Lights, Data, Insight: Uncovering the Story Behind Every Journey

Transcript

Hi everyone and welcome. I’m Katie Klein and I am so excited to be with you today. This is Lights Data Insight and we’ll be uncovering the story behind every journey. So I am the principal consultant at Blue Acorn for data and insights. And I’ve been a long time Adobe analytics user and I’m part of the analytics champion program, which includes CJA. Over the years I’ve worked at brands like Verizon, Audible and Macy’s. And a lot of times I am tasked with helping teams unlock the full potential of the experience cloud solutions. And this is what the goal of driving smarter decisions and obviously better customer outcomes.

I am also so fortunate to lead the New York City Adobe analytics user group alongside some wonderful co-leaders. And I’ve also founded Data on Trend, which is a brand where I share content focused on marketing technology and demystifying what goes into that. And I love storytelling and really sharing how strategic frameworks help to support that and maybe a little bit of sparkle. So today in this session I will be talking about how to bring structure to your data story. And this will help you to go from scattered scenes to a more seamless customer journey that makes us ready for center stage. This will include customer journey analytics, realtime CDP and target and journey optimizer. And the goal again is to transform disjointed data into these meaningful measurable experiences. We can think of the session like a play. Data is our cast, tools are our script and insights are a standing ovation. Let’s start with Act 1, chaos. Imagine your data as scattered props on a stage. It’s a mess. Different tools, different sources, different systems, everything is telling their own story. Analysts are stuck trying to reconcile plots. It’s noisy, it’s fragmented, it’s really difficult to act on. And this is a challenge that I’ve seen some organizations face, many of us face this today. So the goal is for us to find our north star. Imagine stepping into new act, this is one where our tools are aligned, data is flowing cleanly, the entire cast is working from the same script. And with CJA in the spotlight, we can bring harmony to the chaos and we can enable everyone, including marketing, product, ops teams to see and act on what actually matters. When we think about how to write the script for our play, we want to also add a camera and the web SDK is how we can capture those interactions. It’ll collect events across the website and send them to experience platform. And this really allows you to align your data and those interactions to your defined schema, which I’ll go into a little bit more in depth in just a moment. All of these things can help to enable the real time behavioral data that you need for things like targeting for personalization, and one of my favorites attribution. So every great story has structure. In experience platform, that is the XDM schema. So the XDM schema is critical to how you structure your data. It defines how your data should look. This includes which fields exist, how they’re related, how they’ll be used across the platform. This is a visual that allows you to see how these things are established within the overall structure. Your XDM schema can equal your class plus your field group. And the goal is for this to create your unified profiles and allow you to feel really confident in your data and your analysis within CJA specifically. So going back to our analogy, if you think of it like a screenplay, your XDM schema ensures consistency, alignment, and seamless handoff between your cast of characters, which includes your tools like CJA, RT, CDP, and AJL. So going a step deeper and really thinking about web SDK, this is like choreographing a scene. Your implementation has to be precise. This is super important. So setting up roles and sandboxes and AEP, then mapping the schema and ensuring that you’re thinking about things like privacy and regulatory considerations, especially when it comes to consent that you’re now thinking about with omni-channel data. Each step can make sure that web SDK is not only capturing the right data, but that it’s actually doing it responsibly and reliably. And you’re thinking about your data in the most ethical way possible. So these are some of the steps to actually implement the web SDK. I think sometimes this can feel like a daunting task where you’re not sure where to start. You think about all the different steps that have to go into it. And the goal is to think about this in the most straightforward way possible. So when you go ahead and establish your AEP environment, then you want to go ahead and install the web SDK. And that’s by adding the JavaScript snippet to your site. Ideally, you’re doing these things through a tag manager, best practice, anytime you’re adding something to your site, you want to be able to do that. But it can also be done other ways. You want to go on to track events. This would be adding fields which contain your data types, and then you want to name and describe your schema. And then you can integrate consent management, which again, super important. We want to think about being ethical, respecting privacy regulations, and also thinking about the things that need to happen just to really have good governance. And that’s across the board. So for this, it’s thinking about privacy and consent. But in other ways, you really want to think about strategic frameworks, measurement methodology, and I’ll go into a little bit more of that later. So then testing and validation is incredibly important. Using debugging tools to make sure that the SDK is sending data correctly to AEP. I really go with the test and learn methodology in pretty much everything I do. I think it helps to scale implementations, it helps to scale new processes and steps, anything that you can do to get a sense of how things are, how things are going, I think is really important as early as you can. And then finally, leveraging that data is the reason that we’re here. Really being able to use AEP and the associated platforms to drive insights, drive personalization, and then integrate with the other solutions.

Now it’s time to set the stage for your personalized journey. With your data foundation in place, we can move to the spotlight. Nearly every business wants to be able to enable personalization. I always say that the buzzwords are omni-channel personalization at scale, but it is a real goal. So CJA can help to act on real-time behavior at a cross-channel level to be able to scale that. From segmentation and cross-channel journey visibility, thinking about predictive insights, CJA can actually give a lens to understand customers as people. It goes beyond IDs in a table. So one of the great things that you can have within CJA is a people metric. And this allows you to really think about how visitors, how those who are interacting with touch points, how segments are performing as actual people. Going on to think about a world of personalization, you really need to also think about thoughtful schema planning. And this helps to define the attributes that actually shape our narrative and shape our story. So your data view configurations can give the opportunity to a great scene, bringing together things like data sets, dimensions, metrics. The goal is to really tell a cohesive and powerful story. So real-time data integration, segmentation and audience insights, cross-channel tracking, predictive analytics, content optimization, really bringing together a lot of the things that we’ve been talking about already, and that’ll go into more detail on. One of the great ways to think about CJA and a lot of the solutions, but really CJA is where I think a lot of these features shine is predictive analytics. So this is where your analysis becomes action. CJA can use machine learning. This helps to analyze past behaviors and to actually surface the trends that you would need to be able to optimize things like marketing efforts. This could include the likelihood of a purchase or the risk of something like churn as well. And you can use things like intelligent captions, pathing analyses, predictive segmentation and model scoring and outputs to help support this. So you’re not just reacting to customer behavior and behavioral data, you’re actually trying to anticipate it and proactively shape your next steps. CJA can aggregate the data from multiple touchpoints and multiple data sources. And this can be done with the various touchpoints for your brand. Models can fuel this and they are refined over time. So as the data improves, as more data comes in, as you have more for analysis, this can really improve your prediction accuracy. And the goal is to be as accurate, as contextualized with the highest data quality as possible. So now moving along in our journey, we’re thinking about segmentation and audience insights as your casting process. So in CJA, you can take a look at the different types of data. Behavioral data is one and that’s very related to what we’ve been able to do in Adobe Analytics. But it can include the connection with data that is maybe a little bit different than what we’ve had access to in analytics before. So this could include interactions with your website, app or email. But it can also tie that together with these other types of data, including something like demographic data. So as I mentioned before, privacy and regulatory considerations when it comes to consent are super important. You also want to think about what’s ethical for your campaigns in the way that you’re treating your data. So demographic data can include location, age, gender, gender identity. But these are things that you want to consider in the context of how you are communicating with your customers as well. You also have access to transactional data if you decide to bring that in. We’ve had access to transactional data before in Adobe Analytics. But if you have an offline experience for customers that is encompassing transactions, this is something that you can look at that purchase behavior and history alongside your digital experience, which contains things that are maybe more traditional like card abandonment. You also have custom variables just like you’ve had in the past. But this might look a little different based off the different types of data you can bring in and where those sources are coming from. Customer IDs are a great way to be able to look at how your business is performing at a customer level and not to have to rely on some of the IDs that we’ve had in the past that might be more device related. But also looking at something like loyalty program status and thinking about how your segments are actually transitioning through the different interaction points or thresholds for a loyalty program is another way that you can look at this and really be able to get those deeper insights through CJA. And the great thing is that these segments not only fuel insight, but they can actually drive personalized experiences through the other solutions including AJO, Target, and RT-CDP. So once the segments and audiences are enabled, you can really hyper target the messaging and personalize the content. And this can be activated and analyzed in a way that gives you, again, those deeper actionable insights. CJA, again, can also provide insights, but thinking about content performance has been really interesting. Content analytics is one of the newer solutions and a great way to think about how to assess the metadata and leverage something like CJA to be able to look at that at an omni-channel view. You really want to look at your data sources and also the point at which somebody is interacting with the brand. And I’ll go into the tracking and tagging that is involved in that in just a bit. And the goal is also to enable that continuous optimization of your digital experiences. And you really want to have that North Star goal of driving better engagement. So going a little bit further, again, into behavioral data, an example of this is an abandoned cart. So if you think about those who are abandoning cart as people who are showing interest, and they’re engaging in some way, but then they’re not converting. One way to look at this is really think about how you have that dynamic segmentation of users. The way that can be created would be to include those who visited a page, which would be considered your behavioral attribute, those who spent more than two minutes on that flow, so that would be your engagement indicator. And then if they did not purchase what they added to cart within that session, that would be your conversion behavior. There’s all sorts of interesting ways to apply things like attribution to this. You can also customize how you’re thinking about the timing. So maybe for some businesses, spending more than two minutes on that flow would be really important. But if it’s less or more than that, you can actually go on to update that within your segments and your strategy. Another example of how you can use the data that I was discussing before is when it comes to demographic data, you can define a group by age, kids, geography, there’s all sorts of demographic data that you have access to as long as you load that into your instance of EEP and CJA. You could create a dynamic segment of users. That may have indicated that they have children under 10. Again, going back to things like explicit consent, I think it’s pretty important for people to actually opt into providing this information, but there are ways for you to be able to think about this at a behavioral level and at a product level if that’s related to your business. But in this instance, we’re thinking about this from a household composition perspective. And then you can also look at a location attribute. So you can update this in your real time for your segmentation. This will allow you to adjust the messaging and then also adjust your offers across email, web, or app. Moving on to activation and motion, this is where things start to get really exciting. We’ve defined our segments, and we want to activate them. So this would likely start with tracking the campaign source, but you could go about this a couple of different ways. And when we think about tracking an attribution, this is the mystery moment in your customer story. You want to know what actually caused the conversion. So with attribution, you can test different models. This might be a more robust view, an omni-channel view of which touch points would matter the most across your digital and offline channels. So choosing an attribution model that works best for your business also includes taking that a step further to actually choose an attribution model that suits your campaign. Now the plot thickens. Campaign tracking. Let’s get tactical. To enable campaign tracking, you would want to add UTM parameters to your campaign URLs. You would ensure that your analytics instance captures those parameters on entry. And then linking those touch points to conversion metrics like purchases or signups will give you an opportunity to trace marketing success back to the source. So this is something that people have been doing for a long time. This is something that’s been available in analytics as well. But again, we’re thinking about this in terms of bringing lots of different data sources of different types into your instance. So you can actually think about how this works at an omni-channel perspective. And just to take this a step further for UTM parameters, you have things like UTM source, medium, campaign, all of these things allow you to go on to do that robust tracking.

Thinking about touch points like actors, each contributes to the outcome. With attribution models, you can actually control how that credit is distributed. So you might favor early interactions and that might be specific to your awareness campaigns. But then you might think about last touch for high intent conversions and that would be slightly different. So you can actually look at different models and apply them through your data views in ways that are not necessarily that will help to drive the deepest insights for the types of campaigns that you’re doing. So you can go on to customize your attribution. You can go on to make sure that the data view covers both offline and online interactions. If you want to get that full picture, you can go on to also segment for insights, which would include creating segments based on those campaign interactions and really trying to understand those different customer responses. Then I always take a test and learn mentality when it comes to testing out anything new or even looking at the overall setup and implementation or success of the things that I’m putting into practice. So getting a read early and being able to make sure that you’re using things like A-B testing to go on to adjust your strategy and your approach or your attribution is a great way to really see and refine what’s driving conversions. And then measuring that campaign impact and tracking things like lift in the behavior allows you to really see the success of your efforts. Segments aren’t static. They’re actor players in your experience delivery. So you can build and refine them in C-J-A. Then you can share them out with target or journey optimizer and personalize at scale. So this is one of the reasons that I really enjoy working with these solutions because you can go on to do implementation insights and send things out for activation from one centralized place. And then because a lot of these segments can be refreshed in real time, they stay super relevant as the customer behavior changes because you do want to drive changes in customer behavior. That’s the goal driving more engagement.

So in the spotlight, we are building and refining those customer segments. You can go into Segment Builder in C-J-A and go on to define your criteria and make sure that you have all of the things that you want to consider for when you’re building your segment. Then you can apply filters and you can set exclusions and make sure that you’re really refining this for the highest level of accuracy that you can. Then you can go on again to share them with the activation platforms and really drive that targeted delivery and then go on to monitor your performance and make sure that you’re doing things that will allow you to refine and iterate as you go along. That is how your story evolves and how you move from analysis to action.

Now we shift from storytelling to directing. Target and Journey Optimizer are your tools to orchestrate those personalized scenes across channels. So alongside C-J-A, they can allow you to ensure that the customer sees the right story. That’s based on their behaviors, their segments, and their timing.

So now that you’ve set up your UTM parameters and you have the ability to use your campaign data, a great way to use this is to trigger personalized content. You can tailor offers or experiences as users move through the funnel. So as they’re going through the different touch points, you can actually update those experiences and you can use your tracking to trigger that. And then to be able to really expand that to that omni-channel experience, you can also use Journey Optimizer to then trigger messages and those interactions based on the events. This can include card abandonment, but it can also include something like location check-ins. And this allows you to get out of a reactive space and you can be the director in your show and do so in real time.

So we’re at our last act. This is where your loop closes. And this is something that I think is so important when it comes to that virtuous cycle. Your C-J-A audiences can be exported to A-J-O and you can really drive that real-time activation. Your traits can become your triggers, card abandonment, we keep mentioning this, but this is a great opportunity to be able to do this. But in this instance, that might trigger an email or a push notification. Then A-J-O sends delivery data back into C-J-A. So that’s really taking those insights and that data and being able to activate off of that, work on attribution, update that and ideate as you go along, and then further optimize that experience for the customer. And the feedback loop is what actually keeps the story sharp. So we really want to have relevant messaging for customers so that they continue to come back and engage in lots of different ways.

And finally, we get to our vision for a better tomorrow. Journey Canvas is one of my favorite features. This shows you how to think about users that are moving across channels and how they’re doing so over time. So what’s wrong, what’s working, what’s broken. This can be applied to campaign optimization and touch points. This can also apply to what’s broken at a product level. So just as important oftentimes as how people are interacting with the brand to be able to make a purchase or conversion. It’s also really important to think about how features are or are not working. So being able to identify what’s broken, where people are dropping out and being able to have a visual representation can allow you to optimize and make sure that the customer experience is not just relevant, but it’s actually working for customers so they can move through the flow and continue to drive that engagement. This is also a really good tool to rally those cross-functional teams around those important moments in the journey. Cohort analysis is also a good honorable mention, I’ll say, to look at your retention and behavior patterns over time by group. So thinking about the point at which they were acquired as a customer or as a segment and showing the impact of campaigns on things like user retention or lifecycle engagement.

And finally, we are at our finale. This is where you want to turn your analysis into a repeatable practice. So it is super important not just to think about driving performance impact, but also to think about the framework and the best practice to make this scalable. So thinking about the KPIs and aligning those to your business goals, really thinking about how to enrich data sets and incorporate that into your strategy. And then also labels and classifications are incredibly important. So standardized naming conventions allow you to leverage the data that you have and all of the important work that you’ve done for metrics, dimensions, dashboards, everything that has data associated with it. So taking a standardized naming convention is just going to make your life easier. And then also leveraging annotations and setting up alerts are a great way for you to be able to look back at things or also be alerted to when something might be going wrong or might be different than anticipated. And then you can go on to optimize your campaigns or check to see if something is impacting your business or your efforts in a way that may not have been anticipated.

So thank you so much for joining. I hope you enjoyed our show. I am excited to answer your questions on the Q&A and appreciate the time.

Thank you, Katie. That was great. Loved how clearly you explained the components of implementation, which was my favorite part of the session and gave us real world segment examples. I learned a lot and I’m sure our audience members did as well. Let’s open up the Q&A now. If you’ve got questions for Katie, please drop them in the chat below.

OK, Katie, so related to configure data collection and track event steps, are these low code, no code activities, or is it like a fully manual activity? Yeah, that’s a great question. Thanks, Jeffrey. So what I would say and something that I typically say about this is it’s definitely a very significant effort to go ahead with implementation. But what I’ll also say is that it’s totally worth it. So quite a bit is going to be custom and manual. There are definitely out of the box capabilities that you’ll be able to use just across the entire solution and all of the different features. But what I would say is you’re really going to need to get your hands dirty in terms of going in and doing a decent amount of manual effort. There are a lot of ways to be able to harness these different features and capabilities to really further the business. A lot of tangible value realization. So again, completely worth it, but just really get ready to do quite a bit of manual work on this. And I think one of the key benefits that I’ll also say is by leveraging the capabilities more on the front end within the UI, even though you’re doing a very significant lift up front in the process of doing the implementation, by the time you get to it being really usable for the end user, there is really a lot of kind of plug and play, drag and drop capabilities that are very similar to analytics. So you’re kind of paying for it and the effort that you’re doing in terms of what’s manual initially in the process. So operationally, technically, but by the time you get to the area where the end user is going to be able to take advantage of the dashboards and the capabilities, you really get to that place where they’re taking all of the benefits of that manual hard fought effort that you’re putting it up front. Yeah. And can you point to some technical articles or references for folks in the audience about how the mapping is actually done? Yeah, sure. I will say that as somebody who’s been doing this for, you know, for CJA for a number of years with analytics and all of these different solutions for a little over a decade, Experience League, I’ll give a huge plug for that. So I think there’s ways to really like customize it for yourself and make sure that you’re getting the right level of detail, which can always go in there and get the technical documentation. And there’s also a lot of really great community members within the different user groups and the different communities. So I would say going to Experience League, you know, doing a search, setting up your profile there, so you’re getting really relevant content right up front, and then being able to go through the community is a really great way to be able to give yourself the foundation you need to walk through it. Yeah. And thank you for plugging the Experience League. I agree. It’s an excellent resource.

So this is a cool one. What are the biggest innovations Adobe is focusing on next year? Yeah, go ahead. Oh, sure. So I will say not a surprise. AI is at the forefront of things. But I can really, you know, speak from my own experience and opinions on this.

GenTech AI is super exciting. I think a lot of the capabilities that have been founded within Gen AI also, especially as you’re thinking about connecting content through to data and analytics, especially with content analytics. That’s a really exciting one for me, too. So I would say just the concept around AI, you know, insights agent is also a really great way to do it. You know, I know I’m kind of going back and forth with this, but it’s a lot of favorites and things that I’m excited about. I think when, you know, intelligent captions rolled out within CJA, it was something where I started to feel like there was a tangible way to start to get those insights and understand the trends in large data sets. And now that it’s actually coming to fruition and able to be these kind of thought starters within the solution itself and to be able to have more of a natural conversation with the solution and the data and the way that the AI agents work, it’s just going to really take off, especially as things continue to progress. So I would say probably not a surprise. The thing that I am especially excited about is really just to get my hands on these things even more and get a sense for where that value can really be derived. Awesome. So I know this is a really relevant to a lot of people who are kind of in the implementation journey. But so what needs to be done to ensure the existing Web SDK is compatible with CJA and what are prerequisites to move to CJA from Adobe Analytics? So what I would say is I’m going to answer the second part of that first. When you are thinking about going ahead with moving to CJA or in some instances adding CJA to your suite of solutions, because I think there can be both perspectives depending on what you’re looking to do with the solutions, really taking a step back and even going a level up from the technical implementation to think about what does my data look like? How many data sources do I have? What kind of use cases am I looking to build or accomplish based off of what I have available, whether it’s within my ecosystem of technology, both Adobe and potentially not Adobe. This is that kind of assessment where I think it’s really important to be able to take a look at that, think about team structures and how people are going to need to access the data. So I would recommend people want to kind of jump into the schema, they want to jump into the Web SDK, they want to jump into tagging and understanding all of these different nuances. But my recommendation is really to go even a step before that and really think about how are you developing this forward thinking strategy to be able to set yourself up for success. As you go through that and you think about the quality of the data and really the level of granularity that you need, I think it’s only going to set you up for success to be able to then go through and design an effective schema that’s going to be able to accomplish your goals. And so when you go ahead and do that and you think about the team structure and the capabilities and who needs to access what, I think that puts you in a really good position to say, do I have teams that are well versed in implementing analytics that they can go on to understand the implementation around CJA and that way they can adapt their skills to be able to implement that. So for me, it’s really founded in people and process. I would say taking a look at the documentation, thinking about what those steps are that need to be taken, and then making sure that that’s in line with the assessment that you’re doing of your data are all really good exercises to go through that are going to put you in a position to be able to say, am I ready? Have I done this kind of like either self-assessment or working with a partner to be able to go ahead with these readiness assessments, I think are really opportunities and things. They’re activities that I really enjoy doing, especially with the folks that I work with in my current role, because I think we get a chance to look at these unique situations and then take what is a really solid detailed process and adapt it. So as you go through, just going back to that first question that was asked, as you go through and you think about how much custom work do I need to do? What is the manual effort? What does the lift look like? What’s the feasibility? And you go through those different checkpoints through your process, you can actually map the two together and say, yes, I’m really well equipped to do this. I know that the quality of my data is high. I’ve gone through corrective measures, just in case there are things to consider and, you know, kind of custom work that needs to be done in advance of it. And that way, when you get deep into that implementation, you’re feeling especially confident in the type of data and the capabilities and everything that you have in front of you. Yeah, great answer. And this is a natural one, I think, to follow up with that. So here’s a question. Where can we find best practices for naming conventions across CJA or your opinion, what the best practices are? Yeah, I think there’s a lot of great content online from, I’m very fortunate to be part of this Champions program with a lot of people that I have an opportunity to learn from. And they’re part of this broader community. And I often say when it comes to the analytics community and a lot of the folks that are involved in, you know, advocating for or speaking about these solutions, especially within Skill Exchange, so many wonderful folks and presentations that are taking place during this event. They’re really great resources to be able to look at and see how different people are going ahead with, you know, everything from naming conventions. I know that there were some recent, I think, experience leak or perspective articles that were done around things like UTM parameters and variables and all of the different things that go into the solutions themselves. Those are great places to look. I’ve also published some content myself. But what I would say is doing a search on experience league and within the communities is a great place to do it. Also going on to actually follow a lot of the folks in the community on social media, LinkedIn, there’s a lot of great articles that are written. But there’s a ton of documentation out there. Again, plug for experience league because I think it has so much great information and I think a lot of the community is going to continue to publish things on there. One kind of final comment on that, go ahead and interact with the community. I think it’s a great place for you to also be able to contribute because I think a lot of the folks who are even asking some of these questions probably have their own learnings. And there are times when things come up during implementations that maybe we don’t expect. But those are really good learning opportunities for us and for the folks within the community who may face the same challenges. So I would say, you know, experience league again is a great place to start, but really leaning on community members, looking at social media, thinking about where some of these great resources are is a good way to just continue to take part in the community. Those are terrific ideas. So here’s one that’s comparing CJA to analytics. So does CJA have limitations like 50,000 rows for downloading data like we do in analytics? Also, if we want to filter certain numbers of customer IDs inside a segment and it has more than 500 values per container, what are your thoughts on that? Yeah, so I will say that I don’t have the numbers exactly kind of at my fingertips right now, but I know that there’s definitely some details again within the documentation that talks about that. But what I will say is in the instances that have been especially relevant to the use cases and the implementations that I’ve done both on the brand side previously, and then also working with some of the clients that we work with now, there are a lot of things that are really opened up when it comes to CJA. So aside from being able to use data that’s different from just behavioral data and a lot of the ways that people have even taken creative solutions to incorporating even more beyond kind of traditional web data, there are things that have been opened up. So thinking about parameters and naming conventions, that’s one of the areas where we don’t face the same limitations. So I’m sure everybody who’s worked with analytics remembers low traffic or faces that when you’re doing a marketing campaign and you’ve got these very long tails of campaign tracking codes.

You know, there’s not the same issue with cardinality, essentially. So that’s one of the really great areas where you can start to really think through what is campaign tracking look like and open that up for yourself. Similar to kind of the nuances that a lot of us have gone through when it comes to, you know, 250, I think was the number that I remembered at one point when it came to how many, you know, E-bars and props I had access to. And now you don’t face the same kind of thing because really the construct of how the solution works is quite different. So, you know, when it comes to those individual aspects of implementation that you’re going through, I would recommend again, I’ll just continue to say there’s nothing wrong with going and searching for it when it comes to needing to understand kind of those precise numbers. But there are a lot of things that have really been opened up and a lot of limitations that have been lifted to be able to do, you know, even more in-depth kind of omni-channel analysis when it comes to CJI. Yeah, and those are huge benefits of CJI too.

So back to an implementation question. So can we implement and I think this might be our last question, can we implement Web SDK without having a data layer implemented? I personally am not aware of a way to do that. I know that you may need to kind of re-orchestrate the data layer in order to use different tag management systems. So one of the nuances was, you know, we did an implementation through a TMS that was different from tags, what some of you may know as launch, you know, the Adobe tag management system. And so, you know, there are questions around re-orchestrating a data layer and how we do that in advance of going ahead with Web SDK. I do know, however, that you can use Connector. And so this is one of the things where, you know, maybe the answer is just that it’s not as straightforward as saying yes or no to that. I think I’ll kind of amend my answer. You can go ahead and use Connector to be able to bring your data over from analytics into CJI. I actually say sometimes that this is a good way. And actually, I had a conversation with my friend, Lukash, from Lenovo at one point a number of months ago where we talked about this. You can bring the data over. There are going to be some limitations. My recommendation on that is really to do that when you’re thinking about enablement, not when you’re thinking about leveraging CJI for really harnessing the full power of the solution. So I’ll amend that to say yes, in theory you can. But I think my follow up to that is there’s so much benefit to implementing Web SDK and also being able to leverage a data layer to be able to facilitate a lot of the ways that you want to be able to not just understand what customers or those interacting with your brand are doing, but also to be able to activate off of that, especially in near real time.

Yeah, agree. Awesome conversation. Thank you so much again, Katie. Thank you so much, Gentry. Appreciate it.