[Music] [Nate Smith] Hi, everyone. Thanks for joining us today. If you've come to watch the 2024 Power Couple, Your Customer Analysis Tech Stack and Customer Journey Analytics, you are in the right place. By way of introduction, my name is Nate Smith. I am Director of Product Marketing here at Adobe, and I am joined by the one and the only Katie Cipollone, Principal Expert Solution Consultant here at Adobe as well. Let's get right into it. Katie, I'd love for you to maybe take us a little bit down memory lane.

[Katie Cipollone] Yeah, Nate. So by now, we all take web analytics for granted. But I'm sure some of you remember back to the beginning. Maybe you remember when JavaScript revolutionized the industry, and we moved from log files to tag managers. Or maybe you remember the first time we were able to connect marketing campaign data with website activity, the opportunity to actually report on web events connected to specific emails, for instance. I personally remember being able to report on orders from an iPhone for the first time. The device breakdown and site catalyst coupled with the advent of mobile devices that enabled people to shop from their phones was a watershed moment and so exciting to witness. Now we're evolving to the point where pixels aren't enough and we need to be able to measure people and not just in digital experiences but across all channels of interaction. And who knows what will come next? Thanks, Katie. You know, analytics is often one of those things that may not seem important until it is. And the reason we're taking this little trip down memory lane, I think, is to really illustrate how often analytics is important, which is all the time. It has a critical seat at the strategy table and the importance of analytics in modern business operations for modern customer intelligence really can't be overstated today. The goal is to go from mass marketing to one-to-one real-time personalization at scale. And leadership is looking for future-proof, modern, strategy-focused solutions where they can get out of this reactive mode and into real-time decision-making. And 2024 is a pivot point for many brands as we have a really unique coalescence of market and technology and regulatory changes that are all happening simultaneously. The cookies have in fact crumbled. You've got major changes in privacy requirements for mandatory consent, new laws and updates to existing and happening all of the time. You got customers demanding and then having all sorts of different needs that have to be addressed, really causing an explosion in channel engagement and the associated data that comes with that. Now while that data may be centralized, for most brands, it's actually not accessible for analysis, or at least not customer analysis. Just because you put data into one place physically doesn't mean it isn't siloed. It's still separate sources, tables, identities, etcetera. So the question comes, I'll pose a question here, ask anyone in your organization how easy it is to access customer data. What do you think the answer will be? And think beyond the usual executive reporting.

Now what does all this mean? Well, let's start our analogy for the day. And I promise it's not going to be any old American football analogy. These challenges mean it's not easy for an organization to find repeatable success. It's almost like you're playing a game where the other team has an advantage. It's stacked with extra players on the field.

Now football is generally speaking about scoring points to win the game. The players do that by passing and running and kicking a ball or a funny shaped ball towards an end zone. Now business is generally about driving sales to increase revenue. Business stakeholders do that by courting and converting customers. Now in both cases, it's not so much the what that ultimately makes a difference, it's really about the how. So when it comes to customer experience, the kind that makes repeat customers and brand advocates, the how is a challenge and it's particularly acute today when it comes to customer data. As you think about all of the newer go-to-market approaches, that requires new use cases to be established, new data types to be used, and data structures to be rationalized and related integrations that need to be built to drive marketing, sales, and buyer-related actions. And that's because customer data is special data. It's special because of the properties. Customer data is more than traits or even rows in a CRM. It's event data, it's behavioral data, it's data that ultimately needs to be connected, up-to-date, sequenced, real-time, and shaped for optimization. And we hear from brands all of the time that the current approaches that they're doing just aren't fast enough. They're not unified enough and they're not precise enough for the customer data needs to drive high growth for their brands. So we're going to introduce to you the greatest Power Couple in the world today. One may easily assume that we are talking about Travis Kelce and Taylor Swift, but no, we are referring to your analytics stack and Adobe Customer Journey Analytics. Now together, they can crush any analysis obstacle in their way for modern customer intelligence. So let's talk about the game plan. Here's how we're going to move through the key takeaways and get to the Super Bowl, if you will. Now our game plan simple, we'll be covering what is going on in the playing field, the analytics team, bringing in some outside talent to provide some catalytic value enhancement to your brand, and then advancing the ball or the insights, I should say, downfield, and then ultimately putting it all together for the big event for success. So let's talk a little bit about the playing field.

Let's look at a generalized customer journey just to start with. At the acquisition phase, you're looking at who might be the segments with the highest value and then targeting those prospects. But these are really broad and expensive brushstrokes. And then as customers start to engage with the brand more and more across different channels, it enables the brand to collect more data, which then leads their personalization tactics to become more robust. And that way you can actually really start to truly serve the customer with the most relevant messaging at the right time in the right channel. Now for the brands that consumers have decided to have a relationship with, right, think about us, we as consumers, we expect those brands to know us well enough to be relevant and offer tailored experiences that meet specific needs. It's like when the game is being played, as you get further down the field to the goal, the pressure is a little bit different, right? Even when you're going from first down to fourth down, that pressure is different. And this is why it's important to not only know who your customer is from the data, but to understand where they are in their journey with you. How many times have you gotten close to making a purchase, but then left? There's more on the line for that brand at that moment. So in your organization, does anybody else ask you about where that customer went? I'm guessing the answer is yes. But that's, you know, often the reply is, well, that's too much data or it's too hard to sequence or whatever it might be. And then if you don't have that visibility, ultimately that means latency. It's like not being able to see where open receivers are. And if you're that quarterback and you're dancing around too much, you're going to get sacked and that opportunity is going to pass you by.

So I'd love to turn the time over to Katie to talk a little bit about the playbook and how should we be running plays going forward? Yeah, Nate. So quarterbacks and coaches make decisions in game based on the conditions as they're changing. They choose the play based on who they're playing, the context of the game, and the status of the players. The playing field and the conditions are always in flux. Likewise, your customer will not always follow a prescribed path. They may start online and end up in store or in branch. Or they may respond to an interaction on a mobile device and come back to that same device or another later. Customer journeys aren't actually something a brand creates. They are, in fact, created by your customer. That's why we call them customer journeys. Our brands design the experience they want a customer to have, but the actual journey a customer takes is all on their own. So if it takes marketing three days to get the data in order to determine where a customer is, or if it takes two weeks to launch a campaign based on that data, I can promise you you're missing opportunities 'cause your customer is always moving.

So in this evolving, uncertain world, how can you continue to provide the best experiences to the right people if you don't have a complete view of your customers? That's a great question. And I can tell you, Katie, game time can get intense. And we imagine that this phrase has been uttered more than once around your organization. What are our customers doing? So with that, Katie, I want you to tell us about some outside talent that can really help out.

Yeah, so a few years ago, no one could have imagined Taylor Swift having any impact on football. Nate and I believe that adding CJA to your business is like adding Taylor Swift to the NFL. Did you think you needed it? Probably not. Can it amplify your business? Absolutely. And numbers don't lie. Here are some of them. Two million. Three times. 470%. 330 million. 400%. And this Super Bowl, 448 million impressions worth about $9.6 million. That's the impact Taylor Swift has had on the NFL and on the Chiefs in particular.

So now we know that your data lake is a source of pride. It took time, resources, and lots of effort to put together. And I bet the management and maintenance is no joke either. We know it's a valuable part of your business. It brings all of your organization's data together in one place. Data lakes are great. They're great for your specialized resources to conduct deep dive analysis, for data modeling, or for special projects. They also make it a lot easier for those resources to connect multiple data sources for standardized business reporting that can be delivered to the business at regular intervals.

But sending data to a centralized repository is like Mahome throwing a pass to Kelce. It's a necessary part of success. In order to respond to the dynamic experiences your customers are having with your brand though, you need to be more nimble. While useful for routine reporting, predefined reports are static. The questions have to be defined ahead of time and any breakdowns or deeper dives also have to be predefined. So the data is available for that breakdown. It's like having one play to run and that's it. You'd never imagine your favorite football team running the same play over and over and over again during a game. Now in order to win, you need options on the field. Your whole team needs to be engaged. And ideally, they all have access to the information they need to make decisions in real-time.

Having CJA is like having an extra set of players on your team. You have the data engineer who can take all of the customer centric goodness from your data lake and connect it sequentially at a customer level. Then you have an inherent data scientist who can write SQL queries in milliseconds against fully correlated data, while you and your users only need to drag and drop elements in a panel. The data scientist can also evaluate all of the data in the results set for anomalies, applying attribution and data sequencing. And finally, you have built-in visualization with dozens of options for converting the results of those queries into graphical depictions to clearly illustrate the insights from the data itself.

When CJA was being designed and built, we knew that our customers love the data processing power of Adobe Analytics. So we took that processing power and we improved it for omnichannel journey analysis in CJA. What it has is APIs for configuration and reporting that provide for data consistency in queries across all tools. Workspace, Report Builder, mobile apps, and even the SQL Connector to Power BI and Tableau. So every user has the same version of the truth. Also, contains under the hood intelligence. So when CJA processes the data, it applies out of the box intelligence. So you can easily run an attribution analysis, build and apply segments, perform cross-dimensional analysis, all at report time. So without requiring any new code in order to run those queries or have any new code written in order to deliver those results. CJA also gives you the ability to encode business logic into a metric or a dimension and standardize it across the organization. These are your analysis building blocks. And we all know how hard it is to merge and combine datasets simply using SQL. Combining and stitching together data from different environments can take hours, even if they're in the same data lake. And hundreds of lines of code to ensure the data is properly stitched together and accurate. In CJA, this is done in a point-and-click UI by your end users.

And finally, CJA can bypass steps that most query engines today require. It can rapidly query your omnichannel data because of how the data is inherently structured. Queries that can take several hours to run in most data lake environments can be executed in seconds in CJA.

That's great, Katie. And that's a lot to think about when we think about the path to value from data ultimately to business objectives, right? And I'm going to argue that there is a path to value. And while what you see here on the slide might be a little oversimplified, I would argue it's still very simple, right? We start with storage, collecting data, managing that data, bringing some shape and meaning to it through ETL processes and data transformation processes, and then ultimately providing visualizations and reporting and some data exploration for our stakeholders. But when it comes to cross-channel analysis of customers, tying online and offline actions together, we see many organizations, they try to pull together a solution using the components they have today. It makes sense, right? They have data being collected by a variety of tools and systems, they feed that into a data warehouse and they add the great data scientists that they have, their SQL skills, other expertise, and then they pipe that end result into that BI dashboard for the end user to consume. This is phenomenal for many different types of use cases and applications such as reporting, modeling, forecasting. However, when it comes to the rationalization of customer data around profiles for activation, this is where a lot of organizations hit a line of defenders, and they can't advance that customer ball down the field. So when you think about it, dashboards and visualizations are good and are interesting, but in terms of action, they're ultimately, may I be so bold as to suggest, they're ultimately a waste of time if you can't do anything with the insights or measure the results of the action being taken. And this is something that we, like I said before, we see brands struggle with this on their own, taking those outputs of analytics, whether it's a segment or a predictive score, whatever it might be, and then using it in a system of action. That's an entirely different build initiative than what has taken on before in your customer analytics stack, right? It's more than API integration, or, you know, think about building for cross app scalability and resolving multiple profiles, then governing that data, like that's big, right? That's a big initiative. And this is where the outside talent and the Swift effect or the CJA effect can really help you out. By plugging in CJA to your stack and to your workflows, you can start to execute on customer use cases around things like optimization and cross-channel attribution and so forth. So you can start to see things like cross device, types of insights very, very quickly. You can understand questions like, why did conversion double? Once you saw someone move from one large view format to say a small view format, or being able to take audiences that you want and you can start targeting them with content or advertising optimization. And then being able to stitch together data around a common identifier at a profile level really helps you understand your marketing a lot better at that person level. Big changes really start to happen when you can stitch activity there. So you can start to see significantly more conversions actually attributed to your marketing. And that's not just your marketing, but it can be things like affiliate programs. Now I could talk all day about this, but what I'd rather do is actually have Katie show you how this works. So, Katie, let me turn it to you for a minute.

Yeah, so this is CJA. This interface with embedded visualization and query capabilities can enable any end user to drag and drop elements on a page for cross-channel analysis that in a traditional reporting environment would be next to impossible to recreate. In CJA, I can easily pull over things like page names and metrics like people counts and calls. And because of the attribution capabilities in CJA, I can configure the calls to a time decay setting of 15 minutes. And by constraining it to the session, I am essentially building a query that says, what are the top pages visited by people within 15 minutes of calling the call center? I can then drag over the call reason to get a better understanding of why those calls are being made. So within about a minute, I can create a customer analysis that relates two very distinct datasets with the events connected sequentially by customer. And the fact that CJA sits on top of the Adobe Experience Platform means that all of the data is housed in an enterprise class environment that includes privacy controls, the ability to remove data for compliance reasons, and HIPAA readiness, an environment that extends to every solution across the platform, Workspace, Report Builder, CDP, and again, even the data connectors to Power BI and Tableau.

That's phenomenal, Katie. So we recently did a study with Forrester to find out how good the CJA Effect is. Is it truly of a Swift level? And the numbers don't lie from actual customers. You can see 30% increase in analyst productivity. You can see the present value of the working hours associated with analysis in an organization. You can see the net gain in revenue in millions of dollars. And just overall revenue increases and improvement in marketing efficiency. So hopefully, as we've gone through this, you can see why CJA can in fact be your Swift effect.

So let's talk a little bit more about the team.

One of the biggest challenges that organizations face is unlocking customer data. And it's easy for customer data to be trapped in silos. And the current solution for most organizations is to take all that data, put it into a data lake, but again, that isn't stitched. It goes in siloed and the data for the most part, isn't usable in meaningful ways for customer types of use cases. There's still a need to unleash the data and to tie it together to better understand customers and to make it usable for your stakeholders internally. So I'll go back to that question I asked earlier, ask anyone in your organization how easy it is to access data, what's that answer going to be? My wife and my daughter are huge Swifties. I've noticed a few things about the Swifties. First, they are a pretty dedicated subculture of fans. They're very invested. And they're also very good at creativity. And as a force, they can even make politicians worry about their careers due to their collective action. You need to be able to leverage the whole organization and not just to specialize few in order to efficiently drive business outcomes. So by democratizing access to insight, customer insight in a controlled way and getting data to your Swifties, getting it into their hands, you can amplify your reach as an analytics center of excellence. You can augment your ability to take action and respond appropriately to your customer actions and provide more meaningful experiences as those Swifties who touch that customer experience can make data-driven decisions on holistic up-to-date customer data. In terms of data and people, don't leave your best players on the sideline.

That's right, Nate, and many organizations do. They do a great job with their digital initiatives, but when it comes to accessing insights beyond digital engagement, they have to rely on processes that are lengthy, inefficient, and ultimately expensive. With the focus on customer experience, questions are often asked by leadership, wanting to understand the whole journey the customer is taking, and how each channel plays a role in that journey. That task is often placed on the BI team, with workflows that aren't built for efficiency and quick answers. Business questions take time and require specialized resources to answer. A typical process looks like this. A marketer wants to understand how a specific digital campaign affected an offline transaction. So they submit a request to the BI team's queue. And depending on that queue, it could be days or weeks for the team to be able to address it. We like to call this the ring around the office. The BI team writes many rows of SQL to acquire and transform that data to support the request. Once the raw data is retrieved, the BI team validates it with the business unit until it's approved, and then they export the dataset. On top of that dataset now, someone needs to create a visualization, which also takes time depending on the complexity filters and the views that are needed to be developed. The original requester then spends hours to days combing through the insights and possibly extracting audiences for activation in another environment. If there are any follow-up questions or issues with the data, it could be days before those questions are addressed and the cycle will just continue. This process is inefficient, as we said, and it causes significant delays and being able to react and adjust journeys. In that time, your customer has moved on. Delayed time to insight leads to misspent marketing dollars, decreased ROI, and IT resources that could be better spent elsewhere.

So what we like to say is put CJ in coach.

All right, so let's advance the ball. Okay, how do we advance the ball? You can't score unless you advance the ball. So you need to take action from your analysis. Otherwise, you have just a really expensive, maybe it's pretty, but it's really expensive dashboard. It's not as useful as it could be. Action means streamlining the path from uncovering audiences in your analysis to sending those audiences directly to activation systems without having to recreate in other environments before being able to reach the customer. And this could take days. And by then, again, the customer may have moved on. So if you don't call the play, nothing happens.

So what are you doing with all of that data if you can't take action? Well, we want to go to the Super Bowl.

And why now? Why are we talking about this now? Again, as Nate said earlier, the game has changed. Today's analytics practice is not the same as it was yesterday. It was fun walking down memory lane, but that was a long time ago. The customer identity paradigm has shifted from cookies to customer, from pixels to people. And that's happening now. You don't want to pass up the opportunity to go to the Super Bowl. So, Nate, do you think everybody's ready? I think we are all ready. Let's do it. Thanks, everybody. [Music]