Don’t Give Customers Away. Keep the Delivery Promise — with Chris Rupp

"Listen to your customers and understand what their use cases are."

Chris Rupp, who launched the first-ever Amazon Prime Day, says this idea matters more to last-mile delivery than most businesses think. Understanding use cases means knowing whether a customer needs delivery in an hour or whether the business needs better exception management when something goes wrong.

On this episode of Deliver: The Last-Mile Performance Podcast, Bringg CEO Guy Bloch and Sales Engineering Lead Raquel Zanoni sit down with Rupp to discuss why delivery promises fail in execution, what promise reliability looks like as a metric, and what AI readiness actually requires before tools can make an impact.

Rupp currently serves as a board member at Canadian Tire. She was EVP and Chief Customer and Digital Officer at Albertsons, Chief Customer Officer at Victoria's Secret, VP of Amazon Prime, VP of Global Fulfillment at Amazon, and GM of Xbox and Microsoft Store. Beyond her work on Prime Day, she expanded Amazon's global fulfillment footprint from four to nine countries.

Watch the full interview to learn:

• Why delivery promises break in execution, not in boardroom strategy, and how one holiday weekend at Albertsons exposed the gap
• Why promise reliability matters more than same-day speed, and how retailers should start with wider windows and tighten from there
• The real story behind the first Prime Day and the cross-functional alignment it required
• Why same-day delivery survived in some categories and collapsed in others
• What AI readiness actually requires before tools can make an impact
• Why most retailers can't measure incrementality, and how that leads to political investment decisions
• Why CEO sponsorship, not delegation, determines whether CX and operations teams align

Get the key takeaways and full transcript below. Subscribe on Spotify, Apple Podcasts, or YouTube to get the latest episodes.

Key takeaways

Delivery promises fail in execution, not in strategy

• Retailers usually do not lose the customer in planning. They lose the customer in execution, where labor shortages, demand spikes, and local constraints break the promise.
• Chris shared an Albertsons example where a holiday surge and staffing gaps led to poor order accuracy at the store level.
• The solution was not simply more labor. It was better control over incoming orders based on real operational capacity.

This is the core operational lesson from the conversation: strategy can look sound at the top, but delivery performance depends on what happens in real time on the ground.

Promise reliability matters more than delivery speed

• Chris frames delivery as a contract: deliver what was promised, when it was promised.
• Customers do not see late or incomplete orders as partially successful. If timing or accuracy fails, the promise fails.
• Her recommendation is to start with a wider, realistic delivery window, meet it consistently, and tighten it over time.

The broader point is that retailers build trust through consistency, not headline speed. Reliability is what protects loyalty.

Same-day delivery only works when the use case justifies the cost

• Many retailers expanded same-day delivery during COVID, but not every category could sustain the economics.
• Same-day pickup endured because it fits store operations more naturally and does not add transportation complexity in the same way.
• Same-day delivery remained viable in categories where urgency truly matters, such as beauty and event-driven purchases.

Delivery strategy should reflect customer behavior and category economics, not competitive imitation.

Cross-functional alignment requires CEO sponsorship

• Customer experience leaders often push for growth, while operations leaders focus on cost control.
• Those priorities rarely align on their own, especially when teams operate with separate incentives.
• Chris argues that CEOs must sponsor the trade-offs directly if they want the organization to move.

When executive leadership delegates alignment instead of owning it, transformation slows down.

Incrementality is the metric too many retailers still cannot prove

• Chris argues that many retailers still cannot prove whether delivery investments create net-new demand or simply shift existing demand.
• Without incrementality data, decisions around faster delivery, tighter windows, or new service models become political.
• She connects this directly to customer lifetime value, which should inform operational decisions, not just boardroom reporting.

Better measurement leads to better investment decisions. Retailers that cannot prove impact struggle to scale the right delivery strategies.

AI cannot fix broken processes or bad data

• Chris is direct that AI only works when the underlying processes are standardized and the data is clean.
• Without that foundation, organizations simply automate confusion and produce low-trust outcomes.
• She also points to internal adoption as a key factor, since teams that do not trust AI will resist using it.

AI readiness starts long before the model. It starts with operational discipline, data quality, and organizational buy-in.

Real-time decision systems will separate leaders from the rest

• Chris points to Amazon and Walmart as examples of retailers already making fulfillment, payment, and delivery decisions in milliseconds.
• These systems detect problems mid-order and adjust before the customer feels the disruption.
• She believes those capabilities will become more accessible to mid-market retailers as costs continue to fall.

The long-term takeaway is that real-time orchestration will become a competitive advantage. Retailers that invest early will be better positioned to protect both margin and customer experience.

What is a chief customer officer and why does the role matter?

Guy: The chief customer officer role is relatively new in retail, and it means different things to different companies. In some organizations, it sits close to marketing. In others, it spans digital, logistics, and services. Chris, throughout your career, from Microsoft to Amazon, Albertsons, and Victoria's Secret, you've been at the intersection of customer experience and complex logistics. What is the role of chief customer officer, and what originally drew you into it?

Chris: This was a role made for me because I've always enjoyed the psychology of customers, figuring out what they want and how best to give it to them, going back to when I was an assistant buyer at Sears in the '90s. As I went through my career, I spent a lot of time analyzing the customer, using data, going on the sales floor, getting close to sales associates who understood what customers were doing. I've learned that if you focus on what they're trying to solve, and then, in Amazon fashion, work backwards from the customer, you can figure out what to do back in corporate headquarters to serve them better.

The chief customer officer is responsible for tying all of those functions together so that we don't show up in front of the customer like an org chart. The fulfillment teams drive to get costs down and efficiency up. The marketing teams think about how to get more customers through the door. The merchandising teams think about how to bring great [product] to the customer. But somebody's got to bring it all together so that when it shows up, it's an integrated promise and solution. That's the role of the chief customer officer.

Where do CX strategy and last-mile execution fall out of sync?

Guy: Many retailers design customer promises like delivery speed, time windows, and tracking in one part of the organization and execute them in another. That disconnect grows as a company scales, and it often surfaces as a broken delivery experience that customers see but leadership doesn't.

From your experience, where do you see the biggest gap between customer experience strategy and last-mile execution inside a large retail organization?

Chris: A couple of places. The first is when the store team doesn't know what great looks like for the customer and it's left to interpretation. The store team is thinking about what a great order looks like. They might decide, "Hey, you've ordered a hamburger for dinner tonight and we don't have the exact thing you ordered, the 80/20 in the one-pound serving. If I can't fill that exactly, maybe I should just cancel that order so you're not mad at me." But if I've ordered five other things and I'm trying to put together taco night, I really want the ground beef. It's okay if you substitute something else. Helping store teams understand what great looks like for the customer is very important.

And then fulfillment teams will often optimize for things on their direct scorecard, which would be speed and cost. But accuracy may not be on their plate as something important to them. And if it's not, then they're not optimizing for the whole customer experience. These things don't break down in strategy. They break down in execution, in front of the customer, when you don't have all of those fine details thought through and the organization aligned around what great looks like.

Why delivery accuracy matters more than speed

Raquel: I have a follow-up to that because you mentioned accuracy. We just released a report around customer expectations, and we found that speed and price are still important, but they're not as important as accuracy, which we define as getting the thing you want when you expected to get it. Is there a customer promise as it relates to accuracy that you see repeatedly fail once companies try to scale it?

Chris: It's in the precision. Delivering in two days is easier than delivering today in a two-hour window. By the time you've got a tight window and it's got to be today and the customer ordered just a couple of hours ago, you're going to find labor issues where capacity is going up and down, whether it's in store or in the delivery network. Those things make it difficult to hit [delivery] promise accuracy and might also jeopardize the accuracy of the whole order if you're rushing through it to get it out on time.

The breakdown of those two things is what makes the customer upset. You said you'd get it here in this two-hour window. You said you'd bring me the five things for taco night. And somehow you showed up without all five things or too late for me to make dinner. All of those things are upsetting.

A great example: when I first got to Albertsons, we noticed a failure rate that was too high on on-time, accurate orders. Our orders were mainly getting shipped the next day, not the day the customer placed the order. So we had time to look at that order and try to get it right. But we had this one weekend that was really low in terms of accuracy. I wanted to get deep in the details. I went to one store and had some conversations.

It was the Fourth of July weekend. They were supposed to have a certain number of people on staff, let's say six people to pick orders. Two of them called in sick; teenage boys, their moms took them camping for the weekend. And then Fourth of July fell on a different day of the week than the year before. It was a Saturday morning, and the store got slammed with orders between 7 a.m. and 10 a.m. pickup because everybody was leaving for their camping trip.

Between those two things, they didn't have a chance of filling all those orders and were having to cancel just to let customers know. You can imagine how many disappointed customers we had. But that was back in 2020. What we figured out right away is that we needed a way to throttle the orders so that if two people called in sick, we just took fewer orders in the first place, and we only had so many orders we could take in a given hour.

If a large order came in, it might fill several order slots as opposed to just one. And when we got that system running, our accuracy went through the roof.

Why same-day delivery failed for some retailers and survived for others

Raquel: That's interesting. I know you said 2020, which is the year everyone jumped on the delivery boat. There were a lot of retailers launching same-day initiatives. Victoria's Secret was probably one of them. Lululemon. All of these brands launched same-day [delivery]. But many of them didn't maintain it. They tested the waters in a few markets and very few kept it. I'm curious if you have thoughts on why those experiments didn't work…or what they realized that made it fall apart.

Chris: There's a difference between same-day pickup and same-day delivery. In many places where same-day delivery didn't hold, the store decided to keep same-day pickup available. It's not difficult inside the store environment to have sales associates picking orders as they come in and setting them aside for a customer to pick up. What got more difficult was arranging the transportation and how expensive it was, and whether the customer was really willing to pay for that portion of the order.

A lot of stores decided that customers were more willing to say, "Look, I have a gift. I need it today. I'm happy to place the order online to reserve the inventory for myself. I'll run over there and grab it." That's probably what took root more in the fashion arena. Whereas in groceries or other categories, if you can't go pick up your groceries, delivery is a very important thing. But in the fashion arena, it's a different psychology to the purchase.

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The cross-functional alignment behind the first Amazon Prime Day

Guy: Let's talk about cross-functional alignment between customer experience, fulfillment, and last-mile teams. Easy to say, hard to do. In reality, these groups often operate with different goals, different data, and different definitions of success, which leads to stalled decisions and inconsistent experiences.

Going back to the Amazon days, launching Prime Day required massive alignment between marketing, customer experience, fulfillment, and last-mile teams. What did that coordination look like behind the scenes?

Chris: One of the most important debates we had in designing Prime Day was what day of the year to have it. I had done a bunch of research and concluded that Amazon was already doing a great job winning customers during the holidays but was doing less of a good job winning customers at back-to-school. That was much more local. Walmart, Target, and other retail chains were doing a much better job in back-to-school than we were.

So I thought, if we're going to have a Prime Day, a day to give members these great discounts across the assortment, why don't we do it at a time where we have something else to prove? I wanted to put Prime Day in mid to late July, ahead of back-to-school, so we could stand alone in that time and space and win the customer's mind pre-back-to-school.

When I went to the operations team and said that, they said, "No way. We build warehouses for the holidays. We need to do this in November." And I said, "Wait a minute. We're already winning at Christmas, and I know that's when we'll have more capacity for an event like this. But what we really need to win is more hearts and minds, and we can do that in July." We had this big debate about November versus July. My point was, if you put it in November, all you're doing is taking their Christmas spending and moving it. It's not going to be as incremental as it would be in July.

Eventually I won the argument, and we had that first Prime Day on July 13th. It did an amazing job at winning hearts and minds for back-to-school.

Now, you asked about the coordination work. That was just the beginning of it. Operating an event like Prime Day, all the merchants brought in the best deals of the year. We checked every deal to make sure a better deal hadn't happened in the prior six months because we wanted to make sure customers were getting real value. And then the day of, it was more like working in air traffic control than working at a retailer. You have all of these systems throttled around the capacity to handle the traffic and the orders, not to mention all the shipping capacity. It was a constant throttling and moving around of deals.

Our traffic was so much higher than we expected that first Prime Day. We ended up doing more business on July 13th than we had on Black Friday six months earlier. The whole time, we were a little nervous that the site was going to go down and we'd stop serving deals to customers. But it didn't, because we just kept moving things around until we could manage the traffic properly. We had a war room set up working around the clock for two days because this was a one-day event, but it happened around the world in different countries at different times.

How retailers align CX and operations around shared goals

Raquel: I want to go back to the debate between you and operations and the vision you had for what Prime Day was going to be versus what they wanted. I think we can expand this beyond Amazon. Where are other examples where you've noticed this disagreement between customer experience and operations? And how do you force that decision?

Chris: Working in any big company across many teams, and I've done that several times, the very first thing that's important is that you have some form of aligned goals. It's really difficult if you have an operations team who only cares about cost, and then within the cost structure they've built, there are different speed requirements.

Very strong companies will say, "Look, we're going to have this portion of our business at two-day shipping or same-day shipping because the customer demands it. It is a higher cost. But within that channel, how can I reduce cost?" So they're at least looking at a portfolio of shipping services. Companies that are newer to this started out looking at it as pure added cost. They didn't understand the upside to the business.

Over time, it's been proven enough that if you reach customers through a different channel, and a different channel can just be a different speed of delivery, you're actually adding incremental business. It's all about the metrics at the very beginning. Is the senior-most team aligned on what the overall company has to accomplish? And are they all measured on those metrics such that they can come to aligned decisions? It works if that happens. It does not work if it doesn't.

Promise reliability is the most important last-mile metric

Guy: I want to pick on the metrics, specifically the metrics when it comes to unified customer experience and last-mile operations done well. Is there a specific KPI or set of KPIs that best reflect unified success?

Chris: The way to think about it is promise reliability. I do what I said I was going to do, which is both about time and about the products you receive. It's not just one or the other, because either one is going to upset me at this point. You have entered a contract with me if I am your customer. You have told me that you're bringing me the things so that I can have taco night in time for taco night. So please don't show up at seven o'clock. Please don't show up with half the order. Either one of those things is completely wrong to me, not partially right.

The more teams can look at overall promise reliability, "Did I do what I said I was going to do?", the better. And then don't just look at the fact that you're 99% right. Spend all your time on the 1% that’s wrong and fixing those use cases. Because that scales up into an operation that really keeps trust.

Raquel: You mentioned "don't show up with 80% of my order." You also mentioned this bottleneck at Albertsons that was adding friction to the customer experience. Those were two great examples of where this friction shows up. Can we hear a little more, maybe going back to Prime Day or a different initiative, where those friction points showed up and you had to scramble to find a way to remove them? And what impact did that have for your customers?

Chris: It's one of those things where it's such an everyday activity that there are hundreds of examples. Let's talk about substitutions, because we haven't covered that yet. If you can't deliver exactly what the customer asked for, you can make decisions on the fly that will still save the order and save the customer experience. But you really have to be inside the psychology of the consumer to do that. Sometimes it's obvious: if you have the five things for taco night, a different form of ground beef would be fine. Go ahead and substitute that.

But sometimes it's a lot less obvious. What if you don't have any iceberg lettuce? Can you put arugula on your tacos? Nobody really wants to put arugula on their tacos. This is a specific case where technology can really help. First, there's asking the customer what they'd want substituted, so you already know in advance. There's letting the picker call the customer right there in the moment and ask, which would be better than getting the order wrong. But then there are also many other contextual things you can bring to bear on that order. You wouldn't want every picker making these decisions independently, but you can surface it in technology. When a picker comes down to, "I don't see the exact form of lettuce the customer asked for," AI can look at that order, look at prior order history from that customer, and suggest a good alternative. Using technology all the way through the experience to flatten out those points of friction is what the best of the best do to make orders more reliable and accurate.

What last-mile AI readiness and its impact 

Guy: Let's go to the unavoidable topic of AI. From what we see working with so many customers in the market, many organizations invest in AI-driven tools before they align the teams and processes those tools depend on. In many cases, it actually adds complexity rather than removing it. How do you see AI shaping the relationship between customer experience teams and last-mile operations over the next few years?

Chris: What I think is fabulous about AI is that you hear about what agents are going to be able to do. My agent's going to have these goals, your agent's going to have those goals, and now we're going to send our agents in to negotiate with each other and come to different solutions. That's really interesting to think about.

But if I take a step back and think about what has to be in place for AI to be successful, first, you've got to win some hearts and minds. Every person has heard that AI is coming to take their job. That's how it's positioned in people's minds. Good luck getting AI initiatives across in a place where the news has told people that AI is coming to take their jobs, because they'll give you every reason why it won't work and they just won't use it. So the first thing you've got to do is make people see how much more efficient their day can be by using the simple AI tools. Help me write my emails back instead of me having to think about everything I want to say. Just the simple ways of using AI to get comfortable with it.

Once you do that, you need to get the organization aligned around what the right inputs for AI are. It's not about going out and buying the first cool tool you see, because your organization's not ready for it if your processes aren't standardized and your data isn't clean. If those two things aren't in place, AI cannot fix what systems have already broken. It's the same old garbage in, garbage out that software has been for decades.

Organizations getting ready for AI need to first get their team bought in that this is a good thing for the company, a good thing for the customer, and a good thing for them. Once they've bought in, you've got to do the hard work of getting the structure of your data right and clean. And you've got to make sure your processes are standardized, because you can't automate what isn't standard in the first place. Once those things are in place, now you're ready to roll. AI can be very exciting for what it can do inside your company to make you more efficient, more creative, and help you scale better. But first, you've got to get some of those standard things right.

Guy: Based on your time in global fulfillment and digital transformation, where do you see AI making the biggest short-term impact? Is it in prediction, routing, forecasting, service?

Chris: There are two exciting ways to think about what AI is going to do, and I'm going to have a hard time picking between them. Maybe I'd say forecasting, only because the value of inventory and the value of labor are usually two of the biggest line items on your P&L. Just having a little bit of inventory savings, a little bit of time savings in labor, it really can have a big impact on the P&L.

It would be hard to ignore those things. But those are still things AI is going to do inside of a system. If you have a forecasting tool, a SaaS tool, that tool is getting better by the minute. Because if it isn't, all of its competitors are. There are going to be great tools available where AI is ingesting your data and making your forecast better and better.

However, here's where I think the real value of AI is going, and it's not really in the short term. When you think about companies like Amazon and Walmart, the biggest companies, they have all of that software. It's often homegrown, but sometimes it's SaaS they've purchased. The difference is in the way those companies have woven those things together to coordinate across systems and make decisions in real time. Medium-size companies haven't done that. There's usually a data lake somewhere where you can do batch jobs and make some decisions, but it's not happening in the moment the customer is going to make a payment or place an order.

If you have a real-time coordination system the way those big companies do, when the customer is standing in line to place an order, whether online or in a store, and something goes wrong, the inventory isn't there, the payment doesn't go through, one of a hundred other issues, that system can make good decisions on the fly to save the order. And it can make those decisions in milliseconds so the customer experience isn't impacted. Big companies like Amazon and Walmart have those systems, and they're starting to become more popular in companies that are $10 or $20 billion in size. But the middle market really doesn't have tools like this, and they can be expensive to put in. What I see coming is that the price of those systems is going to come down to a place where all of those tools can be easily integrated to make better overall decisions for the consumer.

What mid-market retailers should focus on first

Raquel: The Amazons and Walmarts of the world have all this money they can throw at AI and the customer experience. Smaller companies are more limited in what they can focus on, especially from a last-mile perspective. If you were to work with a mid-market retailer trying to revamp their AI strategy as it relates to the last mile, what would you tell them to focus on? Is it promise reliability? Accuracy? Fulfillment itself? What's the thing they should focus on to provide a great customer experience?

Chris: I would counsel someone to think about these different modes of delivery as different channels. You're going to reach different customer use cases. This is not the customer who's going to drop everything and run into your store for a shopping trip. You have to decide how much those use cases are worth to you based on the products you sell and the customer mindset when buying them. For some companies, that may not be important. For others, it is.

Once you get straight about that, we talked about the importance of forecasting and getting that right because there's so much money to be made on those line items. But the main metric I would focus on is promise reliability. Just don't say it if you can't do it.

Say I wanted to deliver groceries within a one-hour window. A customer places an order at 10 o'clock and I'd be at their house sometime between 11 and 12. In order to do that, you have to start with, "I just want to be able to deliver today in a three-hour window and get that right all the time." Then I can start tightening around the edges so it's faster and in a smaller window so customers don't have to wait around. But don't try to go immediately to "I'm going to deliver in an hour within an hour window," because you're going to get that wrong. And every time you get it wrong, you're losing an option with that customer. They're saying to themselves, "This company can't do that. I'm going to look somewhere else." Don't give your customers away. Make sure you're getting it right in the first place and then tighten your promise from there.

The metric most retailers can't measure: incrementality

Guy: Let's talk about leaders and laggards, because I think we're getting to the point where you either lead from the front or fall behind. Consumer expectations around delivery continue to rise, but what we see is that retailers pulling ahead aren't just investing more. They're investing differently. And the gap increasingly comes down to which capabilities and metrics organizations prioritize. Over the next five to ten years, what metrics will matter most for retailers trying to deliver a seamless customer experience and last-mile experience? 

Chris: I'll say promise reliability again. But I'm going to say something completely different as well. Incrementality. A lot of companies struggle to understand incrementality. And when they can't understand it, they can't make good decisions. If you have to debate whether adding same-day delivery got you incremental customers, and someone just says, "I'm sure those people would have come to the store anyway," then there's no belief in incrementality. You have to be able to do the math to show whether you've got it or not. And you have to spend the time and energy necessary to get that right.

That'll tell you what your customers want and what you need to spend more time and money on in the first place. Otherwise, it's a shot in the dark. I love what you said about retailers out there who know exactly who their customer is: they want this but not that, this is important, that is not. Their money doesn't go very far in being able to buy all of the different SaaS services they could possibly buy. Being very focused about which few matter most is very important. And you don't know if you can't measure what's giving you more business. Ultimately, that's what's going to win the day for a C-suite team deciding where to put their investments.

Raquel: That ties in nicely with something you said earlier about not giving your customers away and being hyper-focused on what matters to them. Going back to the retailers who launched same-day, maybe I don't need same-day makeup or same-day Lululemon leggings. It's a nice thought, but it's not necessarily what's going to keep me coming back. What technologies or capabilities do you think will help retailers understand their customers better and differentiate in the last mile?

Chris: This is a little different than what you asked, but it strikes me that in looking at incrementality of customer behaviors, companies should be looking at customer lifetime value and whether adding these different products or services is really contributing to customer lifetime value on an individual basis, with many customers, and then in the aggregate.

The biggest and best companies use those measures not just as a strategic measure but literally as an operating measure to make on-the-fly decisions. "Will this offer increase customer lifetime value with this customer? Should I make this offer?" Those kinds of things are very important. Not just using them in a board meeting to say, "Here's the incremental customer lifetime value we've added," but right in the payments workflow, right in the place where you're deciding whether to lean in on a potentially fraudulent order. Once you see the customer lifetime value of that customer, "This one's already spent $1,000 with us, I think we should lean in and say this is not a fraudulent order," versus another customer who's brand new and unknown. Bringing that type of data into these decisions is going to be really important.

Convenience and faster delivery won't slow down

Raquel: You're talking about the checkout page and conversion, and there are so many trends we've seen in that process. Delivery windows at the product description page level, green delivery, delivery within one hour. Is there a trend you see on its way out, or one on its way in?

Chris: It's just going to keep getting faster. DoorDash, Uber, Instacart, there are lots of companies that have made their fame and fortune on delivering fast. They don't own the goods, they're not a retailer, but they're doing billions of dollars in local retail because somebody wants something fast and can't or doesn't want to go get it themselves.

This trend towards faster and more convenient, "I don't want to leave the house today," or even "I live in a city, I'm not going to own a car, I'd rather pay $10 extra when I need a fast delivery," those trade-offs are being made. Faster is going to be the way of the future, but for the right use cases. The beauty sector is using these fast delivery options more often than I would have thought. If you're getting ready for an event and you realize you don't have the right makeup, you've run out of your favorite mascara, there are people who are saying, "I'm just going to have Uber or someone go get that for me." Whatever convenience means to someone, that's what we should be focused on, because the demands around it are going to get higher and higher.

Raquel: Convenience is not going away. Faster delivery is not going away. Definitely investment areas if you're a retailer not already thinking about that.

Chris: Right. You just need to know the right use cases for your customers. That takes a lot of analysis. Just go stand on the sales floor and listen to why they're walking out of the store. Because a lot of the time it's, "I needed something today and you didn't have it."

CEO sponsorship determines whether CX and operations align

Guy: Chris, last mile, the more we work with our customers, we see it's really a transformation that goes across not just technology but different functions, people, adoption, processes. It's a true transformation. What should leaders of last mile and customer experience adopt in terms of strategies to be successful here?

Chris: Often last-mile leaders are focused on cost alone. Ideally, you'd want that part of your organization to focus not just on cost but on how they contribute to the top line as well, which means serving different use cases for customers. It gets tricky when you have a fulfillment organization only focused on cost, because sometimes the most convenient options cost more money. And these are really very different customer use cases.

When you take these decisions up to the senior team, how are those cross-functional decisions getting made between the customer experience leaders and the fulfillment and operations leaders? I see some CEOs jumping in and saying, "Here's the strategy for the company, here's how we should make trade-offs," and they're really a sponsor for moving the company towards more use cases and understanding the incrementality of those decisions. That's powerful.

But if you have a CEO who says, "I have six direct reports, they each own their own function, I'm going to let those functions fight it out to decide how they're going to work together," you're stalled. The customer experience leader wants to grow sales but only in a way that helps customer experience, and the operations leader doesn't want to pay more cost. You're not going to get past those stalemates. CEOs need to step in and have mandates around how they're going to sponsor new technologies and new use cases for consumers.

Advice to improve last-mile performance

Guy: This has been such a fascinating conversation. But I want to leave you with one question. We see organizations where the coin has dropped and they realize that online is taking a big portion of revenue today. They invest, they move forward, they're bold. And we see the incrementality: conversion rate gets better, customer lifetime value grows, on-time accuracy improves. Say what you do, do what you say. But then we see so many still left behind. If you could give one piece of advice on how leaders can improve their organization's last-mile performance, what would it be?

Chris: I'm going to give a couple pieces of advice, not just one. The first is listen to your customers and understand what their use cases are. Retailers are saying, "I know what drives my customer," and broadly, maybe it's having the right fashion or the right product or the right aspirational look. Those things are very important to brands. But it's an "and," not an "or." You can be expanding the use cases you give your customers. Gifting, as an example, in a fashion environment is important. Using last-mile delivery as a way of serving those use cases is important.

Think of all the technology around online as a way to serve your customer in new ways. And invest now in getting your data and your processes standardized so that you're ready to use AI. Because by the time you realize you haven't, and everybody else already has these real-time coordination systems in place, their service is going to be so much better that it may be impossible to catch up. Everything your customer has loved about your company in the past is true, but this is an "and," and you have to get it right. AI is going to change the cost structure of companies, and companies that don't participate won't be able to compete at some point in the future. But it's also growing the pie and creating more business for the retailers that choose to use it. I would be getting my data estate in order right now.

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