Improving Customer Engagement with Unified Data

Kailey Raymond on September 8th 2022

Good Data, Better Marketing |  Episode 08

Subscribe to all episodes on Apple Podcasts, Google Podcasts, Spotify, or wherever you listen to your favorite podcasts.

In this episode, Pepe Valiente, Senior Management Consultant at IBM Garage, talks about the risks of digital transformations and the importance of personalization, simplicity, and privacy for the customer experience. 

Guest Bio

Over fifteen years of program and project management experience delivering complex initiatives within multiple industries including aerospace, electronics, consulting, electrical, software, and retail. In addition, experience in Management of Acquisitions and Integrations for International Companies (Market Research, Commercial Analysis, Financial Analysis, Due Diligence, Negotiation, and Integrations). At IBM Garage, Pepe is responsible for managing this platform through initiatives that generate value from the early stages of the process to its implementation, ensuring scalability and world-class standards. 

Episode Summary

This episode features an interview with Pepe Valiente, Senior Management Consultant at IBM Garage. As a competitive dragon boat steerer, Pepe is used to interpreting data from the elements and his team to stay on course. He translates this skill into consulting strategies by breaking down data into valuable pieces. 

In this episode, Kailey and Pepe discuss why digital transformations fail, how our brains react to personalization, and why simplicity is key to customer experience.  

Key Takeaways

  • Customers do not expect you to be perfect. So long as you’re improving your strategy day by day and daring to try new things, customers will judge your intention.

  • Digital transformations are risky business, with about 70% of them failing. This happens because there isn’t a clear understanding of why a digital transformation is being implemented in the first place. To be successful, you have to agree on what technology to use, friction points you’re trying to eradicate, and what the customer wants.

  • Personalization, simplicity, and privacy are key to creating a top-tier customer experience. Personalization creates a chemical reaction in the brain that translates to trust within a company. Simplicity helps customers understand that their needs are being met. And privacy compliance ensures that customers trust the brand and will continue doing business with them.


“When we include the person's name in our communication, that creates chemical reactions in our brain that makes us feel good. That may create some excitement and unconscious signals that create empathy, trust. The importance of personalization translates into how much we care about our user.” – Pepe Valiente, Senior Consultant, IBM Garage

Episode Timestamps:

‍*(01:49) - Pepe’s career journey and dragon boat background

*(06:28) - Pepe’s role at IBM Garage

*(11:47) - Trends in customer experience from a consulting perspective

*(24:44) - Why 70% of digital transformations fail

*(26:19) - How Pepe defines “good data”

‍*(32:11) - An example of another company doing it right with customer experience (hint: it’s American Express)

*(37:45) - Pepe’s recommendations for upleveling your consulting strategy with data

Connect with Pepe on LinkedIn

Connect with Kailey on LinkedIn

The views and opinions expressed are personal and do not represent IBM’s point of view.

Read the transcript

Kailey Raymond: Much like the crew of a dragon boat. Steering a business toward a common goal is no easy feat. To get everyone rowing in the right direction, you need to build trust and work together. The distributed work of paddlers and the steerer of dragon boats takes into account a lot of data points, the wind, currents, paddler strength, and rowing cadence to name a few. Similarly, in business, this happens by understanding what data will help you reach your goal, breaking it into smaller pieces and implementing that data into your strategy. Pepe Valiente knows all about this. Not only does he compete in dragon boat races, he's a senior consultant at IBM Garage. They're consulting practice dedicated to accelerating digital transformations. Here, they use data to drive value for their clients by encouraging them to take a customer first approach.

Pepe, the way that I like to start this is just to get to know you and your career journeys a little bit better. So, tell me a little bit more about your career. How did you get to where you are today?

Pepe Valiente: First of all, thank you for having me. It's a great opportunity to talk to you.

Kailey Raymond: I'm so excited. You're here.

Pepe Valiente: Yeah. So, well, I have listened all your episodes and let me tell you first. I'm a big fan, so everything that's going on-

Kailey Raymond: I love a fan, Pepe. That's great to hear.

Pepe Valiente: Yeah. And I can tell you all the guests before me have been amazing and their careers have been very impressive. So, if you allow me, I would like to tackle this in a different way, so I will give you three fun facts about me. So, I think that's one way to describe myself and instead of getting into my bio and everything. I can give you three different fun facts, which two are going to be true and one is going to be a lie.

Kailey Raymond: And am I going to have to guess at the end?

Pepe Valiente: Exactly. So, I will ask you to guess-

Kailey Raymond: Oh, Lord. Here we go.

Pepe Valiente: ... which one is a lie? So, here we go. Are you okay with that approach?

Kailey Raymond: I'm so ready. I am excited to embarrass myself and let's go.

Pepe Valiente: All right. Awesome. Let's see. I am a dragon boat steerer, that's a number one. Number two, I am a certified data science professional, and number three, I teach digital transformation in a business school. Which one you think is the lie?

Kailey Raymond: Oh, man. I don't think, and I don't want to offend you because I know you're really good at this, but I don't think you're certified in data science, but you're a wizard at it. Is that right?

Pepe Valiente: That is correct. Actually, I am not a data science professional certified, although I dedicate a lot of my life at work with data. That's why I'm here actually. So, no, you got it perfectly and for those that are not familiar with dragon boating. Visualize this, so imagine a 40 feet boat, 20 paddlers, one drummer, and one steerer. So, that is who I am, I'm the steerer. I'm the person who is in charge of the safety, setting the direction of the boat. I'm the only person who's actually looking at where the boat is going. That is a level of responsibility and linking this to data. I remember once I was training in New York City, there is this very competitive team I used to be part of and we were preparing for this important race and in between drill the coach start asking for feedback and, "Hey, how the drill started looking and how do you feel about it blah, blah?"

And because they're very competitive team that start giving opinions about the direction of the boat and some of them start saying, "Oh, the boat is not going straight. I think that we're going to the left and to the right." And that is a direct criticize of what I was doing, my job is to keep the boat straight. And as soon as I docked the boat, I start downloading the data that I have on my watch, and I was able to share that with the coaches and share, "You know what? These are the drills and the boat is completely straight." So, that day they stop actually with those ideas or opinions about going left or right, so they can know that they can rely on me and that I execute my job. And that is the piece that where data by itself is useful. It got me out of trouble and I could be useful for the team. And by the way, if they are listening, catch me too, I love you guys, so that is the anecdote about dragon boating.

Kailey Raymond: I love that. Shout out to the team. Building consensus with data.

Pepe Valiente: Exactly. And about teaching, which is the other piece that I added is because you not only have to be good at the industry level, or at least from my perspective. I also want to have the opportunity to prepare professionals and get into this world with real life experience and we will talk more about this as we progress in the podcast, but I do believe that you can get to know better someone by understanding how their use their time, what the hobbies and what they do. And in case you're worried about where I'm going. Yes, I will talk about data and my role at IBM.

Kailey Raymond: Listen, let's talk about dragon boats all day and just make this a very weird episode.

Pepe Valiente: I feel like it can be completely disruptive.

Kailey Raymond: Fun for me. Okay. Very cool. So, you're teacher, you are an enthusiast around dragon boating, and you also happen to be incredibly sharp at data. Your first role, as I understand, at IBM was a project manager. You've also been in product management, a steppingstone to your current position in consulting at the IBM Garage. So, tell me a little bit more about what you're doing at the garage and how that relates to the customer engagement journey.

Pepe Valiente: Right. So, first of all, I have been at IBM for, well, it's going to be 10 years in October.

Kailey Raymond:A decade. Wow.

Pepe Valiente: Exactly. So, I never imagined, but, well, those are the things that just life push you. Funny fact is I joined IBM as a consequence. It is something also to share. So, they were looking for a person to help them with finance and I joined IBM as a finance analyst and then because, my previous experience with, well, the PNLs and the balances, and so, so, so. I was asked to join IBM as a finance analyst then because of my previous experience, I start working as a project manager and I transition organically into project management and that is where I feel very comfortable about leading projects, organizing scope, getting daily activities, and getting this-

Kailey Raymond: Watch data to get people on the same page about dragon boat steadiness.

Pepe Valiente: Exactly. Alignment is very important. So, I used a lot of my time and project management and different projects in IBM. So, I started with electronic payments. So, for example, how you get the money into the bank, which is a very important component-

Kailey Raymond: Important. I like when that happens.

Pepe Valiente: Exactly. And you get the very traditional methods where you have the MasterCard, the Visas, the American Express. That's cool. But then we start disrupting a little bit and say, "How can we get PayPal on it?" Or "How can we get now nowadays a little bit more disruptive and get crypto?" And those things like, "Okay, how can we get paid and be creative around?" So, that was how we started in the project management world at IBM, then I moved to a Salesforce implementation.

And this is interesting because we as consultants and IBM has the biggest practice of Salesforce consultants, so I just use as a background information. We implement Salesforce internally for IBMers, for IBM sales teams. And just to give you an idea on how big this is, back then, when I was in this project, we were talking about 9,000 sellers onboarded in three different areas, different locations, 50,000 business partners, more than 530 clients. So, it was a massive implementation and it was a market to cash so, you can see all the platform across.

And my responsibility was on the CRM side, the CRM component, and I had exposure to product management and I love it. So, that idea of, okay, so start defining what the team need to build, start working with data elements and understand what is the user journey so we can simplify our seller's life and we can foster that idea of start selling. And there is where I start working on product management. I start shaping my skills on that land, and that allowed me to land a job in what the... Well, that is known as the IBM growth team and the IBM growth team is with actually my journey with Segment began, and we were a composition of different platforms that we can find in the market where the intention was to acquire data from different data points, get IBM sites, IBM applications, products, and so on, and consolidate that information and send it to different destinations so we can activate the data.

And there, I was responsible for the IBM and Segment relationship for three years and many good memories. Now, many good projects together. We did the biggest protocols implementation together as one example, and also to share how big this was. We manage within the platform more than 130 products, more than 500 sources, user at 70, and multiple destinations way of working. We use WalkMe for user journey, Amplitude for analytics, Braze for email and marketing campaigns transitioning to Marketo right now. So, there are many things that are going on, but because at the end, the data is useless if you don't have a use case to support it, if you don't activate the data, and so we use a lot of that creating campaigns. So, this experience allowed me to understand platforms, understand people, the methods, and how important they are to understand the business and relate to whatever the value that can bring to a company.

And I wanted to put that in a good use on a customer-facing role. So, I had some background at IBM, different areas, different organizations, different cities and suddenly I found myself, "Look, you know what? I want to do that for customers." And that's how I landed in IBM Garage and in Garage is what we do. We identify those components, get alignment and make it real, so that is what we do. I'm part of a very talented team. We are on a mission to scale IBM Garage. So, every engagement that we are having with different or with multiple with our customers, we are using IBM Garage as an example on how creating alignment is important, how we can build up a good vision, how we can understand the value, materialize or translate that value into something that we can quantify, and ultimately that we can help our clients to materialize the vision in my own way.

Kailey Raymond: So, as a consultant, you are in front of clients all day, every day and I'm sure that you see and hear a lot of the things that they're talking about in terms of trends that are really impacting their businesses. So, what are some of those trends that you're seeing as it relates to customer experience?

Pepe Valiente: Sure. So, when talking about trends, I would like to share three things or three different trends that I see in the industry in general. So, let me start with personalization and here, I would like to ask you, tell me a name of a singer or an artist that you follow?

Kailey Raymond: Beyonce.

Pepe Valiente: Wow. Okay. It's perfect. That's a very good example. So, imagine that you're in stadium and you win an award and Beyonce is going to deliver that award to you. Okay. So, you're in the audience and Beyonce is saying your name in front of the multitude. How would you feel?

Kailey Raymond: I would faint. I would faint, Pepe.

Pepe Valiente: Right. So, it feels amazing. You would feel over the moon, so the same happens. There are studies that tell us that person's name is the sweetest and the most important thing in any sound and languages. So, when we include the person's name in our communication, that creates chemical reactions in our brain, that makes us feel good, that may create some excitement and unconscious signals that create empathy, trust, and remember, trust is very important. So, why are we using generalizations? Why we go with, "Hello, User123." When we can say, "Hey. Hello, Kailey." That is very important. So, the importance of personalization translates into how much we care about our user. For a company as big as IBM, unified customer data is crucial to unlocking personal experiences. Here's where a CDP can help us connect that data from different places.

So, what the marketing team is doing, what sales is doing, what the CSMs are doing, the partners are engaging with us. So, by identifying common pieces of data elements, consolidating those, sharing these from one business area to another. So, we can personalize our messaging and create one consistent user experience. And one example digging a little bit more on this is we capture from our users, we capture our industry, and we capture the role. And what we can do with this information is we can present them personalized topics that so once they log into a product, we recognize the person, we recognize the role in the industry. So, we can present them pieces of information that could be meaningful to them. So, if I am Pepe and I work in aerospace. I can say, "Hi, Pepe, welcome back," or "Hello. Thank you for visiting us," or "Thank you for joining. I know that you're in aerospace. Would you like to learn what is the latest we're doing with Delta, for example."

And that is what can create some level of curiosity in me and what we have seen is that these experiences foster the possibilities to our clients on how they can use IBM products or how they can leverage a solution. So, beyond resolving a problem, which is why the user came to us in a first place, what we are doing is we're fostering that imagination on what other use cases we can cover.

Kailey Raymond: I love that. I love that you related it to the chemical reaction that happens within somebody's brain when they hear their name. I think that's exactly what personalization does and nobody's actually relayed it to me in that simple of a term before. So I really appreciate that context. And for me, I think that relates back to the fact that when I log into a Netflix or an IBM portal or whatever, and I'm seeing exactly what's relevant to me, it's the simplest way to digest information.

Pepe Valiente: Exactly. And you will be surprised. I mean, furthermore, now with the pandemic and everything, all these personalization became so key that otherwise will be impossible, and one example of this. You know Audi? Audi in UK increased by 59%, their digital sales by leveraging personalization. And while the market was dropping, because the marketing in the UK was around 30% going down, Audi was going up. So, these things, and these tricks are important, so we can try to understand and meet the customers wherever they are. So, that is the first trend. The second trend is about simplicity and simplicity. It is key on delivering superior customer experience and the reason I'm saying this is because if you provide multiple options to users, it is easy to confuse them. It is easy to make them feel that your product or your service is not satisfying their needs and what happened, they will leave. So, for example, the New York subway, they have this seamless payment experience, it's called the tap and go, or OMNY, that you can pay the subway with your credit card, with your watch, with your phone-

Kailey Raymond: That's a beautiful thing. Yes.

Pepe Valiente: You don't need to go and buy a MetroCard all the time, or spend some time with those cashiers, et cetera, or talk to someone. You can directly go and will just pass. So, that is exactly what Amazon is doing with the Amazon Go stores. You don't want to talk to a cashier. Not a problem. You just go wrap whatever you want. So, at the end of the day, it is reducing all those friction points. So, my experience can be better and if that generates some level of satisfaction, if that covers the value that I expect from a company, why not doing that? So, the question here for us is what are the things that we can reduce to simplify the experience without impacting the value? And can you live without that piece of data? Can you make it better or can you derive the data?

A lot of the things we are getting those from browsers, for example, so we know the language, we know the region. So, there are many things that you can know about the person, about the user without being invasive and asking them for things. And the same applies to technology, so if you're using multiple systems for one process or one simple function. Remember your employees are also your users. So, if you are making it difficult for them to track what a user is doing, they're also adding blockers and problems to the organization. So, be careful with the data, make it simple, be careful with the tools that you use, simplify the process, so that's trend number two.

Kailey Raymond: Very cool. Yeah. One of the things that I really like that you're saying, and I forget where I was reading this the other day, but it was some comment about how you don't need to do it 90% better. If you have a problem that you're trying to solve, you don't need to do it 90% better. You need to do every single increment and component of it 1% better and it's the law of exponents. If you make everything 1% better, then over time you will get to 95, 99, 100% better. So, I like this idea around these small little tweaks that are aiding in conversion here.

Pepe Valiente: Exactly. And this is one of the ideal principles. No one is expecting something to be perfect or excellent. Who's perfect? So, at the end of the day, if we're able to improve a little bit every day, a little bit and release to production, try and test A, is this working? Is this not working? Let's go back. Let's try something different, but it is about this experimentation culture. It's about how you can dare to try new things. And if you're ready to get better each day, at the end of the day, what our customers will judge is the intention.

Kailey Raymond: Here's hoping that they'll judge the intention.

Pepe Valiente: And well, that takes us to the number three. So, the trend number three that I like I would like to share today is about security, privacy, and compliance. And do you know that the average cost for a data breach in finance services is around $5.72 million?

Kailey Raymond: I actually just read that in an IBM report a week or two ago, that is an unbelievable figure.

Pepe Valiente: It's a big amount of money. So if there are ways that we can prevent that from happening, why not use that? So, in this case, you can see that the concern about compliance is growing. You can see that from the company's perspective, you can see that from the government perspective and there are more and more legislations around it. And to cover, we need to understand the legislation, of course, and something that it will be interesting is that we can understand and adapt your schemas, so you can be compliant with that. And here's where tools can help us significantly, and I can highlight three elements that help IBM to be in compliance with many of these elements. So, first the front end, so if you're using websites, applications in app, et cetera. If you have the right permission to capture information, sometimes it's through cookies. Sometimes it's directly asking to the user, "Are you okay if I capture this data with this purpose?" And explain them in advance.

So, if you do a good job in terms of user consent, that is the very first topic. Second topic is Segment protocols. It is a very good tool to help us understand what data can be in and what data can be out. So, we created one common schema that goes across all the business units at IBM, and we created one major tracking plan and what this means is that we had that ability to filter in, filter out events and properties, and that can help us defend any issues or any legislation. So, every time that we had an audit, we put the tracking plans in front and we setup. The GDPR says is this I'm compliant A, LGD in Brazil says this and so on.

So, imagine that we did that for more than 500 sources that can tell you why the importance of having this implemented on it. So, we have control over 100% of the events that are flowing into the platform and that is how we did it. And the third point here is the privacy portal, which is also a Segment product here is, imagine that by mistake, we send that social security number or a credit card.

Kailey Raymond: I hope not.

Pepe Valiente: No, well, hopefully not right, but well, sometimes it happen. And you can be in compliant of the event, but maybe not the data that is in those events. So, here is where the privacy portal help us. So, we define thousands of properties where we could identify or catch data that could be at risk, or that can be risky to receive. And between all these three points, the idea was to cover most of the legislations. And at the end, again, this is a matter of trust. If you trust the brand, you will do business with the brand. If you don't trust it, you are losing business. And we talk about personalization earlier. So, you know that two-thirds of the users will share personal information in exchange of some value, so if you consider that then, well, there is a key. So, you give them something valuable. They will give you the data and then it can be a virtual circle.

Kailey Raymond: Leveraging IBM's big data and Segment’s distribution to really activate and make sure that you are building those great experiences for customers. I'm curious as well, is, are any of these industry trends, maybe impacting IBM directly? We hear personalization. We hear the trust as it relates to privacy all the time here. Are any of these or other trends, things that you're seeing directly within IBM, that y'all are thinking about every day?

Pepe Valiente: Everything goes together. And ultimately, we're in a world that things are changing very quickly. So, one thing that work yesterday is not working today. So, we need to frequently adapt. And what IBM is doing is always trying to look at the future. Where are the trends going? Where are the users going? And maybe not at the industry perspective, but maybe as a company, we see the market moving and the enterprise will continue to expand their ecosystems, supported by platforms, open platforms preferably, so that's important to say. They will leverage their people, their workflows, their data. So, that's a hybrid cloud. So you may hear about IBM it is implementing this about hybrid cloud and that's the idea.

So, we know that people or companies will be in multiple environments. We know that the data explosion is big and that will trigger the need for machine learning, artificial intelligence at scale. We will be duplicating or triplicating the data in by two, three years. So, every two, three years is going to be growing exponentially in terms of the amount of information we have that will trigger the need of artificial intelligence for decision making, for automated processing and so on. I have seen for example, that the chief data officer role is also growing. So, that's also a hint. And ultimately when we talk about automation, well, that is giving you a competitive advantage. So, the message is clear. We need good data that we can rely on use and systems that can help us integrate these data across organizations.

Kailey Raymond: That's so interesting about that figure of the growth of CDOs and as it relates, especially to the trend of data doubling every two to however many years, I think that's unbelievably fascinating and probably rolled up entirely within this very macro trend of digital transformation. And as I understand it, 70% of those digital transformations fail, and obviously, that's not good for business. That's lost time. That's lost money. That's lost effort on your behalf. In your experience, why do you think that is? What's happening with the failures of those digital transformations?

Pepe Valiente: So, I would say that there is a big number of variables of why a digital transformation fails and I wish I could know all the answers. Something I have seen is that we don't have a clear understanding on what the digital transformation or why we are implementing a digital transformation. Do we clearly agree on the business goals? What success looks like? What do we build first? What is the technology or the right technology to use and so on. So, fundamentally, I'm talking about what, what is what we need to do, and what is the reason. So, in my experience, everything starts with the user journey. What is end to end process? What are the frictions we want to resolve? How we link this with technology? Follow what users need, not what leadership dictates. Sometimes the idea is that customers wants X, but when you do some research and you start digging around it, you find that the users want something completely different, so being able to articulate ambition is key.

Kailey Raymond: One of the things that I like to learn a little bit more of and how you're doing it and how you're defining it is good data is a pretty vague term. I know the podcast is called Good Data, Better Marketing, but how would you at IBM define what good data means?

Pepe Valiente: Well, at the end, what is the data that can help you with a specific use case? It can have different connotations. How can you make it simple? How can you personalize? So, at the end of the day is where you can find the right pieces of information so you can cover the value that you want to bring to your users. And that is what can bring success or failure about a specific piece of data.

Kailey Raymond: So, tell me how you start to normalize some of the data that you're bringing in?

Pepe Valiente: Normalization is very important. We manage in the platform more than 130 products. So, imagine if we don't standardize or normalize the events that they are going to flow into the platform. If we accept all the information, it will be the wild west, so it will be very messy. So, from compliance issues to event repeatability, we will have thousands of events where many of those could be treated for the same purpose, maybe, or similar properties, nomenclatures, flavors, et cetera. You name it, we will have many events that can fight in each other. And the challenge is that as we capture those events, those events are going to flow downstream.

So, the issues are going to multiply exponentially and the complexity and the confusion is also going to multiply. And remember, considering that our teams in general are relying in our information for reports, campaigns, machine learning, AI, et cetera. If we don't provide a clear signal, it only gets more and more complicated and siloed and the idea of doing all this is that we want to democratize the data. We want to make it available to everybody. So, if we are not using this with that purpose, we're doing something wrong.

Kailey Raymond: I love that data normalization and having that upfront is so important so that when you get down the road, you aren't running into some of these big headaches with data that's not properly within its nomenclature within its schemas, so appreciate you bring that to the forefront.

Pepe Valiente: Another piece that is important related to this is the documentation, because everything that we do as part of the automation is also documented in GitHub patients. And let's say we have a very complex automation process, but if we create a new event, if we modify an existing event and so on, everything automatically. Let's say updates all the documentation. So, if I am a data scientist in X team, I can relate to that information and I know that's going to be updated so I can understand what the event, what the property, what are the required fields, what are the desires, what are the nice to have, et cetera.

Kailey Raymond: That's great. So, as I understand it, what's happening is that you are basically allowing every single team to build upon their own use cases with this common nomenclature of the data that's successful to them and I love the example of onboarding and that's something that I was trying to figure out at the last company that I was at. I was running customer education and that segmented journey and making sure that you are being so personal to every user and understanding what their needs might be massive increases in conversion rates when you do that in adoption and, obviously, that leads to retention, which is good science for business. So, how are you using good data that you helped to find for me a little bit earlier to build customer engagement at IBM?

Pepe Valiente: That is a great question, and we use good data to unify your message. So, what are the campaigns that we're launching? Are they clients or prospects? Do they have a CSM assigned? It is not easy when you have thousands of clients, thousands of prospects, but the idea is to be able to leverage a data and capture from different areas and share across so we can provide a unified experience, so that is a big challenge. So, what happened, we need to correct things, very simple, such as sending an email message to someone to buy IBM Cognos when they were IBM Cognos customers already. So, it is something very simple, but these are the things that a CDP can help us to resolve. So, we can understand what data, in this case, it's evident that the data was not flowing. So, we needed to find ways that we can be more open across departments and identify what data to share, so we can keep improving.

We go beyond historical information. We use examples, industry, market, company-specific data to identify and recommend products. So, this is similar to what Amazon is doing with the books. So, you look at something, there are engines behind that can tell you what else to buy. We do the same with IBM products. So, we understand the company, the size of the company, what products they are already buying, and we are able to create recommendations on what can fit or what can be a good commonality in terms of product. And like that many, right?

So, what CSM is assigned. So, once they are customers, maybe we can connect to a CRM. A CRM, we use Salesforce by the way, and what we can do to connect with an existing CSM and maybe upsell. That is a good opportunity. So, the CSMs have very good input. Why don't we are listening to them? So, we want to create more and more engagement, more sales, or grow an account. They can be a very good source of knowledge and if a user is belonging to an account, so you can send emails to that specific person or send emails globally to an account specific account and so on. So, at the end, that is why we use good data.

Kailey Raymond: I love that. Being able to use it to the upsell example is a great one, but I think that everybody will appreciate because what is it's keeping your current customers and being able to upsell them and retain them. It's a whole lot easier than selling that new. So, certainly advice to follow. I want to switch gears and learn from you in terms of who you think is doing it right. Are there any people that you look to for inspiration as it relates to customer experience?

Pepe Valiente: One company that I believe is doing great job with data is American Express and the reason behind that is they have very interesting programs. For example, my wife, she went the other day to buy something and for some reason we needed to return it, and that, well, the guarantee in the original store was not applied. So, we talked to American Express, and you know what? This is situation. This is what happened. And American Express through their purchase protection program. They refund everything and they know all the historic background. They know how much transactions you have a month, et cetera. And they were able to do that in seconds. So, they didn't ask a lot of questions. They just opened a ticket. They did it.

Fraud protection. The same. First, I received a message in my phone saying, "Hey, are you in New York?" And I was in California. So, I thought, "No, I'm not." "Well, because we identify a charge in this theater in New York." And I was like, "Well, that's not me." So, through the app, I was able to freeze my card and then I opened a ticket right away, and that was solved in minutes. And I can tell you other banks or my experience with other banks has not been that, no, you need to write a letter, send a letter. We need to analyze it and-

Kailey Raymond: That's very your old school, write the letter, call up the CEO.

Pepe Valiente: Exactly. And then, let's see if you have a case because you know what, I don't trust you. You were on a business trip in New York and you're not telling me, and it can be those conversations, but you know what? American Express started with this trust and say, "You know what? I know who you are, and I know that you're not going to trick us, and you're not going down for $50."

So, end of it, there you go. Here's our return and we will investigate, but in the meantime, you have your money back. That's another thing. They have travel experience. They have concierge and they are implementing a store. I don't know if you're familiar with this, but very similar to what Amazon is doing with the Amazon Go. They have a store in some of the stadiums so they're implementing that model, where through your American Express card, you can just go to store, pick whatever you want, get the snacks, get the sodas, whatever, and you just leave, and then you get your charge receiving the American Express.

Kailey Raymond: Oh, I have seen in the airport, the Amazon stores where you swipe your credit card on the way in, and then you just walk out, and it is an interesting, freaky experience. I will tell you, but you're right. It is simplified, but I'm like, "Maybe I'm a lot, maybe a little bit too old school for that." I'm like, "Where's the cashier? Let me smile and say, "Hey, do a little chit chat."

Pepe Valiente: Exactly. And we're going there, but there are some processes that are not adding value, so why not automate those. And you need to be very careful on balancing because at the end of the day, you are talking to humans. So, if you expose yourself to a robotic experience where you're like, "Well, I don't care who you are. You're just a customer, a transaction for me." You're going to lose those customers. So, you need to balance that. Where is that right amount of automation? So, I can get rid of everything that is not adding value, that everything's repetitive and boring, and you can implement something that can create a good experience.

Kailey Raymond: I do love that you just brought that out, which is when automation is what you should lean into, and when personalized one-to-one human interaction is what you should lean into from an experienced standpoint. And I, as a people person, fear that we're going down the line of too much automation and I think that that example of walking into a store and cameras looking at you and knowing what you're purchasing for my personal taste might be a little bit too far, but I understand why we're going there. But exactly that is what is that balance and how can brands find that? I think that's an interesting question that people are going to continue to explore for the next few years, which is now. I also want to ask you about is, do you see any changes on the horizon in the next six to twelve months as it relates to customer data, customer experience, what would those be?

Pepe Valiente: Well, companies are more and more aware of data, so they understand it, the data generation, data enrichment, application to multiple business use cases, and they're struggling to get the right benefit on it. So, what I see in the short term is that companies are going to start looking more and more into how can I extract data, but how can I put that data to work and how can I get a benefit from it? Companies are looking for consulting help to shift away from that paradigm and boost the way of work. How can we implement best-in-class methodology, so we can accelerate the value. Who can get the right speed? How can we be more product-led? Sometimes we take that for granted, but the reality is that there are many customers that they don't understand clearly what does that mean. So, end of the day, what will be important is that we can understand where they are.

Companies are changing, everything's changing for more on day to another, so how can we help them? How can we interact with them? How can we foster that employee experience? Because it's not only the customer, internally also it's important, especially with the pandemic. For example, there are a lot of changes. People are changing jobs, people is moving. So, if I don't see the benefit of what I'm doing, if I don't see the purpose of what I'm doing, I'm out. So, there are challenges inside and outside of the organization as a living entity that they will be linking a strategy so they can continue working on that, so that is in the short-term.

Kailey Raymond: What I want to know, last question, Pepe. What are the steps or recommendations that you have for somebody to uplevel their consulting strategies with data?

Pepe Valiente: So, I would say number one, listen to your clients. Listen to your users, validate what they're telling you, find the right motives, find the right purpose. And then, with that idea, with that understanding, define a clear vision and a strategy of what is you want to do, how you can help them. And then, think about all the different technology that are around and something I can recommend to listeners, hey, identify what is out there. What are the exponential technology that we were discussing about and try to get yourself familiar with that and then try to link that to the strategy. And then, when we jump into execution, try to think in this way, in dragon boat, like in many other sports, the way you win championships is one race at a time. So, try to break that strategy, that value that you're going to implement in a small pieces of value, and then start implementing one by one. Keep listening to your market. Keep listening to your clients. Adjust people if you need, but ultimately focus on delivering the value that the client is expecting.

Kailey Raymond: I hear you. It really is in marketing and consulting and truly, probably everything. It's about anchoring yourself on that, delivering value, making sure that you are speaking to your customers and clients in a personalized way, so that they are getting something out of the interaction. Pepe, thank you for being here. I really appreciate it.

Pepe Valiente: Thank you. Yeah. It was fun.

The State of Personalization 2022

Our annual look at how attitudes, preferences, and experiences with personalization have evolved over the past year.

Related articles

Kailey Raymond on August 25th 2022

Adele Hedden, Head of Customer Experience at Faire, talks about how to build a proactive customer service model built on data and the metrics you should be tracking to improve CX.

Kailey Raymond on July 28th 2022

Liz Miller discusses how to build durable customer relationships by bridging the gap between customer needs and customer experience with open-ended questions

Kailey Raymond on June 13th 2022

Waseem Kawaf built iterative testing into his team's DNA by teaching user-centered design strategies that put the customer first. Listen to this episode of Good Data, Better Marketing to learn from STANLEY Security's success.

Kailey Raymond on August 25th 2022

Adele Hedden, Head of Customer Experience at Faire, talks about how to build a proactive customer service model built on data and the metrics you should be tracking to improve CX.

Kailey Raymond on July 28th 2022

Liz Miller discusses how to build durable customer relationships by bridging the gap between customer needs and customer experience with open-ended questions

Kailey Raymond on June 13th 2022

Waseem Kawaf built iterative testing into his team's DNA by teaching user-centered design strategies that put the customer first. Listen to this episode of Good Data, Better Marketing to learn from STANLEY Security's success.

Become a data expert.

Get the latest articles on all things data, product, and growth delivered straight to your inbox.