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Using Data to Improve Customer Experience

Watch this webcast recording as Dan Gordon, SVP Strategy & Development, presents “Every Step of the Way – Using Data to Improve CX” at the CRMXchange Virtual Conference – March 23, 2016


Dan: Good afternoon everybody. We are appreciative to be a part of the CRM virtual exchange. It's our second year in doing this and a big thanks to you, Sherry. You know, we're last so it's little bit of a tough slot for us. But we anticipate some good questions and answers and we'll keep an eye on the box below in the website itself. So if you have questions throughout the conversation today, by all means ask them. You know CRM exchange provides a lot of great information. And Sherry, like I said, we're really excited to be here.

So everybody talking about this connected customer experience, right? And many, many brands are shooting for this, you know, seamless, easy interaction for their customers or their consumers or their patients across devices and across channels.

And, you know, we'll discuss this as we go along but connectivity often just isn't quite enough. And I was at an Executives In The Know Conference a couple weeks ago. And if you haven't been to one of those, it's CRS, the Customer Response Summit, I encourage you to go. Chad McDaniel puts on a great event. And often Chad asks some of the audience and some of the people that are in the executives that are 150ish people responsible for customer care, customer experience across different industries including healthcare and financial services and banking and insurance. He asked, you know, "What are some of the big challenges in 2016 and 2017?"

And we brought these up and there's three big challenges that we discussed in this event and in this conversation at Execs In The Know. And it started really with emerging channels, internal silos or fragmentation, and data and the integration of data into that customer experience. So these were top challenges that people agreed that were causing them to look at how to create a differentiated experience. Right?

So these emerging channels cause us to kind of rethink the process and channels such as web chat and SMS and SMS Chat are kind of here today. In emerging channels, you know, in the healthcare space, think about Fitbit. I don't know how many of you guys are wearing Fitbit bands today. But think about the data that's being collected just via that wearable device. And in healthcare, wearables, excuse me, are continuing to grow in the data that they're gathering. And that channel is emerging, what we would do with that, right?

Think about this as an example. I was at a conference just recently and all of us or maybe some of us are sitting in a chair. And think about a few years from now what that chair might tell you with respect to your weight, your height, your heart rate, your breathing rate, whether or not you're fidgeting or whether or not you might even be sleeping. So think about even those kind of channels down the road that are playing a part of this customer experience and that journey.

Silos or fragmentation that was another big topic. And fragmentation exists kind of everywhere, right? Certain functions within your organization may own the web or they may own the IVR or they may own different channels or devices with a next customer experience. And that fragmentation of planning and functional silos, operating model silos and then arguably, that leads into this next challenge.

You have data fragmentation and data is sitting in all kinds of channels and in all kinds of devices. And then the question becomes how do I collect, unify, enhance, and optimize that data and apply it into my customer experience and into that journey to create a differentiated experience? So I imagine if you're like, you know, one of those other 150 executives in that room, you're probably nodding your head like, "Yeah, pretty big challenges to solve in this industry."

So we go back to kind of that visual, the connecting customer experience. We'd argue there's a big component missing within the center of that connected customer experience and that's the data, if you will, right?

So if you read "Harvard Business Review" or you're connected in the "Gartner," Harvard described the data as the new oil. It's one of the most valuable resources we have as a business and as an industry. And according to "Gartner," by the year 2018, not too far, a year and a half away. They predict that 50% of all customer service transactions are going to be influenced by real time analytics. Not acquisition, sales, marketing, and/or collection. Not even along the other parts of the customer experience continuum. Just in customer service interactions alone and real time analytics.

And like I said, I was just at a conference around marketing technologies and there was a lot of discussion around data. And most of it was not inclusive of real time. So this train is coming and it's coming very, very quickly. And the integration of data is becoming this connector between technologies, devices, channels, and communication.

So we would argue it sits at the middle of this connected customer experience. But the reality quite frankly is, you know, data by itself, isn't enough. So there's a component of people, process that needs to be combined with data.

So, you know, a lot of you probably multitasking through this webinar and I'm going to cut to the chase. If you remember any three things from this presentation or actually walk away with three things. One, it's not the availability of data that's the problem. It's how do you use the data within your customer's journey. Second, like I said, data by itself isn't enough. You've got to have process and people associated with it. So think data, process, and people over and over and over again.

And the last is when you integrate data with people and process, you're gonna find that your business processes are likely to change. And ultimately, you're gonna find that changing your business process is going to change your customer experience and create a differentiated experience for your user, your patient, your buyer, etc. So those three things. How you use the data and data of itself isn't enough, you've got to have people and process with it. And you really got to look at the impact to your business process.

So today's agenda, two things. We're gonna walk through an example of a brand that used data within their customer journey. And then at the end, we're gonna share five or six small, applicable ideas that you can take and put into your business. That will help you think about or help you integrate data within your customer experience.

So we asked the question the Q&A, how many were using data in some form or another within their business and in their customer journey? And to Sherry's point, we got a lot. We got a few yeses and a lot of noes. And I think that's the difficult part that we'll try to help you solve at the end is where do I start and how do I get started and what's this magic bullet.

So we're gonna talk about an example of a brand that started with a very specific business problem. And regardless of whether or not you're talking about integration of data or other components about your customer experience, start with a clear business problem. Understand what business problem you're trying to solve and that will help you down the right path.

So in this case, this company was clearly looking at a business case, a business problem around growing revenue. Now, that might surprise you. That's not you fighting with your data. This is about a specific use case that this company had around creating revenue through an emerging channel. By using data to get smarter about when and why those subscribers would buy a pay-per-view fight. So I try to be really specific. This brand looked at how do I grow revenue by offering a pay-per-view UFC fight via proactive notification. Can I get people to respond to an SMS notification to buy a movie in that SMS itself? Not go to the website, not make a phone call. Buy the fight in that SMS channel. So that was their business challenge. That was very narrowly focused and they had three basic goals in this example, in this business problem.

Increase revenue. How do I grow my revenue? Through that emerging channel using a very focused approach. And I think it's an interesting goal and maybe a lesson at this point for all of us. To look at, you know, a specific business use case. What we've found is people get so concerned or overwhelmed by not just the amount of data. But where to start and how to begin to look at data and apply it to their business. And in this case this company was very, very targeted with a very, very narrow approach around we're gonna use this outbound SMS channel to grow revenue by ordering a pay-per-view UFC fight. So very, very narrow opportunity or very, very narrow use case for them to think about, could data influence our ability to grow revenue.

So, they embarked on this process of how to integrate data into this channel to grow revenue. So you'll see this process across the top of the chart here. And I think if you take some notes from the slide, look at this process because it's a fairly simple process that you can use across multiple parts of your business.

So this brand, this company started with what they knew. They created a hypothesis, a sort of what-if scenario, a set of those what-if scenarios. They looked at their known data against that what-if scenario. And then said, "What other data do we need?" And then they built a model and said, "Okay, now, we have this data known and other appended data. We've got a hypothesis. We've got a series of what-if questions and opportunities." Then they built the model and then they started with the pilot.

So in this case, this brand used some basic demographic information. They had name and ZIP, account status, whether or not that account was in default, it was disbanded, it was in collection. So they had an account status information and they had gender and email and SMS opt-in, opt-out information.

So they started with the known data, right? And they took a look at "Okay, if we're gonna solve this business problem of creating revenue through an SMS offer." That's a hypothesis. What are we gonna test against? And this brand in particular looked at timing. So this would be hit number two out of this presentation. We talk about this all the time internally. Data is all about timing and sequencing. And in this case, you know, it was no different. So what this client looked at was what they knew and hypothesis around time. And the precise time to market this fight via SMS.

So they used RFM data which is recency, frequency, and monetary data. So the more recent, the more frequent, and the more money you spent with them, the better. So it created sort of a score, a customer value score using that data. And then they looked at timing. They looked at past behavior. What sort of other events, movies, action movies, sports events were these subscribers buying. And then they started to build up this hypothesis around time. So time is the most important variable in all of analytics, okay? So that was their hypothesis.

They started looking at what other data they needed in order to test this hypothesis. And this is almost like three silos if you will, right? So the vendor-owned data, it's probably a little bit of a misnomer. But think about the data that might reside in your vendor's databases in its organization. So it might be your data and it might be in your vendor's. So it's a silo within a vendor.

The second was, you know, what other data did they have within their own organization that they could get access to. And then what other third party data was available to them that they could then sort of append their known data, right? So they looked at things like average household age, average household income by ZIP, historical CRM and transaction data. They looked at multifamily home ownership and single-family home ownership. So they appended their known data with all of these other third party sources. So collecting the data, starting [inaudible 00:13:57], to test against the hypothesis. So that was the third step in this process right?

So then they developed a predictive model. And says business people on the phone, I'm gonna simplify a definition of a predictive model. And for the data scientist and data modelers and miners on the phone, you know my apologies upfront. Because it's much more technical than this and you guys can appreciate that. But I'll describe the predictive model in this way. It's an equation that finds patterns in old data that then can be applied to new data. So I'll say it again, predictive model is an equation that finds patterns in old data that then can be applied to new data.

So a quick example, you can take sales from your website from the prior year, put it in an equation, and begin to draw some conclusions about buying behaviors based on that past behavior, based on that past historical view, right? So again, predictive model is a little more complicated than that. But for the purposes of what we're talking about today, that's as business easy as I can describe it for us.

So in this example, the brand uses old purchase behavior and these are variables that they had access to and started to run it against their predictive model. So here's the other point in this pilot. They started really small. They started really simply and they started very small and simply because they wanted to gain momentum. So they knew, much like you know, people on the phone have shared already, you know, this idea of data and how to apply it into your customer experience can be overwhelming.

So again, this brand went back to drive revenue through a pay-per-view UFC fight via a direct SMS to any consumers that they could literally click a link and order that fight against a small segment of their population. What they did is they took their top 10% of their customers according to this our RFM score, this recency, frequency, and monetary score and then they segmented it into two groups. They segmented it into a groups small or excuse me. A group that was comprised of young, urban professionals in apartments and multifamily dwellings and then they looked at young professionals in multifamily or excuse me, in single-family homes. And they started to run tests in this predictive model.

So think about it this way. Think about like A/B testing in this case. They took a variable of time and started testing it against all of these other demographic, purchase behaviors, against all these other components to figure out when was the optimal time to send the SMS offer. So they basically did A/B testing in this predictive model and kept a common element around time. And started A/B testing against time, okay? So that's what they did in these two segments.

And what they found in doing this work. They ran 10 different campaigns over the course of about a year using the same data sets. And they found that the optimal time to send an outbound SMS offer for a pay-per-view UFC fight is two days, two days before the fight. And what they realized in results was an 80% lift in revenue that equated to about 2 million bucks. And for this organization, doing this again very small segment of populated workflow, they found that 2 million bucks meant a lot to them. And the result in the momentum they've started to gain as a result of this test is starting to spread throughout the organization.

And so what's happened is other parts of this company are starting to open up their sources of data and starting to look at, "Geez, where else could we integrate data within our customers journey?" And so like I said, they started small. They started with a use case and they started to gain momentum. And now, they're really starting to realize some valuable benefits of using data, okay. So that's the use case. That's the story.

Great. Dan, how does this fit my business? Where can I use it? What makes sense for me? Where do I get started? We get that question pretty frequently and we hear that question when we're in conferences and other events throughout the country. So we'll give you five practical ideas of how you use and how to integrate data into your customer journey.

First, map customer journey. I know there's been probably more people in the last two or three days have talked about customer journey mapping. Have you gone through your business as a buyer, as a patient? If you can put that in the Q&A box, that'd be great. Have you gone through your business, bought a product? Did it as a patient, bought a policy online, gone through that? We're getting some yeses, some noes, a little bit mixed reaction, right?

So you've got this journey that you go through. And then you're gonna look at what's the desired journey and have you mapped what you'd like that journey to look like? So do that and then you're gonna find some friction points. You're gonna find some opportunities where the desired experience isn't necessarily what the practical experience looks like.

And instead of reacting to where can technology fit and create opportunities using technology, I'd encourage you to think about where data might play a role in that. And can data help you solve friction things. Can data lead you to improving your first-call resolution in your IVR for example? Can data lead you to send an outbound notification to prevent that inbound call? Can data lead you to finding an opportunity not just to save cost but to grow revenue in that customer experience? So think about how data might fit it in that journey, not just where technology might solve that. So that's practical idea number one.

Second, use data as a diagnostic tool. So we're gonna talk about a couple things here. One, combining your data and two, think about using some visualization tools. So customers don't behave in functions. They cut across channels and devices to interact and converse with you. Your data needs to do the same. You need to start looking at how your customers go through that journey, channels they cut across, and where that data fits so you can start to collect and unify that data. Combine it into a single source.

So I ask this question, how many of you are combining but... So let's start simply, how many you are combining all of your reporting information into a single source? If you put that in the Q&A box, that'd be great. Again, we're seeing some noes, quite a few noes, a few yeses, right. Combining your data.

So I'll go back to this conference I just left. There's a lot of opportunity in the marketing end of things, marketing side of this customer experience continuum. And just looking at historical reporting and starting to mine that data for what customers might do, what patients might do in the future. It's pretty simple. It's pretty similar, excuse me, to the example that we just walked through with this pay-per-view fight.

And then secondly, think about using visualization tools to identify or diagnose friction spots. There are some visualization tools available and if you've heard of some of them, go ahead and put those in the Q&A box. There's a couple that I'm familiar with but these organizations literally can take your data. And create a visual for you to evaluate and identify where problems might exist in your customer journey.

So I'll give you an example. There's a couple of companies Datameer, Data Dimension. They have some visualization tools. You can load IVR data into them and they can literally map out where customers are transferring out of your IVR. And will help you diagnose where those friction points are.

So think about combining your data across all those channels and devices and unifying it. And then running it through a visualization tool that compares it your customer journey. You'll find some opportunities where, you know, friction might be created and that you might be able to make some real business improvements and save cost and save revenue.

Third, brainstorm and hypothesize and think outside the box. So in the story that we shared this example, this is exactly what this company looked at. They started thinking about, you know, "Geez, we could be looking at data to save money but maybe we should be looking at how do we use data to grow revenue." And they started, you know, looking at that data and inventorying that data. And starting to figure out what could we create with this, what-if scenarios and hypothesis around the business problem they had and using the data. And they used some outside expertise to help them really think, you know, differently about their business.

And so this is where, you know, think about people process and data. This is where some of the people started to play a pretty critical role. Business people, business analytics people, IT people really started to come together and started to look at, you know, what about this scenario? What if we looked at the business problem this way? And so this is where people started to get a really important or started to play a really important role in the process.

And so that gets us to this next practical piece of advice, get some resources. You know in our business, I'd refer to these as the really smart people. You know they're the data scientists, the data miners, and modelers and the people that really enjoy spending time with data and digging into it and really understanding data in depth.

And if you don't have those skill sets in-house or you don't plan do to add them to your staff... Actually, let's do this. Let me ask you a question. How many of you have a business analytics group, a business analytics function, data scientists, data modelers in your company that can help you create a hypothesis and help you analyze some data? Yeah, we're getting some that do. That's great. Yup, yes, no, kind of, we got a kind of in there, that's kind of interesting. I know what kind of means but... So we're getting a little bit, maybe about 50/50, Right? So there's companies that can provide this service to you. But we'd encourage you to look at, you know, outside of your organization. Get some input to help you analyze and interpret the data and draw some conclusions and hypotheses around what it could mean. So that then you can start to predict some behavior around what your customers might do through that journey. And I think when you find that, you're gonna end up, you know, looking at your business process and really starting to change what your business does as a result.

So fifth point, good piece of advice is to be patient and keep going. So execute, test, refine. Start small, start simply, and start with a specific business problem and specific use case in mind and be patient. This is an iterative process, right? It takes time. The brand that we talked about, ran 10 tests against the same data sets over time and they created some results with it. So start small, start fast, [inaudible 00:26:32] fast and recover faster. But be patient and keep going. Look at very specific use cases that you can identify that there's problems to solve, and start small. Start simply and don't get overwhelmed that you have too much data or that the data you have isn't valuable or it doesn't lead you to that hypothesis. There's all kinds of ways to get that data, analyze that data, create those hypotheses. So be patient with it. It takes time. All right?

So, let me go back to the beginning and encourage you to remember a couple of things. The integration of people, process, and data will help you create a differentiated customer experience. An experience that creates trust, transparency, and predictability. Think about how to integrate data in your customer journey to make that experience trustworthy, transparent, and predictable. And you'll be surprised at the results you get at the backside. And with that, Sherry, I'll open it up to a couple of questions that the audience might have.

Sherry: Thanks Dan. You know, it's pretty interesting that the theme that ran through almost every single session we've done from just starting with a simple journey map to SMS and texting. Almost everything when we really ask the audience, "Why haven't you gotten started or have you gotten started?" And you mentioned sometimes it looks overwhelming and every single time the responses you have is just start. Start small, just start something. And that's been through every single session so that was that was pretty interesting.

Dan: Yeah. And, you know, I would say start small but start with a specific use case in mind. Don't be overwhelmed by what you could do. Start with solving a problem. Start with a clear problem statement that your business has.

Sherry: Right. So you can put any questions you have for Dan in the Q&A. We do have some that have come up. See Dan, you were talking about some visualization. Can you just discuss? Because there's a couple questions here on what kind of tools do you use for that?

Dan: There are couple of companies that provide visualization tools. Some of them are pay models and some of them are kind of free on the internet too. But MicroStrategy and Tableau can help you visualize data sets and create some dashboards. Dimension Data is another organization that can help you do that. Datameer, M-E-E-R not M-I-R-R-O-R but M-E-E-R, is another company has data visualization tools.

And we've done some of this work ourselves on behalf of some of our clients. We've loaded in data for their IVRs for example and we've identified. It helps you create or helps you identify, excuse me, a problem or a place in that journey that is creating friction for your customer. It doesn't help you. It just helps you diagnosed it. It doesn't help you fix it. It just tells you where the problem is. And that's where the people side of it comes in, right? I mean the tools will help you identify the point of maybe where customers are falling out but those visualization tools, Sherry, will solve that problem. So that's where the people side of it comes in. And you really have to spend some time, looking at the business and the journey to find out what's the cause of that, not just the symptom.

Sherry: Okay. I'm not sure if you'll know the answer to that but we have a question that's asking if there's a visualizations used in Salesforce.

Dan: Yes, but it's not very rich in its capability.

Sherry: Okay. The company you were talking about in the case study what was there? What are they doing next? Where are they going after that?

Dan: Well, so they go in a couple of directions here. They're looking at other parts of the organization that would benefit from this type of predictive knowledge. So they're looking at propensity to pay models in their collections space. So they're looking at comparing data sets to different segments of their defaulted customers and then looking at... I'll be specific. The model might say, "Sherry is much more likely to pay her monthly bill based on the these factors than Dan is." And our business process is going to be centered around trying to reach Sherry to get her to pay her bill and we're not even gonna try to collect from Dan. Because our data is telling us he's not gonna pay us either way. So we're gonna save our resources, time, and energy on Dan and we're gonna devote them to Sherry because our model tells us that the propensity to pay is going to be higher, which means an increase in cash flow for them, an increase in margin.

So that's, you know, another space they're looking at and they're looking at how they can integrate some of these channels in that propensity to pay model. So can I send an SMS and can I get Dan or can I get Sherry to pay via that SMS? Rather than continually try to reach her via a phone call for example. So that's a space they're looking at and they're looking at a couple of others as well.

Sherry: Do you recommend any starting point to start to collect and segment data? We have that pay-per-view and they had something very specific in mind. Do you start with a concept, something you wanna work towards and that and you start to collect data towards that or where do you begin?

Dan: So you know, I'll go back to starting with kind of holistically what the business problem that you have and what data is available. So start know go back to this process. What data is known? What data do I have access to? And I'm bumping that up against specifically what business problem I am trying to solve, right? Can I or can my group solve that problem with the data, right?

So I'm gonna suggest being a little bit parochial because the more people and functions you get involved in trying to do this work initially, the harder it is. And the more difficult it is to create traction if you will. So again, try to identify a business problem that your group, your function can solve and that you can kind of control. And what I mean by control is, you know, use the data you have, use the data maybe your vendors have. And start in a place that you have a little bit more license over that other parts of your organization. And then begin to test those hypotheses. So start with actually your business problem in mind, collect that data, and really begin to drive into what other data do I need and how do I build that model. So that I can begin to, you know, connect what I think is going to happen, that predictable side with the data I have.

Sherry: Okay. Let's see. There's a question about that case study again. Did that company share the results with others in the organization? Is it something that you would recommend that when you have something very successful that you then share it throughout the company?

Dan: Absolutely, yes. Yep and in this case, you know, this use cases was very narrow. But this company felt that $2 million over this 10 months of trial was pretty significant. And what happened is as they started, as this functional silo within this company started to share that information, those results. All of a sudden other parts of the organization started to kind of get on that bandwagon and say, "Geez, what else could we do? What other data do you need and what other hypothesis do we wanna test in order for us to, you know, drive revenue and/or save cost?"

So, you know, that that was my point about starting small to gain some momentum. So that it doesn't become overwhelming and you get some traction. And once you get some traction, you're beginning to socialize that and share that, you'll be surprised both internally and externally, the kind of results you're gonna get. And the kind of interest and support and ongoing like help you'll get to continue to expand that data integration into the customer journey.

Sherry: Okay, we're getting questions on your resources when you discussed resources. So I'm gonna try to summarize it into a couple of buckets. One is companies saying, "Yes, we have the resources but how do I get my time with that resource." Kind of how do I make my case to be able to use that resource. That's one area. The other is, "We have no resources. How do we start to pull this together? Are there tools we could use internally? We're a smaller company, how do I do this?" So there's the one that, "Yes, we have resources but how do I make my case to be able to use this." And the other is "We just have no resources, then what we do?"

Dan: Good questions. Good questions. Some of the organization may have, you know, centralized groups that they have to make that case too. So in that case, if you've got a centralized group and you're trying to get resources. You're trying to get attention, I'd suggest starting with the hypothesis. What is it that you're trying to solve and create that business case. And that business case is gonna have to include some idea or projected results that you think you can get, right?

So you can't... it's gonna be hard to walk into the BA group and say, "Well, I just need to save money. And I think I can save money by looking at my IVR." Well that group is gonna kind of push back and go, "You know what, you need to put some guesses around the outcome and you need to at least frame a business case that's gonna create some value for the organization." So that group can help value it against other opportunities that they have.

So some organizations have pretty sophisticated scoring and prioritization models. That they look at these business case and they say, "Okay, what's the revenue impact, what's the saving impact, how does it align to strategy. Does it align the strategy? How does it impact our product development life cycle?" And so some of these BA groups have some pretty sophisticated scoring models. And as you build that business case, I get with the BA group and really understand what information they need, what models they're using to prioritize, how they adjust their work and manage their work. And so that you begin to, you know, give them the data they need in order to make the decision to help you to build your business case. Don't do that in a silo. You don't need to do that in a silo. Those data scientists and engineers, they can help you build that business case. So don't be afraid to get with them and ask them how they're valuing their work. What the intake process is? What their scoring model looks. So that you can get some attention.

If you don't have resources, where do you start? I said this a couple of times. I try to maybe say it in a different way. But, you know, identifying what you're trying to solve is the key and where you think your customers are either experiencing friction or where you think you can grow your revenue for example. And so, you know, is there a big enough problem that will get you, you know, results and resources you need, right? So I think you got it from a business perspective even though you may be a small business. Look at, is the problem I'm trying to solve big enough that it warrants putting resources to it? And is it big enough that it warrants putting funding and budget to?

And you probably have three or four you're gonna identify pretty quickly. And as you start there, you're probably gonna expand. You're probably gonna find a couple of others that you're like, "Geez, I didn't realize we had this problem. But now that we've dug into problem A, problem B and C have arisen. And they're big enough and require some resources and funding as well."

Sherry: Okay. And before we wrap up, I just wanna remind everyone to visit the West booth in the exhibit hall and they do have a prized drawing. So just by exhibiting the booth and there's plenty of great content in there to exhibit, I mean to visit anyway. But they do have an Amazon gift card. So somebody will win that in a prize drawing.

So just to do a quick wrap-up, Dan, what would you say... if they're gonna remember, let's say, three best practices when using data, what would that be?

Dan: I would start with clearly identifying the business problem you're trying to solve. Start there. Because if you don't have a clear definition and really detailed scope around the problem you're trying to solve, you'll end up getting overwhelmed. Second would be develop that hypothesis. Develop what you think you can do to solve that business problem. And then look at the data. What known data do you have, what other data do you need? And begin to collect that data and unify that data in a way that your business analytics can consume it.

Sherry: Okay. Thank you. And that seems to be all of the questions we have for your session. Thank you, Dan, and thank you to West for bringing this session on data to the virtual conference.

We have one final live event for today which is a "Best Practice Idea in 60 Minutes" and Dan will be with us again for that session. So we can hear Dan again. And there's other presenters and we have Sheryl Kingstone who is an analyst in this industry, kind of spearheading that. So you do wanna make sure that you attend the best practice session at 4:00 Eastern. So Dan, thank you so much for this webcast. And we will see everyone at 4:00 Eastern. Thank you.

Dan: Thank you Sherry. I really appreciate it.

Sherry: Thank you. Bye-bye.