Using Data to Personalize the Customer Experience with Steven Maskell

Using Data to Personalize the Customer Experience with Steven Maskell TW

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In this episode of the Customer Service Secrets Podcast, Gabe Larsen is joined by Steven Maskell, Vice President of Customer Experience at Zones, to discuss how to create a personalized, data-driven customer experience. Learn how Steven does so by listening to the podcast below.

Creating a Data-Driven Customer Experience

Steven Maskell has successfully led service teams for nearly 30 years. Throughout his time in the CX industry, he has figured out how to integrate data into providing the most excellent customer service possible. He says, “I see the people have a very high expectation and a short fuse. And so what that means is that they will give you the data or they accept that you’re going to take the data, but by golly, you had better make it worthwhile.” In discussing tips in which data can be attained, Steven mentions knowing your customer, who they are, what they’re doing, and how they interact with the brand have all proven to be greatly effective when building brand loyalty and curating to the customer persona.

Data can also be used as a helpful tool when advertising to the customer. Customer data shows shopping interests and purchases. Based on this, the company can decide how to advertise to the customer in the most effective way. Rather than advertising the product a customer has already purchased, a brand could advertise a warranty on that product, ideas for how to use that product, etc. Proactively using data to shape the customer experience can ultimately lead to brand loyalty.

Starting Small Makes a Big Impact

The next step to personalizing the customer experience after finding the data is figuring out an infrastructure to store that data and to organize it to be more useful. Steven knows that it can be overwhelming and difficult for companies to change their current methodologies to becoming more data driven. He mentions, “I wouldn’t say start an Excel spreadsheet, but start somewhere small where you can just get the literal basics structured. There’s great relational databases out there. There are some really good tools out there. As I mentioned, there’s off the shelf sort of relationship management products that are out there.” The easiest way to implement this change is to start small and to invest into the basic essentials of data storage and framework. Starting small to get the basics structured into a system is highly recommended by Steven to allow for more structural growth as new data is added. Once the company figures out what they really want to gain from each customer interaction, they will be better able to configure their databases to become more data driven for a more personalized experience.

Integration of AI into CX Operations

Artificial intelligence has become somewhat of a controversial topic in the CX realm. Becoming more normalized, AI can be found in a lot of customer service organizations as an implemented aspect of daily customer interaction. On this topic, Steven notes:

You’ve got to be very flexible in my opinion about how you react to the data and what you have and really what you’re trying to achieve. So… have very realistic expectations. Please don’t think you’re going to double the company’s revenue because you’ve done AI implementations or some nonsense like that. But please know that you can have a significant impact on it.

AI, while certainly helpful, is not without flaws. At its current state of development, AI is not a perfect system, nor is it a valid replacement for human intelligence. AI can be helpful in guiding customers to finding answers to their simple questions, similarly to questions answered on FAQ pages. However, nothing can replace the genuine human connection between a customer and a CX agent. It’s this connection that ultimately builds a sense of trust between the customer and the brand.

Steven urges CX leaders to take an honest look at themselves and to reevaluate how they amplify their brand and its products. He believes that in doing so, leaders will produce better CX outcomes.

To learn more about the secrets to personalizing the customer experience, check out the Customer Service Secrets podcast episode below, and be sure to subscribe for new episodes each Thursday.

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Full Episode Transcript:

Using Data to Personalize the Customer Experience | Steven Maskell

TRANSCRIPT
Intro Voice: (00:04)
You’re listening to the Customer Service Secrets Podcast by Kustomer.

Gabe Larsen: (00:11)
Welcome everybody. We’re excited to get going today. We’re going to be talking about how you can take the customer experience, personalize it, all using data to do that. And got a special guest, Steven Maskell. He’s joining us as the Vice President Customer Experience from Zone. Steven, thanks for joining. How the heck are ya?

Steven Maskell: (00:32)
Absolutely wonderful to be here. Happy days to everyone so it’s a joy to be here.

Gabe Larsen: (00:37)
We just got Steven before he’s going on vacation so I appreciate him jumping on and doing it quick before he jumps on the week long vacation. Before we jump in Steven, can you tell us a little bit about yourself, maybe your background? Give us that quick overview.

Steven Maskell: (00:53)
Background is that I’ve been in the customer experience space for about 25 to 30 years and have spent a lot of time both on the research side, on the consulting side, and now on the implementation side. So I’ve spent my career both learning what customers want and then helping other organizations better understand how to deliver on that. Then actually being a consultant and helping organizations implement that. And now as the Vice President of Customer Experience, I am on the complete opposite end of the spectrum. Designing, building, implementing and measuring against KPIs.

Gabe Larsen: (01:27)
Yeah, such a fun background. I think it’ll be a fun talk track today. So let’s dive in, big picture as you think about this. Personalization is obviously an important word that people are using a lot more. Data is something that I think people want to use more. AI is a buzz word that people haven’t figured out. How do you start this journey? How do you start to think about using data to personalize? Because I think we all want it, but we don’t know how to do it.

Steven Maskell: (01:54)
Yeah. It’s a great place to actually start this conversation. Here’s the thing about personalization and about customer experiences as a data-driven methodology or practice, you have to, first of all, have the data. You have to know who that person is. You have to be capturing the data. You need to be in a place that they want to give you their data because there’s value in giving it to them, by giving it to you. So, where do you all start with it is what do you know about your customer? Are you able to actually see how they are interacting with you or is it anonymized? Are they sharing with you information that’s important that you can use? We can talk a lot about that in a little bit, but all of us are doing our level best to understand how to really drive a customer experience and make their lives a whole lot easier. And customers are doing their level best to say, “I don’t want you to know too much about me.” So it’s balancing that and making sure that they understand what they’re giving up and what they’re getting, but then you also have to have a robust set of data so that you don’t recommend the completely wrong product service, a path to someone just because you’re trying to put them in a persona that doesn’t make any sense.

Gabe Larsen: (03:05)
But this collision, right? Where do you typically stand? Do you feel like people are more open to give you more data nowadays, or you feel like you’re seeing kind of this tightening up where people are saying, “I don’t even care if you give me value, I don’t want to get the data to you?” What’s the trend you’re kind of seeing there?

Steven Maskell: (03:25)
I see the people have a very high expectation and a short fuse. And so what that means is that they will give you the data or they accept that you’re going to take the data, but by golly, you had better make it worthwhile.

Gabe Larsen: (03:42)
I love that.

Steven Maskell: (03:42)
If you go on a website, you do something and then you start seeing an advertisement for the item that you were looking for. Yeah, I kind of expect that. But then you show that to me six months later, no. I’ve moved on. You look really, really ridiculous. Or the next step on that will be, let’s say there’s a product that you purchased and really, stop advertising it. Start telling me what a warranty is or how to use it, or really taking it to the next step. You’re using my data, make it worthwhile. Inspire me. I bought something, now give me a recipe to make with this unusual ingredient that I might’ve purchased off of an obscure website. So people have a short fuse and then if you don’t do it right once, they can be bothered with you. You’ve lost credibility pretty quickly.

Gabe Larsen: (04:33)
Isn’t that true? I can’t argue that point. And maybe I’m acting the same way. I just, short view’s a good way to say it. It’s like people don’t, we just don’t tolerate. It’s that effort word? I just don’t deal with high effort anymore. You’ve got one chance and if it was hard, I’ll go to somewhere else. I don’t care if you’re a big brand name like Nike, I’ll go somewhere else to get my shoes. When you look at the different data sources and trying to create a customer experience that does matter, are there certain things you feel like they’re either the basics or they’re the must haves? It’s kind of like, look, if you’re going to start to take advantage of that one opportunity, that short fuse, it’s this or that type of data to really start to build that personalized experience.

Steven Maskell: (05:21)
Yeah. There’s a lot that goes into it and they fall into, I would start with two large buckets. Bucket number one is who is the person? And bucket number two is what are they doing? What’s the intersectionality of those two things? So is this person a procurement person? Are they a legal professional? Where do they sit within their profession? Where do they, who are they overall? We’re not talking about highly granular, but if you have a procurement person they’re looking for X. Generally, they’re looking to get the best deal and the best whatever. If they might be a lawyer, they might have something specific, a highly unique need that they want. So now you have an understanding of who they are a little bit about what their drivers are. The second would be then, what are they actually doing? How are they actually purchasing things? How are they actually interacting with your brand? Are they looking at your advertising? Are they responding to your blog posts? Are they actually making purchases? Are they open to conversations? What are their actual behaviors so that you can start building a good understanding of who they are? So you also want to keep testing your hypothesis. This person is A, and so this is what’s important. Their data suggests that that’s what they’re going down. That then would drive you as a deliverer of consumer or customer experience to follow that path. But the second you start seeing them doing something different, now’s the time that you have to pivot. You have to understand what’s going on. And so the two areas where I would say the best understanding is, is frame it around, who are they? And then what are they doing? And then how are they influencing each other?

Gabe Larsen: (07:01)
Yeah, I think those are great big buckets that you can kind of build around. I think as soon as you start talking about data though, the word technology kind of comes into play and you start to think about, “Okay, that makes sense.” Behavior, who they are. I don’t know how to store that stuff. I don’t know where to store it, or it’s stored in so many disparate systems that I don’t think I can bring it together to make a difference. I don’t necessarily want you to be, sell some technology with this question but, quick thoughts on building that infrastructure to actually do something with it or capture it from a technology standpoint? Because it seems like once you know what data to get then you’re going to say, “Well, how do I get it? Where do I store it?”

Steven Maskell: (07:48)
Let’s just take a deep breath on that one, because there’s so much that happens. There’s some great off the shelf products. There are bespoke products. There’s custom work that people do. The thing that is most intimidating is there’s just so much data. And it comes down to a point of taking a deep breath, in my opinion, and saying, “What do I really want to drive with this? There’s so much that I can and so many interactions.” Well, there’s these silly things like, how do you eat an elephant? One bite at a time. You boil it, you can’t boil there. So we all have these things. The exact same thing applies. You know, I wouldn’t say start an Excel spreadsheet, but start somewhere small where you can just get the literal basics structured. There’s great relational databases out there. There are some really good tools out there. As I mentioned, there’s off the shelf sort of relationship management products that are out there. But once you start actually figuring out what it is that you want to learn about, someone build that and feed it and keep it going. Then something will come along where you want to add a new entity or a new attribute, or something that’s a little bit different that’s associated about that person. Grow with them and only them, don’t try and build this behemoth of, “I want to know everything about everyone and everything.” You’re never going to succeed. Rather, just get the basics. Who are my top customers? Why are they my top customers? What do my top customers look like? What do my top customers buy? What do my top customers not buy? That’s enough. That really is enough because now you can start saying, “Okay, these seem to be my large product central services. Now I can look at my other customers that look like my top customers, maybe from two years ago, are these the same things that I should be sending to them? Should I be nurturing them in the exact same way?” Let me tell you something, that’s more than enough.

Gabe Larsen: (09:38)
Yeah. Yeah. I really appreciate the crawl, walk, run strategy. I’ve often referred to it as it does get overwhelming fast and narrow it down to some of those key points and to start to manually capture. I’ve always found if I can build it and get it in an Excel spreadsheet first, or you’d mentioned that, that’s just, I got it. I’ve kind of felt it. I’ve tasted it. I’ve touched it and may only be three data points then it’s like, “Okay, how do we automate this?” And then pretty soon I’m moving on to kind of phase two. I think that’s really important. So you kind of frame that, but I’m curious as people go down this journey, what are some of the other gotchas? We know it intuitively the data, we need it. Personalization, do it. We’re not, a lot of us aren’t doing it very successfully. Is there a couple of gotchas that, and maybe one of them is, it’s that crawl, walk, run, you don’t try to boil the ocean to start with the day. Anything else you’re seeing where people are kind of stumbling on this journey?

Steven Maskell: (10:36)
That’s like a two year podcast to have conversations around that. And I’ll just hold –

Gabe Larsen: (10:43)
Of course you’re going on a vacation tomorrow, so we don’t have to –

Steven Maskell: (10:47)
Yeah. Look, there’s so much that the people botch. I think some of the things are expectations and it’s having very realistic expectations. We hear a lot of mumbo-jumbo around machine learning and AI and all these sorts of things. And it took IBM a really long time to build Watson and Watson still screws up. And what I would say is this, don’t expect that it’s going to solve everything. Really what it’s going to do is it’s going to help you understand a little bit better, a little bit better. That’s what you’re trying to do each and every time. There’s also going to be some gotchas especially in a B2B sort of environment where the user or the person you’re trying to interact with is anonymized. And so you then have to switch your mindset around, “Okay. I was trying to do a one-on-one between me and you, Mary the buyer, or Jane the seller, but now it’s just a buyer. And how do I understand that?” That’s a bit of like, “Oh wow, I can’t succeed.” Actually, you really can. You’ve got to understand that someone’s making a purchase, and you have to switch your mindset. You’ve got to be very flexible in my opinion about how you react to the data and what you have and really what you’re trying to achieve. So the gotchas would be, have very realistic expectations. Please don’t think you’re going to double the company’s revenue because you’ve done AI implementations or some nonsense like that. But please know that you can have a significant impact on it. Two is also making sure that you have a lot of people on board with you on this data amalgamation and centralization and then pushing out of insights and, or next steps is fantastic. Yay. But really what it comes down to is you’ve got to have everybody understanding how to use that. How are you actual sellers? What is your salesforce using this information for? The wisdom for them, you’re going to make more money by knowing more about your customer, which means you have to get more so that I can help you and all that sort of thing, would be some of the other things to really consider in the entire equation. And it is an equation where one plus one plus one, there’s a lot that goes into the chain versus, “Okay, pull a lever and then suddenly something will happen,” but that’s human interaction. And my data also may suggest something, but then I’m having a bad day and I completely throw a fly net on them. So I would just keep the realistic expectations. Know that you’re not always going to get the data and that you also need to make sure that everyone’s, there are a lot of people are on board with the entire process of getting it. And please don’t think that AI is going to be the solution. Please don’t think that machine learning is going to be the solution. We’re a ways off on that. There’s some great stuff that’s being done, but it’s not perfect. And it’s never going to get rid of, never’s a strong word. It’s never going to get rid of people actually understanding someone else.

Gabe Larsen: (13:45)
Yeah. I mean, I’m guilty. I actually was one of those people who was like, “Oh, I’ll just deploy a chat bot and it’ll run itself.” And it didn’t require a full-time person to program and integrate. So I’m smiling you bring up kind of like the AI thing. So I’m guilty on that one. You’ve talked about it a lot. We hit a bunch of different topics on the data front. If you had to kind of simple it down and just mentioned starting on this journey, where or how would you recommend a CX or CX leader start?

Steven Maskell: (14:25)
When would I start? When would I recommend the CX leaders start? I would recommend that a steep CX leader needs to have a good, honest assessment of where they’re at. The function that I had the delight of being in is the result of that assessment. Where there was a goal, there was a big, hairy, audacious goal. And the bottom line is the infrastructure, the platform, the knowledge, it just wasn’t there. And that’s okay. And you know, so the first thing is the CX leader is what’s there, is there a CRM solution in place? Is there a, is there some way that it’s being fed? Is there a mechanism to better understand, are we engaging with customers? Do we have a way of solutioning and being standardized and how we try and solve for things? It’s looking at your landscape and wondering like, “Okay, what do I know about my customers?” And if it’s sitting on the backs of napkins at the end of the long night of drinking, then it’s not going to do a whole lot of good. But if it’s codified and solidified, and if I use the right nomenclature and no matter how many times I say a certain word, everyone understands exactly what that word means, now that we’re heading in the right direction. And so those would be the things that that would happen. I would also argue that you have to understand that a business, the CX leader is in a place to amplify what a business is doing well. So businesses are the results of delivering of services, goods, and products and they do that really well. So please don’t think that customer experience is going to change your product. You have to remember what your product is and you’re there to amplify it. So, I’m not going to change how airlines fly. I am going to make the whole process of engaging with, in this case an airline, as delightful as possible. I’m going to leave the wings and all that to them. And so that would be the other thing as a CX leader is I am responsible for amplifying what my business does and understanding you also have to be able to really, this is one of the hard things, you got to be able to suck it up when someone says you suck. And understand that they’re right.

Gabe Larsen: (16:37)
Yeah, yeah, yeah, yeah. Sometimes those are hard words to swallow. Sometimes those are hard words to swallow, but well said. Well Steven, appreciate you taking the time. I know you got fun stuff coming up ahead over the next couple of days. If someone wants to get in touch with you or continue the dialogue, what’s the best way to do that?

Steven Maskell: (16:56)
Find me on LinkedIn. Steven Maskell. Happy to have a conversation.

Gabe Larsen: (17:01)
Awesome. Awesome. Well again, Steven, really appreciate the time. Fun talk track on thinking through how to use data to personalize that customer experience. So thank you again and for the audience, have a fantastic day.

Exit Voice: (17:18)
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