What is DRINKS and what problem does it solve for ecommerce merchants?

DRINKS started as a D2C alcoholic beverage company but has since pivoted to a platform that lets any ecommerce site add regulated products like alcohol into their cart sequence. The merchant doesn't have to handle anything — DRINKS manages shipping, compliance, taxation, and age verification. The result: if a site has an audience with high affinity for alcohol (flowers, gifting, grocery, meal kits, media), they can layer in a bottle of champagne or wine with a click, shifting purchase patterns and significantly lifting basket sizes with high-value products.

What is the 95-5 rule and why does Andy say it should change how brands think about growth?

The 95-5 rule (originally a B2B marketing construct) says the majority of your customers are not in the market today for what you sell. Andy's argument: ecommerce teams spend most of their energy optimizing checkout conversion, but they're really just tuning a fixed revenue pool. The bigger lever is changing inter-purchase frequency and visit recency — giving consumers more reasons to come back and bigger baskets when they do. You can lift checkout conversion 1–5%, but you won't solve the fundamental growth problem until you address the 95% who aren't buying today.

How does DRINKS handle compliance, taxation, and age verification for alcohol?

The alcohol industry is still locked into a prohibition-era distribution funnel with state-by-state regulation and taxation complexity. DRINKS automates all of it end-to-end — for both wineries and ecommerce brands. That includes compliance verification, age verification at delivery (driver's license checks), shipping, and tax collection. Andy frames the goal simply: they wanted ordering alcohol online to be easier than buying a Tesla — one click, and the wine is on its way.

Which kinds of ecommerce brands benefit most from adding regulated products like alcohol?

Nearly every consumer-side category, according to Andy. Flowers (UrbanStems) and fashion (Quince) are obvious fits, but he also highlights media (Forbes), grocery, meal kits, premium meat delivery services, and influencer-driven sites like Half Baked Harvest. The thread running through them: brands whose audience has high affinity for alcohol and where a 'better together' proposition creates differentiation and a reason to come back. The pairing has to make sense — a $200 bottle of wine doesn't belong on a $40 flower basket, but the right pricing, packaging, and merchandising (often AI-guided) does.

Why does Andy believe rational decision-making in ecommerce is a myth?

Andy argues one of the biggest myths in ecommerce is that consumers are rational, research-driven, price-comparing shoppers. In practice, people land on a site to buy jeans, get distracted in a boring meeting, and walk away with croissants, a Spritz, and electrolyte sachets they didn't plan on. The implication for merchants: the most powerful job a multi-product brand has is crafting experiences that surprise, delight, and 'open the aperture' on the brand — Quince and Holt Renfrew get this right by extending the buying journey across adjacent categories rather than optimizing a single funnel.

Expert Perspectives

Episode 134

Pampered Chef: Why AI Is a Data Problem, Not a Model Problem

Podcast

Nathan Armstrong

Director of Global Customer Solutions and EU Technology

In this episode we talked about:

Why relying on customer support calls to identify bugs is a losing strategy
How Pampered Chef transitioned from point solutions to a consolidated technology platform
Ways to use AI to simplify internal workflows and business queries
The role of data governance and guardrails when implementing AI
How to evaluate software value and prioritize features during budget cycles
Why the 80/20 rule can help organizations save money while maintaining performance

🎧 Listen now on Apple Podcasts, Spotify, or YouTube

Episode highlights:

1:05 – Identifying issues before customers report them

‍3:28 – Expanding from niche use cases to unified platforms

‍4:40 – AI's role in simplifying internal and external journeys

‍6:59 – The challenge of data structuring and governance

‍9:21 – Moving toward true self-service data

‍11:14 – Navigating budgets with the 80/20 rule

Nathan's Bottom Line: You can keep stacking point solutions and chasing the latest LLM, but everyone's running on the same foundational models — the real differentiator is data structure and governance. Don't try to boil the ocean making every dataset AI-ready; start with the specific business questions your team is burning hours on manually (who are my top 10 performers today, why are customers abandoning this funnel) and let them self-serve those answers without filing a ticket to a tech team for every one.

Nathan Armstrong — Transcript

The Ecommerce Toolbox: Expert Perspectives • Human-Reviewed Transcript

[00:00:00] Nathan Armstrong: People that they're not gonna call us to tell us that they were having a problem adding a product to the cart, not seeing an image. Nobody's gonna call customer support to to let us know those things, and that's where really we wanted to get further into the details to provide a better experience.

[00:00:16] Kailin Noivo: Welcome to another episode of the ecommerce toolbox expert's perspective. Joining us today, we have Nathan Armstrong from Pampered Chef. Welcome, Nathan.

[00:00:24] Nathan Armstrong: Thanks, Cal. I'm glad to be here.

[00:00:26] Kailin Noivo: Nathan, why don't you start with a quick introduction, who you are, a bit about Pampered Chef, and what you oversee?

[00:00:31] Nathan Armstrong: Sure. So I lead director of econ of not director. Sorry. Director of of global, uh, customer solutions and EU technology. So I live in kind of two worlds. One, our customer experience, tech support side of the house, as well as leading our technology strategy for our EU markets.

[00:00:53] Kailin Noivo: Very cool. We got we got hooked up in during the COVID era, so probably twenty twenty one ish era. So talk to me a bit about what the needs were of Pampered Chef when you started initially working with Noiboo.

[00:01:05] Nathan Armstrong: Yeah. Yeah. So it's been been a while now. And, yeah, as we as we stated, when we initially came to Noiboo, we were looking to become more proactive at identifying issues that customers were having going through our funnels on the site that we were unaware of. So people that they're not gonna call us to tell us that they were having a problem adding a product to to their to the cart, not seeing an image, nobody's gonna call customer support to to let us know those things. And that's where, really, we wanted to get further into the details to provide a better experience to the customers and solve those and solve those problems in advance. So that's where we started looking out and came to Noiboo. And, yeah, we started in just post co post COVID in there in the 2021 time period.

[00:01:51] Kailin Noivo: Very cool. And at that time, like you mentioned, people were very invested in buying point solutions. They were trying to solve very specific problems because growth was infinite. But kind of as actually Berkshire Hathaway's Warren Buffett would say, the tide kinda tide came out after, and a lot of things started to to to kind of maybe change. Consumer demand softened in the market in 2022, 2023. How did that lead to maybe a change in how you guys are looking at procuring software, an evolution in in what your needs were.

[00:02:24] Nathan Armstrong: Yeah. So I think a lot of, like, a lot of organizations that are out there of we're always trying to act like act like owners. So money that we spend, treat it like it's our treat it like it's our it's our it's our dollar, and continuously improve, continues to continually to to continue to challenge vendors to do to do more, provide provide more. And that's really where, not just with, uh, with our relationship, but any of our partners that we're constantly looking at, is this solution still providing value the same value that it was when we initially when we initially bought it, or are there now other tools and other partnerships that we have to where we can consolidate those pieces? I think that's that that's just the natural evolution inside of the technology space. You know? People are gonna start with one area and be super successful with that partner to start with, and then things are gonna evolve, and we're gonna mature and look at ways to consolidate those those platforms to leverage to get to leverage that spend.

[00:03:28] Kailin Noivo: Yeah. And just getting a bit more specific as well. Like, initially, when you guys bought us, it was like, we want to solve two main problems. Number one, we have customers calling in, and we don't know what they're talking about. They're talking about errors, this, that. Like, let's solve that problem, uh, with, like, session replay effectively. And then a mutual was like, hey. We wanna understand where we might be behind the ball, and maybe people are calling or they aren't calling, but there's, like, these heavy hitter bugs that are, like, hiding somewhere in plain sight that are hurting our business KPIs. So those are, like, kind of the initial two use cases. Well, then from there early on, you would actually engage us in, like, hey. We have this heat mapping tool that our our product managers are using for user research. So that was kind of a third use case we couldn't support at the time. Obviously, since then, we've launched our page analysis, which is our heat mapping, our scroll mapping, and our journeys, and now the product team's using that. Okay. That's great. We then got heavily into release management. So, hey, as you're doing releases, how do you understand if there's regression? And then we also added in performance, so understanding how your core of vitals are changing over time. So when we start to look at all these things, it was very like, hey. We we we looked at Noibu for a very niche use case, and then we started to see that different needs were popping up in the business that we wanted to consolidate from other other vendors. So that's kind of the journey that we've gone on as well. And, obviously, you guys are still on your technology transformation journey. But, really, for us, it was really important for us to meet you where you guys were going in terms of multiple use cases, multiple teams collaborating out of a single tool. On that note, how are you guys starting to think about, like, how AI might start to change, how you look at tooling, or how you look at workflows?

[00:05:14] Nathan Armstrong: Yeah. I mean, AI, obviously, continued buzzword even as we're now into into 2026. I think, really, the pieces that we're looking at for it is, you know, what are the journeys? What are the tasks both from an internal perspective internal business operations perspective and external operation where AI make where AI makes sense? So, externally, it may be a chatbot. How do you simplify your customers' your customers' experience on your site, getting post purchase help or getting pre purchase help, asking questions about a product, specific specifications in there? So that's one journey that we're looking at. And then two, for internal tools that that we that we use, we want the tools to be, you know, almost, I guess, chat g p to where I can ask it I can ask the tool questions. I don't have to go hunt and pack to find the data that I'm looking for. It understands the query that I'm the query that I'm giving it and bubbling up that in that information for me, whether it's date and time driven, it's on specific order flows that we're talking about, you know, those different pieces in there. That's really where we're looking to also bring AI in to help simplify all of our jobs. It's not about replacing headcount or anything like that. It's about making our jobs easier and allowing us to find that needle in a haystack data point that us as humans potentially can't get to super easy by diving through all that data, but a but AI can help us get there.

[00:06:45] Midroll: If you're listening to the ecommerce toolbox, you're entitled to a podcast exclusive website audit. Go to noiboo.com/podcast-audit for a free scan that uncovers the hidden friction blocking your conversions and shows you where you're leaking revenue.

[00:06:59] Kailin Noivo: Makes sense. On the back of that, what we're seeing is that data structure. So data capture is one thing, and most people have the data. But data structuring in a queryable way is really challenging with, like, data governance. How are you starting to think about that out of curiosity? And I'll talk a bit about how we are, and maybe there's some products actually that you haven't seen yet because I don't think when you're at our last customer advisory board, I don't think we had the mock ups yet. Um, but, yeah, I'm curious how you guys are thinking on the data side because candidly, everyone's using the same foundational models. Claw this, open source that. Like, no one's reinventing the LLM wheel. And if someone's telling you that, then they're they're they're straight up lying, in my opinion, or they're naive. Yeah. And it's really a data structuring problem. But, yeah, curious how you guys are approaching that.

[00:07:46] Nathan Armstrong: Yeah. It's totally totally agree. It it it is that data structure. It is that data governance that we're really looking that we're really looking at. We've gone starting down the initial journey part through some of our partnerships with with a with with AWS in there of really understanding how the data that we're connecting into, are we putting guardrails around that data so that whether it's an internal user or external services that are in there, we're keeping those those queries segmented based on the user's permissions in their AWS offers, AWS guardrails that's part of their AI their AI platform that's in there that allows us to keep that that data governance in place. And then the other pieces that are there as well, instead of looking at it from a technology solution perspective, first and foremost, we're trying to understand what are the business questions that we're trying to answer to help guide us into more of that that data governance piece, that data structure piece. So what data do I need right now? What problem do I need to solve right now versus trying to boil the ocean and say, hey. All of our data is not structured in a way that's going to be success that's gonna make us successful for to for AI in there and instead have it be more point solution to start with to learn and continue to grow and evolve from there.

[00:09:16] Kailin Noivo: What part of the data flow are you most interested in solving first out of curiosity?

[00:09:21] Nathan Armstrong: I think the big piece for us is simplifying some of the basic business questions that our internal team member that our internal teams have, and how do we make it easier for them to self serve without uh, you know, I think a lot of times we talk about self-service in there, but self-service still means that I've gotta build a business case. I've gotta send it to a tech team to go and and build those pieces out. Instead, we see AI being the the AI tooling, being able to help the business just have access to the data that they need and answer those and get answers to those structured questions without having to build specific use cases for every single thing that they're trying to get information on.

[00:10:02] Kailin Noivo: Yeah. I think we even see that in our in in our business as well. Like, if you're for example, let's say you have an analyst like, hey. What are my top performing products selling in Florida? Before you would take that, you'd send it to an analyst. They probably have to pull data from multiple sources, put it in Excel, clean the data, run it, and then you have that at a point in time. And if you wanna check it again tomorrow, you have to redo that work. Right? Yeah. I think, like, AI is really, really good at that.

[00:10:30] Nathan Armstrong: Yeah. And for us, I mean so paper you know, being a direct sales being a direct sales company, we have consultants spread across across the across the globe. So being able to get easy access to, hey. Who are my who are my top 10 performers today, last month, this year, and what makes them a top a top 10 performer? Being able to ask those simple business questions and get feedback real time versus taking hours or days is super key for us.

[00:10:58] Kailin Noivo: Yeah. That's that's really, really cool. As you're looking, budgets aren't getting looser for most of our businesses. Expectation for SaaS providers are are increasing, especially with your AI story. What are kinda some of your top themes for this year or when you're doing your budget for this year?

[00:11:14] Nathan Armstrong: Yeah. I mean, it's always it's always do more with do more with less or show me the value that you're able to deliver with the same dollars or a higher amount of value that you're able to deliver with dollars. Nobody's ever willing to just open up the pocketbook and continue to dish out dollars unless you're giving unless you're giving value back there. So we are continuing down the similar paths to understand what are our vendors doing, what are differently this year or in the subsequent years that match up with some of our journey pieces as we talk about AI. What are you doing to simplify the the experience? What are the new pieces that you're getting into that help us better problem solve conversion ish conversion problems, increase, you know, carts cart sizes, limit the amount of tools that that that we have. So if I can do the same thing inside of one the one tool and get 80% of the features and functionality that I had from a different tool, I'm gonna play the eighty twenty rule all day long and go down that route to save a few dollars and deliver that that same value to be able to then focus my time in other areas.

[00:12:22] Kailin Noivo: That's literally what I'm consistently hearing. So now that's such a good good framing. This was great to catch up. Really, really, really appreciate you hopping on and talking through your journey with us and kind of how you your use cases are evolved. And I'm excited to show you what we're working on on the AI side as well because I think, uh, a lot of what's in your head is is kind of what's in ours. So I appreciate you hopping on and being such a great partner.

[00:12:44] Nathan Armstrong: Yeah. Awesome, Kellen. I appreciate it. Thanks for having me.

[00:12:48] Midroll: The ecommerce toolbox expert perspectives is brought to you by Noiboo. To find out more about Noiboo and how we can help you debug your ecommerce site and rocket your revenue, visit www.noibu.com. That's noibu.com. And then make sure to search for the ecommerce toolbox expert perspectives on Apple Podcast, Spotify, or anywhere else podcasts are found, and click subscribe so you don't miss out on any future episodes. On behalf of the team here at NOIBO, thanks for listening.

FAQ

Pampered Chef came to Noibu in 2021 to get proactive about the issues customers were running into but never picking up the phone about — broken add-to-cart flows, missing product images, silent friction inside the funnel. The relationship started narrow (session replay for inbound support calls plus surfacing high-impact bugs hurting KPIs), then expanded as Noibu's platform grew. Page analysis (heat maps, scroll maps, journeys) is now used by the product team for user research, release management surfaces regressions tied to deploys, and performance monitoring tracks Core Web Vitals over time. Nathan's framing: they bought Noibu for a niche use case, then consolidated several other tools into it as the platform matured.

Nathan splits AI into two journeys. Externally, it's about simplifying the customer experience — chatbots that handle pre- and post-purchase questions, product specs, and order help. Internally, it's about making business tools feel more like ChatGPT: ask a question, get an answer, instead of hunting and pecking through dashboards. He's explicit that this isn't a headcount play — it's about making jobs easier and surfacing the needle-in-a-haystack data points humans struggle to find at scale.

Everyone's running on the same foundational models — nobody is reinventing the LLM. The real differentiator is whether your data is structured, queryable, and governed. Pampered Chef is tackling this through AWS guardrails that segment queries by user permission, so the same AI tooling respects who's allowed to see what. Just as important: Nathan's team refuses to boil the ocean. Instead of restructuring all of their data upfront in the hope of being "AI-ready," they start with specific business questions worth answering and let the data work follow the use case.

Nathan's critique: most "self-service" still means a business user has to build a case, file a ticket, and wait for a tech team to build a dashboard. For a direct sales company with consultants across the globe, that's too slow. Real self-service means a sales leader can ask "who are my top 10 performers today, last month, this year — and what makes them top 10?" and get an answer in real time without anyone touching SQL or Excel. That's the bar AI tooling has to clear to actually change how the business operates.

Nathan's team treats every dollar like it's their own and runs the 80/20 rule hard against the stack. The two questions every vendor has to answer: is this tool still delivering the same value it did the day we bought it, and can another partner now cover what it does as part of a broader platform? If a single tool can hit 80% of another tool's feature set, that's the consolidation play — save the spend, redirect the time, and push the remaining vendors to do more, especially on the AI side.