Experts of Experience - #2 Matt Dixon: Master Customer Experience with The Challenger Sale Approach

Episode Date: November 2, 2023

How does AI redefine customer success and sales strategies? Tune in as Matt Dixon, author of ‘The Challenger Sale’, unveils the transformative journey of customer feedback and experience.Matt Dixon is the founding partner of DCM Insights and author of The Challenger Sale and The Effortless Experience.Matt kicks things off by sharing with us his journey as a “customer experience anthropologist.” We delve into his books, with a specific focus on their key themes and the importance of comprehending customer preferences.We explore the impact of technology, specifically AI, on reshaping sales strategies. Matt explains how AI has transformed sales data analysis, enabling predictive customer insights and real-time feedback.The discussion shifts to the significance of real-time data in predicting customer satisfaction and the challenges traditional survey methods pose. Matt advocates for a new approach, emphasizing the importance of using unstructured data.To wrap things up we explore the correlation between employee experience and customer satisfaction, emphasizing the need for companies to focus on both. Matt provides valuable advice on tools that can improve the employee experience so make sure to watch till the end!If you enjoyed this episode, please be sure to rate our show on Spotify and Apple Podcasts. Subscribe Now: https://www.youtube.com/@ExpertsofExperience?sub_confirmation=1 Imagine running your business with a trusted advisor who has your success top of mind. That’s what it’s like when you have a Salesforce Success Plan. With the right plan, Salesforce is with you through every stage of your journey — from onboarding, to realizing business outcomes, to driving efficient growth.Learn more about what’s possible on the Salesforce success plan website: sfdc.co/SalesforceCustomerSuccess (00:00) Preview and Introduction(00:32) Unpacking Matt Dixon's Career & Books(03:52) From Traditional Research to AI-Driven Analysis(07:15) The Future of AI in Sales and Customer Insights(11:47) Four Major Roles of AI in Sales(16:07) Unstructured Data & Predictive Survey Scores(24:15) The Impact of Real-Time Coaching(26:26) Evolving Sales Methods & Customer Metrics(34:58) Exploring Customer & Employee Experience(41:50) CX Tools For Better Customer Experience

We were experimenting with a tether was using unstructured data like call recordings to predict the customer SAT score or the customer effort score, the NPS score. A customer would have given on their survey, but without having to ask the customer to fill out a survey. Great salespeople do a lot of homework before they reach out to a customer. The best coaches deliver coaching in near real time bursts that are actually pretty short. So in they're delivered as close to that example that they want to reference as possible. Think of generative AI as like a new member of your team you're going to hire. If there is anyone who knows about customer experience, it's Matt Dixon. He literally wrote the book on it. Matt is the founding partner of DCM Insights and the co-author

of multiple best-selling books, including The Effortless Experience and his latest, The Jolt Effect. Matt has spent years gathering and analyzing data around the customer experience and the sales process, and he's sharing his wealth of knowledge with us on this episode. We're going to get into everything from generative AI to measuring customer and employee effort. I know you'll walk away with a better understanding of what it takes to create a great customer experience from the inside out. I'm so excited for you to hear what he has to say. Let's get into it. Hello, everyone. I'm here with Matt Dixon, who is someone I'm so excited to chat with because he has so much knowledge and information about the full customer experience.

He's an author. He's the founding partner of DCM Insights. And I'm just so stoked to hear all the knowledge that you have for us today. So let's kick it off by just telling us a little bit about yourself and what you do in the world. Yeah. Thank you for the kind words of introduction, Lauren. It's great to be here with you. It's hard to explain what I do for a living. I think I've been doing it for a long time. My parents still don't understand what I do. They know I live in the DC area, what my job sounds mysterious and confusing. My company has an acronym, so they assume I'm a spy, which I'm not. But I think probably the best people have kind of called me a sales anthropologist or a customer experience anthropologist.

And I think that's a pretty good description. What it maybe suggests is that I'm a researcher, so I use research-based methods to really understand how customers are evolving their preferences towards companies they do business with, what they're looking for, what they like and dislike. But also, as importantly, what are the best companies doing, whether that's a customer service organization or service reps engaging customers and providing post-sales service, whether it's customer success teams, whether it's sales teams. And I think what we always find in the research is that customers are going one way, most companies are still going the opposite way. But a few companies, a few sellers, a few gifted frontline-facing call center reps, they've kind of figured it out. They've adapted their approach in light of the changes they see

in customer preferences. And so I try to go out and, again, use research to document those things. And I've been doing it for a while now. Awesome. Well, I definitely want to get into some of those insights, but before I do, I know throughout this career that you just spoke about, you've also been writing books and you're a Wall Street Journal bestselling author. I'd love to hear a little bit about some of the books that you've written and kind of what inspired you to write them. Yeah, that's a really, the inspiration question is a really good one because I keep asking myself why I keep going back and doing it again. So this is not, as anybody who's written a book knows, it's not for the faint of heart and is an arduous process. But we wrote our first book called The Challenger Sale in 2011, which was a study of business-to-business sales effectiveness. We followed that up with the Effortless Experience, which was a study of customer experience effectiveness,

and really actually more specifically customer service effectiveness and how customer service interactions impact customer loyalty. A few years later, we wrote a sequel to the challenger sale called The Challenger Customer, which looked at the problem of consensus buying and dealing with large scale buying committees, especially when you're talking about complex solution sales of the sort that B2B vendors are known for. And then most recently, we just finished a book last fall called The Jolt Effect, which I think is sort of like Chapter 3 in the Challenger saga, if you will. It deals with the problem of no decision losses. So maybe the framework I might offer just for those three books, Challenger, Challenger Customer, and The Jolt Effect is any sale moves through three phases.

You've got your customer in their status quo. This is like the way they do things today. The second step is to get them to agree on a vision. And part of that is getting not just that individual to agree on a new way forward, but getting all their colleagues, getting the whole buying committee on board. And then the last step in the journey, the third act in the play is to get them to actually sign the agreement and buy the solution. So Challenger was really a story of like, how do you get the customer off their status quo to agree on a vision? Challenger customer was a story of how do I get that individual to convince all of their colleagues? And then the jolt effect is really about how do I go from stated intent to purchase? And that's actually where a lot of deals go sideways where, you know, and it turns out, I would say somebody said to me recently, you know, despite our best efforts, it feels like we're seeing a lot of no decision losses today in the market. Just given the economy we're in right now, customers say they want to buy from us, but they just won't put the ink on the contract or execute that DocuSign.

And it just kind of, the customer starts to ghost you and they go radio silent. And I think what the Jolt Effect showed us is that sometimes it's because of your best efforts that happens to you. So it revealed some pretty counterintuitive stuff around why no decisions happen in sales and what the best people, salespeople do to avoid that. Effortless Experience was, again, it's our only book we've done in customer experience specifically, but I think what's interesting, and I'm sure we'll talk about this, is the other books to the extent that they talk about the experience a customer has learning about your solution, the experience they have buying the solution. As you know, I think having a foot in the practitioner world and dealing with the post-sales, everything that happens in the post-sales world, that a lot of what happens during the sale then translates or

transfers over both good and bad to all those folks on the team who've got to take that customer from signature to value. So when it comes to the research that you've done, can you tell us a little bit about how you approach actually gathering these insights? Because I've been doing research on you and just listening to kind of how you've been doing this. And I just want you to tell everyone, because to me, it's a little bit mind-blowing, like the amount of data you're collecting in order to gather these really juicy insights. Yeah, I think we are very much seeing what am I call like the Moore's law of research into customers, customer experience, sales, effectiveness, etc. What I mean by that, and I think many folks are familiar with that concept that and I don't know what the

exact specifics are, but it's like processing speed will double every X increment of time was Moore's law. And what we found is actually the amount of data out there that we've been able to collect and study at scale has continued to increase. So if I look back on, if I go all the way back, like one of the books that I love and was a real inspiration to me was the book by Professor Neil Rackham called Spin Selling. And this is a book that really started the whole solution selling movement in business to business sales. He did this research back in the late 70s, early 80s. And the way he did it was he and his team of, I think it was about 12 people, spent

almost 10 years physically sitting like physically sitting in on sales calls that they were invited to. They were just a fly in the wall, quite literally. They would sit in the back of the room with a clipboard. These were all like psychologists and IO psych professionals. And they would like take notes on what the salesperson said, how the customer responded, et cetera. They sat in on 30,000 sales calls. So, you know, that's why it took them 10 years. And so that was back in the 70s, 80s. You fast forward to books like The Challenger Sale, The Challenger Customer, and The Effortless Experience. Those were, we collected the data there by using kind of web-based

surveys in which was, think about these platforms that many CX practitioners rely on, like Qualtrics and others, SurveyMonkey, et cetera. So this technology allowed us as researchers to distribute electronically a data collection instrument and then collect data that we could then analyze. But still, if I take any of those books, Effortless Experience just as an example, that was like a five-year-plus research project. I mean, it wasn't as long as Neil Rackham's study. We collected, though, hundreds of thousands of data points, customer survey responses. We did several drill-down surveys. We did hundreds of in-depth behavioral interviews with leaders, managers, frontline representatives of companies, as well as with customers.

Now, you fast-forward to the Jolt Effect. The Jolt Effect, we just wrapped up last year, and now we're in this era of AI. So to do that research, we actually collected two and a half million recorded sales conversations starting in March of 2020. This is, remember the heady days of Sourdough Bread and Tiger King when the whole world went virtual. So in sales, this was an interesting time because everything went virtual overnight. And so we launched a study in March of 2020. We spent about 18 months collecting data from several dozen companies, got two and a half million sales calls, transcribed those using Lauren, is that that was before ChatGPT and a lot of this generative AI stuff. So that was still kind of like pseudo-manual AI, but it was machine learning on a big data set. And the insights we got are just really mind-blowing, just what we're able to find with that amount of data. So what's next?

I don't know, but I'm excited to find out. It's amazing the amount of data, if we think about every customer touchpoint, whether it's sales, even pre-sales, like where customers are learning about our services and our products, our solutions, the sales experience, the customer success interactions, those account management interactions, customer service, and customer support when things go wrong and the customer needs our help, the expansion conversation, the renewal conversation, you know, advocacy and things that happen after that fact when we kind

of create raving fans. And there's just so much data we collect at each of those touch points. And I think now we have the technology to study it in a very powerful and accurate way. So it's kind of a great time to be in this business of customer understanding and research. Totally. I mean, it's interesting, you know, you saying that you collected two and a half million sales calls that you were able to assess in 2020. And I don't know about most of our listeners, but back then I wasn't really thinking about how can I use AI to really track and understand, you know, what's happening on these calls for my team. So how would you say, you know, even today, where are we at with the technology and what are you seeing in the future if you can be a fortune teller at all to tell us kind of what's going to happen? It's a great, great question.

I wouldn't profess to be an expert. I mean, there's way smarter people on gen AI and kind of where AI is taking us. But if we took, just as a specific use case, take sales as an example. I read an article recently from Boston Consulting Group, I thought did a really good job summarizing the different ways you want to think about generative AI for your sales team. And the article said, think of generative AI as a thing you would, like a new member of your team you're going to hire to do four specific jobs for your team. So the first job was generative AI is a super efficient, productive, accurate sales assistant. So a sales assistant is somebody who is going to take notes on your call. They're going to update the CRM records. They're going to help serve up information to prepare you for your next sales call or your next sales meeting.

But do a lot of that admin work that ends up bogging salespeople down. The second use case was or the second person you would hire onto your sales team, the second job you would hire generative AI to do is as your personal sales researcher or data scientist. So your personal data scientist is going to get into your data and they're going to steer you towards across your whole territory. Which are the opportunities that represent the best fit right now? You know, where the timing is right. Our value proposition is properly aligned. Which customers with what value prop? When?

Who do you reach out to in kind of helping you avoid wasting time, but making you way more efficient in your pursuit and your, because time is a salesperson's scarcest resource. The third job you might hire AI to do is as your personal marketing assistant. So their generative AI is helping salespeople craft emails in outreach, pitch decks, proposals based on a huge body of data, helping them create, you know, just take email outreach, for example. It's helping us craft an email that is timely, it's personalized, it's tailored, it's contextualized,

it's relevant, and it resonates with the customer versus like the stuff that you and I probably get every, you know, 10 times a day, which is just random from vendors you've never heard of. Like, would you be interested in X? And you're like, my company doesn't even do X. So I have no idea why you're reaching out to me. I'm just on some random list. And it's just the spray and pray kind of approach. But that personal marketing assistant helps create those warm kind of outreaches, which resonate and get read and responded to at a much higher rate. And then the last job you might hire AI to do is as your personal sales coach. So what are the things that I could do differently as a seller? If you looked at my recorded sales calls, if you look at how I am pursuing deals, what could a really smart technology platform

like Gen AI come in and tell me I could be doing a little bit better? And it could be simple stuff like, you know what, don't bring up price until this point in the sale because that's what best performers do. Or handle this objection a little bit differently. Or you miss the signal from the customer that they were a little bit indecisive about this and you just kind of rode roughshod right over it, but you might've camped out on that a little bit more to, to unpack it and help instill some confidence with that customer. So sales managers are very busy. We know sales managers are ultimately the right people to be coaching salespeople, but it's really cool to think about if you were like Iron Man or Iron Woman that you've got Jarvis, right? This, This like suit of AI armor that's feeding you data, feeding you insight, telling you where to go, what to do, how to do it, and just making

you better as a professional. So I think, you know, if I think about those things, what I would say is that right now, most organizations are in that kind of sales admin data science thing. Like help me get targeted on the right opportunities, do a lot of the grunt work for me, right? And free up time. Where they're going to go to though, is I think in more of that marketing assistant and ultimately that sales coach world. Like help me actually, and even in the moment, pick up on signals my customers are sending me that I might miss, you know, but high performance sellers wouldn't miss, you know, serve up suggestions and things I might use again, the same way Jarvis is feeding Ironman, like lots of cool insight and data on the fly in real time to make Ironman a superhero, right?

Yeah, totally. And I can see how that would also really apply to other customer facing teams. Oh, for sure. Absolutely. Customer success, like in any case where you're having conversations with your clients. Yeah. service, customer success, like in any case where you're having conversations with your clients. I mean, wow, it really changes the game. Are there any tools that you know of that are like really stand out to you that like customer experience leaders should be watching? Yeah, you know, I think, so there's a few categories of tools. So first of all, I came back from the Salesforce Dreamforce conference a few weeks ago, and I find what's so fascinating is when you look at some of the big cloud players, whether it's the pan-industry ones like Microsoft Dynamics or Salesforce, but then you look at some of the ones that are specific to different verticals. So they're really focused on law, accounting, consulting, investment banking, et cetera.

And all of these companies are talking about embedding generative AI capabilities into their platform and thinking through a lot of those use cases we just talked about. So the first thing I would say is a lot of your existing platforms that you use and existing technologies, whether that's CRM or it's knowledge management or what have you, a lot of these vendors are baking AI capabilities in. So if you don't know what those are, you should be talking to your CS manager. You should be talking to your account manager. You should be talking to the product people at your biggest vendors and finding out what is their AI roadmap. How are they going to be infusing Gen AI capabilities into what they deliver and what is that going to mean for your team? Then I think separate from that, there are, I think, a number of really cool technologies. I'll give just one example. And this is actually a personal example because we use this technology to do the research and the jolt effect, which is conversation intelligence. So if you think about,

now, of course, for call centers, they've used this technology for a long time. They've all been recording calls for forever, right? Recorded for quality assurance and training purposes. They've been recording these calls forever, but only recently have they started to mine that data, which is just a gold mine of customer insight. And I mean, it's just absolute gold. What's it buried in there? And only recently have they started to unpack it with conversation intelligence technology. I think that what's super exciting, especially for the CX audience, we are moving from a world where we assess effectiveness and we make decisions off of structured data, like survey responses, into a world in which we are assessing effectiveness, making decisions, prioritizing investments off of unstructured data. So call recordings, email

exchanges, social utterances, interactions, SMS interactions. And there is, if you compare it, like the amount of survey data to the amount of like unstructured data, the unstructured data, like it makes the structured stuff look like a rounding error, right? It's just huge. And now we have the ability to unpack it. Last thing I'll say, because I think it's a really interesting question, a specific use case I'm pretty excited about and it's something I worked on when I was at Tether, but you're starting to see really grab hold in the market and I think it's especially important for CX practitioners. Many of us have been asked by our companies to be the owners of the customer survey, right? So we own the loyalty survey. We're the torch holders of NPS and CSAT in our companies. And that's fine. But I think we're also all seeing as practitioners a secular

decline in response rates. And I see this in almost every industry, every company, with a few exceptions. And it's so much work to get your customers to fill out the survey. When you're using Salesforce to tackle your company's most important goals, failure is simply not an option. That's why their most highly skilled advisors, Salesforce CTOs, are available to help you succeed with expert guidance and implementation support at every step of the way. Learn more about Salesforce CTOs at sfdc.co slash professional services. And you know, what happens is then to try to get them to do it, we make the survey shorter.

And then when we do that, the knock-on effect is maybe we boost response rate, but the amount of insight we get is so much lighter, right? And so then what do we do about it? Read through the lines, be like, this is what they probably meant. Yeah. Like, oh, we got an NPS score or we got a customer effort score, we got a CSAT, but why? Like what caused them to give that score? So I think what's really cool is thinking about using unstructured data to predict what survey scores would have been. Because I don't think we're ever going to enter a world where CX practitioners are like, hey, guess what? I decided we're not

measuring, we're not doing NPS anymore. We're not doing CSAT anymore. It's not going to go over very well because it's in, you know, for whatever, whether it's truly an operational metric that we focus on or it's just in executive bonus structures. But whatever reason, companies are, they focus on the number. But what we were experimenting with at Tether was using unstructured data, like call recordings, to predict the customer SAT score or the customer effort score or the NPS score a customer would have given on their survey, but without having to ask the customer to fill out a survey.

So that's kind of like the cold fusion. Imagine this world in which you have 100% response rate and you no longer have to worry about people filling out surveys because every time you interact with the company, a machine in the background is saying, off of this unstructured data, based on other customers who had similar interactions, you would have given us a four on the NPS scale and a seven on the effort scale or what have you. I mean, that's huge for a CX practitioner to then take and run with it. Plus, because you have all the unstructured data behind it, it's your hop, skip, and jump away from like, why? Because I have the call recording, so I have all the unstructured data behind it, it's your like a hop, skip and jump away from like, why? Because I know I have the call recording, so I know why the machine said they would have dinged you because it was an awful call. And we, you know, we left our customer high and dry or we wronged them somehow or what have you.

Totally. And I can imagine it's really helpful when it comes to, one, forecasting because, you know, we can now be using more data, understand what is leading to what will come in the future. But then also enable us to really tackle challenges early on. Instead of waiting for this lagging indicator of NPS to say what happened last quarter or, you know, last year, we can actually be seeing what's happening in more real time so we can action on it. Yeah. So I give you two specific things that I think are really, really cool. So take that example, like a predictive survey score, not an actual survey score, but something that predicts off of your call center interaction, off of your website visit, off of your, you know, SMS exchange or chat interaction with the company and says, this is the MPS score Laura would have given

us. Now, imagine, and we actually were working with companies to do this, that that MPS score was a bad one, right? You were flagged as a detractor based on that interaction. How powerful it is for that company to then close the loop and go back and say, Lauren, I know you just had a chat interaction with one of our agents or you had a call center interaction or an SMS exchange about an hour ago. And based on our understanding of this interaction, it didn't go as well as either of us probably would hope. And we'd love to get your feedback. What can we do better? And how can we make this right for you right now? That is grabbing that customer before they churn out or before they get on social media and badmouth you before they lean over the fence and tell their neighbor never to do business with you. That is a really powerful outreach that tells, and that's actually, you know, to be honest

with you, one of the biggest shortcomings of surveys is we ask our customers to fill it out and it's a black box. They never hear anything in response. But imagine the power of that kind of, that connection or that closed loop interaction where it's like, whoa, this company could hear the frustration in my voice. They could tell this wasn't a great interaction. Yeah, I'll tell you what you can do differently. I'll give you some feedback. That's just amazing. And then imagine those, and this is, I think, really bleeding edge stuff. Imagine while a rep is interacting with a customer for them to be able to see how that score is fluctuating through the course of the call. It's a little bit like those as a baseball fan,

I look on ESPN and, you know, based on what the score is and what the ending is, it'll have the win probability, you know, usually my team's the one heading down, but that's kind of what it's like for a frontline person to be able to see that this thing is trending. I've got this thing on in a good spot. Oh, it took a dip down. I need to course correct. Right. But having that as sort of an indicator while you're interacting with the customer helps avoid those bad interactions or prevent them from even happening in the first place. And I know you've spoken about this before, that real-time coaching, whether it's maybe a manager or even AI, is actually really helpful for developing the employee's skills rather than waiting until, okay, once a month we have a manager sit down

and listen to all your calls, write notes and come back and tell you like what you did well or not well. This is kind of bringing that real-time feedback for the employee as well so that they can be improving quicker. Yeah. That embedded coaching. And we did a study on this that I think you're referencing a number of years ago. And we found that the best coaches, and this is true of in sales, it's true in call center, it's true in retail settings, customer success. The best coaches deliver coaching in near real time bursts that are actually pretty short. So, and they're delivered as close to that example that they want to reference as possible. So, you know, we just,

I'm your manager. We just got after off of a customer success call and I'm giving you a quick five minutes of, of coaching. And we're having a little mini coaching interaction versus I sit down, take, you know, in a call center setting and I had my once a month coaching session with my reps and I'm sitting down and I'm talking about calls that happened three, four weeks ago that the rep has no doesn't even remember, right? And it's like, what call was that? Like, I don't even know. Oh, like, and it tends to be small sample size. So that's like, oh, you're talking about that day. Well, I just broken up with my significant other and I was had a bad morning. And that's why I was in a bad mood. And I should, you know, but so you need to go check out more

calls, because that was my one bad call this month. But in this world where we're providing broader sample off of which to work and we're providing those more embedded in the moment coaching interactions really makes that behavior change stick in a way where it's harder to ignore and it's more likely to be acted against by the coachee, if you will. Totally. I think it's really interesting, too, just how AI not only is going to change the way we work, but it sounds like it's also going to change how we organize ourselves within companies. Yeah. Do you have any thoughts on that? Like, what are you seeing? How are you seeing some companies maybe readjust

in terms of management structures or cross-collaboration? Yeah, yeah. I think so. So one is, I think we'll see a lot of the things we've been trying to push in companies like collaboration actually hopefully take off now because it's, you know, it's enabled and made easier by AI. I mean, I think that, you know, when I look at, when we look at what best, take, for example, again, salespeople, it's long been known that great salespeople do a lot of homework before they reach out to a customer. They're going to reach out and they're going to try to understand who is Lauren?

What is her role? What company is she in? What's going on at that company? What's their strategy? What's going on in their market? Is their share increasing, decreasing? What's going on with their revenues? Have they had a funding round recently?

Let me get that context. And then, by the way, who else is Lauren connected to? Is it somebody I used to work with or maybe somebody we used to be a client of ours, et cetera? So that when you reach out to the customer, again, you're reaching out in a tailored, personalized, contextualized way that's relevant, it resonates. And it's just really hard for a prospect to ignore it and put it like send it to spam or just delete. Right. Or worse, send you a nasty note response like that's the kind of outreach that somebody says. Yeah. OK. Maybe I'm not interested, but I appreciate the time, the thoughtfulness of the outreach. But more often than not, you're going to get. Yeah, that's great.

I would like to hear more about this. This is something we're working on and glad to know you worked with so-and-so before. And I respect that person's judgment and love to hear more what you guys are talking about. Doing that has always required a lot of background work, right? It's a lot of legwork. It's a lot of reading. It's a lot of homework. It's a lot of due diligence to craft that one perfect outreach. And that's the reason that most average performers don't do it because they end up getting behind goal and then they don't have time to do it. They might know deep down they should be doing it, but their managers are breathing down their neck because they're behind goal and they're just looking to get some activity and some pipeline built. And so then they go into spray and pray mode and then they start burning through leads

by sending generic outreaches. It's like, clearly you don't know who I am or what I do, or otherwise you wouldn't have sent me an outreach like this. That's impersonal. It's irrelevant. It doesn't resonate, et cetera. Now, I think AI helps kind of democratize those high performer behaviors, right? So it's this tide that kind of lifts all boats. Now, I think it also, to your restructuring question, I think one of the biggest things is it's going to free up customer facing time. It's going to reduce the amount of resourcing we put into things like admin, you know, whether

sales administration, customer service support, things like that, that, you know, are really just there to provide more leverage to the customer facing team. But really in this world, don't need to be done by administrators or by sales professionals or frontline facing customer service reps or CS practitioners because you've got AI doing it, right? So I no longer need to ask my CS team to like, oh, make sure you take detailed notes on every interaction and then upload them into Salesforce because I got a machine doing it for you. You know what's great about that? That allows you to prepare for your next customer interaction or better yet, have more

customer interactions and deliver more value. And so I do think that kind of administrative operational layer will be thinned out or redeployed based on AI. I think it's going to make managers way more effective. And I think in some ways, you know, I said this on another, on like a webinar a couple weeks ago, but I think it's going to make the job a lot more fun, whether you're talking about CS, you're talking about customer service, you're talking about sales, like nobody got into any of these professions so that they could update CRM records. So this allows them to get focused on the things that they love doing. Take a CS manager, loves getting that customer from signature to value and creating those raving fans who want to buy more, who want to expand their usage, take it across the enterprise. And this allows them to get the time back to do the stuff that

they were built to do when not updating CRM records and taking notes and that kind of admin stuff. Yeah. Or chasing sales to update their CRM so that we know what happened. Yeah, exactly. Yeah. And I love that example you were just sharing about the sales rep, you know, being able to spend time in the right areas instead of the spray and pray. And I think that the spray and pray is really driven by the KPIs that we're holding teams against. Yeah, for sure. What are you seeing in the realm of customer experience as a whole or the entire customer

journey kind of broad, but what are you seeing as some of the metrics that are really mattering or things that companies are holding themselves metrics that are really mattering or things that companies are holding themselves to that are proving to be effective? Yeah. If I take, may I just take customer service as an example, but I think you could take this and apply it to CS and other functions as well. There are people out there, I think, who are like NPS haters. I'm not an NPS hater. I do think it's a useful metric to help assess the customer's overall loyalty towards your brand. The problem with it is it's a very noisy metric, meaning it's got a lot of, if you ask me how likely I am to recommend your company, there's a lot that feeds into that. Do I like the

product? Do I think it's priced competitively? What do I think of your competitors' products? What do I think about your values as a brand? What do I think about that call center interaction I just had, or my website visit, or my use of your mobile app? There's a lot that feeds into that. And that's why I think companies are always confused when they have clearly great call center interactions or other interactions at certain touch points, and then they get NPS scores that are really low or vice versa. And so there's always this disconnect where it's like, I don't understand. That was great. Why did we get panned on NPS? Or that was awesome. Or that was terrible. Why did we get such great NPS scores? Clearly,

we didn't deserve it. But it's because there's a lot going through the customer's mind when they answer that question. But I do think it's a good directional indicator. And for no other reason, it's on a lot of executive dashboards. So it's going to be hard to get rid of. I think the other thing that I tell practitioners to think about is, well, let me talk about customer effort, because it's something we talk about in the effortless experience. So not just your loyalty to the brand or how likely you are to recommend the company, but also how difficult was this interaction? How easy did we make it for you to do X, whether that's learn about our products, whether that's buy the solution, whether that's get value

out of it from your CS team, whether that's get a problem solved by the customer support team. So understanding the effort level of the interaction is what we found in the effortless experience is one of the biggest predictors of churn and disloyalty. The high effort begets churn and disloyalty. And so you want to be on an effort reduction like mission in your organization. And the effort score is a really good way to understand not just are they loyal to us, but how easy was this interaction? Then I mentioned customer satisfaction. I am not a customer satisfaction hater, but I would say I think people ask that question

the wrong way. What they should focus on is how satisfied are you with the outcome that you received? So how satisfied are you with the ultimate result of the sales process? How satisfied are you with this customer success interaction you had? How satisfied are you with the answer you got from this customer support representative you interacted with versus are you satisfied with the person or are you satisfied with the experience, which I feel like are not super helpful. So, so again, overall loyalty to the brand effort of this particular touch point or, or experience, and then, um, satisfaction with

the outcome with what you actually got at the end of the day, those three things I think we, we found are highly predictive of loyalty. Other metrics, if you take call center, for instance, I've seen a lot of companies start to move away from kind of legacy metrics like handle time. They might still measure it in the background because they need to do workforce planning, right? And they need to staff for peaks and valleys in call volume and customer interaction volume. But they don't dangle it over their rep's head saying, you've got to finish the call in X number of seconds or you're going to get dinged. They just use it to manage outliers. Because I would say in that example, we usually think about the outliers as like the people are taking too long and maybe they need some coaching. Maybe they just need to be shown some shortcuts on their keyboard or how to get information more quickly so they can get on to the next customer

in the queue. But oftentimes it's the opposite problem. It's the reps who are not spending enough time with customers. Their handle time is really short. And what is that telling you? Well, it's telling you they're probably not taking enough time to fully explain the resolution of the customer or to see what else they could do for the customer while they have them on the phone. And so it's the extremes that can be kind of telling. But anyway, I digress. But yeah, that's kind of what I'm seeing on the metric front. That's awesome. I really love that you brought up customer effort. I mean, I know you were going to because you basically wrote a book on it. But I'd love to share with our listeners, like, what are some of the best ways to actually track that metric?

Because I think it's difficult and I see a lot of different approaches to it. So I'd love your thoughts there. Yeah. So we've iterated on that question. So if you just took the survey question itself, we've iterated on that a little bit over the years. In 2010, we wrote an HBR article called Stop Trying to Delight Your Customers, for which we got no shortage of hate mail.

And in that, we introduced the customer effort score, which was a question that quite literally was articulated as follows. How much effort did you put forth to get your issue resolved? And I think what we found was it was kind of prone to misinterpretation. And it also came across as a little bit accusatory. So it's a little bit like, you know, when I asked my wife where the milk is in the refrigerator and she's like, did you look for the milk in the refrigerator? Which the answer of course is no, but I, you know, it can come across that way. Like, did you really try to handle this issue on your own before you called us? So it was effort as a concept is hard to translate into other languages. So we actually

changed the question to more of a scaled respond to the following statement. The company made it easy for me to handle my issue and, or to get X done, right. To buy the product or to, you know, made it easy for me to get value out of the, out of the solution that I bought. You know, so there are different ways you could ask it, but it's more of a stated, it's a statement. Then you go from like strongly disagree to strongly agree. And we found that that was less prone to misinterpretation, less prone to false positives and false negatives, just easier for people to understand and was more of an accurate indicator. I will say though, I do, back to what I said before around AI, I do think the future is probably not to ask people effort questions at any touchpoint,

but to use unstructured data to predict the effort score they would have given you on the survey. You're still going to need to collect some surveys to provide that dependent variable to calibrate your model against. But, you know, if I've got, so imagine I've got a set of several thousand call center interactions or customer success calls. And I have effort survey score responses for each of them. We asked a survey and the person gave us an effort score. Then I've got all the unstructured data. I've got their known outcome score.

And then I can just apply that to others where I don't know the known outcome, but I do have the raw data. Now you have to go back and refresh that from time to time so the model doesn't get stale and it's always going to evolve as products change and customer preferences change and markets change, et cetera. But again, I think the future is getting more into predictive effort measurements, less kind of survey-based effort measurement. Yeah. That makes a lot of sense. And I think, yeah, at the end of the day, the surveys are dependent on what people felt like telling us that day. And we actually have a ton of data in the back end.

But it does definitely require us to work with our product teams and make sure that we're keeping track of all those moments that we're actually collecting data around. Absolutely, yeah. Which is there and it's great. Awesome. So, I mean, we've gone through a lot of different things. I'd love to just understand what's one piece of advice that you would give to a CX leader? What's one thing on your LinkedIn profile, one thing about you that's not on your LinkedIn profile? And I was like, ah, like deer in the headlights, like, I don't know what to say. And I could only come up with like really lame answers. But I think I do have a good answer for this one.

I think, so take effort reduction as an example. I think a lot of companies, CX practitioners are very quick to want to, you know, create a prioritization list of the things that are causing friction in the customer experience and then get after those things. Quantify them, understand their impact, prioritize them, triage, boom, and go. Assign project teams, work with cross-functional partners to eradicate those sources of effort. And that's really important. I'm not saying we shouldn't do that.

But what almost every CX practitioner overlooks is the effect that the effort of the employee experience has on the effort of the customer experience, meaning that it's really hard to be an easy company to do business with, to be a low effort company, if you are bogged down by antiquated systems, policies and processes that don't make any sense to them, a lot of bureaucracy, it's just really hard to ask those people to overcome that and then make it easy for our customers. You know, this is not our research, but the tie-in and the connection between great customer experience and great employee experience is very, very clear. It's very well documented. And the same thing is true of effort. So what I tell practitioners is, absolutely, I know you want to get after-effort reduction for your customers, and you should do that. But you'll be surprised at how much your reps and your frontline people, the people interacting with customers every single day, can overcome and how much easier they can make the experience for

their customers if you make the job a little bit easier for them. So spend a little bit of time trying to understand what are the friction points in the employee experience? What's getting in their way? What could you do differently as a management team to provide them with the right tools, the right resources, and the right training, the right metrics to reward performance, the right kind of coaching? And when you do that stuff without changing anything else, your website, your product, your pricing, you don't have to change anything else. Your people will step up and that ease of the employee experience will start to bleed over into providing an easy customer experience. They're going to put forth more discretionary

effort. They're going to try harder to make it easier for customers. And then we can get on to reducing effort in the customer experience, for which I think as an old mentor of mine said, there is food for many winters when it comes to making things easier for customers as well. But start with your employees. Yeah. I don't know if you looked into me and my work at all, but this is literally what I'm dedicating my career to. So you're preaching to the choir here. That's God's work. So you're on the right thing. A hundred percent. I think we, anyone who's worked in a company, especially in a client facing team, you know, the pains of having that friction internally while you're trying to

create this frictionless experience outside. And it's just super counterintuitive. It's so counterintuitive. It can't, it really is very, very hard to do. And it's not fair. And it comes across as tone deaf. I think that's probably the biggest problem with it is, you know, we want to be all about effortless experiences for our customers. And you're thinking like, then why do you manage us in this way? Why do you saddle us with this old technology, these metrics that don't actually reward me for doing what I know is right for my customers? And it's just, again, it's very frustrating to frontline folks. Completely. And then their engagement goes down. Absolutely. How do you expect someone who's not really engaged in their role to really engage someone else? You can't.

It doesn't pass on. So yeah, it's a big one. Are there any tools, just one last follow-up question on that, are there any tools that you find have been really helpful in creating an easier experience? I mean, I know Salesforce is one that many companies use and work with, and I'm just curious to know if you've seen anything that's been really helpful at easing that employee experience so that we can... Yeah, I actually go back to the same technology that I used before. So conversation intelligence, what's interesting about this, take call centers or customer success or sales, doesn't really matter but in these interactions remember you're recording

both sides of the conversation you're recording the customer side and recording what the representative is saying on your side and so what's interesting about that is that there's a lot you can figure out by moments where the think think about, I mean, how many times have we been on interactions with service representatives from different companies we do business with? And the service rep says, yeah, I don't know why that's the policy, but it just is. Or stupid systems are like, I've got to reboot my system, so frustrating. Or they're just quiet, right? They don't know the answer to our question. That's a feeling nobody likes having when the customer stumps you and you don't know the answer because you haven't been trained on it and the company hasn't equipped you to handle that issue

or that question appropriately. So there's a lot you can figure out by listening to the representatives, to listen to your side of that customer conversation, which is going to indicate to you what are some of those friction points for your people? Where can you pick up even just through voice inflection, like increased frustration or again, just that friction, like this is a pain for me to do this. Frustration with the company, you can pick up a lot from your side of the conversation, let alone all the things you can pick up from the customer side. So I think that is a great thing. And then the other one I would probably flag, and there are a lot of platforms for this, obviously, you know, Slack is a big one, but there's lots of, you know, there's Teams or other instant messaging platforms.

But one of the things we found in the research over the years that you, I'm sure, can attest to, and you probably saw in our prior work, is that a lot of these are team sports, whether it's sales, CS, customer support, where we rely on harnessing the wisdom of our colleagues. That's hard in today's environment where we're all working from home or part of the time we're not all in the office anymore, at least for most organizations. And so these tools can really kind of help overcome that and create more of that over the cube wall conversation where I can say, Lauren, I've got a customer on a call. They're having this issue. Have you ever heard of this before? Or like, what do you recommend? And get that instant response. Those kinds of technologies, I think, are even more important in this current environment.

I think we're using them before, but I've talked to teams who said, look, when my CS team shows up in the morning, the first thing they do is fire up like the group Slack or the group text or the group IM so that they're all plugged in and they're sharing their insights across the day. And we're all getting better as a result. Definitely. Sharing that knowledge is so important because I mean, in the realm of supporting our customers, you never know what's coming your way. And there's always going to be edge cases that you haven't seen before. For sure. Wonderful. Well, I have learned so much on this conversation. I hope that all of our listeners have as well.

If anyone wants to go and find out more information about you, your work, your books, where can they find you? Yeah. I mean, LinkedIn is – I spend a lot of time on LinkedIn, so connect with me there. Tell me you heard me on Lauren's show and tell me what you thought of it, and let's get connected. And if you have follow-up questions, feel free to hit me up there. And then you can also check out our company at dcminsights.com is our website. And it talks a little bit more about our research and kind of what we're doing for companies out there. Awesome. Well, I hope you have a beautiful day

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