Self-Service, Digital Transformation, and Omnichannel Customer Service

Kailey Raymond: In 2024, we can expect to see more digital transformation, self-service, and automation. But an overlooked trend we need to pay attention to is social media, not just for marketing, but for customer service as well. Social channels allow us to actively engage with customers, address their concerns, resolve their issues, and build lasting relationships. We can meet customers on the channel they're familiar with and speak to them like a human, creating a powerful mechanism for communication and community. Dr. Tiffany Perkins-Munn is creating this feedback loop and her day-to-day work at JP Morgan Chase.

Kailey Raymond: In this episode, we discuss opportunities in AI, the power of self-service tools, and improving customer experience through social media.

Kailey Raymond: With me today, I have Dr. Tiffany Perkins-Munn. She earned her PhD in social personality psychology and an interdisciplinary... Wow, that's a hard word, Tiffany.

Kailey Raymond: It's a mouthful! Interdisciplinary focus on advanced quantitative methods. Her insights are the subject of tons of lectures on psychology and statistics and their real-world applications. She is the head of data and analytics at the very innovative CDAO organization at JP Morgan Chase. Welcome to the show, Tiffany.

Tiffany Perkins-Munn: Thank you very much. Thanks for having me.

Kailey Raymond: Absolutely. I'm stoked to talk to you. You have a fascinating career journey and are obviously a wealth of information. I'm curious, in your own words, tell me how you got to where you are today at JP Morgan Chase.

Tiffany Perkins-Munn: So as you picked up in my intro, that whole story is a bit of a tongue twister. So I really characterize it as like my background is in statistics and psychology, and I usually describe it as the practical application of measurement to behaviors, right? Could be investor behaviors, consumer behaviors, student behaviors, patient behaviors, etcetera. But it's really, how do we use analytics to predict and understand outcomes for a myriad of people? And probably at this point in my career, I'm probably somewhat of a unicorn in data and analytics. I started off in market research, which is really tied to the customer experience, focusing on customer satisfaction and NPS and value and ease of use and similar metrics.

Tiffany Perkins-Munn: And I moved into financial services and I've been fortunate enough to work in investment banking, sales and trading, retail, and now consumer banking at Chase. So I have had an acute view into data and analytics across the customer and client continuum throughout my 25 plus year career.

Kailey Raymond: Wow. So yeah, you are a wealth of information, tons of deep experience across lots of different sections of especially the financial services industry. I'm wondering, with your role specifically at JP Morgan Chase now, how does it tie back to the customer experience and journey? What are some of those things that your team works on?

Tiffany Perkins-Munn: So really excitingly, this role at Chase brings the customer experience journey to life, right? So we offer a myriad of consumer products, right? Lots of different credit cards. You probably have a Freedom or Sapphire in your wallet right now.

Kailey Raymond: I do, Tiffany, I do.

Tiffany Perkins-Munn: Deposit accounts, investment accounts, auto financing, home lending for mortgages. And our goal at JP Morgan is really to provide a seamless experience for our consumers. So from a data and analytics perspective, which is where I live, it's important to anticipate customer needs based on where they are in their life's journey. And it's also important to understand where the process breaks down. So how do we make sure that we continue to have a seamless experience for our consumers across the various products that they might engage with us in?

Kailey Raymond: Interesting. I'm sure that relates to cross-sell, up-sell, making sure that you're identifying those key moments to make sure that you're improving and continuously getting great NPS scores. Very, very interesting.

Tiffany Perkins-Munn: Yes, we are acutely focused on NPS. A lot of our metrics are tied to what do we need to do to make improvements? Where are those improvements necessary? What are the smaller sort of double-click, more operational things that we can do to make processes better?

Kailey Raymond: And I'm sure in a lot of this data that your team is assessing every single day, you're identifying trends and you're kind of keeping up with the market and trying to understand exactly where consumer trends are going. So you can kind of be ahead of the curve in developing the best customer experience possible. And so I'm wondering if there are any trends in particular that you have your eye on that you're seeing that relate to the customer experience and that journey that you're building?

Tiffany Perkins-Munn: Yeah, there are so many trends, but maybe I would classify the top five just to make it simple. The number one being digital transformation, right? Artificial intelligence, machine learning, data analytics to deliver personalized experiences. That's all about the digital transformation that's happening across our broader landscape, not only in financial services, but across all industries. Number two, I think would be more automation, like self-service and automation, right? So we have all these digital platforms now, and as it turns out, there are many customers who like to solve their issues independently. They really want to be able to get from the issue that they're having to the resolution of the issue without any human intervention.

Kailey Raymond: I'm that person.

Tiffany Perkins-Munn: Me too, me too, by the way. So as a result, businesses are actually investing in self-service tools, right? So you'll see the rise of lots of chatbots. Now when you go to websites, in addition to being able to send a question via the online portal, you can typically chat with someone right there to help you resolve your problem. And the chat right there could actually deliver the service for you, like walk you through the process for filling out the application or take the order and get your information to send it to you and things like that.

Tiffany Perkins-Munn: So businesses are investing in more of these tools, including online forums as well, like places where, online places where people can meet to chat, to talk about services and solutions to service problems. The third thing I would say is probably omnichannel customer service. So this is a place, a space where I feel that businesses that have omnichannel capabilities have not quite figured out that single line of sight across the omnichannel experience, right?

Tiffany Perkins-Munn: But customers, you and me, we expect seamless experiences across all of the touch points. If we go on to the website, if we go on to the app, if we communicate via email, if a business sends me something in email, if I go into the store, if I'm on my phone, even snail mail, 'cause snail mail is still a thing. People are still doing direct mail.

Kailey Raymond: It sure is. You should look in my inbox right now. It's every time I go to the mailbox, I'm how is this much paper being produced?

Tiffany Perkins-Munn: Right. I literally just moved here in June and I'm why is there all this junk mail in my box? How did they even find me?

Kailey Raymond: I do not know.

Tiffany Perkins-Munn: But as consumers it's about the continuation of that story, right? We don't want disjointed messages. You don't want me sending you something on your phone, in your app, and you open it up, and then you end up engaging with it, buying it, purchasing it, and then you get something in the mail two weeks later that say, why don't you buy this thing? And you're but I just bought that thing, or?

Kailey Raymond: You should know that.

Tiffany Perkins-Munn: Right, exactly. The businesses should know that. Or in the early days of Amazon, because Amazon has gotten much better and I'm an online shopper, I would buy something and then the next day Amazon would recommend that same thing. Not a different color, not a different style, that exact same thing. But I am giving them grace because building algorithms myself, I know that it takes a a period of trial and error and learning, like now that we have machine learning, it takes an amount of learning that has to take place in an iterative fashion in order for the algo to really start to know who you are as a digital customer. So we'll allow them that trial period that they needed to get that right.

Kailey Raymond: Okay, we'll give them grace. We'll give Amazon grace. Like they need it.

Tiffany Perkins-Munn: Right, like they need it. The fourth thing I would say is like data privacy and security. So all of these businesses are collecting much more data and customers, I think, are becoming more attuned to what that means. Like historically, I feel like customers were oh my God, there are these businesses that's collecting my information. What are they doing with it? It's creepy. And they're following me, they're tracking me, they're data breaches. I think now customers are becoming more engaged with the way that data is being utilized and they're starting to take more of an interest in understanding.

Tiffany Perkins-Munn: How is that information being used and to have expectations around if the information is being used, what are you getting out of it? So it's a little bit more of a quid pro quo from the customer perspective between the business and the customer, which I think is very important because I think for data privacy and security to work, like customers really have to be debated. They need to be really knowledgeable about the information that they're sharing, how that information is being used. We want to be able to build consumer confidence in our ability as a business to protect their data and information.

Kailey Raymond: Absolutely. And it's interesting because you're mentioning as well, like personalization, you want to make sure that you're touching everybody at the right, on the right channel, with the right message, at the right time. Some people might think that that data privacy and that personalization kind of are at this inherent conflict. That's not necessarily true as long as consumers know exactly what's happening with their data, including that consent and it's trusted first party data, right?

Tiffany Perkins-Munn: Right. And I think historically consumers just haven't been that connected to that piece of it because it's all been very black box. But now with every GDPR and all of the privacy rules that are coming to play and the engagement with that being more front face and center for consumers, like you log onto the website and the first thing they say is we're going to do X, Y, and Z. Are you okay with that? That's a new process that I think really helps to fortify the data privacy and security because then it becomes a two-way street between the business and the consumer.

Kailey Raymond: I was just in Paris and I can tell you, yes, all of those pop-ups are very real and you definitely need to have your opinions on whether you consent, whether you go into the additional forms. Like yes, GDPR is in full force in Europe.

Tiffany Perkins-Munn: Yes. Yeah. I would say the fifth and the most obvious is social media because highly regulated businesses like say financial services, for example, have been, especially as the consumer moves away from the consumer and more to like the business as a consumer like B2B, they've been slow to move into social media for obvious reasons. However, there are lots of businesses that are using social media, not only for marketing, but also for customer service, like we were talking about before. So actively engaging with customers on the platform, addressing their concerns, building relationships.

Tiffany Perkins-Munn: So this to me is a real opportunity for digital transformation and AI to maximize the work that's happening, particularly within marketing, which is where I think the customer experience lives and how you should be engaging with the customer, what you need to know about them and what their expectations are about how they should engage with you.

Kailey Raymond: That's really interesting. Surprisingly, I actually haven't heard social media as one of the trends so far, but I do think it kind of ties back to something that I'm seeing, which is this trend towards community. I feel like community was a buzzword 10 years ago, and I think it's kind of coming back where it's making sure that you're speaking like a human to the people that are consumers of your business and meeting them on a channel that they are consistently on, in a format that they are familiar with. Really interesting.

Tiffany Perkins-Munn: Don't you remember when, Elon Musk, love him or hate him, he did something that was transformational when people started tweeting about Tesla, and he started responding to individual consumer tweets, and it just blew everyone's mind. They were like, Oh my God, Elon responded to that person directly. And it seemed as if anyway, it was actually Elon himself who was responding to the person and going back and forth. And that's a very powerful mechanism for communication and community that I think we're starting to really understand the benefits of now.

Kailey Raymond: That's really interesting. Yeah, you can have a direct line to a consumer and somebody that is, for better or for worse, a world leader.

Tiffany Perkins-Munn: Yeah, who you wouldn't otherwise have spoken to, or run into, or had any engagement with, right?

Kailey Raymond: Totally. That's really interesting. You've mentioned this really lightly. And I think that a lot of this kind of digital transformation, automation, kind of the first two, certainly, I would say, are topics related to AI, but the buzzword didn't necessarily come out of your mouth. So I'm curious, where do you think kind of AI fits into the picture as it relates to kind of these mega trends that are happening in 2024?

Tiffany Perkins-Munn: Oh my goodness. So I would say that of the five, I think, that I listed, I most closely follow the digital transformation and the utilization of AI as a marketing tool. Because everything that I mentioned in the top five can really honestly be better understood with AI. So we talked about data. AI can analyze vast amounts of data in a very short period of time. It has taken processes that we have it's been like three months to generate a report because dealing with that many petabytes of data, sometimes it would leave a process running overnight for multiple nights, right?

Tiffany Perkins-Munn: So imagine that, you leave something running overnight for multiple nights, and then you come in and there's an error. What happens? Oh, let me just start, right? Let me just go through all this code and figure out where the error is, and then let me start this process over, right? But AI can analyze vast amounts of data really quickly and understand individual consumer preference and behaviors like in the minute. And if there's an error, it can also highlight for you where that error occurred.

Tiffany Perkins-Munn: So you can hone in on that particular part of the code, fix it, and then sort of rerun it quickly. That's a big deal. For people who sit in data, like really sit in data, that is a huge transformative deal. AI can also though, do things like quickly identify patterns and trends in data. So then you become, a business starts to build a muscle where they can become increasingly predictive, right, in their abilities. Because you can look over patterns, not only to see what's the last few things that the customer has done, but this person has been a customer for 20 years. What have they done over the lifetime of their relationship with us?

Tiffany Perkins-Munn: And how have those abilities or those behaviors changed over those two decades? And given that information, where do we see this trending? And are there others like this customer who we should also be predicting future outcomes for? AI can automate repetitive tasks, right? Think about email marketing, you're doing social media posting, you're doing ad bidding, it just can automate all of those tasks that are repetitive, that maybe require some nuance, sort of editing and changing, but don't require, shouldn't require full power for an individual to actually execute. I'm gonna keep going unless you tell me to stop.

Kailey Raymond: Listen, I can listen to you, I can listen to you talk about AI all day. Please keep going. I'm just like in 2024, what are the top use cases that you think businesses should really implement with AI? I know we're talking about, oh, it can predict audiences. Oh, it can take massive amounts of data and synthesize insights for your team to take action on. Like, if you were to pick three or five use cases that you're every business should do this year with AI, what do you think it would be?

Tiffany Perkins-Munn: Yeah. If I think about what, where are the places where there are a lot of challenges, as you know, and maybe we'll talk about that with AI, but if I think about where the places that AI can be very positive or have positive outcomes, I think of things like maybe online shopping, right? So online shopping post-COVID. If you don't have an online presence now it can be problematic because since COVID online shopping has exploded, using AI to sort of engage with your customers on a digital platform. Let's not call it, maybe it's not online shopping, but whatever that looks like in your business is very important. Also making everything mobile friendly, right? Because the use of mobile devices, people want to use mobile devices to do everything.

Tiffany Perkins-Munn: So we really need to help them get to a place where they can use the mobile device, that information, and their behavior is being assessed on the device so that the next time they come back, maybe they are presented with something new and different because the system has learned that they don't even pay attention to that upfront stuff and they go right to, well whatever the next thing is, so really using that to help a system learn. I think any scenario where an organization has an opportunity to incorporate machine learning, where a behavior can be tracked, monitored, and learned, and then you can use that to create predictive outcomes for your consumers.

Tiffany Perkins-Munn: That's another opportunity for, I think, real opportunities for AI. And then I think the third would probably be something related to what we were talking to about before, which is more omni-channel purchasing, how many times, well, the way I do it, I'll look something up online, right? And there are certain things that I have to have, I have to touch. I can't just look at the couch online and think to myself...

Kailey Raymond: I've made a mistake.

Tiffany Perkins-Munn: I'm going to buy that couch. Because sometimes the dimensions look a certain way and you measure it and you don't measure it right and it comes, it's too big, it can't fit in the door.

Kailey Raymond: Oh, the most uncomfortable quirk of my life is sitting in my living room right now. Such a mistake. I won't say the brand name.

Tiffany Perkins-Munn: But they don't, but you get what I'm saying. So I'll go online and I'll look it up and I'm Oh, I like that. Where is a store near me? Or where can I buy this near me? Right. I'll look it up. I'll go to the store, even if I have to drive 30 minutes to get there. I'll drive so I can like really look and touch and I'll think about it. And I might do that for a couple of things. And then I'll come back, I'll pick up my phone. And once I've decided I'll order. So this idea of understanding the omni-channel purchasing process is uber important. And sometimes people, people think, Oh, well, we really only deal in one product. So we don't really need to worry about this omni-channel purchasing process. It's that one product is the sofa that I'm talking about. And it is important whether you are dealing with multiple products where you want to make sure you are sending a consistent, concise, easy to understand narrative to the consumer But it also applies if you just sell one thing and you want to make sure that the experience that they have about that one thing is consistent across channel.

Kailey Raymond: 100%. Yeah. You just described something where it's, you're on either the web with your computer or your mobile. You might be on an app, who the heck knows, you're going to a store that's in person, there's some POS data perhaps being collected, you're going back online, you're ordering it. So like, you mentioned like three, four or five channels easily in one experience.

Tiffany Perkins-Munn: Exactly.

Kailey Raymond: And it's a necessity for that business to be able to understand that is you, Tiffany, all doing the same exact thing so it can coordinate its messaging and appropriately hit you with the right communications. You mentioned some of perhaps the pitfalls or some of the scarier parts of AI. I'm wondering what might those be? What are some of the limitations of AI or what are some of the things that we should be on the lookout as it relates to AI?

Tiffany Perkins-Munn: I don't know if we need to be on the lookout as much as we just need to be aware of what some of the concerns are and just understand them better so that this idea that AI is taking over the world doesn't propagate our thought processes and become like the overarching narrative, right? So the first thing is job displacement. I'm sure you hear that a lot. Like AI is taking everyone's jobs. AI is going to replace the human and AI is definitely going to make a lot of human tasks easier. I have zero fear of AI replacing humans. And in fact, AI is creating new jobs. Have you ever heard of a prompt engineer before?

Kailey Raymond: No, but tell me about it.

Tiffany Perkins-Munn: A prompt engineer is someone who is trained to write the question correctly in ChatGPT so that you get the correct answer from the system. It's a job.

Kailey Raymond: Whoa.

Tiffany Perkins-Munn: With training. Because the, what is the interesting thing about ChatGPT is that, and systems like that, is that they hallucinate, meaning they will give you a beautiful, well scripted, well thought out answer with a lot of confidence and bravado in its voice, if they had a voice, and it will be completely wrong. And the reason it hallucinates is that it doesn't know everything. It's like pulling from all of these various places and its goal is to give you a structured, solid answer. So you perceive that to be a correct answer when in fact it's an incorrect answer and that's called a hallucination. And so when that happens, as a result of that, and as a result of the increasing use in ChatGPT and OpenAI and systems like that, a new role has emerged, which is called a prompt engineer.

Tiffany Perkins-Munn: And if you look it up online, you will see lots of requests for prompt engineers, you'll see the training associated with becoming a prompt engineer, all around crafting the perfect question for a ChatGPT or a generative AI system, which is amazing. And that's just the beginning of many different roles that will come into play as a result of AI. AI still needs someone to train it. AI still needs someone to understand how to interpret it. Like all of these roles, some of them already existing, like I'm a statistician, so clearly I know how to interpret the output of a statistical analysis.

Tiffany Perkins-Munn: However, similarly, but different, there's a separate and unique set of skills that go with interpreting what comes out of an artificial intelligence or machine learning analysis. And as a result, there are all these new roles that are coming into being. So this is where I think people who are in school, in college, trying to think about what they need to do in their career early on, or even late stages where they want to make a change. Like data, like really manipulating, wrangling, understanding data is a hot spot because it's pervasive. It covers every single industry. You will learn about machine learning and AI, and then you will be better positioned for any new roles that are coming as a result of all of these new technologies.

Kailey Raymond: I love that. I think that's really astute, which is, you're right. I think a lot of folks are saying, oh, we're coming for your jobs. AI is eating the industries. But at the end of the day, you're right. Like with new technologies comes new opportunities. And one of the kind of like unsaid things related to AI is, obviously it requires an unbelievable amount of data and it requires good, structured, high quality data to get the right outcomes for your business. So I'm hoping at a certain point, hallucinations will become less, or at the very least, like we'll train LLM specifically on, for instance, if you're a lawyer, feeding it the right books so that it could actually take out the right laws for you to be able to interpret some of your cases perhaps.

Kailey Raymond: So you can't use ChatGPT right now in a really niche industry probably because it's just, it's not trained specifically on that. So hopefully we're going to get to a better place, but where this is taking me is that you're probably the perfect person to ask this question to that we ask quite often, which is the namesake of the show. So Tiffany, how would you define good data, which is really kind of the input of everything that we're talking about?

Tiffany Perkins-Munn: Yes. I would define good data as data that is accurate, it's reliable, it's timely, and it's relevant.

Kailey Raymond: Boom. She's got it down.

Tiffany Perkins-Munn: Drop the mic.

Kailey Raymond: And you know what, in particular, when you're talking about timely as a marketer, my ears perk up, I would also say like able to activate, like making sure that it's something that can be put out into the world. That idea of you mentioning earlier of like running a report for nights on end. That's a marketer's worst nightmare. If that's a segmentation report that your campaign's launching on Friday and that still thing's still running on Thursday night, like you're probably not going to get the campaign out on time.

Tiffany Perkins-Munn: Yes. Accessibility is key. Like can you actually get the data that you need?

Kailey Raymond: A hundred%. And I'm wondering in across all of your work, I'm sure you've seen some things within some of the statistical analyses that you've been a part of that have surprised you. I'm wondering, if you have any examples of anything that you've learned that surprised you based off of the data that you're collecting from your customers?

Tiffany Perkins-Munn: Oh, yes. So sometimes you go into a scenario and I can't give you all the details here, but you go into the scenario with a hypothesis, with an expectation, right? And you're pretty sure about it. And you're just doing the research so that you can basically confirm your hypothesis. Like I just wanna make sure that this is the way this process works, that they behave in this way, in this scenario, so that the solution that I'm building for them is accurate or makes sense. And then you do the analysis and you realize that something completely different that you had not even considered was the thing that was actually driving the consumer to behave a certain way.

Tiffany Perkins-Munn: Like something you hadn't even considered. Wasn't even one of the variables. And this is a case where natural language processing wins, right? And historically there's been this it's like quantitative, qualitative. Most people use quantitative because it's easy to categorize and analyze. And the qualitative has been like oh, we'll supplement the quantitative with some verbatims. Or we'll read through a few of the qualitative just to get, pull some themes out of the text. Or in early days, you'll use like a text analyzer, but now we have natural language processing via AI, which basically allows you to read through and understand, allows the system to read through and understand data, information, text, verbiage, everything from millions and millions and millions of customers simultaneously, and then bring to the forefront, what are the key things that they are saying to us or that are meaningful to them or that are the most important?

Tiffany Perkins-Munn: And that supplement to our traditional way of doing analysis, I think, has been groundbreaking. Because then you have a real tool for listening to the customer's voice without the customer actually being in front of you talking to you, right? You have a real tool for then taking that and looking across millions and millions and millions of records to understand what some of the issues are that you've missed because you've made an assumption about it's service or it's product or it's this or it's ease of use or it's value, like all the things that you think is the challenge or could be a challenge, and then you run through the verbatims in the system and you realize that there are two or three things that you had not considered that pop to the forefront in terms of customer needs. So natural language processing has been a real game changer for me and some of the work that I do around the customer journey and experience.

Kailey Raymond: Ties back really well to your trend of social media, too. I'm sure that you're using that for social listening to be able to aggregate those trends and really understand if there's anything that's kind of popping up there. That's interesting. Actually, my last company did that. I worked at AlphaSense and they did exactly that. They basically take documents and they kind of pull out the key trends and kind of pilot even tone analysis. And we're doing a panel coming up here. And I took all of the transcripts from all of the podcast episodes from the past couple of years, put them in AlphaSense and looked at the top trends that people are talking about.

Tiffany Perkins-Munn: Amazing.

Kailey Raymond: So I'm like okay, cool. I know exactly what is going on in 2023 and let's see what's up for 2024.

Tiffany Perkins-Munn: And look how quickly you did that.

Kailey Raymond: Truly took me minutes, which would have taken somebody...

Tiffany Perkins-Munn: Days.

Kailey Raymond: Imagine a financial analyst years ago. Days and days and days, actually.

Tiffany Perkins-Munn: Yes, exactly.

Kailey Raymond: I love it.

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Kailey Raymond: I'm wondering if you have any folks that you think are doing it right as it relates to customer experience?

Tiffany Perkins-Munn: Well this is that trick question that you always get and all the typical players always rise to the top, Amazon, Apple, Disney. I think Zappos does it right. Ritz-Carlton does it really well. But being from Philly and being this year, one of my resolutions is really to buy local, so I have been really focused on being very local in my purchases recently. And I would just like to say that I think there are really, when you think more locally versus globally or nationwide, there are many local coffee shops, independent bookstores, like this boutique fitness studio that I participate in around the corner, local restaurants. They're all really acutely attuned to customer service and the customer experience, and they pay really close attention.

Tiffany Perkins-Munn: Now there's smaller businesses in scale, clearly, and they may have more bandwidth to be more attentive to the customer, but I think if you look in local places, you can find great examples of how, what the customer experience and having attention to customer experience really look like, what it needs to look like.

Kailey Raymond: And I think it's something that companies try to mimic at scale, right? Like it's, they want every interaction to feel like it's one-to-one so that when you go around to your fitness studio around the corner and they say, Hey, Tiffany, good to see you. Last time I saw you was last year, are you ready? You have that information too, if you're at scale. So I think it's a great place to draw inspiration from.

Tiffany Perkins-Munn: But I don't even think that they, like an Amazon. How one-to-one is Amazon really going to be? You know what I mean? It's never going to happen. Like play in your lane. Like I feel like an Amazon has scale as its power. Right? And so it's not as so much about, yes, I want them to know and understand what it is that I want and need, but they have data from the rest of the world and they know what the rest of the world wants and needs, and they also know who in the rest of the world looks the most like Tiffany, and I want them to know the rest of the world well enough so that they can tell me, people like you also like these other things and you should consider these, but in a really smart way, not just oh, they watch the show, you know how Netflix is like oh, so-and-so watch the show, maybe you want to watch it, but in a really smart way so that you can start to get to content and information that doesn't sit at the surface, but it's like a double-click, like Netflix, get me to those independent films that are two or three clicks down that I might be interested in, not the blockbusters that are everybody's interested in?

Tiffany Perkins-Munn: So really using their scale to get smart about personalization. That's what the Amazons and Apples and Netflix of the world need to do.

Kailey Raymond: I love that. Hot take, you don't think that they're going to get to a one-to-one level, even with AI?

Tiffany Perkins-Munn: No, and it's creepy anyway. Like for Amazon to be one-to-one with me is a little too close, personally. Jeff Bezos calls me up and says, did that echo order work out for you? I might pass out.

Kailey Raymond: All right, Tiffany, we're gonna round this out and ask you the final question of today, which is if you had any steps or recommendations that you might have for somebody that's looking to up-level their customer experience strategies, leveraging data, what would they be?

Tiffany Perkins-Munn: In order to up-level customer strategies using data, I would say cast a wide net, but be very intentional. Meaning get your hands on all the data and information that you can. A lot of people restrict themselves to certain pieces of information and then they use it to extrapolate to a full story, but just be very comprehensive in the data that you bring together. And be very mindful of technology, tools, AI, systems that you can use to manage that data, right? Because there are lots of, lots and lots, I could list off tools, Alteryx, and just a number.

Tiffany Perkins-Munn: I don't want to call out any in particular, but just a myriad of tools that we can use to better understand data and information. And so the more knowledgeable we are about all of those tools, meaning you don't have to know how to use all of them intimately, but you need to know A, that they exist, B, what they do and C, how they work so that you can use them to help you better understand the data, the customer, and what you need to do to provide a better customer experience.

Kailey Raymond: I love that. Awesome. Very much appreciated, Tiffany. I learned a ton today. I really appreciate your time.

Tiffany Perkins-Munn: Thank you so much. I appreciate you guys giving me the platform and the forum to discuss a topic, which is near and dear to my heart. So thank you very much.