Katya Allison: 

Welcome to the GRIN Gets Real podcast, a show for marketers by marketers to talk shop and share insights on the ever-changing landscape of the digital world. My name is Katya, and I am your host on this exciting journey as we talk to our experts who join us. 

 

In every episode, we aim to help marketers maximize their potential by getting real with industry experts across multiple industries and disciplines. From influencer marketing to ecommerce strategy, we talk through it all and leave you with actionable tips to help you and the day-to-day of marketing. 

 

My guest today is Ella Dillon. Her areas of expertise are in customer experience and the buyer journey, having previously led customer experience at DocuSign and AT&T. Now, as the Chief Customer Officer at Conversica, she and I sit down to chat about AI and how we can create a front-end experience for our customer, regardless of industry. So put your AirPods in, turn up that volume, and get ready for my guest today, Ella Dillon.

 

Ella, welcome to the GRIN Gets Real podcast. I am super excited to be talking to you today about all things kind of AI.

 

Ella Dillon: 

Great to talk to you today.

 

Katya Allison: 

Excellent. So let’s get started. Share a little bit about just kind of your background as it pertains to kind of customer experience and, honestly, how it’s veered into the world of AI as well, too.

 

Ella Dillon: 

I think before customer experience was really understood, I had the privilege at AT&T—actually, I was asked to be a part of their inaugural customer experience group. And nobody even really knew what that was. So it was wonderful at the time to define it. 

 

For me, I realized if you truly wanted to design a customer-centric or customer experience-oriented company, you designed everything from inside-ou—or outside-in, I should say. 

 

Katya Allison: 

Mmhmm. 

 

Ella Dillon: 

So the products you decide to build, the organizational design, the metrics that you track, the compensation of what you pay your executives on, or you know, you incent them on. Like, the whole thing, the whole platform of the company has to truly be designed if you’re going to be totally customer centric. 

 

And I kind of became a student of this thing. I left there and was at DocuSign for about 7 years back in the day, and I helped them build their global support organization. And for me, I approach it from an experience perspective that is one of the highest touch but still scalable ways to capture the voice of the customer. 

 

You know, they’re talking—all these customers every single day. How do you take that and turn that into learning that makes a better product, or better training, or better alignment with sales messaging upfront, so people aren’t misaligned with what they’re actually getting? 

 

So that was super fascinating. And I went from there to sort of build out their success operations and customer analytics function. And anyone worth their salt, especially in SaaS, you know, they want to have the revenue go up hockey stick to the right, and they want your exposure to be relatively flat because in between is a company that lasts, right? You have great margins, etc. But you can’t do that if you don’t have data. 

 

Katya Allison: 

Yeah. 

 

Ella Dillon:

But you certainly can’t do that if you can’t build on insights on top of the data to inform the right kind of actions. Sort of a decision engine, if you will. And I think there are a lot of companies that get a lot of data. And I think it’s a lot harder for them to extract true insight from it and then tie that to action in a way that allows them to take their human capital—their people—and apply it to delivering customer value really well. 

 

Katya Allison: 

Yeah. 

 

Ella Dillon:

So I became very—I was always very interested in this, like, “Allow me to hire four really smart senior people instead of the 52 wrote people just to do a bunch of busywork because we don’t know.” 

 

Katya Allison: 

Yeah. 

 

Ella Dillon:

So that was sort of a lot of my experience. And then again, sort of had been a student across sort of the post-sale journey of how do you design this whole thing from implementation and onboarding through customer success, and training, and enablement to customer support to the operational background, the systems, and the tools, and the analytics to actually deliver a consistent experience?

 

And so that—that has always been kind of my—my love affair, I think, if you will. It’s like how do you build a customer-centric company? I have this sort of platform in my mind about what that means, but it has to be fueled by insights. Otherwise, it’s sort of—you’re not very smart about it. 

 

So that’s really my origin story.

 

Katya Allison: 

So the insights that you’re gathering is that the AI that we’re talking about—actually, let me backtrack because, in our previous conversation, we were using the term AI. I was using it very—kind of very flippantly like we’re talking about it almost like it’s just jargon. And then you had mentioned, “Well, a lot of people say AI, but to me, it looks like this back-end, this workflow automation.”

 

What do you mean by that? Like, what is the difference between AI and workflow automation?

 

Ella Dillon: 

I think AI is jargony right now. Kind of everybody tacks on AI like it makes them kind of a little extra special. So it is probably an overused term. 

 

And I will say almost every company—well, that’s probably not true. But AI—companies have AI usually in the back-end machine learning; like they’re taking their data, and they’re trying to learn, but sort of from the back-end. 

 

Katya Allison: 

Yeah. 

 

Ella Dillon:

What I think is different in the company that I joined—because I’m super excited about it—is true artificial intelligence applied to a front-end workflow. So most companies have workflow automation or rules-based routing, you know, like, send an email, and it’ll have a scheduled follow-up. But the minute that there’s a customer interaction, you need a human intervention to actually go carry the ball. 

 

Katya Allison:

Okay.

 

Ella Dillon:

True AI, I think, applied to the front-end is when you actually can have this dual-directional conversation without another human. Natural language processing, polite, persistent, personalized, read intent. 

 

So if someone has an actual issue, and they respond, you know, all of a sudden, just route it to a human. You can actually read it. You can actually, “Oh, you want a blue car and not a green car. We can address the order, or I can schedule that call, or you just gave me updated information, and I can append it and update your records.” 

 

Like, again, it’s the—AI can do this. It’s not like someone gives you an answer, and you now need a human to read it. And I think that’s new.

 

Katya Allison:  

And I appreciate you articulating the difference because I definitely want our conversation today to be about that front-end AI, right? Like, because the front-end is where you develop that customer experience. So tell me, how can I leverage, then, AI for customer marketing and—or customer success or improving the customer experience in general?

 

Ella Dillon: 

So the use cases are endless, and I think a couple of trends in the marketplace that I think are super fun is—and you probably see, and I’d love; you know, I listen to all your podcasts about it—is sort of the wedding, right, between sales and marketing, and marketing and success. 

 

You know, I do believe that the people are starting to think about an integrated revenue journey. 

 

Katya Allison:

Yeah.

 

Ella Dillon: 

Not just siloed. Like, old marketing was top of funnel, get new, like, hot leads, or whatnot. 

 

Katya Allison:

Yeah.

 

Ella Dillon: 

So how do I think that this can help is right now, again, it’s a lot of—I have a Fortune 500 company. They love this because what they do is they use our virtual—our intelligent virtual assistant to nurture leads, get them conversation ready, i.e., they have intent where they actually want—they’re not just saying, “I’m on vacation.”

 

Katya Allison: 

Yeah. 

 

Ella Dillon: 

You know, it’s like, they know they’re ready for a conversation, and they can hand it to a business development representative. Humans want to go where it’s easy, so they might make calls. But if it falls out, and they don’t actually close it with a customer, that comes back to the intelligent virtual assistant, who then nurtures them again. 

 

So this is this nice living, breathing ecosystem where the actual close rates are the actual conversation-ready that leads to—actually leads that lead to actual revenue. It’s completely different because of this augmentation we have between, like, the human and the IVA. So that’s one example, like, from a marketing perspective. 

 

The actual, like—again, it used to be called hot leads—we’re starting to call it conversation-ready—is a completely different order. From a success perspective, again, at Conversica, we totally use this for ourselves, where if we see a health score, we can actually launch our IVA to reach out to the customer, do discovery, understand what’s going on, so that we can run sort of health playbooks. 

 

Can use it to sign up webinars if we see intent. We can get that. We can schedule meetings with it. There’s across, you know—we can use it for referrals, or for upsells, or capturing, again, back to marketing—customer marketing is critical. 

 

Right now, customers are feeding the top of the funnel, now, to help you get a customer advocacy and a customer voice. We can use our own assistance in these very explicit use cases to reach out to customers and capture that.

 

Katya Allison: 

Forgive me because you have used this term, “IVA,” V as in Victor, and if so, what does that stand for first? And then I have a follow-up question to what you were talking about.

 

Ella Dillon: 

So we call it the intelligent virtual, with a V, assistant.

 

Katya Allison: 

Okay. Excellent. And then so my follow-up question because, first of all, that sounds amazing, right? Like, especially from the sales side, you know, being in marketing, I see the full scope. I see the sales side. I see, like, the customer success side. And being able to be just kind of that bridge in between that. 

 

When you had described, you know, this IVA just kind of knowing when someone is going to be conversation-ready, and if it turns out it’s a bust, it goes back to marketing. What we typically see is like, okay, then marketing has to decide what’s that next route. 

 

In this kind of AI/IVA world, does marketing have to then say, “All right, if this person comes back, this is what I want to serve them?” Or is the learning happening in that entire process so that the AI knows what to do next with something that kind of went bust?

 

Ella Dillon: 

Yeah. So we have, of course, you know, billions of sort of instances here where we’ve trained our AI. So our AI can actually make a bunch of decisions without it again, but that’s the whole sort of thing of beauty of it is, again. If you have rules-based or work automation, the minute a customer responds, a human has to read it and then make a decision. 

 

The AI, the virtual assistant, here can read intent, can say, “Oh, you had an ‘out of office,’” or, “You were—you asked me to follow up in 45 days,” and it can make the decision to schedule something in 45 days. Or if someone says, “Go contact this person instead. It’s not my job anymore,” the virtual assistant can just go update the record, and instead of a follow-up, schedule and send all that information, including a calendar, for the marketing person or whatnot. 

 

So it can do a lot of those things. And I will say that we invest a lot because AI is only as good as your investment. 

 

Katya Allison:

Yeah. 

 

Ella Dillon: 

So that, you know, if someone says, “Hey, how was Super Bowl Sunday for you?” We can train. You might go out to the queue and trigger, like, a review so that we can clean it up when you see this. 

 

So there’s a lot of investment in natural language and the human use of it to make sure that we deliver a really good, and valuable, and personal customer experience.

 

Katya Allison: 

That definitely makes sense. I think that it’s just like this continued learning, which is a little scary for me. I feel like listening to it, like having a machine just kind of learn natural language, what an appropriate response is so that I am served well because I think at this point now, any experience that I have had with an IVA has felt like “Oh, I know this isn’t a person.” 

 

Like, I’m throwing things out there to, like, test it out. Like, you’ve said my name, you know where I’m at in, like, the funnel. It sounds amazing, but do you feel at all that introducing, like, IVA removes a little bit from, like, the relationship between the consumer or the customer and, like, the product or service? 

 

Ella Dillon: 

You know, so I don’t, for a couple of reasons. One is—well, one is that our IVA is so good that they get presents or have been invited to go use someone’s cabin or whatnot. But that gets to the ethics of AI, where, from my perspective, you disclose. Like people should always know that they are. 

 

But people make decisions now—another big trend in the marketplace is people are making buying decisions based on experience, not just the product, right? 

 

Katya Allison:

Yeah. 

 

Ella Dillon:

So I think it seems, like, presumptive. Like, everybody knows this is the future. What they want is the ethics of it, but—so I think as long as you’re delivering a valuable, personal experience for the customer that gets them faster to what they really want, they’re good with it. 

 

In fact, they want it. I think it’s just how you do it, and making sure that it’s personalized for the customer, and that you, again, have integrity about what you’re doing.

 

Katya Allison: 

I want to talk about how AI can be used to create an exceptional customer experience, right? Not just kind of solving that problem, that immediate need. What you had described just right now was a little bit of, like, this time to speed. Get me what it is that I need as a customer sooner. 

 

But is there another way that you can leverage AI so that you’re creating an exceptional customer experience? Like what can I do above and beyond to create that exceptional experience?

 

Ella Dillon: 

So I’ll give another example of running success. You always have the question of, “How do you deliver a human experience at scale?” Because you can’t hire a customer success for a cost of millions, right? 

 

Katya Allison: 

Yes. 

 

Ella Dillon: 

But things fall through the cracks. So if you’re a small customer or whatnot, and you’ve sent a note, and your customer success manager hasn’t reached out to you, or they forgot to schedule a meeting, or you didn’t know about the webinar, there’s a certain amount of persistence, consistency, and following-up that’s just easier to do with a virtual assistant that gets the customer what they want, and the follow-through and the follow-up is easier to do with a human. 

 

So I think just again, it’s like, when you read a lot of, like, customers, they love having the Zappos experience about exceptionality. 

 

Katya Allison: 

Yeah. 

 

Ella Dillon:

What they really—what they want is predictability, consistency, and to trust you. That you’re getting what they need, right? And I think that’s—that’s where the AI can come through and really make sure you’re following through on their needs and things aren’t falling through the cracks.

 

Katya Allison: 

Absolutely. And delivering in a timely manner as well, too. There’s nothing worse than making a customer wait. That if you’re delayed, you know, think of, like, how long it takes for a website to load, right? You’re gonna lose me if that bad boy doesn’t come up as soon as I click on there. Like, there’s the next website that’s gonna load a lot faster and all of that stuff. 

 

Now, do you see AI being leveraged across just B2B? DTC? Do you see there being, like, a role for AI across industries? 

 

Ella Dillon: 

Absolutely. I mean, I think again, with the evolution, like—I think the barriers and the silos between marketing, sales, success are being removed. And I think across industries as well, whether you’re pure SaaS or not, people understand that they have to deliver a consistent experience and that they’re competing not just on a product but on a service level now, as well. 

 

Katya Allison: 

Yeah. 

 

Ella Dillon: 

And I think it’s absolutely applicable and necessary. And even, like, health care and things, you know, people are really, like—right now, when you’re looking at the trends of the great resignation, or the great reformation if you want, you know, people working from home and all of the things. How do you deliver a consistent experience sort of in this emerging world? And this is a really wonderful tool to do that.

 

Katya Allison: 

I love it. Now, what is the most exciting AI capability that gets you excited about the future for brands leveraging AI?

 

Ella Dillon: 

So I think we all talk about personalization. I mean, everybody’s been talking about that. But how do you actually do that one-on-one? Like, I think it’s a marketer’s dream. Like, how do you specifically do personalized marketing one-on-one? 

 

And, I mean, you can sort of do segmentation. And you can figure out, you know, I think I have the right sort of personas. But to actually have the opportunity to actually do one-on-one marketing, where you’re delivering what a customer needs, is, I think, super exciting. And I think it’s, again, sort of where we’re all going.

 

Katya Allison:

Now, what about for the consumer? What’s exciting from the consumer side about AI?

 

Ella Dillon: 

I guess I have it from the same point of view of, you know, if I’m a consumer and I had—I had a very bad bot experience the other day. I was trying to, you know, get something figured out. And honestly, there, I had thought my account was compromised. And I got stuck in this bot cycle, where, you know, I kept erroring out of its script, and it kept sending me back of,  “What product are you trying to do?” 

 

And, of course, my blood pressure started going up.

 

Katya Allison: 

It does! Again, it is that, like, “Oh my gosh, if I have to reset my password one more time—.”

 

Ella Dillon: 

Right? You know, we’ve all had the experiences. You go through an IVA in the phone tree, and you have to—you have to tell your firstborn child’s name and your neighbor’s dog, and then you get through to the human, and they’re like, “Who am I speaking with?” And you do it all over. And you’re just—you’re wondering why you bother.

 

Katya Allison:

Yes. 

 

Ella Dillon: 

There’s not enough system integration on the back-end, etc. But I think the applicability is that—for the consumer—is that we actually start delivering on the promise of, when we have you go and have an experience and ask you, we actually can nurture you. We can actually have this personalized, “I know you. I’m consistent. I’m persistent. I’m real-time. And I’m going to make conversation ready for real so that when I do hand you to a human, you know, they actually know what your intent is and what you need.” 

 

Versus, we just use you for a bunch of analytics, by the way, because, mostly, they do all that stuff so they can capture the analytics and learn later about what the customer experience is. But that customer, in that moment, has their blood pressure up and all that.

 

Katya Allison: 

It’s interesting that you say that because now what’s going through my mind are all of those instances where you’ve called, you know, some sort of customer support, and you know, you’re pressing that zero to just get to a human. And you’re talking to a human, you’ve described it, and they’re like, “Okay, I’m gonna route you here.” 

 

And then inevitably, my questions—I was like, “How much do I have to tell you about the story that I told the person before?” Right, like? Yeah, I think it happens with, like, the human-to-human stuff. 

 

And I think my earlier question where I was, like, “Are we are we kind of, like, losing that connection by introducing AI?” 

 

I would welcome that. I would welcome someone just kind of knowing what I had said beforehand because it’s true. Every time you repeat yourself, it is the blood pressure. It is the, “Oh my gosh, I have no more patience because I’ve told my story three times, and no one’s solving it. And now you’re telling me that you’re going to have to call me back?” 

 

I love it. 

 

So in your opinion, what does it mean to be, though, kind of customer centric in general? And is it removing, like, those friction points? And what do you think brands are just kind of forgetting, especially nowadays, when it comes to the customer experience and keeping things customer centric?

 

Ella Dillon: 

I do think that brands forget it’s an ecosystem, you know? So you say that you really care about the customer, but then someone calls support and waits on hold for eight hours because you haven’t staffed it. There’s a bit of an “I don’t believe you” thing. 

 

You know, so I think you need to really think through and design for, “If we say that we are a customer-centric brand end-to-end, what does that mean?” And you’re designing it end-to-end, you know, it shouldn’t be, you talk to a salesperson, and they’ve promised a bunch of things, then it goes to a success person, and they don’t have any of that data, so the customer doesn’t feel seen, they don’t feel known. What we try and deliver them is completely different than what was promised. 

 

You know, the same thing where a person can’t see any of that, and it’s troubleshooting the wrong product or doesn’t have the right information to actually solve the problem, or your product is inconsistent, or it doesn’t have good uptime. 

 

I mean, again, if you’re really a customer-centric company, then you’re trying to design for all of that. You know, stability, predictability, personalization. I know you. 

 

And I do think a lot of the future of marketing, again for your listeners, is customer marketing is top-line funnel now, right? You know, your cost of customer acquisition, the referenceability of your customers, all of those things. And so if you’re making it very easy for a customer to do their business, all the blood pressure we just talked about before, of how frictioned it was, you know, if you’re making it very easy to get someone’s job done, then I think that’s sort of the power of all of this.

 

Katya Allison: 

Oh, I couldn’t agree more. I actually just—I presented at a virtual event yesterday where we were talking about—I presented the bowtie funnel, right? Like, I think as marketers, we’re always so focused on, like, that top, middle, bottom. 

 

Great, I’ve converted them. Hands are washed. Now they’re a customer. They’ll be a customer forever. 

 

That actually doesn’t happen unless you take that time to invest in that customer. It’s like, it’s a whole different sales cycle. Like, now your goal is, let’s make them brand advocates. Like, let’s make them brand ambassadors, and what can we do—what can we do? What do we put in place, right? 

 

Is it, like, the personalization? Is it the education when it comes to the customers? Is it support? What else do they need? 

 

And I think across the board, regardless of industry, like, if it’s an ecommerce brand, as a consumer, I want to continue to be served as well, too. You know, tee something up so that I continue to purchase from you, but it’s also service-based as well, too.

 

Ella Dillon: 

Yep. Yep. I couldn’t agree more.

 

Katya Allison: 

All right. So it’s prediction time. Okay. What do you see happening in the world of AI in maybe the next year or two? I don’t know if that’s enough time to see big shifts in AI or not?

 

Ella Dillon: 

Yeah, we won’t have Jarvis. I was—I was thinking about, like, all of that. Reading with my daughter–I don’t know if you’ve heard of them, but Thunderhead—Scythe and the Thunderhead. It was like, “Oh yeah, AI and those big dreams.” We’re not going to do that. I don’t think. I’ll be surprised. 

 

But I do think a little bit to your question about how this has helped build relationships with brands. I think the adoption, and the comfort, and the assimilation, and the expectation of AI to be part of your every day, and the welcoming of it—it’ll become just kind of mainstream, like just another tool. And the seamlessness of the human and sort of an intelligent virtual assistant interaction will become, again, I think, fairly normal over the next couple of years. 

 

And I think that people will welcome it, again, because we consider ourselves workforce augmentation. I mean, we’re not here to replace humans. But everybody wants to work smarter, not harder. You know, no one wants to do the same rote activities over and over. Nobody wants to have to go clean up their database all of the time because everything kind of changes. 

 

So if you are leveraging a virtual assistant, that sort of—you know, we have a couple of customers that actually have awards for their virtual assistant, or they have their team meetings and the virtual assistants, like, at the team meeting, you know? It’s pretty cute. Like, they’ve actually made it a part of their DNA. They really have pulled it in as a tool to their success and their customer success. 

 

And I think things like that will make total sense. Again, we’re here for making everything smarter, more personalized, and predictive. And I think that’ll become much more normal.

 

Katya Allison: 

I love that. Now, typically, that’s my last question. But you said something that has a follow-up for me because you use the word comfortable, which I think with AI is—it’s an interesting term. I feel like there’s a level of comfort of use. But do you run into a level of comfort of security when it comes to that? 

 

Like, are you finding that people feel like maybe it’s an invasion of privacy? Or are there security concerns when it comes to AI?

 

Ella Dillon: 

Actually, none that I’m running into. Sort of with our business, it hasn’t been that. I think it’s actually really just an education to how we started the hour. Everybody says AI, and everybody kind of wants to be in AI. But what does it mean? 

 

I think more, it’s the, “Are you Jarvis? Are you workforce automation? Like, what—what is it?” 

 

And I think a lot of the—there’ll be a lot more clarity over the year or two of, like, what actually it is and what it can do and unlock for you.

 

Katya Allison: 

That’s such a good note. Education is, like, the biggest piece of even knowing that. Like, talking to you today and having talked to you before as well, too; it really did open my mind because it’s interesting. 

 

I ask this prediction question at the end of every podcast, and it’s interesting how many people have said, “AI, AI, AI. Like, be on the lookout for AI.” 

 

So it’s nice to be able to now dive into, “All right, what is AI? What are we really talking about?”

 

I can’t thank you enough for coming on here and just taking the time to educate us on AI, IVA, all the terms, all the jargon.

 

Ella Dillon: 

It’s been a privilege. Thank you for having us—having me on.

 

Katya Allison: 

Thanks so much.

 

This was such a fun and interesting interview for me. Really being educated on what we mean when we say AI, artificial intelligence. Ella did such a great job of just kind of explaining that back-end of workflow automation that we typically kind of put under this bucket of AI while also diving into the front-end, the customer-centric side of AI, leveraging IVAs. 

 

What I really walked away with was, there’s still so much more education that really needs to happen for brands and consumers alike so that we can really truly get comfortable with having conversations with AI and, honestly, how you can use it as a brand to create that exceptional customer experience by removing friction points and expediting the time to a satisfied customer. 

 

Want to hear more? Be sure to subscribe to the GRIN Gets Real podcast to get the latest episodes. Give us some stars. I wouldn’t mind five of them. And leave us a review. 

 

Connect with me on social. You can find me on LinkedIn, Katya Allison. And if you’re interested in learning more about GRIN, visit our website at grin.co. Until next time, keep grinning.