Insights Unlocked
How AI + user feedback transformed puppy training with Zigzag's Jack Mitchell
Mon, 10 Mar 2025
Episode web page: https://tinyurl.com/ywpva5x5 ----------------------- In this Insights Unlocked episode, host Michael Domanic sits down with Jack Mitchell, Head of Product and Operations at Zigzag, the #1 puppy training app. Jack shares how his team built Ziggy, an AI-powered virtual puppy trainer designed to provide personalized, science-backed training advice at scale. Jack explains that scalability was a major challenge for the certified B-Corp startup. Originally, Zigzag provided WhatsApp chat support with real dog trainers, but as the company grew, this wasn’t sustainable. They needed a way to offer science-backed, consistent training advice while still maintaining the personal touch that users valued. Additionally, user behavior played a key role in the decision to develop an AI-powered chat. Zigzag found many customers hesitated to ask simple questions because they didn’t want to bother a human trainer—even though they were paying for the service. AI eliminated this friction by making users feel more comfortable asking quick, everyday training questions. Zigzag is continuously refining Ziggy’s AI model based on user feedback, with a 96% positive rating of the feature. "Trust is key," Jack said. "Users need to know they’re getting the right advice—every time." Looking ahead, Jack said they aim to expand Ziggy’s capabilities and explore how AI can further improve the pet training experience. "The goal is to keep making the AI better, smarter, and more useful for puppy parents everywhere," he said. Key Takeaways: 🐶 AI as a training tool – How Zigzag built Ziggy to provide AI-driven, real-time puppy training advice. 📈 From concept to execution – The strategic and technical hurdles in implementing AI, from user feedback to content validation. 🔄 Engagement & retention – How and why AI chat features have increased user interactions and overall app retention rates. 🎯 Measuring AI’s business impact – The importance of A/B testing, user feedback, and ROI tracking to validate AI investments. 🚀 AI Beyond the product – How small teams like Zigzag use AI to streamline internal workflows and accelerate product development. Episode links: 🐾 Try ZigZag: Download the app (if applicable) 💬 Connect with Jack Mitchell on LinkedIn: Find him on LinkedIn 🏁 A comprehensive guide to A/B testing: A comprehensive guide on conducting effective A/B tests to enhance user experience and conversion rates.
Chapter 1: What is the main focus of this episode on AI and puppy training?
Welcome back to the Insights Unlocked podcast. Today, we're exploring how AI is revolutionizing puppy training with Jack Mitchell, head of product and operations at ZigZag, the number one puppy training app. Jack and his team set out to solve a big challenge. How do you provide expert science-backed puppy training at scale while keeping it engaging and personalized?
The answer, Ziggy, Zigzag's AI-powered virtual puppy trainer. But building trust in AI-driven training wasn't easy. Jack will share how user feedback played a crucial role in shaping Ziggy, helping the team understand what pet owners needed most, where they hesitated to ask for help, and how AI could make training more accessible.
We'll also dive into how ZigZag measured AI's impact with A-B testing, tackled user engagement challenges, and ensured their AI solutions delivered real business value. It's a fascinating look at the intersection of AI, UX, and pet care. So let's get started. Enjoy the show.
welcome to insights unlocked an original podcast from user testing where we bring you candid conversations and stories with the thinkers viewers and builders behind some of the most successful digital products and experiences in the world from concept to execution
Chapter 2: How did Zigzag transition from human trainers to AI with Ziggy?
Welcome to the Insights Unlocked podcast. I'm Nathan Isaacs, senior manager for content production and user testing. And joining us today as host is Michael Dominick, user testing's head of AI. Welcome, Michael. Hey, everyone. And our guest today is Jack Mitchell, head of product and operations at ZigZag. ZigZag is the number one puppy training app. Welcome to the show, Jack. Thanks, Nathan.
Jack, thank you so much for joining us today. Look, as we get started, I'd love for you to tell us a bit about your career journey. Tell us about Zigzag. Tell us about the work that you do there.
Of course. So I've spent over 10 years in B2C startups. So I started out at a scaling fintech credit report company. And during my eight years there, like pretty much anyone on the planet, I got a dog in lockdown. and had the opportunity to join Zigzag as a second employee. Zigzag is a 20-person B Corp, and we're a corporate-backed startup.
Our users describe Zigzag as Duolingo for dogs, so we offer users daily bite-sized lessons and AI-driven puppy answers. So, you know, what does this mean? I've gained deep expertise in user-focused growth and rapid scaling, and now applying that know-how to create engaging science-based training solutions here at Digvac.
So, Jack, before we unpack all of that, what's your dog's name? What kind of dog do you have?
My dog's name is Peanut. She's a four-year-old Labrador. I didn't have zigzag during puppyhood. So, you know, when I found zigzag, I was like, this would have been great during those early years. You've really got to get started early. So, yes, still have a few underlung issues to resolve. But, yeah, I can get a little bit of help with our training coaches and the app.
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Chapter 3: What challenges did Zigzag face in developing an AI-powered puppy trainer?
Yeah, fantastic. So putting the focus back on Zigzag a little bit here. So Zigzag was among the first dog training apps to integrate a generative AI chat. So can you tell us a bit about that feature? Also, it'd be great to hear what were some of the biggest technical and strategical challenges that you face in developing that AI powered solution? How did you overcome those challenges?
So tell us a little bit more about that.
Cool. So Ziggy is a cartoon AOM coach trained by real dog trainers and is personalized to each dog's profile when they sign up to Zigzag. So when we built it, the biggest strategic challenge was that we were in growth mode and still are. So at the time we had a WhatsApp chat support for all real life training coaches. And as you can imagine, that's not really scalable as we grow as a business.
So we needed to find a way to provide science-backed advice through a scalable method while still incorporating that core value proposition for our users.
The technical challenge there was balancing personalization with a need for consistent answers that were validated through proof of concept testing from a technical side, but also getting trainers to review the answers to ensure the responses given were suitable to our training methods, but also science-backed.
So in terms of how we overcame these challenges, first, it was all about continuous user interviews, really knowing what problems our users were coming to solve, looking at the data that we already had around that. And we found that out through things like chat tagging, As you can imagine, collaboration was key throughout that process.
So we had data people, we had non-technical dog coaches, we had engineers and product designers. So it was really about bringing all those people together to understand that problem and deliver a relevant solution. And then on top of that, we needed to deliver an AI solution which uses trust. So why wouldn't they just go to chat GPT rather than us?
So that solution testing was really important throughout that process to make that not only trusted, but engaging and valuable.
Yeah, can you unpack that a little bit? Because I think I often have the same question when using a product's AI features is, why am I using this as opposed to just going to one of the frontier models directly? So why would someone use your feature as opposed to going to ChatGPT?
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Chapter 4: Why is trust important in AI-driven solutions, and how does Zigzag build it?
And that's really through continuous data gathering, whether that's through our user interviews and qual research or the quants research we get on a continuous basis through things like ratings.
Yeah, that's fantastic. Yeah, you touched on something that I've been thinking about through all of this and I think about this wherever companies are integrating AI features is that trust thing, right? A lot of folks approach AI not knowing what to trust and what level of trust that they should be putting into it.
Are you noticing a change in the level of trust that people have in the product as they continue to use it?
In terms of the feature itself or just generally the product?
the AI feature itself?
I think people just learn more and more how to engage with it. So once they've used it, once we see high retention, what we found is that You know, our response usually really traps our responses and we get really good positive ratings. We have a 96% positive rating with our responses because we capture feedback every time we give a response. That's great.
So yeah, the trap is certainly there and hasn't diminished.
Yeah, I think another thing that you touched on that was really interesting to me as well is I guess for the traditional kind of chat feature where folks are engaging with an actual human trainer, they feel a little bit of hesitation to reach out to those individuals for silly questions.
So yeah, there's just kind of like a whole can of worms, I guess, that can be opened up with the way that users engage with your product if they just feel like they're not actually bothering someone, they're just asking an AI chat. So that's interesting. I mean, are you seeing a lift in engagement from folks because they know that they can just engage with a AI chat feature as opposed to a person?
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Chapter 5: How did user feedback shape the development of Ziggy?
Very early on, this was 18 months ago now, that solution was a great match for problems our users have. When we go back to the business side, we knew we were going to A-B test this feature right from the start. So we needed to know that it would drive real business value for us.
And obviously that maps to, you know, solving real pain points for users, like training confusion and things like a lack of guidance or, you know, coming in to solve a biting issue, stuff like that. So that relentless focus on those results and that sort of qualitative insight that maps to that is really important. And having clear success metrics from the outset.
So whether that's subscription rates, just general usage and feedback ratings. So when we write up a design brief, that is front of mind for a product designer to really design with that in mind. In terms of the key takeaways, I'd say, you know, I think every founder, what most founders will go, yeah, I need like an AI roadmap and we're going to just like nail that roadmap.
So, yeah, it's not something that... Yeah, you should do just for the sake of it as a business. But yeah, if you are going to try anything out, yeah, make sure it solves those problems. I'd advise starting small, iterating quickly and tracking that usage, getting those feedback loops from the outset. And yeah, use that to continuously refine any AI features that you build.
Because yes, it can be relatively straightforward to set these things up. But to those points, you need to get usage. There's no point in building a feature that is dead in the water and no one uses from the outset.
Yeah, Jack, that's great that you guys are tracking some metrics around success. So there's this conversation that happens across the AI landscape, the AI product landscape, and that's this conversation around ROI, right? Like every company, again, we're trying to figure out how do we integrate AI into our products, into our services, into our companies, right? To help us
do things better do things faster more efficient um and then there's this conversation around like what is the roi of this so can you just double click on what those success metrics are that you're tracking and is there any kind of like tight roi calculation that you attach to those metrics as much as you're comfortable sharing yeah yeah of course the key
Those two key metrics we were looking at on top of things like usage, but primarily one was about reducing our load on the training coaches. So we needed to create a solution that enables us to scale and maintain some of our core feature sets as a key selling point.
And the second was when we A-B test, we're A-B testing around things like subscription rates and average revenue per user, ultimately lifetime value. So we A-B tested the version with AI chat versus the version that had nothing. So we knew categorically what impact that had to subscription rates, our average revenue per user, and our other core metrics. So yeah, having...
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Chapter 6: What impact has Ziggy had on user engagement and retention?
Especially in small business, it's a lot more possible to do this stuff. Yeah. Working out what you can do as an individual within a business is probably one of the main things because the technology is advanced the whole time, so you can do more and more stuff. And there's loads of cool new tools, things like Craftful, which you can use at product teams throughout the development process.
So looking forward into the future, obviously we're looking at a very quickly evolving space, right? So as AI continues to evolve, how do you plan to keep refining your AI chat experience to further enhance the experience that your users receive from it?
Yeah, so continuous metric tracking is key to what I'm doing and what we're doing as a team. We're only refining Ziggy based on real interactions. So where we see opportunities and we're looking at what responses people are giving our AI in terms of both the questions they're asking, but also the responses the AI gives. That's getting reviewed the whole time to enable us to improve the model.
but also looking at expanding into things like new problem spaces where we can add a lot of value for our users, and even exploring about how it can fundamentally define how we're building the product experience as well.
Like, you know, you're going to see, I think we're going to see startups who just build things in completely different ways because the way that incumbents have built it just don't make any sense. It's a lot easier to build and build a lot of interesting stuff.
I generally say our approach is we're very data informed and combining that with user focus enables us to keep track on an ongoing basis of how users are engaging with specifically Ziggy, our AI feature, but also doing it in a way that generally helps dog owners and that.
In our stage of business, that isn't necessarily all... A part of that would be about improving what we have, but another side of it is solving new problems as well.
Yeah. So I think what I'm hearing is a lot of generative research for generative AI features.
Yeah. Yeah. It's really massively important for us. I think, yeah, that... It's marrying those two things up as, you know, it enables you to jump when it makes sense to jump and act quickly when having that generative backing really allows you to play around with all this cool new stuff that's coming out the whole time.
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