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Transcript

My name is Jas Singh and I’m a Senior Customer Success Architect here with ÃÛ¶¹ÊÓƵ and I help our customers overcome their existing challenges or roadblocks to achieve their marketing North Star with the ÃÛ¶¹ÊÓƵ Digital Experience Stack. And my expertise, like Katie mentioned, is within the Campaign Orchestration Rong and Customer Journey Domain. So let me do this. Let me stop the camera and let’s focus on the content. OK, so today we’ll be talking about data and specifically one of the challenges around data that’s too much data, right? So that is data stream prioritization. What do we do when we have too much data? Our agenda first things first, right? Let’s we’ll talk about what is data stream prioritization. Then we will go over why is that important to even consider. And we should go over how to achieve data stream prioritization. And last but not the least, what are the best practices around data stream prioritization? So by definition, data stream prioritization is the process and ability of a system to prioritize and manage data from different sources based on certain predefined rules or criteria. And this criteria, it can vary depending on a few factors. For example, the significance or importance of the urgency of that data. The ÃÛ¶¹ÊÓƵ Marketing Technology Stack, it frequently encompasses that collection of data from different sources, a very diverse set of sources such as CRM systems, loyalty programs, the web data, mobile data through apps and SDKs and other digital points, depending on the vertical or the industry that you’re part of, the enterprise that you’re part of. And this data acquisition process, it can occur in either batch or real time formats, real time aka streaming. And different types of data streams may include first party data, second party interactions, web visits, ad impressions and many more. Now, there are a few things that make up that data stream prioritization to what it should be. And to be able to explain this concept a little bit better, I’ll be using ÃÛ¶¹ÊÓƵ Experience Platform as a central point of reference. So that would make a lot more sense since it has a lot of data sources coming in in order to create that complete customer profile. So first thing, real time data collection. Because whenever we talk CDP specifically AP, we talk real time because it has some of the capabilities, some other data management platforms or data platforms they don’t have. So the ÃÛ¶¹ÊÓƵ Experience Cloud, it often involves real time data collection for different sources. Like I said, websites, mobile apps and other digital points. And these different types of data streams may include customer interactions and ad impressions and more. Second is the prioritization criteria. So the criteria or the rules for these prioritization, they can vary and they can be based on the importance of that data, the urgency of that data. For example, customer interactions during critical marketing campaign that’s going on might be assigned a higher priority than the routine web visit. This best example could be like the holiday seasons or Thanksgiving sales. Next is business rules and objectives. So the prioritization is typically aligned with the business rules and objectives. And different businesses or enterprises, they may define these rules regarding whichever data should be processed or analyzed first based off their own organizational and strategic goals. Next is optimizing the resource utilization. That means basically that data stream prioritization will help optimize your resource utilization and ensure that critical data is being processed promptly. It can also prevent delays in processing high priority data, allowing your enterprise and the business to respond even quicker to some customer interactions that just pop up and overall market trends. Next being customization and flexibility. Now, like I mentioned, ÃÛ¶¹ÊÓƵ Experience Solutions, they may provide customization options and allowing you to tailor your data stream based on whatever your unique requirements are for a particular use case. And the users, they might be able to configure and adjust that priority settings to adapt whatever you have changing your business models based on, let’s say, the holiday season or business goals. Yes, so on and so forth. Next is integration with other ÃÛ¶¹ÊÓƵ solutions. So the ÃÛ¶¹ÊÓƵ Experience Solutions, like I said, it consists of AP, ÃÛ¶¹ÊÓƵ Analytics, Campaign, Target, and many more. So prioritization settings, they can extend across these solutions to ensure that seamless flow of data and insight. So when we talk about best practices, I’ll talk a little bit more deeper into that on why is this important. Now let us dive into why is data stream prioritization even worth considering when it comes to campaign orchestration and customer journey. So prioritizing that data streams, it becomes essential and you need to prioritize the construction and updating of that customer profiles and segments for that effective campaign orchestration and activation. You need to ensure that the most pertinent, precise, and timely data is given precedence, especially for those real-time use cases. The objective of prioritizing data streams is to optimize the quality and the relevance of that customer data within the platform. But this not only doesn’t stop here, like in turn, it’ll allow your org to elevate your customer insights and tailor your marketing efforts to enhance overall customer experiences. And that way you’ll have the most valuable and up-to-date data at your disposal. And once you have that, the marketers have that, the activation and the personalization, you can scale it very well. All right, so we talked about what data prioritization is, what’s important, but let’s talk about how to achieve this, the data prioritization. Now the process of achieving data stream prioritization, it basically necessitates a blend of you need technology, you need strategy, you need tools. So we’ll begin by delineating the metrics and the criteria that ascertain the significance of data streams with your specific context of force. Now these may encompass the variables like the customer engagement part, the transaction values, or if the use case has to be real-time, so there’s some real-time relevance factor there, or any other indicators that is specific to your business. So what you need to do is you need to harmonize that data stream prioritization with your overall arching business goals and objectives. Now these alignments, they’ll ensure that your strategy, your prioritization strategy is in sync with your org or enterprise broader strategic initiatives and goals. But you don’t stop there, right? You have to regularly, you have to put like scrutiny on that performance of how that strategy, the data stream prioritization strategy working out for you, and continuously assess how well your systems will respond to this dynamic conditions, right? Marketing is never staying and there’s changes coming all the time, so it has to be dynamic. It has to be able to respond to the dynamic conditions and whether it’ll align to your business goals or not. And you can always adjust as necessary, right? So if you see some change coming up, you should be able to adjust as necessary and engage in a continuous process of iteration and reiteration and continuously improve your data and your data stream prioritization strategy. Now this will involve incorporating the feedback, right? So you need feedback, you need to analyze the performance of the metrics and then overall adaptation to those changes in the business requirements. This approach is both flexible and adaptive and is crucial for have that prioritization strategy effectiveness over time. Now, I know this could be overwhelming or tedious process, right? So far what we talked about what it is, why is it important, how to achieve it. It’s a lot of strategy work, but we’ll talk through these on how to start with a few pointers one by one. Number one being source reliability. Now there are so many different data sources that will vary in terms of their accuracy and reliability. Like data reliability, you can ask any data engineer, that’s their first problem. Like they’re not getting the IDs that are needed or the data is corrupt by the time it reaches the management system, the CDP. Now some sources they may provide trustworthy data than others. So that’s another reason to prioritize one over the other. And it’ll allow the CDP to assign higher priority to sources that have been historically more accurate and reliable than the others. Second one is timeliness. Now the freshness of that data is essential for an effective customer engagement. Why? Because that timely data, it’ll update and it’s crucial for understanding and responding to a customer behavior. You need that latest data, especially for the real-time use cases. And like I said, to ensure that real-time or near real-time from certain sources is processed and incorporated into customer journey profiles way more quickly than historical data. A little bit double click into the source reliability is the actual data quality. So the prioritization may consider the quality of data provided by different sources. It could be high quality data, free from errors and inconsistencies overall, and it would prioritize over low quality data. And the CDP itself may have mechanisms to identify, CDP in this case being AP, right? It has its own mechanism to identify and prioritize the data sources that are consistently providing accurate and well maintained information versus some stale ones. Another thing to look at is the business rules or the overall criteria, right? So the rules, they’re often aligned with whatever your business or goals are at hand. So for example, if you’re making a marketing campaign go, the data from sources related to the campaign interactions might be given higher priority than some other ones. And we’ll talk a little bit more. I have an architecture and a use case example later in the slides. We’ll talk a little bit more, an example at hand. It’ll make more sense then. Next one is user defined rules. So the CDP itself, it may provide you the flexibility for your own users to define their rules for the data prioritization. Like this will enable your team to tailor the system to their own specific use cases and preferences. Last but not the least is the integration with a third party system. So the data, it may be a source from various internal external systems, right? So the prioritization, it’ll extend to integrating data from third party systems as well. So we need to ensure that the relevant external data is considered in building those customer profiles. Let’s dig right into the challenges. So while data source prioritization, especially with the CDP is essential for managing that diverse data streams, it may comes with its own challenges. So first one being the data quality variability. Now, different data sources, they may exhibit different varying levels of data quality. It’s not everything’s going to be this at the same level. So we need to ensure the consistency and reliability across the sources that can be challenged. It’s not easy. So that’s why it’s one of the top challenges that you run into. Because low quality data from certain selected sources can negatively impact the overall accuracy of your customer profile. Next one is real time data integration. So prioritizing the real time or near real time data, it can be challenging, especially when dealing with large volumes of data. So that is something you need to look into. You need to ensure that timely integration and processing of that data without compromising anything on the system performance. It’ll require robust infrastructure and technology. And quite honestly, AEP does a really good job at that. Next one being data governance and compliance. So complying with that data governance and privacy regulations while prioritizing data can be complex. So that is something you need to look into. Do you meet the regulations or you need to prioritize the data? So answers always meet the regulations. There’s always a balancing that’s needed for prioritization with this requirement. And you need to adhere to that data protection laws too. Because it poses a challenge for CDP, right? But the data governance within CDP and the labels and already CDP, AEP does a really good job at that. Next is dynamic business environments. So different businesses, they often experience changes in their own internal strategies, campaigns and overall priorities. So you need to adapt your data source prioritization or data stream prioritization rules to reflect that dynamic business environment. And that can be a continuous challenge because business end requirements, especially my marketers on this call, they can confirm that it keeps changing constantly. So it’s a constant challenge at the same time. And ensuring that the prioritization strategy, it remains aligned with that evolving business goal is crucial. Next is integration complexity. So the CDPs, they often they’ll integrate with various systems, both internal and external. And managing the complexity of that integrating data from different sources requires great amount of planning and coordination. And this is where your EAs and your data engineers, they come into play. The changes itself that happen in the source systems or addition of overall new data stream, it needs trigger to be make adjustments to your prioritization strategy. So that’s a constant challenge as well. But if everybody’s on the same page, especially like I mentioned, the EAs and the DAs, this challenge can be definitely smoothed. Next one is user adoption and training. Now what that means is, as users, they’re responsible for configuring and managing data source prioritization rules. And they need to understand the capability of CDP thoroughly. Because if they’re not trained well, if they don’t know what the CDP is capable of, it’s really hard for them to actually manage the data source prioritization well. So the like I said, the adequate training and support will be very crucial to ensure that the users who are managing the data platform, they can effectively utilize and prioritize the data using the features of the CDP. Next one is scalability. So as your volume of the customer data grows, which certainly I would say 99.99% cases, that will be the case, the volume of the customer data will grow. You have to ensure that your prioritization strategy and mechanism, it can in fact scale effectively and does not become a challenge down the road. So scalability issues, it may arise, right, dealing with the increasing number of data sources or larger data sets. So that is something you need to almost like nip in the bud if you think that is something you will face down the street when your data scales and grows. Last but not the least, data silos. So data silos are a very important part of the data. Last but not the least, data silos. So in enterprises and organizations with multiple departments or business units, data may be siloed. And we see this in practical examples all the time. Marketing has a different data, loyalty has a different data, the CRM systems will have a different level of data. So data siloed can happen and are very common. So our job is to ensure that relevant data from different parts of organization is in fact considered in that prioritization process. And that can be challenging. So that’s why hence the call out here. But after like you see all these challenges, but overcoming these challenges will require a very robust technology and well-defined process. And it’s a proactive approach to be able to adapt to these changes in the business and the landscape that’s happening. So what you need to do is regular assessment, updates to your prioritization rules, and overall just focus on that data governance part as well. And they’re essential for that successful data source prioritization strategy within a CDP. All right. So getting a little double-ticking it a little bit more practical examples on what this is, data stream is about. So this is how a typical architectural representation is with ÃÛ¶¹ÊÓƵ Experience Platform being at the center of the data flow. And you can see the data flow is from left to right. So you can see the dotted red box. I called it out just so you see it on the left. Those are the potential data streams or sources that get fed into the AP. As you can see, these could be either web or mobile data streaming with SDK or even batch data sources like email data, CRM, MDM, loyalty and whatnot. So what we can do is let’s do a use case example using this architecture that’ll make more sense on how data stream, what it is and how it works. So the use case is a typical use case, right? Unknown customer to a gourmet restaurant chain. It’s being presented with some targeted ads on about a certain menu item and the user or the prospect in this case, they’ll click on the ad. And based on the UTM parameters that are collected from the ad, the user will be presented some relevant creative throughout the ordering experience. So based on the referring ad, a certain creative will appear on the here page and overall menu list item. Now, when the user will scroll down the menu, the menu item from the ad, it should go on top of the list. So a very typical use case, I used a restaurant example because this has been resonating with me lately because of GrubHubs and DoorDash. It’s just so easy to order. So and I’ve seen this practically happen. On the next slide, we shall look into how the data flows given in this example and what data source need to be prioritized for the simple use case. All right. So, like I said, based on the use case, the data attributes needed to show that relevant menu item at the top of the page. We will need the particular UTM parameter that are collected on the web through the ad. And since this is a new or unknown user or customer, like I said, truly a prospect at this point, we don’t need or even have any of that data from the customer loyalty or previous interaction. This is a very net new prospect for us. So hence, there’s only one data source, so we only get to prioritize this. And before I get into a little bit, making this case a little bit more complex, I want to quickly walk through the flow of the data, how this interaction works, going to AP and the target and then coming back. So just follow the steps. If you see the bullets in the yellow colors, those are steps. Number one is the user will see a paid media ad on a paid media channel. The ad includes a link that has that UTM parameter attached to the URL and it’ll persist through the restaurant website. It’ll be set up that way. So that UTM campaign code, it’s set into that data layer and gets picked up with the rest of the data layer into the initial load of the page and the first call to the ÃÛ¶¹ÊÓƵ Edge for that ID sync and personalization. So the visitor context now is set. At the edge, the edge segmentation engine, it’ll qualify the user for any segments based on that event data received and in the request along with the context data. So here the user gets qualified into the, let’s call it a new customer segment based on those UTM parameters. At the edge, the experience edge will start orchestrating a call to ÃÛ¶¹ÊÓƵ target edge with visitors context. Now within, this is number five, sorry, I should have said the numbers before. Apologies. Number five, within the ÃÛ¶¹ÊÓƵ target edge, the target receives user segmentation from that edge segmentation and it’ll start analyzing and add any additional parameters sent from the request. From here, the number six, when the experience is identified for the prospect, the content itself, the selection that is made, it gets delivered to the experience edge. And finally, number seven, the experience then finally delivers the content to the requesting application for the rendering. Now, this is, like I said, very simple use case. We only had one data source, so we hence, we only prioritize that one, but let’s assume that this was a known customer with the previous orders and interaction and related loyalty program data, or even previously presented offers, the whole nine yards basically. Now in that scenario, we will start prioritizing the data streams to be brought into AP based on the criteria defined by that new use case. For example, new use case being if we need a segment for returning customers that we presented, let’s say offer X in the past, we will pull in all the data streams except loyalty data, because we don’t care for loyalty data in this particular segment. It’s not required. But if the use case was a little different and our segment were able to pull all the gold level returning customers that were presented with offer Y in the past and days, in that case, we need to bring all the data sources that I mentioned earlier, loyalty, interactions, previously presented offers and whatnot. So, yeah, but like I said, Rome wasn’t built in a day, right? So neither was this use case. So you don’t start with that. You start with the MVP. And we’ll talk a little bit more about that on how to approach this data set, data stream prioritization strategy step by step in our next section, which is basically the best practices. Right. So, like I said, implementing that effective data stream prioritization is very crucial and you need to maintain that accurate and valuable customer profiles. So best practice, first things first, find that clear prioritization criteria. So you need to precisely outline the standards for prioritization data sources. You need to engage the stakeholders from different departments to guarantee a comprehensive and comprehensive list of priorities. Next is assess and monitor data quality. So consistently, you need to evaluate the accuracy of that data from diverse sources. You need to introduce data cleansing and validation procedures to rectify any problems if you see for the related data or any inconsistent or inaccurate data. Next one is consider real time and batch processing. So you need to have, need to be able to understand the difference between the real time and batch processing requirements according to your business needs. So I want to take a moment here and go back to the example that we talked about. So. Here. So in here, the first use case was very much real time, right? It needed real time data, but the following complex use cases that I talked about, which needed, you know, certain gold member customers in the past X days, certain X offer, that is not really that real time. And that can be sufficed with just batch data. So that is the kind of prioritization you will be able to do as a best practice, means based on the use case, selecting the data, data stream prioritization. So you need to strike that balance right between that necessity of that real time versus the corresponding batch processing based on what your infrastructure also demands. Next one is aligning prioritization with your business goals. I can’t stress this enough. You probably heard me say this a few times already in this session. You need to guarantee that the prioritization of that data source, it should be in harmony with any existing business goals and strategies. And you also have to periodically assess and revise and adapt to these prioritization rules in order to mirror those shifts within the business goals, priorities, campaigns or objectives. Right. Next is implementing user defined rules. So you need to grant all the users or marketers in this case, the freedom to create and personalize their rules for prioritization of data sources, because if you empower these different teams to tailor the prioritization strategy to suit their individual use cases, it will help evolving into the requirement. It helps while the involvement of the requirements. Now, at the same time, you also have to make sure they’re on the same page because you don’t want one team to be making changes that negatively affects the other team. And in that case, you need to encourage collaboration across different departments. So promoting that collaboration among different diverse departments for sharing those insights and priorities, that will certainly help and is one of the top priorities when it comes to best practices for data stream prioritization. Having that guarantee and the inclusion of data from different sources across the organization and departments to be able to construct a complete view of the customer. Next is establishing the government’s framework. So we talked about data governance and how it should be number one priority when it comes to designing and having your data stream prioritization, because having and establishing that strong data governance framework overall will ensure as you adhere to the privacy regulations and even internal policies within your organization, there shouldn’t be any trade offs for this. This is a no negotiation situation. And you need to clearly outline the roles and responsibilities of the management while updating these prioritization roles. Next is prioritize the consistency across all different channels. So as when you’re using a CDP in the middle, the activation is happening on so many different levels and channels. So you need to sustain that uniformity in the data stream prioritization across the sources, across the diverse channels and touch points. And you need to also guarantee the consistent updating of customer profile irrespective of the channel that they should be, that they’re having touch points on or where the data has been gathered. Next is providing training and support. So you need to provide that extensive training to the current users or marketers that are tasked with the configuration and overseeing overall data source prioritization and the rules. And to be able to deliver continuous support, right, then you’ll be able to handle any challenges or any inquiries that may be arising during the implementation itself. And you need to be able to maintain these prioritization rules. Next is regularly reviewing and optimizing. So it’s a constant, I won’t say challenge, but definitely a constant process. So the challenges are handled smoothly. The plan of periodic assessment of that data source prioritization rules, they need to pinpoint areas for enhancement as you see fit. The fine tuning of this prioritization strategy will come with some feedback addressing any building up or evolving business needs or adapting to any of the changes that the data landscape may have. Last but not the least is scaling incrementally. So you need to prepare for scalability across the board, and this can be done by incorporating gradual changes or upgrades. Now, keep a close eye on the overall system performance, right, and keep modifying that prioritization strategy as necessary to be able to accommodate, let’s say, increase in data or user activity or change in data sources and so forth. So basically, by following all these best practices, the enterprises organization, they can truly enhance the overall effectiveness of the data source prioritization strategy within their CDP. And it also leads to more accurate and actionable customer insights. The regular doing reviews on a regular basis and to be able to adapt to changes, they’ll help contribute to these ongoing success or the prioritization strategy. All right, so this is going to be a summary real quick about what, so what, and now what. So what did we talk about today? We talked about data stream prioritization, its definition, what it actually is and encompasses. So what is, basically, why is it even important? Why are we even talking about it? So we talked about the importance of it, data stream prioritization, and the related challenges. And we also talked about the architectural representation and use case examples. Oops. And now it means what are the next steps and best practices? So in the last couple of slides we discussed and went over what could be the best practices when you are considering data stream prioritization for your own organization. And this is towards the end of it. So these are certain sources. These are hyperlinks. And I believe Katie will later on share the deck and this content with you so you can click on them. These are sources for the Experience League website. Some of the concepts that were used in driving these slides, you can revisit them here. And that was it from my site. Katie, over to you. Wonderful. Jess, thank you so much for taking us through all of that today.