Microsoft Azure for Industry Podcast

Helping Retailers with Actionable Data

Episode Summary

We’re joined by Richard Hammond of Uncrowd who explains how retailers can use data-driven insights to decrease friction in the purchasing experience. Learn how, for a limited time, Uncrowd is offering their solution through the Azure Marketplace at cost and how you can take advantage of that offer.

Episode Notes

Transcript

Uncrowd’s Friction/Reward Index, or FRi, platform answers retail's most fundamental question, “Why do customers choose retailer x over retailer y?” It then shows exactly how to win in more shopper missions, identify safe cost reductions, and optimize operations.

The Friction/Reward index is fundamentally about showing when and why a customer will prefer you to a rival. In the era of total and easy access to competing choices, knowing where you stand among those choices is your number one priority.

Guests

Richard Hammond

Richard is the Uncrowd’s CEO and the leading global expert on friction versus reward. He is the author of both Friction/Reward and Smart Retail.

Richard on LinkedIn
Uncrowd on LinkedIn

Episode Transcription

David:

Welcome to the Microsoft Industry Experiences Team Podcast, I'm your host David Starr. And in this series, you will hear from leaders across various industries discussing the impact of digital disruption and innovation, sharing how they've used Azure to transform their business. You can find our team online at aka.ms/indxp or on Twitter at Industry XP. So today we're talking about Uncrowd and we're joined by Richard Hammond. Richard is Uncrowd CEO and leading global expert on friction versus reward. He's the author of both Frictions/Reward and Smart Retail. Welcome Richard to the program.

Richard:
Thank you David, glad to be here.

David:

I suppose the logical thing to ask Richard as we start our conversation here is simply what does Uncrowd do for retailers?

Richard:

Sure. We for the first time, can show any retailer when and why a customer will choose them instead of a competing alternative.

David:

And I understand the data that you provide, this is the friction reward index and helps customers understand why to choose retailer X over retailer Y. Do I have that right?

Richard:

Yeah. That's the fundamental point that we started from is, I've been a retailer for 34 years. So I started in 1985 in an electrical retail business as a fresh face kid. And even at that point when I was 15, I wanted to understand why customers would come to us instead of the other places in the town that I grew up in Oxford, why people would come to us instead of those competitors with their money. I felt like we were selling broadly the same products, we were selling at similar prices, so what was driving that choice? The most extraordinary thing is that 34 years later, retailers were still asking that question and not being adequately supported with an analytics tool that could give them an answer to it. In our current era of extraordinary ability to manipulate, understand and put data to our will, not being able to have an answer to that question was very frustrating.

Richard:

You mentioned very kindly my books at the beginning and I was working on the fourth edition of my bestselling book, Smart Retail, and I thought it's finally time to answer that question. It's finally time to look, is there a data-driven, math spaced metric that can tell us the likelihood of a customer trying to do a particular thing who is in a particular kind of need state. Is there a metric we can find that will tell us if that customer is going to go to retail A or retail B, C, D or whatever. So start as an academic idea, I found there wasn't a metric. So I set about putting together answer to this question, and actually we discovered that if you took the theory of economic utility, theory from maths, the 200 year old theory from mathematician Jeremy Bentham, whatever century it was.

Richard:

If you took that theory, combined it with the heart and modern behavioral economics in the form of two system thinking from Amos Tversky and Danny Kahneman, if you put those two things together, you could start to build up sets of data drivable variables, that would tell you about a person's likelihood to choose direction A or direction B. And it became quite clear that we could construct a metric and then prove out that metric to be able to do that. What we looked at was what you just mentioned David, this idea of shopper friction and purchase reward. Shopper friction is very literally what does the customer have to get through to get to you as a retailer? What does the customer have to do to shop in that channel? What are all the things in the way of them between them making the decision to come and shop for X and achieving that shop for X?

Richard:

But on the flip side, what does the customer get from choosing to shop you in that particular way? Because it's not just a case of being the lowest friction option. If it was only ever the lowest friction option, there would be no retail on the planet apart from Amazon because that's the lowest friction way of fulfilling most things. But it turns out customers are much more sophisticated and that customers are prepared to put up with certain frictions if the reward is high enough. And it's probably worth illustrating that with the real world examples. So if you look at Aldi, the food retail, if you've got Aldi's laundry detergent brand Almat, Almat has a very high market share, given that it's a laundry detergent that you can't buy online, you physically have to visit a store for, you physically have to pick it up and put it in a basket and take it home.

Richard:

Aldi make no secret of the way in which queuing is part of the experience. You have fewer choices. All those things that kind of drive traditional retail, and yet the Almat brand is incredibly successful because even though it's a high friction purchase, it takes a lot of effort to buy it. There's a huge amount of reward for customers who choose Almat. The price is very low. The product is a very high performance product, but it's also very easy to buy at the shelf. You're not looking at a six yard run of laundry detergents to pick your way through as a customer, you're looking at Almats, the wool one, the color one, the bio one, you pick the one you want and you go and pay for it. It's a very simple, straightforward process to choose.

Richard:

So that combination of friction versus reward, if you look at those in combination, if you look at them in the whole, you can start to be able to say, well, a customer who is trying to buy laundry powder, so perhaps somebody who is price sensitive, less brand sensitive and maybe habitual, whatever it might be, a combination of those need states. You can start to map that that customer is the one who's going to go and buy Almat instead of the customer who's going to order their laundry powder from Persil. The customer is going to buy that from Amazon Fresh, was going to buy it from Orkin or it's going to buy it from Ahold Delhaize. So once you're able to identify those sets of frictions and rewards, you can then start to make really useful comparisons and you can start to really understand the flow of customers between brands in given situations.

Richard:

What it tells you is the fundamental way in which our platform works is that, if you're a retailer and you want to know who's going to buy from you and when, we structure the platform for you so that we then can feed it with our proprietary data process and we turn that experience into data. That data then

drives a really clear picture of, in this situation, your customers are going here, this mission you're winning, but you're winning for personal reasons you didn't realize. This mission, you're losing it and here's why you're losing and here's exactly what you can do about it.

David:

With that in mind, your company is doing some very interesting things right now and I'm wondering how Uncrowd's data services might help retailers in this time of COVID-19.

Richard:

Yeah. That has been really important to us because as I mentioned, I've been a retailer for 34 years, my business depends on retail, it's our lifeblood. Part of my identity as a person is the retailing world and our colleagues are struggling, and our colleagues are struggling in a number of ways. They're not just struggling because of the drop off in sales, but our colleagues in insight are flying blind. As an industry, as retailers, we're very good at the supply side contingency. So thankfully, particularly in Europe at the moment where we're only the immediate kind of threat almost of siege, on the supply side, grocers in particular have done a fabulous job of creating that supply side resilience, and quite often using coincidentally, fabulous AI tools and machine learning tools from the likes of Microsoft to understand better how to maximize the effectiveness of a supply chain under pressure. And that's been amazing.

Richard:

So the food is available and the way of getting food into stores is available. What's missing is understanding the effect on demand. So obviously when stores are closed completely, that's one story, but what happens when we do start to emerge from this? But if we're a retailer that's disproportionately affected earlier than our colleagues, what is going to happen if customers sensitivities are changed permanently by their experience of being under a lockdown during COVID-19 times. Those are all questions is vital for our colleagues to have in retail at their fingertips. And in the same way, there isn't a metric that reliably explains customer preference to one retailer instead of another. There isn't a metric going to last. There wasn't a metric that's able to answer those questions.

Richard:

So we then find ourselves at a crossroads. We have a valuable tool that can answer some really important questions and it has a commercial value to it. Well, we decided that we should ignore the commercial part of this and actually make available this analysis to our retail colleagues with no cost from us. So if there's a cost to physically go and raise some data, then we'll pass through those costs or whatever they actually cost us. But in most circumstances, actually they won't even be any of those costs. Our retail colleagues can get this analysis from us free of charge and get some light on those questions. That feels like the right thing to do. That feels like something that you have to do when things are exceptional as they are. And hopefully longterm people remember that we've done that and that would all be lovely. But the heart of it is really straightforward. We have an analysis that can paint a picture that's going to be helpful in recovery for retailers, so we're providing it free of charge to those retailers.

David:

That's absolutely wonderful. So the idea here is that you're going to be helping retailers perhaps get back on their feet when this is over by offering these solutions at cost, that's a very benevolent thing for you to do. What made you decide to go this direction?

Richard:

I think it's the right thing to do. We could just run along the path that we were already on and keep building the proof of concepts, keep building the licensed versions of the platform, all that kind of stuff, which is fine. But actually if we find ourselves doing that in an industry that was in some way made smaller by some of the challenges of the COVID crisis, then I wouldn't feel good about that. None of the people who would work for us would feel good about that. And actually longterm it would be shooting ourselves in the foot anyway because a thriving retail industry is our best possible environment in which to operate. And much as kind of Microsoft have done yourselves, the measures that you put in place very early on to support things like hourly wage paid workers in your facilities is all being part of contributing to the community that you depend on thriving and being successful.

Richard:

And that's exactly the kind of ... And we're seeing that in some fabulous places. There are all sorts of retailers who are trying really hard themselves to support their local communities, but essentially at cost and capacity and resource to themselves. And it feels better as a human being to be part of that than the alternative, which is just money grubbing in a time of need, nobody wants to be that.

David:

So Richard, this problem of customer buying behavior has been around for a long time. How is Uncrowd solution unique in that space?

Richard:

Looking at the customer buying behavior, it's been a critical question for a long time, there are lots of people trying to answer this question, but there isn't a metric that answers this question. And in the data driven world, we should be using maths and data and real kind of strength of modern tools. Like for example, and this is why we've put our platform on the Azure Marketplace, we've built it on Azure, is that opportunity to turn huge, great sways of data into meaningful insight is how this question should be answered. And we're the only company that have a metric that's able to do that reliably and be a platform to be able to present it to retailers so that they can actually action what they see in front of them. So there are other tools in play, you've got things like [inaudible 00:12:49] analysis and all those sorts of interesting data-driven almost side issues.

Richard:

And those things are really valuable, we don't replace those things. What we replace is very fundamental, is we for the first time bring the answer to the question, why is that customer gone to me or my competitor? That's been unanswerable until now through data and through a metric, we are the first opportunity to do that. That's why it's simple, that's why I had to turn this into a business and I had to deploy it on a platform like Azure because it needed to be done.

David:

And you're taking advantage of things like Azures, artificial intelligence services and machine learning to derive your answers to these questions?

Richard:

Yeah. So we are deploying AR machine learning in probably quite interesting areas of the platform. The platform itself doesn't depend on those things, but being able to get the best combination of queries and the best combination of answers is a lovely opportunity to bring AI into kind of finding the path through the data. So in very basic terms, we can start to learn what are our user's favorite queries, what queries are adjacent to that that query that a user to also be seeing. So we can start to show them things they might not have thought to look for themselves by using machine learning, by using AIS to understand how use is actually happening.

Richard:

But we can also use, and this is part of our medium term plan, is we're teaching, training Ais at the moment to say, okay, cross all of our friction raw data, what's my best possible combination of solutions and service and proposition development to improve say loyalty, or to improve average spend per transaction or to improve the likelihood of a customer to return to us more often. Those become really interesting because let's take a gross away. You might have half a dozen different formats. You might have a very wide competitor set. You might hundreds of shopper missions, potentially dozens of combinations of customer type. Being able to deploy an AI into that is really powerful and that's part of our development plan. And again, obviously this is a Microsoft podcast, but the tools that Microsoft provide to us to make that straightforward and easy are just out of this world. It transforms our ability to do really cool things with the platform over the medium and long term.

David:

That's very good to hear. And you mentioned that your solution is available in the Azure Marketplace. Is that how people get started?

Richard:

Yeah, the easiest possible way to get hold of the power of friction road indexing is to go into the Azure Marketplace and leverage your existing, if you're a client, your existing Microsoft and Azure relationship and snap up some version or other of the platform has offered there and we can tailor it to whatever circumstance you've got. There are some lovely sample versions at relatively low cost there. There are from time to time specials that we're able to provide. So for example, NRF this year, we were able to get together with the Azure Marketplace team and offer a very special price and combination for people who were in NRF. We're able to do that sort of thing incredibly flexibly through the Azure Marketplace.

Richard:

But it also means that if you're a client, you get to buy a solution that Microsoft have already ensured meets their standards, that is immediately accessible and that can be procured through a mechanism that you've already internally approved. And Azure Marketplace is a game changer in those terms, not just for the vendors like ourselves, but also if I was sitting in a retail insight or a retail marketing role right now, I would be snapping up all sorts of really interesting solutions from Azure Marketplace as fast as I can find them.

David:
You've described a very compelling platform here Richard, and how would they get up and running?

Richard:

So we've made it accessible to a whole range of functions within a retailer, marketing, insight, operations, strategic commercial development, and also to the information services and management information teams and all levels. If you're the CEO and you need insight right now, boom, it's there. If you need to get deeper under the skin of it, you're an insight manager and that's your day in debt. You have the elegance you need there as well. We provide the platform and the platform is really straightforward. We've deliberately made it incredibly easy to get answers from fast but also to be strategically actionable. So you can see at a glance, okay, my opportunities are A, B, and C and my remedies are X, Y, and Z, and you can see the direct connections between those. So the platform is easy and straightforward.

Richard:

We handle the process behind the data so we can go and source that entirely for you, onboard it into the platform. It's a really straightforward process. And essentially to go from sitting in a room and saying, where are my customers and why my customers are there, you can go from asking yourself that question to doing something about it in an incredibly fast period of time. And you can do all that through Uncrowd and the Azure Marketplace.

David:

And of course we're going to put a link to your solution on the marketplace in the show notes. And with that Richard, I just want to thank you so much for being on the show and exposing us to your solution.

Richard:

It's been absolutely my pleasure, and the commitment that we've seen in Microsoft to supporting the retail industry in good times as well as challenging, is one of the reasons why we are so delighted to be part of this ecosystem. All of the tech giants talk retail, for us, Microsoft is the one that is really walking that talk and delivering into the industry that we know enough.

David:
Thank you for your time.

Richard:
My pleasure, David, thank you.

David:

Thank you for joining us for this episode of the Microsoft Industry Experiences Team Podcast, the show that explores how industry experts are transforming businesses with Azure. Visit our team at aka.ms/indxp, and don't forget to join us for our next episode.