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6-steps-for-Conversational-Bot-success-A-primer 

Make the most of your conversational bot investment with our easy-to-follow guide featuring best practices that can be applied to your digital transformation journey.

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(Featured) 6-steps-for-Conversational-Bot-success-A-primer

Six steps for conversational bot success: A helpful primer for chatbot implementation

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Driving efficiency with digital automation for a financial services brand

Discover how TELUS International implemented a comprehensive intelligent automation
solution for a financial services client that enabled operational efficiency, a
reduction in employee effort and an overall increase in customer satisfaction.

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On this episode of TELUS International Studios, we’re joined by Seth Earley, consultant and award-winning author of The AI Powered Enterprise. Seth has more than twenty years of expertise in data and information architecture, knowledge strategy, search based applications and information findability solutions.

In the conversation, Seth shares advice for business leaders looking to incorporate AI solutions into their support model. Tune in to hear Seth’s insights on navigating uncertainty in AI development while keeping customer experience at the forefront, and his take on the biggest challenges when it comes to human-machine partnerships.

To learn more about TELUS International and our digital CX solutions, contact us today.

Click here to read the first chapter of Seth’s book, The AI Powered Enterprise.

Intro: TELUS International Studios where customer experience meets digital transformation.

Patrick Haughey (PH): And you are welcome back to TELUS International Studios. I’m your host, Patrick Haughey, and today we have a really interesting conversation for you with Seth Earley, the CEO of Earley Information Science, a consulting firm focused on organizing information for business impact. Seth has a 20 plus year expertise in knowledge strategy, data and information architecture, and search-based applications. He’s also the award winning author of “The AI Powered Enterprise.” We get into some pretty interesting questions about AI applications in CX, moving humans up the value chain, and where we see this technology going. Without further ado, we start off today’s interview talking about the ethos behind Seth’s book “The AI Powered Enterprise.”

Seth Earley (SE): I wrote that book because there’s a lot of stuff that’s right at thirty thousand feet, right? It’s like, Oh, AI is going to change the world or AI is a threat to humankind or AI’s going to do this. And it’s all that arm-wavey stuff, right? And that’s a lot of it’s true. You know, some of it exaggerates. Some of it goes too far. Superintelligence, I’m not sure you know how far we are from a Superintelligence or artificial general AI, artificial general artificial intelligence. You know, which is an AI that can answer anything in any context that we’re very far from that. But there’s also other books that get into the details and the weeds of, you know, n-dimensional free space vectors and, you know, backward propagation, deep neural networks and all the math and all the heavy engineering. People don’t need to know that stuff. You know, business people don’t need to know that they need to know about their use cases, about their business processes, about their data sources, about their business objectives, about their metrics. And all of that is what the book is about. The book, you know, a colleague who is a very experienced technical AI guy, machine learning guy. He’s deep in the weeds, you know, PhD quality stuff, level stuff, really, data engineering, data science, deep, deep stuff. And he said to me, you know, it’s less about AI and it’s more about kind of what you have to do to make AI work. And I’m like, bingo, you know, like ding, ding, ding. Yes, that is what it is.

PH: One of the things that organizations have found a very strong use case for when it comes to AI is their customer experience  operations. So AI and CX this has proven when used right to be a very powerful combination in recent years, is that right?

SE: Absolutely, absolutely. And it’s one of the areas that I really focus on in the book. You know, I focus on the customer journey. I focus on product data. I focus on customer experience. I focus on customer data and attribute models. But a lot of the heavy lifting has already been done, been done by the big vendors. So you, as a business person, as a technology leader. You as a business leader need to look at the capabilities of those platforms and say, OK, what’s my business problem? Right? And it’s not going to replace a human. It’s not going to do the end to end process, and not going to figure out a process you don’t understand. You can’t automate a mess and you can’t automate something you don’t understand. If you don’t understand it, the AI is not going to figure it out.

SE: So, so it’s incumbent upon organizations to understand those processes, understand those data flows, understand the quality of the data, the provenance of the data, the structure of the data, the attribute models. All of those things are basic. Blocking and tackling that should be done. You know, I’ve seen organizations kind of outsource a whole, you know, virtual assistant capability. Now it’s - you need to own the pieces of it that are the differentiators, right? Which is the knowledge, right? What are your products? What are your services? What are your troubleshooting approaches? What’s your knowledge about the customer? Because think of it this way right now. Virtual assistants are not great.There’s some good ones out there, but a lot of times they break. They stumble on silly things like I had one the other day and I left an apostrophe out of Don’t I spelt D-O-N and there’s a T. There was no apostrophe. And said, I’m sorry, I don’t know what you’re talking about. You know, it was a simple. And then when I put the apostrophe in it recognized the intent. Well, that’s silly. That’s not even basic machine learning, intent, you know, intent identification or utterance classification. So my point here is that there’s a lot of basic stuff. And down the road, these virtual assistants are going to be very, very good. They are going to be. I wrote it in my first chapter of the book, and I think you can download the first chapter on our website, www.earley.com, that’s E-A-R-L-E-Y.

SE: Don’t forget the E before the Y, but I think you can download that first chapter. I’ll make sure it’s available in your show notes, but it talks about a vision for the future, and it just walks a person through their day where every part of their day, their walk, they’re talking to, interacting with virtual assistants that work for him - for them, for this person, and they are doing their work. They’re helping schedule flights and checking portfolios and purchasing replacement products and making recommendations and handling scheduling. That is what we’ll get to. One day they will be fluent and they will be seamless and it’ll be just, we know that’s how technology is right. One day we’ll get there. Where we are today, between where we are today and where we need to be is a knowledge and data problem, right? It’s a knowledge and data problem because it’s not about the technology. We can do it the technology, but the data sources are not in the right shape, they’re not curated. And it’s the codification of the knowledge of the organization because organizations compete on their knowledge. Period. Full stop. Knowledge of customers. Knowledge of markets. Knowledge of solutions. Knowledge of potential products and routes to market. And competitive disadvantages and advantages. And so on. That’s all their knowledge and all people do internally. Humans are creative and they work with knowledge and they create that knowledge.

SE: Virtual assistants are chat bots, are a channel to that knowledge. Chat bots are a channel. They need that knowledge. Now, if you don’t have a handle on that today and you don’t get a handle on it in the next few years, you will be caught flat footed. That means you need to build out those content processes. When you talk about omnichannel, when you talk about Customer 360, all of those things depend on the ability to understand the customer in data terms and to be able to present back to them. The messaging that is presented by the orchestration by digital machinery, the digital machinery that orchestrates these things that takes in customer signals and then presents something back. Now, all of that means if organizations don’t do this, it will be existential. If they don’t start now, they haven’t done it and they don’t start now. I guarantee there will be a lot of companies spending tens of billions of dollars trying to catch up, and many of them will not be able to.

PH: Well, based on your experience, what should companies who will set out, let’s say they start today to try and take your advice and build on it? What are some of the things they should expect in terms of the big challenges and the areas they need to be prepared for and to concentrate on?

SE: Yeah, yeah, yeah. So one thing is its executive vision and support. One of the problems that we have in most corporations is the quarter to quarter a short term investor earnings expectations. Wall Street expectations. Sometimes when the stock price goes down or they get a new leader, they go, Oh, wait a minute, why are we doing this? What do we need this for? What? What’s the ROI? And yet that has been established at the early stages of the project. This is a multi-year commitment. This is a marathon and this will take millions of dollars. People do multi-million dollar, tens of millions of dollars, hundreds of million dollars on digital transformations, and they ignore the knowledge. I worked with one organization spend two hundred and fifty million dollars on SAP, and when it came to the knowledge there was about a million and a half dollar investment they needed to make. Whoa, that’s way too much for us, right? In other words, they spent the money on the structured side. They didn’t spend it on the unstructured side.

PH: So when you say knowledge, you mean gathering in just figuring out what we have, what unique knowledge we have and data we have and how to gather it together?

SE: Building knowledge bases, building, you know, standard operating procedures, you know, making the day to day operational things so people can’t find their stuff. What do they have to do? They have to recreate it. That is inefficient. That causes friction that slows down the process when you don’t know, we work with one financial services firm. We had to build the chains of trust throughout the organization for reports that they had to do. What happened was before we built this. People didn’t know the accuracy or the validity of the data source, so they would go back upstream, check every data source and it added weeks to their process and huge amounts of inefficiency. So the point is when people don’t trust the data and the same thing goes for AI. By the way, if they don’t trust the result and they don’t understand the result, they won’t use the AI. But even in day to day work, unless you are sure about your data and your content, where is the latest strategic plan for this? Where’s the troubleshooting guide? Is the knowledge base up to date? What do I need to do my work right? It’s a whole ecosystem of information flows throughout the organization when you look at something like personalization. There are so many people in so many departments and so many processes that go into this.

SE: When you’re onboarding products, you have a whole product operations catalog operations team. You have to understand what the right attributes are that people need to find there. They’re there to solve their problem or find the products downstream. But that’s going to depend on who they are and what their background is. So we need to get that from marketing. We need to get signals from marketing. We need to understand their real time behaviors on the web. We need to provision self-service. We need to do. There’s all of these information. I have an animation that usually blows people’s minds because it shows all of these different flows. But the point is, it’s an orchestration process, and you need maturity in sharing knowledge and sharing information up and down that food chain, collaborating seamlessly friction free so that people can ultimately support those downstream customer facing processes. At the same time, again, you have lots of knowledge being created throughout different parts of the organization that all will go into this idea of personalization. And you need the right architecture. You need the right reference. You need to componentize knowledge so that you can serve up the right piece of content and the right product for the right individual. That means understanding their characteristics as they go through their journey. It means understanding that journey in detail.

SE: It means understanding the messaging and what’s called a messaging architecture, so you can kind of shift in and out like a different call to action, a different hero image, a different target audience that can be optimized by machine learning. But you need those building blocks in order to try those different combinations. And then you need to understand, OK, what? What’s the knowledge of a brand? A brand manager or a merchandiser or customer specialist who knows, well, this is top of the funnel content. This is middle of the funnel content. This is, you know, you know, conversion content, right? For this audience doing this thing with these products, right? That’s knowledge that needs to be captured and applied to this digital machinery. So this whole idea of orchestration personalization through orchestration requires that we have those building blocks. Machine learning and AI can help to optimize all of this and can help to interpret signals. But if we don’t have those building blocks, if we don’t have that data and the data architecture and the content correctly structured and the knowledge captured, none of it’s going to work right and think of it. Think of the best salesperson or customer service rep if you’ve ever worked with. They knew your problem. They knew what you needed. They anticipated what you needed. They could answer the questions.

SE: You trusted them. They can make recommendations that were helpful. And that’s what we want to do in our organizations. We need to build that capability at scale. We need to take the knowledge of the best customer service people and be able to capture, codify and present it to the customer. Now it’s a long way to get there, but the beginning piece is building things like agent assist types of helper bots, right? Bots that will listen to the call in the call center and anticipate, Oh, they’re talking about this. Let me recommend this piece of content to the agent or the agent has a question or the customer has a question. The bot can interpret it. Present a piece of information the agent can say, Oh yeah, that’s the right information. Push it out makes their job much more efficient. Or they say, no, that’s not quite the right intent, or they didn’t interpret intent correctly. So we can correct the frontend interpretation of what the customer is asking, right? Because that’s intent. That’s utterance classification to an intent. Then the intent pulls back a piece of information. Well, is that the right information? Human judgment can tell you that. Now you make the agent more efficient and you teach the bot and you make the bot better. And then eventually, when it’s good enough and it’s accurate enough, you can release it to the wild.

SE: You can give it, you can have it directly work with customers unless you have very narrow use cases with unambiguous results. It’s not possible. It’s not reasonable to build a purely customer facing bot unless it’s very focused, very targeted, clear outcome, clear steps like Amazon does a good job of this with returns, right? It’s such a defined process that it’s repeatable, it’s unambiguous. It has a clear outcome. That’s what we need to begin with. But if it’s anything that requires judgment or human expertise or nuanced interpretation of a complex request, then the human has to be in the loop, right? And we always have to be able to escalate to a human. So there’s many, many different things around, you know, 360 view of a customer around personalization, around improving customer service or on call deflection. First of all, why do people call the call center? Because something’s broken, right? Why we need to fix that stuff upstream, and we can use these tools to do that. We can make that experience better by using these high functionality tools. So anyway, my point, my overarching point here is there’s a lot of foundational work that can be done that will improve processes and efficiencies today while preparing for the future.

PH: It’s gathering the data that you will ultimately need to feed into the next iteration of your AI strategy.

SE: That’s right. The data and the structure and the content and the use cases. So I think when you ask me the question, what do they have to watch out for? They need to get consistent support. They need to have the right vision. They need to have the North Star, even if they believe that North Star is aspirational. One guy said to me when I showed him the orchestration approach that we’ve developed that is getting a lot of pickup right now and we have patents on it. And we there’s also a lot of open source and there’s a lot of techniques that are available to anybody. But this guy said that will never happen in my natural lifetime at this company, right? And I think it’s a bad. I think that’s not the way to look at it. First of all, things happen really much faster. They accelerate much faster than we would imagine, number one. And you have to still have that North Star, even if it feels aspirational because you can build the building blocks along the way and you can get value from those building blocks. So having that vision, even if it’s aspirational, getting the support from executives and then building a roadmap and plan that gets you those incremental wins. It can’t be, you know, the gigantic galactic uber AI project that’s going to solve world hunger or cure cancer. MD Anderson with IBM and Watson, there’s $78 million. Now, they didn’t solve the problem, but what they did was they created a lot of other ancillary technologies and benefits and simple things and processes that actually was very, very valuable. These things become transformations when you do lots of small interventions there that are highly targeted around pieces of the process, then they collect together and they become a transformation.

PH: And that’s - it’s quite clever because what it means, particularly around that executive buy in, if you map it out this way, then you can show results in a consistent basis as opposed to people have to wait 10 years to see the results, so that will continue. That will keep the investment flowing.

SE: Nobody, nobody will wait that long. You’re throwing out your stakeholders. Like what?

PH: Yeah, yeah. Yeah, exactly. So the you mentioned the magic partnership between human and machine. In your experience what are some of the biggest challenges that can come to that partnership?

SE: Yeah. Well, there’s certainly the threat that people might feel of, OK, I’m going to work with this tool and it’s going to put me out of a job. There’s a big cultural fit piece. One of the things that has to happen is you have to gamify it, right? You have to give - you have to help the service rep because again, it’s going to come from front end. People who are on the ground has to be positioned in a way where they understand that this is to help them be better and to take away the drudgery right to do service. Reps want to walk people through yet another ten thousand five hundred and fifty two times the password reset, right? And that’s low hanging fruit that’s pretty much solved. But you know, there’s some stuff that’s just boring, right, that people don’t want to do, you know, and it’s just drudgery. And so and or - And so to see to help them see that this is a tool to take away the drudgery, it’s like the turn of the century,-you know, the turn of the 20th century when steam shovels early nineteen hundreds were steam shovels are being used. People say, What are all those ditch diggers going to do? Well, people don’t like digging ditches. Yeah, and you got to build skyscrapers and highways and in cities.

SE: So we have to think of it the same way we’re taking away the drudgery. We’re taking away the stuff that’s boring, that’s repetitious, that’s mind numbing. And we’re giving people more, greater abilities, but we’re also giving them tools to handle difficult situations. Customer starts getting escalated. Maybe they get triggered. Ok, let’s do a real time intervention. We can do sentiment analysis, identify that hand the call off to someone else, bring in a supervisor, bring in a script to guide them anything right. There’s stuff that’s going to help make their job more satisfying, but it’s also helping them understand that look, you’re building skills by becoming a robot trainer, right? That will be in demand. That’s going to be in demand. There’s going to be a lot of that work that’s going to require human judgment to correct those things because they’re very, very dumb. And so it’s helping to communicate that this is not. And it’s also the business model of the BPOs, right, the people that are handling call centers and building call centers and managing call centers, right? What do you do when your call volume decreases? Well, there’s you have to kind of cannibalize that business in a way because your job is to add efficiency and effectiveness or a competitor is going to do that right? So now there has to be new business models and pricing models to say, are we responsible for just calls, handling calls and looking at the statistics? Or are we responsible for solving problems and ensuring a high level of customer satisfaction? Right.

SE: So there are some inherent changes in the marketplace. We also have to say, ‘Hey, what’s going to happen when we displace more people,’ right? When some of those entry level jobs are automated? While I believe it’s incumbent upon the technology providers, the service providers, the BPOs, the organizations to also use the same technologies that are going to cause the problem to help to address the implication of that. So when people have - need to be upskilled, when they need to be trained, when they need new motivation, when they need to find direction, even if it’s, you know, even if it’s about their own personal value in their own personal motivation, in their own personal meaning in life, there are lots of resources that can help with that, but usually in the form of three hundred dollars an hour coaches or you know or whatever people that are in scarce supply. And we will not be able to scale that at the level that would be required as society goes through displacement. But the same tools can help, you know, take that same time.

SE: It’s not going to replace a human coach, but it can really. We can diet. We can assess people using machine learning approaches. You know, you get those assessments and say, you know, do you like to ride a bicycle or eat ice cream? You know, like what? And then you give an answer. And then all of a sudden it comes up with all your personality characteristics, right? The same things can be used to assess people’s hard and soft skills. In fact, customized e-learning, customized training, coaching, all of those things are going to be based on the same technologies and be able to scale and leverage and amplify human capabilities to address the displacement because we are going to have to retrain large swaths of the workforce as these things evolve and people may think, Oh, that’s a long way away. There’s been predictions about job loss. It’s going to happen, but it’s going to take a little while longer. But when it happens, it’s going to happen at a very massive scale. So again, it’s looking at both sides of those things and looking at the impact.

PH: Do you think that the human machine partnership will lead to more general AI models that can tackle broader sets of problems and become the norm? Or is specialization the future?

SE: You know, it’s such a great question. It’s such a great question. I believe that specialization is the future, that you’re going to have fleets of virtual assistants that are being highly tuned to specific tasks. And then you will have concierge virtual assistants that will decide where to send people when they have a particular type of problem. Now, could you call that a step toward more general intelligence? Perhaps. But I think it’s going to be, just like, you’re human. The human brain works the same way. You have very specialized sets of neurons and neural pathways that are higher - that handle very specific functions. They found that in the visual pathways and parts of the brain, they found specific neurons that lit up for specific actors and actresses and celebrities right? Really specific neurons. So it’s highly specialized. Human brain is highly specialized. There’s lots of different circuits and there’s something called the pandemonium theory of language recognition, where you have these different sets of neurons that are responding to interpreting either visual text or speech. And what happens is the ones that yell the loudest or they have the highest signal are the ones that are amplified. So it’s almost like you have these little competing sections of your brain that are trying to interpret things and the ones with the biggest signal win. Well, guess what? That same concept will be applied to virtual assistants and to bots. So I’m not making comparisons to the human brain because there’s hundreds of neurotransmitters.

SE: I started as a biology major and I went to chemistry. There’s hundreds of neurotransmitters and they’re all analogue. They’re all and a single neuron can connect to tens of thousands of other. Neurons and there’s three billion of them, so the number of combinations it’s just so vastly more complex than what we can conceptualize right now with technology, but there are some analogies, so I don’t think we’ll get to human brain capabilities for a very, very long time, if ever. But there are some analogies in this whole idea of very specialized functional bots. So that’s why one of our patents is on master data for AI, master data for virtual assistants. And what it’s essentially saying is when you have these fleets of tens or hundreds of thousands, tens of thousands of bots that eventually will be built if you need to update them with new products, new services, new offerings, new solutions, whatever, you have to push all those things out to many, many distributed bots. And that’s going to be a large scale information management problem. So, so my point is that I believe that we will have specialist bots that handle very narrow functions really well, and then we’ll have concierge bots that will route those tasks and those utterances and inquiries and intents to those different because they are specialized. And that’s kind of the way the world works, right? Everybody is a specialist. Right? So there’s no reason to think that that won’t be replicated in that way.

PH: Ok, a couple of quick fire ones to wrap up, Seth. What is the most underrated ML algorithm of the last few years?

SE: Well, you know, it’s hard to answer as an algorithm because there’s so many, there’s hundreds and thousands of algorithms, thousands of algorithms, I think really what we want to say is what application areas are either - are underrated. You know, it’s really about, you know, the fitness to purpose. You know, there’s I think the basic stuff around knowledge retrieval is underrated, building knowledge retrieval bots. That is a very fundamental thing. It requires you start to get your data house in order, you start to get your knowledge in order. It is necessary for everything else. It is a precursor and a foundation, and they are not difficult to build. They’re based on search. They’re based on AI powered search is a lot - I just wrote an article that should be coming out very soon on AI powered search and there’s a lot of stuff that is in search that you used to have to build. That’s much, much less expensive. That can give you very powerful results, building question, answering systems and building knowledge retrieval bots. Those will be part of the functionality of your virtual assistant, so building those will solve problems today. It’ll help your employees get to the answers they need. It’ll give - start building some discipline in a knowledge architecture and knowledge curation and content processes.

SE: Again, none of that stuff’s going away, and we can’t do the more advanced stuff without the foundation. So it’s building those foundational applications, understanding your intents, you know, understand the utterances, understanding how people are asking for questions. A lot of the problems that we’re trying to solve with AI and text analytics is based on our past sins in poor data and content curation and the lack of consistency of architecture. But now we can derive a lot of that stuff, but we still have to curate the sources. We still have to capture the knowledge that can solve problems today that can vastly improve the effectiveness of a call center. And again, that becomes a foundation for the more advanced stuff. So I think there’s a lot of different algorithms within that application framework that just need to be exploited and leveraged. So that one is, that’s underrated, right? Because it has to be done.

PH: Ok. Excellent, so lovely answer. And I’ve also got the converse question, what is the most overrated?

SE: Personalization, Customer 360 and virtual assistants that are not using knowledge engineering principles, right? There’s a lot of there’s a lot of discussion about personalization. I’m writing an article in the Journal of Applied Marketing Analytics, another deadline I have pressing, but it’s about personalization, and I’ve written a few about personalization recently as well. And the point is that everybody waves their arms around, Oh, you have to take in what the customer wants and you have to understand the signals and you have to do this. But there’s no detail on how to do it. And it doesn’t point. There’s very few vendors that are really talking about how you need to architect the information in order to truly make personalization work. And I think the other problem with personalization is there’s either simplified approaches like customers who bought this bought this. There can be relationships, product relationships and or cross-sell and upsell, but those are data dependent. They require good curated product data and product data relationships. So they’re oversold and they’re under-delivered because the foundation isn’t there because people try to buy an application to make it work, and it won’t work unless you have these other pieces.

SE: There’s a lack of understanding of the customer journey and customer needs. We built an architecture for an organization a number of years ago that that enabled personalization, and at the end of it, they’re like, Well, how do we differentiate this audience from this audience? Well, we don’t know. So they ended up using the same messaging across those different audiences. So at the end of the day, they had the technical capability. They didn’t have the process, maturity or enough deep understanding the customer needs. So personalization and Customer 360 - Customer 360 is very dependent upon having attribute models that are consistent across your entire tech stack, your marketing and eCommerce tech stack. And then there’s ways of evaluating each of those tools in the context of the customer journey. That’s another article I wrote for the Journal of Applied Marketing Analytics. It was almost the whole issue. But there are very clear processes and ways of tuning these things. And unless you get those foundational pieces right, those other - those aspirational pieces of functionality are not going to work. Companies have a hard time with this because they’re not building the right foundation. So those are very overrated.

PH: Well, look, Seth, you’ve been so clear about the importance of this area and the, I guess, the consequences of not doing anything. But also what I’ve taken is that it’s not - it doesn’t have to be this big, massive multi-billion dollar investment from scratch. That there’s a lot of low hanging fruit. There’s a lot of things that can be brought into an organization and implemented from an AI perspective that are small, consistent, give you wins along the way and help to feed that bigger iteration, maybe down the line with the knowledge of the data. And I think you’ve - you make it very clear in your book that one line jumped out of me. That probably is a good way to wrap. And it’s AI only works when it understands the soul of the business. And I think that’s it - knowing the soul of your own business and how AI can work hand-in-hand with that.**

SE: Yes. It all has to be understandable. It all has to make sense. If somebody tells you things and they confuse you, walk away, you should be able to understand. Hopefully, today we had a conversation that was understandable, you know?

PH: Thank you so much, Seth. It’s been fascinating hearing about you and your organization and also just this this whole area. So thanks very much for joining us on TELUS International Studios.**

SE: Any time I appreciate it, it was a lot of fun. Thank you.

PH: And thanks to you for tuning in. We’re excited to be back with some brand new episodes. So, if you like what you just heard and if you think other people should hear it, share this episode and hit subscribe. While you’re there, give us a review and a rating, it really helps. And of course, if you would like to find out more about TELUS International and the global and disruptive brands with whom we work, check out telusinternational.com. Until the next episode, take care!

On this episode, we’re joined by Seth Earley, consultant and award-winning author of The AI Powered Enterprise. Tune in to hear Seth’s insights on navigating uncertainty in AI development while keeping customer experience at the forefront, and his take on the biggest challenges when it comes to human-machine partnerships.

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Customer experience analyst and best-selling author, Matt Dixon, sits down with TELUS International Studios to share insights from his new book, The JOLT Effect: How High Performers Overcome Customer Indecision. Dixon takes us through the bane of every salesperson’s existence: a long customer courtship that ends with an “I’ll think about it.”

Leveraging a first-of-its-kind study that analyzed 2.5 million documented sales conversations through machine learning, Dixon reveals that 56% of sales loss is actually caused by customer indecisiveness. Listen to the episode to gain more sales growth best practices to overcome indecision.

The JOLT Effect: How high performers overcome customer indecision is available for pre-order now.

Intro: TELUS International Studios, where customer experience meets digital transformation.

Patrick Haughey (PH): And you are very welcome back to TELUS international studios. I’m your host Patrick Haughey, and on this episode I am delighted to be joined by someone who I’ve had the pleasure of speaking with at a few TELUS International events over the last couple of years. Matt Dixon is a CX and sales expert, he has written many best-selling books — one of the best known is The Effortless Experience, but he’s joining us on this episode to talk about his newest book, currently available for pre-order. It’s called The JOLT Effect: How High Performers Overcome Customer Indecision. To prepare for this book, 2.5 million documented sales conversations were studied using machine learning. So get ready to hear some very interesting insights from this first of its kind study. So I thought an interesting jumping off point with Matt might be a line that jumped out at me from the book’s description when I was reading it. And that line reads: “In sales, the worst thing you can hear from a customer isn’t ‘No,’  it is ‘I need to think about it.’ And I started by asking Matt why this is.

Matt Dixon (MD): Yeah, it’s a great question. That’s the beginning of the whole tale. And I love that line, too. Well, I don’t love it: I hate it — it’s terrible, it’s really bad news actually, for sales people. But it speaks to what the whole story is about. You know, I think if I’ve been out there engaged with sales people, I know, obviously, Patrick, you and I have spent a lot of time talking about customer experience, customer service, the effortless experience, etc. The other hat I’ve worn as a researcher is really focused on business to business sales and commercial effectiveness, and how is buying behavior evolving and so on and so forth. And one of the things I’ve been struck by over the years is that when you ask a roomful of salespeople to think about the deals they’ve lost over the past quarter or the past year perhaps, and you ask them to categorize that into two different buckets. On the one hand, deals you lost to a named competitor. So the customer ends up buying but they didn’t buy from you, they bought from somebody else. Versus customers where you lost, but you didn’t lose to a competitor, you lost to no decision — you lost to the customer doing nothing. What you find is, in organization after organization, by far more hands go up around the latter, that salespeople feel like they’re much more likely to lose to the customer doing nothing than buying from a competitor or, as you said, even just saying no.

MD: In fact, I think sometimes in sales we can feel like our sales people are heavily influenced by the last sales call they had, you know the product is too expensive, this competitor is eating our lunch, whatever it is. But in this case, our salespeople are actually onto something. We studied this with data and we’ll talk more about the research that went into the book, but I’ll pull forward one finding, which is that anywhere between 40 and 60% of all deals are actually lost to no decision, to the customer doing nothing. And we studied this across 2.5 million sales conversations. We used machine learning to study it. And what we found is exactly what you said. If you look at all of the I would say hundreds of thousands of things that are said in sales conversations, both on the salesperson side and the customer side, by far in our analysis, the worst thing that can be uttered by the customer is I need to think about it. And the reason it’s so bad is that, you know, the old adage in sales has long been that as a salesperson, you don’t want to you don’t want to chase garbage trucks, right? You don’t want to pursue opportunities that have no chance of closing. So in many respects, for a salesperson, it’s far preferable — it may be painful — but far preferable to be told no. The worst outcome is where you go through the entire sales process with the customer. For many companies that can be months or even years, and think about all the resources, the time investment from the salesperson side, the resources from their organization, not to mention all the resources from the customer side. These are customers who go through the demo, they go through multiple iterations of the proposal, they talk to your reference clients, they talk to your subject matter experts, you talk to all of the sales prevention departments inside the customer organization like legal, finance, procurement, etc., only for the customer to get cold feet late in the game and end up doing nothing. And so that is such a deadweight loss for the average salesperson, for sales managers and teams and for entire sales organizations. We do think that how to overcome that ‘no decision’ phenomenon, how to break through and pull more deals across the finish line is the thing that separates great sales organizations and great salespeople from the rest.

PH: We’re going to come to those traits of a great salesperson who can manage to overcome the hurdle in a few minutes’ time, because it’s a fascinating area. We’re looking at something very psychological here called omission bias. Is that right? Which you cover a little bit in the book. Just briefly explain what omission bias is and what kind of mistakes customers are looking to avoid.

MD: Yeah, perfect. So I think the conventional wisdom in sales has always been that if the salesperson starts to get cold feet, if they start to backpedal, wring their hands a bit, waver, waffle, straddle the fence, whatever term you want to use, that it must be that we’ve not we haven’t put to rest the status quo, because we all know, even as human beings, we are predisposed to stay the course. We’ll pass up on better options just for no other reason than we’re lazy and we don’t like to change. And for salespeople, that’s a really powerful obstacle. But I think salespeople have always been taught that that is your only enemy in sales. And what we found was that in our analysis of 2.5 million sales conversations, when this customer starts to show signs of hesitancy, when they start to show those signs of cold feet, by far, three-quarters of salespeople we found go back and they kind of hammer the status quo. They either try to re-convince the customer that ‘you must not really understand the value that our solution is going to deliver.’ ‘You must not really understand how wonderful it is, the ROI projections and how amazing things are going to be on the other side.’ Or, when the customer doesn’t respond to that, what do we do? We put away the carrot and we break out the stick. And what we try to do is show the customer the cost of inaction. And we do that by dialing up what’s called FUD in sales: fear, uncertainty and doubt.

And what we’re really trying to do is make the customer squirm a bit, make them feel the pain of what they’re going to miss out on if they don’t if they don’t buy the solution. We actually found in the analysis, surprisingly, is that that approach, while it’s commonly practiced, actually makes it 84% less likely you’re going to get the customer out of no decision. So you actually increase the odds of the customer doing nothing by using some of those tactics designed to beat the status quo. And to your question, what we actually found, we realized that we wrestled with this quite a bit because it’s been the conventional wisdom in sales for a very long time. In fact, we wrote about this in the Challenger Sale that the best salespeople are gifted at showing the customer that the “pain of same,” the pain of the status quo is actually worse than the pain of change. And that is one of the things they do really well. And so it came as a big surprise to us that that strategy, that approach backfires so often. And so we took a step back and what we realized was we were asking the wrong question. The question that we were asking was, what’s the best way to beat the status quo? Maybe it’s just not being done well. Maybe high performers have figured out a different technique that’s really effective today. But the question we should have been asking is a bit different, which is not how do we beat the status quo, but rather why do customers make no decision? And it turns out the reason they make no decision is exactly what you talked about, Patrick, actually if you look at the psychology research and this is actually backed up in our own analysis, that there are really two reasons the customer will choose to do nothing. The first reason is that they’re committed to their status quo, they just prefer the status quo. It could be that they don’t believe your solution is superior to what they do today or they may just believe it’s superior, but they believe their current approach is good enough. Right? ‘It’s just not worth it for us to change.’ But there’s a second reason that the customer will choose to do nothing. It’s not commitment to the status quo, it’s indecisiveness about changing the status quo. And I think when people hear that, what they immediately ask is, how are those two things different? You’re telling me that customers choose to do nothing either because they prefer what they do today or because they don’t want to change what they do today. And what we explain in the research is that those two things are massively different. And it’s something that high performers get an average performance don’t.

MD: So if we go to the psychology research, what we find is that there are really two types of loss that people in our customers are worried about. On the one hand, there are what are called errors of omission, which are mistakes and losses that we incur because we choose to do nothing. One of the old stories to illustrate is you’re in a tennis tournament and your competitor has a peanut allergy and you sit down to enjoy a lunch before your match and you know there’s something on the menu that has peanuts in it. The omission bias is when your competitor orders the dish that has peanuts in it and you don’t say anything about it. And so they get sick and they have to drop out and you win. The opposite is an error of commission. Now, an order of commission is when you incur a loss because of something you did, not because you failed to do something right, but because you actually did something wrong. So the same metaphor uses you’re sitting across the table from your tennis competitor and you proactively suggest they order the thing that has peanuts in it because you want them to get sick. Now, with those two scenarios, the outcome is exactly the same, your competitor has gotten sick and had to be rushed to hospital, etc. But by and large, everyone was more comfortable with the first one because I didn’t have anything to do with it. The second one is ‘a thing I did.’ ‘I did something wrong.’ And what we find with customers is that when they’re facing two different options, whenever something could go wrong by me not choosing to buy your solution, we could not capitalize on an opportunity, or I could buy your solution, and then something bad could happen as a result, they will choose to do nothing far more often than they’ll choose to do something and run the risk of getting something wrong. Now, the three types of things they’re worried about getting wrong are:

MD: One, I picked the wrong option. We think about all the options we put in front of our customers. So often they start to wring their hands about, ‘I’m not sure if I’m picking the right contract length’ or the right scope of the agreement or ‘I’m buying the premium version — should I really just buy the basic version?’

MD: The second thing is they start to worry that they haven’t done enough research and think about all the content out there about every vertical, every technology out there. And they start to fret over the fact that it’s the next white paper that they would consume that will have all the answers that they need to be a smart consumer.

MD: The third source of indecision is what we call outcome uncertainty. This is where the customer, before they sign on the dotted line, starts to worry that they don’t have any assurance that they’re going to get the benefits they’re paying for. And despite your rosy ROI projections, despite the great demos and proof of concept trials you did, despite the wonderful reference calls and the great analyst reports they read about your company, the customer is worried that they might be the one company for whom this whole thing goes sideways and if that happens, somebody’s head is going to roll. And the person who is going to roll first is the person whose name is on the contract.

MD: So those three things: I’m picking the wrong option, I didn’t do enough homework, or I’m going to be left holding the bag (if you will) and not getting what I’m paying for, are the sources of indecision, which interestingly have nothing to do with the customer’s preference for the status quo. And so it helps explain why we see this phenomenon that salespeople who are taught that every indecisive customer, every waffling and wavering customer with cold feet, must be struggling with a preference for the status quo. So you go back and, you know, your ‘beat the status quo playbook’ is like a hammer, every indecisive customer looks like a nail. But in fact, if they’re worried about something else, then going back and trying to convince them of the loss that might result from inaction only gives them more to be worried about when they’re actually worried about something quite different. So we found in the research that high performers have figured out a different playbook, a companion playbook not just for beating the status quo, but for laying to rest or overcoming these sources of customer indecision.

PH: And does this take us to the JOLT effect?

MD: Absolutely, yeah. So what we found is, there’s again, there’s two playbooks. We’ve all got ‘the beat the status quo’ playbook. We wrote a book about challenger selling; we happen to think being a challenger is one of the best ways to beat the status quo. But as I said before, it’s only part of the story. If beating the status quo is all about showing the customer the cost of inaction, overcoming indecision is getting them more comfortable with the action you’re asking them to take, making them feel like you’re making a good choice, don’t worry about the other options, you’re picking the right thing. You’ve done enough research, you’re in good hands. I’m your subject matter expert, your trusted advisor, and don’t worry, you’re not jumping out of a plane without a parachute. There are some assurances we can give you that this is going to actually work out well for your organization, etc. So if the beat the status quo playbook is about dialing up the fear of not purchasing with the customer, then the playbook for overcoming indecision is about dialing down the fear of purchasing, right? It’s making the customer more confident and comfortable in the decision you’re asking them to make, which they are quite anxious about, right. I didn’t do enough homework. I’m picking the wrong thing. I don’t have any assurance this is going to work out for me. And so JOLT is a set of four behaviors, and I’ll just explain what they are really quickly, that together form this playbook for overcoming indecision:

MD: The J is judging the level of indecision. And what we found is that high performers are quite good not just at assessing opportunities and customers on their ability to buy their solution, but also on their ability to make a decision. So not just the ability to buy, but the ability to decide. And they do that through active listening, probing questions, and powerful requests they make that elicit responses from the customer that tell them, ‘Is this person somebody who can actually make a decision and do so in a reasonable amount of time?’

MD: The O: offer your recommendation. So salespeople have been taught for a very, very long time that it’s all about diagnosing the customer’s needs, understanding what they want. And the hope is that by asking good questions, the customer will figure out on their own what they really want or what they need. But what high performers know is there’s a time for asking and letting 1000 flowers bloom, and then there’s a time for telling and making a recommendation, because with too many options on the table, the customer again will get wrapped around the axle about what they should choose and they’ll fear I’m going to choose the wrong thing, and that will become an irreversible decision.

MD: The L is limiting the exploration. So in a world where customers want to continue to consume content, they always feel like they’re not enough of an expert on the technology or on the platform or on the competitive landscape or on the decision they’re being asked to make. They will consume information endlessly, and there’s no way that they could possibly develop the level of expertise that we as salespeople have. And so we’ve got to put a limit to their exploration, and the way we do that is we’ve got to earn the trust, the trust and credibility, if you will, the customers trust, and we’ve got to establish our credibility as trusted subject matter experts who have the customer’s best interests in mind, and that we can guide them through this learning journey and that they don’t need to consume all the content because we’ve already done that. And we are their trusted advisor.

MD: And then finally the T: this is about taking risk off the table. So when our customer’s hand is hovering over the contract and they’re worried: ‘boy, this is a really big investment, it’s a really big decision for our organization and what if this goes sideways on us?’ We’ve got to provide some assurance that we’ve got their back, that things are going to go fine. And sometimes it comes down to additional professional services support, not that we give it away for free, but proactively suggesting that starting smaller, getting the customer to not bite off more than you can chew, or creative contract structuring that gets us in a place where the customer feels like they’ve got a safety net if things start to slide, if they start to go sideways. So that is the JOLT Effect. And we like it because it’s memorable, but it also speaks to what’s happening here, right? It’s about jolting our customers out of their indecisive state toward action.

PH: It’s really, really interesting. And actually everything you’re talking about there, there’s a couple of words that are shining through to me, which are customer success, it’s not just ‘make the sale and that’s it.’ It’s not a new area, but it’s certainly been more in focus in the last few years that we come out the other side of the funnel with a whole new funnel, which is making sure that the product is used properly, that we keep we keep them educated, that we’re there for them. Is there an element of this in all of that?

MD: 100%. We talk about actually in that T chapter, taking risk off the table, that one of the best things that that salespeople can do is pull forward some of those customer success engagements, laying out the roadmap for how we’re going to work together, the project plan, here are the key milestones, here are the metrics. Let’s get the quarterly business review meeting set up now, let’s get all of this lined up because we know from working with companies who get a ton of value out of our solution, this is the roadmap for success. So it sends the customer the impression that you’ve done this before. You know where the pitfalls are, you know where the landmines are, you’re going to start thinking one step ahead and you’re going to make sure that we’re going to end up in a good spot — this is a huge confidence giver for the customer.

Seth Earley on setting the groundwork for AI implementation

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