Element for each Datastream. This makes it easy to update Datastream IDs later without digging into code.
2. Create “conditional” data elements per environment
These will check the user’s consent and return the correct Datastream ID. For example:
- If consent for marketing is true, return the AAM-enabled ID
- If consent is false, return the AAM-disabled ID
To do this, we used four supporting Data Elements:
- Marketing Allowed – checks if the user gave marketing consent
- Marketing Allowed Value – what value counts as “yes” (e.g., true)
- Datastream – Prod – AAM Enabled – the ID with AAM
- Datastream – Prod – AAM Disabled – the ID without AAM
You can do the same for other environments (dev/staging) as needed.
3. Apply these in the Web SDK extension
Go into the Web SDK settings in Tags, and change the Datastream input to "Enter values" instead of selecting a fixed Datastream.
Then, reference your conditional Data Elements here.
This allows the Web SDK to dynamically choose the right Datastream based on the user's consent status at the time.
One more thing: handling page loads and SPAs
There’s one limitation with Web SDK: the settings are locked when the library loads. So if the user gives or changes consent after the page loads, the SDK won’t automatically switch to the right Datastream.
This is especially problematic in single-page applications (SPAs) where the library might only load once per session.
To solve this, we also added the same conditional logic in the “Configuration override” section of the Web SDK rules that send data (like “Send Event”).
This ensures that even if the SDK doesn’t reload, the data is still sent using the correct Datastream, based on the user’s current consent status.
The outcome
With this setup, we’re now able to:
- Fully respect the user’s consent choices
- Ensure data is only sent to AAM when allowed
- Avoid unnecessary custom code.
- Adapt to changes mid-session (even in SPAs)
If you’re working with Web SDK and dealing with consent-driven data routing, this approach is a flexible, scalable solution that can save time and keep your setup compliant.
Want to see this implemented in a live setup or need help applying it to your environment? Contact us we’re happy to walk you through it.
It’s been a week since Adobe Summit 2025 concluded in Las Vegas, and after digesting the wealth of announcements and insights shared, it's clear we're entering an exciting new era, one driven by intelligent automation and seamless collaboration.
As expected, Adobe Experience Platform (AEP) once again took center stage, but the depth of innovation this year was remarkable. Adobe has accelerated the transition from assisting marketers to orchestrating entire workflows through AI, bringing practical applications front and center.
Let’s dive into the most impactful announcements and explore what they mean for your business.
Agentic AI: Meet Your New Virtual Team
One of the most exciting announcements was the introduction to Adobe’s Agent Orchestrator in AEP, complete with 10 specialized AI agents. Imagine automating your content production, web optimization, audience segmentation, and journey management, all with AI "team members" managed from a single interface. These pre-built, compliance-ready agents simplify tasks previously requiring significant manual effort or technical expertise, allowing marketers to focus on strategic initiatives.
Adobe Brand Concierge: Conversational AI Made Personal
Another groundbreaking innovation is Adobe Brand Concierge, a powerful conversational AI that creates immersive, personalized customer interactions. Built directly into AEP, this tool not only engages customers but coordinates seamlessly with your existing customer data and content. From answering product questions to scheduling follow-up actions, Brand Concierge is poised to redefine how businesses engage across digital channels.
Experimentation Accelerator: Optimizing Journeys with AI Precision
Adobe Journey Optimizer received a major upgrade with the new Experimentation Accelerator. This AI-driven feature takes the guesswork out of optimization by recommending the next best actions based on real-time, cross-channel data analysis. Businesses can now systematically test and improve customer journeys, significantly boosting marketing efficiency and ROI.
The announcements around Adobe GenStudio and Firefly expansions are equally transformative. GenStudio Foundation streamlines content creation and delivery into a single cohesive workflow, while Firefly now supports advanced generative AI capabilities for video and 3D content. These advancements enable marketers to quickly create and localize content at scale, dramatically shortening production cycles and amplifying content effectiveness across all markets and channels.
Strategic Partnership: Adobe Meets Amazon & AWS
One strategic partnership stood out. Adobe’s deeper integration with AWS and Amazon Ads. This collaboration unlocks powerful first-party data matching and targeting capabilities, streamlines ad creative workflows directly within Adobe Creative Cloud, and simplifies the deployment of Adobe Experience Cloud applications within AWS environments. It’s a strategic leap towards better performance, compliance, and seamless cross-platform experiences.
Adapting to the AI-driven Era
Beyond technology, the Summit emphasized the significant shift businesses must embrace to fully leverage these innovations. As AI continues to redefine workflows and customer expectations rise, marketers and IT teams must collaborate more closely. Adobe stressed the importance of clear executive ownership, cross-functional agility, and ongoing experimentation to navigate this rapidly evolving landscape successfully.
Key Takeaways for Businesses
Leverage AI agents to scale personalization effortlessly
Embrace conversational AI for richer customer engagement
Adopt systematic, AI-driven experimentation to optimize customer journeys
Streamline content workflows with advanced generative AI solutions
Utilize strategic partnerships to enhance data-driven advertising and deployment flexibility
Final Thoughts
Adobe Summit 2025 was a testament to Adobe’s bold vision of integrated, AI-driven customer experiences. The advanced tools announced promise exciting opportunities for marketers and businesses ready to embrace these innovations. However, success hinges not just on adopting new technologies, but on fostering a culture of agility, collaboration, and continuous improvement.
To explore these innovations and their implications further, we recommend joining upcoming Adobe events or scheduling a session with our specialists at Accrease. The future of customer experience is here, so let’s navigate it together. Contact us here 😉
Adobe Summit 2025 is almost here, and we're sorting through all the sessions and announcements to keep our clients ahead of the curve.
With 306 in-person sessions and 28 online, there's a ton to choose from. And if you can't make it in person, no worries. The sessions should be recorded, just like in past years.
Not sure where to start? Whether you're looking to sharpen your Adobe Analytics skills, explore AI-powered personalization, or fine-tune your omnichannel engagement, this year's lineup has something for everyone.
To help you make the most of your time, we asked our experts to share the sessions they're most excited about. Here are their top picks:
Emma Walderlo
Emma is our data-driven Paid Search expert who is always turning insights into high-performing campaigns. With experience from Avanza Bank, she’s all about smart strategies and sharp analysis.
Reason: I’m looking forward to attending the session on calculated metrics in Adobe Analytics. This session feels especially relevant to me as it gives me the opportunity to learn more about how to create more precise metrics and gain a deeper understanding of how I can flexibly generate tailored insights, conduct more accurate analyses, and extract better insights from data.
No matter how much experience you have with Adobe Analytics, there’s always something new to learn and smarter ways to work. I believe that this session will provide me with the right tools to do just that. If you, like me, want to optimize your analyses and take your work in Adobe Analytics to the next level, I highly recommend checking out 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.
Reason: This is the session I always attend at every Summit I have attended, and that is quite a few. It is a super fun format, and I love the passion.
Reason: AJO is one of the solutions we see generating a lot of value for our customers. It allows us to use all of the data we have available in AEP to communicate something relevant to the customer, no matter the channel and wherein the customer's journey the person is. So, learning more about the roadmap and innovations will be super interesting.
Reason: Because Accrease is already working with many leading telcos worldwide, I would love to learn more about how T-Mobile delivers personalized customer experiences using Adobe technology.
Reason: To succeed with Adobe solutions, you need to invest in building the right teams to work with the solutions. Nordea has always impressed me with building good teams to support Adobe technology, and I always love to see one of our customers present at Adobe Summit.
Martine Jørgensen
Martine is our Adobe Analytics and CJA expert, as well as a Danish champion in bench press! Data or dumbbells, she lifts it all.
Reason: A must-watch for any Adobe Analytics user looking to master the latest tips and tricks from industry experts. Learn insider techniques to optimize your analysis, uncover deeper insights, and make the most of your data.
Reason: This can be very insightful for Analysts and B2B marketers. This webinar dives into how Customer Journey Analytics can drive revenue, optimize sales funnels, and improve lead conversion. A must for anyone looking to connect marketing efforts with business growth.
Reason: This one goes through AA reporting and shows how advanced metrics can transform Adobe Analytics dashboards. I would say this is ideal for analysts who want to elevate their storytelling and level of insight with data and drive smarter decision-making.
Martin Björklund
Martin is an expert in digital analytics—mastering tracking, measurement, and setting up Analytics. And yes, he’s a big fan of Excel, believing it’s one of the most fantastic tools out there!
Reason: I typically like the hands-on sessions, and customer journey analytics is the future of analytics tools, so from that perspective, the class is very promising. One of the most difficult things is to show relevant insights so that everyone understands how brilliant they are. This session will give you some tools to master later the ability to tell a clear and good story about your most valuable insights!
Samuel Rajkumar
Marketo specialist unlike any other. Experienced in building digital strategies using technologies like CDP and combining them with Marketing Automation.
Reason: Performance optimization is a recurring challenge in Marketo, and this session offers hands-on insights directly from Adobe field engineers. From Smart List inefficiencies to campaign processing time, it covers the core elements that can impact system performance.
I find this session particularly valuable because performance bottlenecks often go unnoticed until they impact deliverability, scalability, and reporting. Learning how to proactively optimize Marketo setup, identify inefficiencies, and mitigate bot-related data pollution is critical for maintaining a clean and efficient instance.
Reason: Webinars continue to be a core part of marketing strategies, but leveraging Gen AI for enhanced engagement and personalization takes it to the next level. This session dives into end-to-end webinar creation in Marketo, including promotion, delivery, and post-event follow-ups.
I find this particularly relevant because recorded webinars often lose engagement over time, and using AI-powered tools to personalize on-demand experiences and repurpose content dynamically makes a huge difference. Learning how to integrate webinar insights into Marketo for improved lead quality and nurturing aligns well with how B2B marketers can maximize ROI.
Mette Riis Bach
Whether it’s navigating the post-cookie world or optimizing customer journeys, Mette knows how to connect the dots and make things happen.
Reason: I find it interesting to hear more about how companies and agencies can work together in a post-third-party cookie world. Both in terms of ensuring privacy focus and at the same time being able to fully leverage all the benefits of having an RTCDP. Personally, I find this session interesting because it provides insights into how companies can work seamlessly with external partners to enhance advertising and co-marketing efforts across channels like Connected TV, commerce media, and walled gardens. Something I know for sure can be an obstacle for many companies.
Reason: Today, it is inevitable to talk about personalized, omnichannel engagement strategies without mentioning Adobe Journey Optimizer, which fully allows us to put the customer at the centre of the experience. My second recommendation is this roadmap session, which I believe would help anyone better understand the direction of the product and allow us to be on top of new features.
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 still and always will be Adobe Target.
Reason: Obviously there's a lot of focus on AEP solutions and this is of course the direction Adobe would like everyone to move in. However, a lot of customers are still using Adobe Target and Adobe Analytics, so I was excited to see that the solutions listed for the Prudential case were the good old AA and AT.
Combine that with AI personalization and a lift of a whooping 135% and you got yourself a perfect Summit session...if you ask me.
Which session is my runner-up, you ask? Okay, nobody asked, but I'm telling you anyway. As mentioned the solutions within AEP is getting a lot of focus and at Accrease we have a lot of clients migrating from AA to CJA. So keeping it within that space, then Mix Modeler is a really interesting solution and there's been some incredible cases with huge ROI.
Alright, that's our take for now. Check back after Summit, and we'll summarise the biggest and most important takeaways.
Imagine pouring endless hours and resources into designing personalized marketing campaigns only to discover they’re based on flawed customer profiles….It’s like throwing darts in the dark. In today’s multi-device, hyperconnected world, this is an all-too-common scenario.
Shared devices and fragmented data inputs can lead to “collapsed profiles,” where behaviours and preferences from multiple users are mistakenly merged into one. The result? Wasted marketing budgets, diluted brand experiences, and missed opportunities to connect meaningfully with your audience.
But how can your businesses rise above this challenge? In the following blog post, Senior Consultant Santosh Kumar will show you the hidden cost of collapsed profiles and how advancements in Identity Stitching are revolutionizing the marketing game.
The cost of collapsed profiles
The challenge of maintaining accurate customer profiles has long been a headache for marketers striving for precision. Consider this: A father and son share a tablet. The father’s browsing history includes luxury watches, while the son spends his time looking up video games. If these distinct behaviors are collapsed into a single profile, the resulting data is an unhelpful mix. This misrepresentation makes it nearly impossible to deliver truly personalized marketing messages, leading to missed opportunities and diminished customer engagement.
Understanding collapsed identity graphs
To visualize the issue, let’s look at two types of identity graphs:
Healthy identity graph:
In a well-maintained graph, each CRM ID (customer identifier) is correctly associated with multiple ECIDs (device/browser identifiers). This ensures all behaviors across devices are tied to the same individual.
Collapsed identity graph:
In contrast, a corrupted graph features multiple CRM IDs and ECIDs tied to the same profile. This creates confusion and undermines the foundation of personalized marketing.
Breaking through with Advanced Identity Stitching
The good news? Advancements in identity stitching are revolutionizing how we understand and engage with our audiences. With Adobe’s Identity Optimization algorithm, marketers can finally combat profile collapse and regain control over their data.
A new era of identity stitching
Adobe’s innovative algorithm leverages both deterministic (exact match) and probabilistic (best guess) techniques to create accurate identity graphs. This hybrid approach ensures each individual is represented by a singular, reliable profile no matter how complex their digital footprint.
Key features driving its success include:
Unique identifiers By prioritizing stable identifiers like CRM IDs over transient ones like cookies, businesses can dramatically reduce the likelihood of corrupted profiles.
Adaptive algorithms Adobe’s machine learning-powered system continuously evolves, refining its ability to differentiate between users and devices over time.
Data hierarchy Marketers can define the priority of various identifiers, ensuring the most reliable data sources take precedence when building profiles.
Real-Time insights for dynamic marketing
One of the standout features of Adobe’s Identity Optimization is its real-time functionality. Marketers can adapt campaigns on the fly, leveraging up-to-date profiles that reflect current user behaviors. This agility is critical in a competitive digital landscape where timely and relevant messaging can make all the difference.
Implementing Adobe’s Identity Optimization
Excited to take your marketing efforts to the next level? Adobe’s Identity Optimization is currently in Limited Availability, but here’s how you can get started once it’s rolled out in full:
Step-by-Step Guide
Understand your identities. Navigate to AEP > Sandbox > Customer > Identities > Browse. Identify key identifiers like CRM IDs and ECIDs. The rest of the default identities are not in use.
2. Simulate the graph.
Optionally you may navigate to “Graph Simulation” tab and play with the configuration to understand how the new solution help not corrupting the profiles. The order of the things you do is highlighted in the screenshot below.
3. Set up the graph. Finally, go back to the "Browse" tab and click the settings button in the top right corner to access the setup options. Here, start by ordering your cross-device IDs, such as CRM ID, phone number, etc. Next, specify whether each identity is unique. In this example, we've marked the CRM ID as unique to ensure only one CRM ID is added to the identity graph, preventing profile collapse.
4. Adjust browser identities.
Once completed, scroll down to see the list of browser identities, such as ECID, which also need to be ordered.
You can use your mouse to click and hold an identity, then drag it up or down to adjust its position in the list. Note that cookie namespaces cannot be marked as unique within a graph, as it's not feasible in real-time scenarios.
5. Save and activate.
In the final step, review the order of your identities and click the "Finish" button. After saving the configuration, it may take some time for the changes to activate.
Challenges and opportunities
While this innovation is a game-changer, it’s not without its limitations.
Historical collapses: Past corrupted profiles won’t be automatically fixed. Businesses may need to work with Adobe to clean up legacy data.
Data representation gaps: Underserved user groups highlight the need for diverse, comprehensive data collection.
However, these challenges present an opportunity for growth. Businesses that invest in refining their data strategies today will be well-positioned to leverage these advancements fully. The payoff? Highly personalized customer experiences that drive engagement and loyalty.
What this means for marketers
For marketers, identity optimization represents a game-changing opportunity to:
Enhance personalization: Craft messages tailored to individuals, not blurry profiles.
Boost ROI: Stop wasting budget on inaccurate targeting.
Build trust: Deliver relevant experiences that show you truly understand your audience.
Let’s discuss.
Have you encountered challenges with collapsed profiles? We’d love to hear your thoughts and help you out.
About the Author Santosh has more than a decades experience in within Digital Marketing and the Adobe stack. He holds more Adobe certifications than what's human possible. When no one else can fix it, you call for Santosh.
Troubleshooting & FAQs
As you implement Adobe’s Identity Optimization, you will wihtout a doubt run into questions or technical challenges. Check out Adobe’s official documentationor contact us and request to get passed through to Santosh 😉
Do you want to learn how to pinpoint, troubleshoot, and resolve common data issues in the Adobe Web SDK?
In this 20-minute Mini Masterclass, our very own Sampsa Suoninen will walk you through how to spot errors early, resolve them efficiently, and keep your data collection on track.
Why should you watch this Masterclass?
Many organizations are embracing the Adobe Web SDK or migrating from Adobe Analytics (AA) to Customer Journey Analytics (CJA).
When data streams are disrupted, reporting and insights can suffer.
Who should attend?
Developers, analysts, and marketers who use or plan to use the Web SDK.
Teams preparing to migrate from AA to CJA, looking for a seamless data collection process.
What you will learn:
Core concepts: Understand how the Web SDK combines data collection for Analytics, Target, and other Adobe products.
Troubleshooting method: Follow a clear, step-by-step approach to prevent and fix data flow issues.
Essential tools: Learn how to use the Adobe Experience Platform Debugger to track network requests and verify setup.
Data safeguards: Discover ways to record requests and maintain integrity, even when errors occur.
Real-world scenarios: See how other teams tackled common challenges and kept their data flowing.
Who Should Attend?
Developers, analysts, and marketers who use or plan to use the Web SDK.
Teams preparing to migrate from AA to CJA, looking for a seamless data collection process.
Bonus: In the session, you will be able to download a concise PDF checklist outlining the key debugging steps for the Web SDK—your quick reference guide whenever issues arise.
The Adobe Analytics Component Manager by Datacroft is an essential tool for many organizations using Adobe Analytics.
Several of our clients use and benefit from Datacroft's Component Manager. It's a fantastic tool, so a shout-out to Lukas Oldenburg for his work on it.
I strongly recommend you check it out if you're not familiar with it already.
If you are familiar with it, you know it works in Google Sheets, which not all our clients are excited about for various reasons. This has got me thinking that it should be possible to use the Adobe Analytics APIs in Excel.
Kasper Andersen,Partner
Like everyone else, we at Accrease are investigating how we can better use AI internally, with our clients, and in our deliveries.
So, last week, I decided to see how far I could push AI as a developer on something I had yet to gain exposure to. I thought it would be a good project to see if I could integrate the Adobe APIs into Microsoft Excel.
I am by no means a developer. Javascript I can read, but I used to Google it if I needed to write. Nowadays, I GPT it— if that's even a thing?
I use chatGPT daily, from being my personal developer to checking my grammar and summarizing notes. Even talk with it when driving to and from work. I know, I really need to find some friends.
My initial thought was to have chatGPT support me in building an Excel add-on, but after explaining my requirements, it suggested using Office Scripts?!
What are office scripts?
I've had no previous exposure to Office Scripts, so if you're the same, let me give you a quick intro to them.
Office Scripts is a tool in Microsoft Excel that helps you automate repetitive tasks. It makes it easy for everyone to use, even if you don't know how to code. Excel even comes with some out-of-the-box scripts to make your life easier. You can either record an action for it or code it using TypeScript, which is basically JavaScript.
They are only available in Office 365, but you can share them across users and organizations.
Now, you might be thinking. What's the difference between Office Scripts and traditional Excel Macros? Office Scripts are newer and easier to use than older Excel macros. Macros use a language called VBA, but Office Scripts uses TypeScript/JavaScript. Office Scripts also work with the online version of Excel, making them more convenient for using different devices.
Whether I've piqued your interest or not, if you're working in Excel daily, you should do yourself a favor and watch a few YouTube videos on the topic.
The result
It took about a week—a lot of back-and-forth with chatGPT and tests in Postman (for the Analytics APIs) and copy-pasting the office scripts. Whenever Excel generated an error, I would paste the error message back into chatGPT, asking it to address the error and creating a new script.
Once I had a functioning script, I would ask chatGPT to optimize it without breaking its functionality, which often resulted in a much smaller script.
I'm pretty impressed by the results myself and how AI can function as a developer — I have no real previous experience with the APIs and certainly not with Office Scripts, and today, we have a spreadsheet that works both on the desktop and online version of Excel.
For now, the functionality is the following:
Extract Workspaces: See usage and edit them straight in the spreadsheet.
Extract users: See their activities broken down by 4 previous quarters.
Extract report suites: Provides an overview of your Report Suites and Virtual Report Suites.
Edit report suites: Allows editing/enabling/disabling variables in one report suite at a time.
Let's get you started
Enough talk; let's get your set up to take it for a spin.
1. Download the sheet
Start by downloading the sheet by filling out the form below:
2. Configure API connection
Once you have the sheet, the first step is to go to the config tab. You must fill out A11 (Client ID) and C11 (Access Token).
To get this information, you need to create a project on Adobe.io. Here are the high-level steps:
Create a new project.
Click add API.
Select Adobe Analytics.
Select OAuth Server-to-Server.
Once the project is created you can find the credentials on the Project Overview page under Credentials.
Copy and paste them into the cells, and click the button, Get OrgId.
If successful, B11 will be populated with your Global Company ID, and you will be ready to use the other sheets.
IMPORTANT: Be aware that the sheet is not automatically getting the access token; why you just copied and pasted it. This also means that it expires after 24 hours. If that happens, you'll get an error when using the spreadsheet. All you have to do is go to your Project Overview > Credentials and click Generate new Access Token, which you copy and paste into the sheet again.
3. Start using the sheet
Hopefully, the buttons are self-explanatory. The Pull- buttons will extract and fill the sheet. You cannot mess anything up if you don't click the Push- buttons, as this will push changes back into Analytics. But let's quickly go through the sheets:
Report Suite: Start here, as it functions as the source for the dropdown in the Report Suite Editor sheet. Other than that, it only has the purpose of giving you an overview of your report suites.
Report Suite Editor: Select your report suites from the dropdown along with the variable type you wish to load. Click the pull button, and they will load. If you make any edits, the row will be highlighted in yellow - if you edit it back to its original value, it will still be highlighted. So, to 'reset' the sheet, you click the pull button again. Edits will populate the last column with a 'True.' Clicking the Push button will push all rows marked with True back into Adobe Analytics. After the push, you should click the Pull button to refresh and see your updates have been applied.
Users: The purpose of this sheet is to provide an overview of the users and how often they log in. Besides giving a general overview over the past 4 quarters, it will also provide an overview of users who have not logged in the last 12 and 3 months. Please note: The user's list is pulled from within Adobe Analytics (Admin > Analytics Users & Assets). It will not correspond with the list of users from within the Product Profiles in the Admin Console.
Workspaces: This sheet allows you to pull all the workspaces in the account with all the details required to understand when it was created, which report suite it is used on, who created it, etc. You can delete and make edits to the workspaces.
If you edit details for a workspace, e.g., change its name, the row will be marked yellow, and column B (Action) will be pre-populated with EDIT.
If you want to delete a workspace, click the cell in column B (Action) and select DELETE. This will mark the row with red, indicating it will be deleted. If you remove the DELETE value or redo any edits to the original value, it will still be highlighted; you can pull data again to reset.
If you make any edits, the 'Action' column will have a value, and the rows will be highlighted in color. Based on this, the API will push updates for all workspaces with an EDIT or DELETE value set.
After the push, you should hit the pull button to see the updates to the Workspaces.
Final note
You are responsible for using this tool, and Accrease is not liable for any outcomes resulting from its use.
The sheet uses macros, which you should enable. Macros look for edits and highlight the row that is edited. I could not get this working with Office Scripts.
Remember, it requires a work/school account with Microsoft 365, as Office Scripts are unavailable on personal accounts.
Feel free to copy the Office Scripts if you can use them elsewhere.
I'd love to hear your thoughts!
Please comment below if you found this helpful or have suggestions for additional features. We'll continue to expand on the features based on the input we receive.
What if you could pinpoint your most engaged users, understand what makes them tick, and tailor your strategies to boost conversions and retention?
In this webinar, our very own Jude Felix, Data Scientist at Accrease will show you how to activate an engagement score and drive significant business impact. Discover how engagement scoring can turn your customer data into actionable insights that fuel growth and improve outcomes.
Here's what you can expect:
How Can Engagement Scoring Impact Your Business? Understand why engagement scoring matters and how it directly contributes to driving meaningful results for your business.
Introduction to Engagement Scoring: Learn what engagement scoring is, its role in analytics, and why it's crucial for effective decision-making.
Role of Adobe Analytics & Customer Journey Analytics: Compare key features and benefits of Adobe Analytics (AA) and Customer Journey Analytics (CJA), and understand how these tools help bring engagement scoring to life.
Data Requirements: Identify relevant data sources, understand the types of data needed, and address data quality and integration challenges.
Building Your Engagement Scoring Model: Learn the steps to define key metrics, collect data, develop, and validate an effective engagement scoring model.
Implementing and Activating the Model: Discover how to integrate your engagement scoring model into AA and CJA, and how to monitor and adjust it for maximum impact.
Takeaways
Bonus: As an added bonus, all attendees will receive a complimentary pre-counseling offer! This includes a free one-on-one consulting session with Jude to help you get started. This offer is for those who use Adobe Analytics and are ready to activate their data for engagement scoring.
What if you could pinpoint your most engaged users, understand what makes them tick, and tailor your strategies to boost conversions and retention? With Adobe Analytics (AA) and Adobe Customer Journey Analytics (CJA), you already have a wealth of data on user behaviour. The real challenge lies in distilling that data into clear, actionable insights.
An Engagement Scoring Model helps you do just that. By assigning scores based on user interactions, you’ll quickly identify your top-performing audience segments, understand which behaviours drive the most value, and prioritize efforts to maximize business outcomes.
In this guide, Jude Felix, Senior Consultant at Accrease will show you how to build an Engagement Scoring Model step by step. You’ll learn how to transform raw analytics data into a powerful tool for targeting, personalization, and optimization, giving you a clearer view of what drives success—and how to replicate it.
Jude Felix,Senior Consultant
What is an Engagement Scoring Model?
An Engagement Scoring Model is a framework that assigns a numerical score to users based on their interactions with your digital properties. By scoring user engagement, you can:
- Identify which user behaviors or interactions are most valuable.
- Segment users based on their engagement levels.
- Make data-driven decisions for targeting, personalization, and optimization.
The process for creating this model combines both gut-based intuition and data-driven analysis to determine which metrics matter most for your business goals. Below are the steps to make the model.
Steps to create the Engagement Scoring Model
Creating an effective engagement scoring model is a powerful way to understand and quantify user behavior. It helps businesses tailor strategies and drive more meaningful customer interactions. Following a structured approach, you can identify key engagement metrics, analyze their impact, and assign appropriate weights to create a robust scoring system.
In this process, we will walk through 7 key steps, starting with defining the data timeframe, selecting relevant metrics, and conducting correlation analysis. We’ll then benchmark metrics, assign weights, and create an engagement score formula. Finally, we validate the model to ensure it accurately reflects user engagement and drives real business results. This systematic approach helps you gauge engagement effectively and provides actionable insights to refine customer experience and strategy for better outcomes.
1. Decide the data time frame
The first step is to decide how much data you want to include in your model. The last 30 days is typically a good starting point, as they capture recent user behaviour without including too much stale data. However, depending on your business or campaign needs, you may choose a different timeframe (e.g., last 7 days or last 90 days).
2. Select key metrics
Selecting the right metrics is the most important step in building the engagement model. There are two main approaches to choosing these metrics:
The intuition-based approach: Based on your experience and knowledge of your customers, you can choose metrics that you believe are important for driving engagement. Some of the metrics might include:
- Page views
- Time spent on site
- Number of sessions
- Product views
- Purchases
- Revenue
The data-driven approach: If you want a more empirical approach, you can identify the metrics most correlated with achieving a specific goal, such as purchasing or signing up for a newsletter. You can do this through statistical correlation analysis, which we will discuss in the next step.
3. Perform Correlation Analysis
To validate and refine the chosen metrics, you can use statistical methods to find out which ones are most closely associated with user success or your defined goals. The goal here is to understand which metrics have the greatest impact on user engagement.
How do you conduct the correlation analysis in Excel?
Gather your data:
- Export relevant data from AA or CJA for your selected metrics over your chosen timeframe (e.g., the last 30 days).
Enable Analysis Toolpak in Excel:
- Go to File > Options > Add-ins.
- In the Manage box, select Excel Add-ins, and click Go.
- Check the box for Analysis Toolpak, and click OK.
Run the correlation analysis:
- Organize your data in columns, with each column representing a metric.
- Navigate to Data > Data Analysis and choose Correlation.
- Select the range of data and choose Output Range to display the correlation matrix.
Analyze the results:
- The correlation matrix will show you the correlation coefficients between each metric and the goal metric (e.g., purchases, signups).
- A correlation coefficient ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no correlation.
Figure 1 below shows the results of a correlation calculation in Excel based on a few selected metrics.
Figure 1 Correlation calculation in Excel.
4. Benchmark a Score Using the Correlation Coefficients
Once you have your correlation coefficients, the next step is to decide which metrics to include in your engagement model. To do this, you can benchmark a threshold score by calculating the average correlation coefficient across all metrics.
- Metrics with correlation coefficients equal to or above the average should be included in the engagement model.
- Metrics below the average threshold may not significantly contribute to engagement and can be excluded.
Based on the coefficients of correlation from the Figure 1 we can create a benchmark chart like in Figure 2. The metrics number 3, 5, 6, 7 and 8 are he metrics that is highly correlated.
Figure 2 Benchmark chart
5. Assign weights to the metrics
Now that you have selected the most relevant metrics, you need to assign them weights. These weights determine how much influence each metric will have on the overall engagement score.
Normalizing Correlation Coefficients to Assign Weights:
Take the correlation coefficients from the previous step and normalize them so that they add up to 100%. This can be done by dividing each metric’s correlation coefficient by the total sum of all selected metrics’ correlation coefficients and multiplying by 100.
For example, if you have three metrics with correlation coefficients of 0.8, 0.5, and 0.3, you can calculate the normalized weights as follows:
These normalized values become the weights for each metric, ensuring that the metrics most closely associated with user engagement have the greatest impact on the score.
6. Create the Engagement Score Formula
Now that you have both the metrics and their respective weights, you can calculate the engagement score for each user. The formula will look something like this:
For example, if you score based on Page Views, Time Spent on Site, and Purchases, figure 3 shows how this can be created in AA or CJA.
Figure 3 Calculation of engagement score
7. Validate the Engagement Model
After building the engagement scoring model, it’s crucial to validate it by applying the model to a sample of users and comparing the scores with actual business outcomes. As shown in figure 3, we can Do higher engagement scores correlate with higher conversions or more purchases?
If the scores don’t align with real-world outcomes, you may need to revisit the metrics or re-run the correlation analysis to fine-tune the model.
If the model accurately reflects user engagement and correlates with success, it’s ready to be deployed.
Why use an Engagement Scoring Model?
An engagement scoring model provides businesses a tangible way to measure and track user engagement over time. Here’s why it matters:
Identify high-value users: By scoring users, you can quickly identify your most engaged users, allowing you to focus your marketing efforts where they’ll have the greatest impact.
Target personalization: Use engagement scores to deliver personalized experiences based on the level of user engagement, creating a more tailored and relevant experience for your audience.
Optimize user journeys: By understanding which behaviors drive engagement, you can optimize your website, app, or marketing efforts to encourage those behaviours, improving overall customer satisfaction and conversion rates.
Conclusion
Building an engagement scoring model using Adobe Analytics or Customer Journey Analytics data is a powerful way to turn raw user interaction data into actionable insights. By following a data-driven approach backed by statistical analysis, you can create a model that accurately reflects user engagement and drives better business outcomes.
This method combines both intuition and rigorous analysis, ensuring that the metrics you choose are grounded in actual data and that the engagement scores you generate align with your business goals. Whether you’re focusing on retention, acquisition, or conversion optimization, an engagement scoring model provides the foundation for smarter, more effective marketing and product strategies.
Start building your model today to maximize your Adobe Analytics data.
Want to dive deeper? Join Jude Felix for a 20-minute Mini Masterclass: 'How to Activate Engagement Scoring to Drive Business Impact.' In this session, Jude will walk you through building and activating engagement scoring models using Adobe Analytics and Customer Journey Analytics (CJA). Don't miss out, sign up here: Mini Masterclass
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 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.
Pass DeclaredID to Adobe
Configure Customer Attributes (import CRM or other data sources)
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!
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 NewScroll 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:
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]
