Retail's Pause is Over: Let's Look Where Tech Investments Might Be Headed Now

In the blink of an eye, retail has changed forever. As COVID-19 has spread across the globe, retailers have been forced to explore untested business models just to keep operating in a new, restrictive reality. Even for an industry accustomed to disruption, the widespread shuttering of doors has been like a kick in the teeth to some. But retailers are resilient. They’ve found new ways to survive — and in some cases even thrive — during this unprecedented crisis. They’re brushing themselves off, adopting new SOPs, and deploying tech on the fly to ensure customers can still purchase the goods they need. Tech Investment Post COVID-19, a webinar hosted by RIS News and presented by Alteryx, dives into the following questions, using the RIS 30th Annual Retail Technology Study as its foundation: When this crisis is behind us and everyone has a chance to breathe, where will retailers begin to invest? What tech will they deploy to shore up holes in their supply chains, demand prediction and emergency response? What’s the No. 1 issue facing many retailers right now when using data in their recovery plans? What’s the future for live-streaming video shopping or other emerging CX technologies? How can artificial intelligence help retailers move forward in a unified manner? The full transcript is below, as well as the slides from the presentation. Tim Denman: Welcome everyone to the Tech Investment Post COVID-19 webinar, which is hosted by RIS News and presented by Alteryx. I am Tim Denman, editor in chief of RIS, and I thank you for joining us today. Joining me today to discuss the ongoing COVID-19 crisis and the technologies retailers will be investing in once the dust settles is Alteryx's senior solutions marketing manager, retail and marketing, Anne-Queline Keller. Anne, thanks for joining us today. Keller: Thank you, Tim. And safe wishes to everyone. Denman: Many of the investment plans we'll be discussing today were recently covered in depth in the RIS News 30th Annual Retail Technology Study, Investing in the Data-Fueled Future, which was sponsored by Alteryx. The research was conducted in early 2020 pre-COVID-19, so obviously some of the short-term IT investment priorities have shifted since the onset of the crisis, but the long-term plans, which were the key feature of the research are still accurate and a strong indicator of where IT dollars are headed. To set the stage for the conversation to come, we're going to discuss some of the biggest findings right now. For our research reports, RIS only surveys headquarter level of executives. We do not feature field-level staff. As you see here, more than 70% of respondents are director level and above, and more than half of respondents work at retailers with annual revenue north of $500 million. And some very encouraging news is that 86% of respondents report that revenue has increased year over year. And the final point I'll make about the demographics of this survey is the breakdown of respondents by retail segment. The biggest group of respondents come from the food, drug and convenience vertical with more than 40% of the total survey pool. Coming in second was apparel and accessories with nearly 30% of respondents working in that vertical. As we discussed in the previous slide, 86% of retailers report an increase in year over year sales, but savvy forward-thinking retailers are not just pocketing that increased revenue, but rather they are reinvesting in the enterprise. As you can see here, retailers that plan to increase spending in IT compared to 2019 outnumber those that plan to decrease spending three to one. Almost 20% plan to increase their IT spending by more than 10%, which is certainly impressive and noteworthy. Numbers on current IT spending in the face of the coronavirus are not available at this time, but based off of anecdotal conversation that I've had with retailers over the last couple of months, spending is certainly on the rise, especially on the supply chain and fulfillment fronts. But where's all this investment heading? Well, as you can see here in the next three years, retailers name retiring legacy systems, application integration, store bandwidth, infrastructure, data security, employment engagement and wages as the top obstacles they're facing. To help meet these challenges, retailers will be beefing up their network security, re-examining their unified commerce approach, continuing to grow their mobile commerce capabilities and exploring new advanced analytic tools. Two areas that are always getting upgraded and enhanced are the store and the supply chain. And those two critical pieces of the retail operation are even more vital now in the face of COVID-19. Again, this data was collected prior to the outbreak, but much of the findings still ring true. A large portion of retailers are currently involved in upgrades to their mobile hardware for associates, as well as focusing on in-store analytic capabilities with real-time monitoring and shopper tracking. When the timeline is increased to about a year out, you'll see that store monitoring and mobile devices are still important parts of the investment roadmap as is in store pickup and return of web goods. But as we're all aware, that last item, return of web goods and pick up, that's been thrusted at forefront in the age of social distancing, and the timetable for those enhancements has certainly been accelerated. Now, if you look even further out, we see computer vision, smart devices like digital signage, interactive mirrors, as well as location based marketing pop up on the investment roadmap. Turning to the supply chain, we see dropped shipment, order management, inventory visibility and returns are all important now and over the next 12 months. The driving force behind all of this tech investment is certainly analytics. It's become a bit of a cliché and everybody says it, but it's true now more than ever that analytics is the fuel that powers the modern retail enterprise. We asked retailers about their analytic upgrade plans, and as you can see from this graphic, there's a lot of activity around analytics currently and into the future. Notable technologies to point out that are currently being upgraded by more than a third of respondents are customer behavioral segmentation, loyalty of tracking and inventory optimization. Top technologies that will be upgraded in the next 12 months include pricing, markdowns and space optimization. When we look out on a three-year perspective, pricing, markdowns and shopper tracking, location analytics, they all stay hot offering key investment areas. Now we're going to dive a little bit deeper into the current state of analytics in the retail industry where cutting-edge retailers will be investing in the short and long term. So now I'd like to bring Anne into the conversation. So Anne, why don't you take the floor? Keller: Thank you, Tim. As retail organizations across the globe face off with the effects of COVID, many are already applying lessons learned as they plan for their recovery. And there are many takeaways from this crisis, but as we speak to other retailers, the No. 1 issue many faced was definitely the lack of granularity into their data across multiple channels. This lack of granularity was self-evident in three major functions. And one was the ability to visualize and understand their business in a unified way. The second was the ability to optimize their business processes. And finally, the last was understanding of their customer's behavior, such as being able to view your customer with a 360 degree perspective. This is going beyond the looking for typical drivers beyond the standard pricing and promotional response that you usually track. In fact, the latest quarantine consumer trends, which were published by Ford on this was collected from data that was collected from Amazon shows that e-commerce trends as well as click and collect trends have now penetrated across all age demographics and socioeconomic layers. According to the latest report from RIS News, these three factors that we show here, unified commerce analytics tools and personalized market are going to remain top of mind for retailers as they explore the next 18 months. The question is: What are some of the biggest challenges that retailers face today as they look to become data driven organizations? Retailers have been creating multiple source of data for many years, and they understand they need to monetize that data. They use data in many ways to do what they do today, whether that's assortment or evaluating new locations for store placement or expanding their brands. I refer to this method as traditional. It's business as usual. But as they look to recover and even grow, retailers need to gain efficiencies by collapsing function. They need to ask: How can I use the not so obvious in order to create reaction speed in the market? So what are the not-so-obvious insights that can be gained about their market and their customers to challenge the status quo. The data they're gathering and creating today through multiple channels is in a sense creating a web of information, which requires automation of business and analytic process. The data being captured is good, but it's not good enough. The challenge is how to put this data into a three-dimensional context. To do that, you must bring in additional sources of data externally to derive business insights and to find the not so obvious. As retailers compete in this complex time-driven market where days have gone to seconds, where spending hours at the mall has turned into placing a quick order online for pickup, they need to shift their thinking. The premise of time has accelerated for everyone, which means everything must be collapsed, including reaction time, processes and conversation time. Analytics is the gateway to accelerating your insights, to act at the speed of your business and as a business at the speed that your business demands. How can retailers begin to use advanced analytics to optimize operations and drive efficiencies? The RIS report investigating the data fueled future cites that over 50% of retailers that responded are investing in one of the major technologies today and within the next 24 months, which will be advanced analytics. One may ask if the retailers are already using, for example, an ERP or any other point solution to manage digital marketing, social marketing, as well as supply chain and logistics, don't these applications already come with BI capabilities and even in some cases, some analytics? And the answer is, yes, they do. But when you dig a little deeper, here's what you find: Most of these organizations want to focus on enhancing three functions that BI and basic analytics cannot solve. One of them is optimized inventory, the other is multiple channel royalty tracking, and the other is price optimization. All of these functions are driven by more and more e-commerce transactions. When dealing with e-commerce dynamics, agility and granularity are two important factors. And these same factors are equally important when trying to optimize your inventory, track loyalty customers or optimize pricing across channels. Inventory optimization is all about driving efficiencies by understanding when to buy and who to buy it from, how to create it close to your targeted demographics, increase your turn and how to not overstate your safety stocks. When you are managing tens of thousands of SKUs and dozens of physical stores as well as online orders, this can get pretty complex. The key is to find an analytics platform that is not a one-trick pony, because somewhere in the process, you know that you will want to add financial data or perhaps push the results of your modeling back into your ERP workflow. Tracking loyalty customers is not the issue. The tracking breakage in financially recognizing the liability for these programs, now that's tricky. First, you need to abide to financial rules on how you can recognize these incentive programs. Understanding when point or gift card expires is critical to your balance sheet, best-in-class loyalty programs, see at least 20% uplift in sales. And to meet these expectations, you will need to update your loyalty incentives every three months to keep it exciting for your customers. Again, here's a perfect storm of data, people and process. Combining all of the data sources required and analyzing this data will demand a business-savvy user. And, oh, yes, it's pretty obvious, this is also going to require automation. Finally, to manage your inventory and demand through a loyalty program is not enough. Your online business has now mixed with paid digital conversions, with redirection when customers shop from competitors, social selling with influencers and collaborative e-commerce platforms. All this is about consumer behavior and time. We mentioned time as a factor earlier, but now here is where it becomes crucial. You don't have time. That's the bottom line. Reaction times are getting smaller, customer attention spans are contracting, and the average time someone is willing to wait for their product is three days. Does automation comes to mind? Does AI and ML come to mind? Make time for your business. Let an analytics platform do it for you, one that includes automation, scaling, sharing, collaboration, as well as ease of use of the tools to let you incorporate machine learning and artificial intelligence. So how can a retailer best combine data from a variety of consumer touchpoints to build meaningful and actionable customer profile? In order to get a three-dimensional view of their customers, you will need to blend multiple sources of data. Retailers need to use geocoding, GIS data such as physical data, non-physical data, sentiment data, and physiological data such as behavioral data and logical data. This is the trifecta of data. In order to do this kind of modern retail analytics to move the needle and impact your bottom line, you need to have an analytics platform. A spreadsheet is not able to perform this kind of trifecta. Not all analytics platforms are equal. The reason retailers have not done this in the past is due to employee skillset. Many retailer teams do not have a master's degree in mathematics and don't have data scientists on their teams. This is the single reason why retailers are unable to adopt this new approach. But with self-service analytics platform, you don't have to be a data scientist, a statistician or a python coder. This is removing the fear and the roadblocks of retailers to pull these three-dimensional data together. All you need to do is to answer the question, what problem am I trying to solve today? And with assisted modeling for predictive analytics, the ease of use is amplified. You don't need to understand algorithms. Alteryx built them for you based on the data that you're importing into the platform, and you're empowered. So to optimize the value requires the adoption again of the self-service analytics platform like the one provided by Alteryx, one that allows users to move from traditional practices to more modern ones, as well as allow them to democratize analytics beyond a center of excellence. And here are some of the key tenants of a transformation platform that you will want to follow. Does the platform guide the way to analytics AI and ML? Does it make advanced analytics accessible? Does it allow users to engage with a human-centered experience? Does it automate data driven processes? Does it ensure transparency and traceability? And does it enable quick wins? Where can you recognize quick ROIs? How can you get ROIs within weeks and not months? If you're a new Alteryx user and interested, we do provide a 30-day trial, so please visit the resource listed that we've made available here. We've included a zip folder containing data and folders showcasing today, and look for Alteryx training sessions to up level your designer skills if you're current Alteryx users. Those are made available through our Alteryx community. So we hope this webinar added value for you today. Please share your feedback with us, and I thank you very much. Tim. Denman: Thanks Anne. Great presentations and great points there. We have some questions coming through from the audience. The first one here, Anne, says: You've been speaking to lots of retailers and brands as part of your daily job. What is the No. 1 factor that is making the difference between the ones that are able to adopt and the ones that are closing and failing? Keller: That's a really good question. And so there's definitely a pattern there. And, by the way, this pattern is true across multiple industries. I think that what we've seen is organizations that had already started down the road of driving analytics or driving a culture of analytics inside their organization are faring far better than the ones who have not. And it really depends on where they are in their adoption level and their maturity level, but it's true that because they had already adopted a culture of analytics, they had already done some of the block and tackling necessary. And the first blocking and tackling, when you're becoming an organization that's driven by analytics is really your data and the condition of your data. Having clean data — data that's not missing pieces, that's not a piece of Swiss cheese — is really important because if you don't have that taken care of first, you can't get to the next step. We can't even do any kind of meaningful analysis and certainly not any of the advanced analytics capabilities. So a lot of organizations have already spent the time necessary. Of course, using a product like Alteryx really accelerates that process to really blend the data, clean the data, get it ready, place some data governance whereto the data was already clean and ready to go for analysis. And the second step would be that a lot of them were already starting to do some of that analysis already, which gave them that granularity in order to be able to pivot and adapt as they needed to. Denman: Another question here is kind of a timeline question: How long on average does it take an organization to kind of adopt a culture of analytics, and where do they start? Keller: There's multiple schools of thoughts on this, but I think it's a top-down and bottom-up type of approach. I think it's really important that your chief digital officer or your chief e-commerce officer, that you do have some vision and direction, definitely from a center of excellence perspective that starts with that individual. For some organizations, they actually will hire a CDO, a chief data officer, for example. So those roles are very important, and they may very well hire chief data scientists or data scientists under them. Those data scientists are really there to bring vision into some of the bigger projects, for example, but also to set standards, and to say, okay, if we're going to standardize on analytics, what's the platform we're going to standardize on? What are the tools we're going to use? How are we going to approach problems? And, again, that whole other question I talked about just earlier about, how do you clean the data and how do you know that you have data integrity? So they deal with that, but that's not good enough because obviously that can't scale. A small team like that of very high skilled individuals cannot possibly tackle the thousands of projects that are in place and the day to day aspects of running an organization based on what I would call real-time or near-time analytics, which any organization who's doing a lot of e-commerce is going to require, right? So you have to do that from the bottoms up. And that's where the self-service analytics and the democratization of analytics becomes ever so important. When we're looking about timeframes for adoption, how long does it take an organization to move in this kind of culture? We've seen organizations really accelerate. Using the hybrid approach that I've just stated really is the point of acceleration. And it really takes about anywhere between 18 months and 24 months to get to that point where you've cleaned up your data, you've done all of that, you've taken a lot of the reporting that you do today, you've automated that to make it easier for you and to get that report on time faster so it's no longer stale reporting, it's meaningful. You've scaled it out to colleagues and users across the globe potentially, and you have built that transparency between different lines of business in order to gain efficiencies and be able to mitigate. And then at that point, then you're ready to do some of the more advanced types of analytics. Denman: You talk about advanced stuff, and that kind of feeds into the next question that we got here. Someone's asking about AI and machine learning, which I know you've touched on in your presentation. How does a retailer know that they're ready to institute an AI and ML environment in addition to their standard analytics? Keller: I think at that point it really comes to convergence. It comes to converging processes in order to collapse the business model. I think once an organization feels they have the transparency and the ability to function between lines of business and share insights and data, then you'll start seeing some of what I talked about, which were the not so obvious, right? You're looking for the not-so-obvious that are going to challenge the status quo. And those things that you're going to find, those insights, that are not so obvious are where the business convergence comes in. An example of that would be taking your new product development or your new service development and really collapsing that with your digital marketing in order to design test and iterate quickly so that you can get to market faster as well as gain considerable amount of cost reduction and synergies because you are no longer iterating and going through the traditional phase gate approach of taking a new product or service out to market. But you're not going to know that. You would not know those kinds of things unless you've taken some time to really take a look at the not-so-obvious, those little diamonds that are out there. Those diamonds, they exist in the data. They are there. And it's a combination of looking at those things and looking at those trends, the not so obvious, and then qualifying that with your business users. It's not just about the data, it's also the story behind the data. And that's another important piece that people have to get to. Once they get to that, then they're ready to converge. They understand their business very well, and they can adapt. They can be agile, and they can pivot. Denman: Perfect. We've got a couple of a nice little fun questions about the future of retail based off of what's going on right now, the first one being: What do you believe the future is for streaming live video shopping? Keller: Well, I think that that is definitely something that's going to happen. I'm already hearing customers talk to me about having virtual shopping parties. What a great idea. For instance, jewelry. Jewelers right now are trying to figure out, it's June coming up and we already had Mother's Day. And, of course, a lot of them were struggling with the Mother's Day situation. Their business is pretty seasonal. Missing Mother's Day is a problem, right? So you surely don't want to miss June because that's when people are getting married, you got bachelorette stuff going on, people are getting engaged. Talking about having virtual bachelorette parties where people are looking at engagement rings and picking engagement rings, those are fun things. And that's what people are looking for. I think we're going to have to step outside the box like that. And that doesn't go just for those kinds of products. You can do apparel trunk. Why can we not do virtual runway trunk shows, right? And do private parties where people are doing virtual private parties, enjoying a glass of wine, God help me, and looking at clothing and sharing. Girls like to talk about this thing and guys too quite frankly, "Hey, I love this." So I think it's definitely there. I think it's definitely going to develop, and that's all about staying relevant. That's why sentiment analysis, and this is that more modern practice that I was talking about. You're going to have to start looking and really keeping a pulse every day on what's happening with the behavior and the sentiment of your shoppers. And it starts with your loyalty shoppers first, because you have access to that first. You start monitoring that and really understanding what's happening with these folks, they will give you ideas on where to go and what to do next. Do they like the shopping experience? It's that fast designed, fast testing and iteration I was talking about. You're going to want to get there, right? Test it out, test it out, put them on social media, test it out. See what people say. Immediately you'll know. Was it a success? Was it not a success? If you're going to fail, fail fast. If you pick up a success, then by all means just propel it like a rocket. Take it out there and take it fast. And that's, again, bumping against the status quo, shaking the tree in order to do something different. Denman: This next question kind of piggybacks on that one. Customers obviously are looking for a contact-less experience similar to what we're talking about with the streaming video. What technologies do you feel will lead customer experience in the post COVID-19 world? I'll take a swing at it, and then I'll hand it back to Anne. I think it's no surprise that all those supply chain technologies are going to be huge in the customer experience post-COVID-19 world, in terms of pick-up at the curb, contact-less delivery. I think you're going to see in some bigger metro markets, you might see an accelerated acceptance of robots or drones. It's already out there for some restaurant delivery with robots. That's been on the roadmap. People have been talking about that for years, but it's always been “Someday we'll do it.” I think you're going to see that kind of accelerate. Anne, what are you seeing in the customer experience tech that's going to be big after this thing hopefully blows over? Keller: I totally concur that the major changes happening is supply chain and technology implementation and advanced technologies and supply chain, from delivery to order management, to how customers have access to products and how they secure products. All of that is a convergence of a lot of different technologies today. There is not one platform for that. So you're looking at bringing on a variety of different pieces. To your report, Tim, which was right on, it's about the integration of applications. And I forget what the exact number was in your report, but there were a considerable amount of folks that were really focusing on that, immigrating applications, and that's the reason why. It's because the technology's out there, it's a lot of point solutions. And then somehow you got to bring all those point solutions in a unified way and unify commerce. And it goes back to what this particular webinar is about. The only way to do that is to use an analytics platform. That's what's going to unify those applications so that you get that unified point of view, and it's going to be very important. The other part of it too, again, is going back to that concept of time. Whatever you do, you got to do it to where you're getting it in a timely manner that is what your business is demanding. Because when you're thinking about the fact that it only takes three seconds for somebody to push a button, one of the big fat buttons on the mobile device to secure a product somewhere, and then another three seconds to put in an address and send it forward to have it delivered, again, reaction time and being able to react quickly is important. And this is where the AI comes in, right? You're not going to be able to have a human being on the other end react that quickly, so that means that AI has to come to play to start doing the follow-up questions if somebody has forgotten to enter the right information, or they can't find the address, or all those kinds of pieces. So this is where that integration, the cross-point comes in. And that's what's going to have to happen. Denman: Talking about integration, we have a question here about another technology called Snowflake. Are you seeing Snowflake used in the mix? And if so, how? Keller: Oh yeah, absolutely. Snowflake is a great technology partner for us. We love them. They absolutely have a great product that is out in the cloud. It allows you to stand up a data environment very quickly in a cloud environment. And if you're choosing a cloud infrastructure, obviously there's Azure, there's AWS. They all provide that pay as you go kind of product and infrastructure. And that's a great way to accelerate getting something up and running really quickly. Snowflake enables that. We also are enabled on those technologies. It's a great way, again, to accelerate and bring an infrastructure up and running pretty quickly. Denman: Gotcha. Another question here: How do you manage the democratization of data with the ability to ensure that different departments don't have different versions of the truth? For example, our planning team might have a different definition of something as our regional retail teams do. So if two data analyses are trying to solve for different things, and we have a limited amount of time to determine who is correct, how do you deal with having different departments using the same data and potentially coming up with different results? Keller: That's right on. That's the first question anyone should be asking. Because it goes back to what I was talking about with the first thing you do is you've got to deal with your data. That's bar none, the first thing you're going to do. And so within Alteryx we have the capability of collaborating on data, whereto you can automate the cleaning of the data and the fixing of the data. It's not just cleaning. It's also fixing it because again you're going to find your data is like Swiss cheese, and you're going to have holes everywhere where you can't do any predictive analytics. You cannot do any advanced analytics if you've got holes in the data. It just won't do it. It won't work. So you got to fix all of that first. So all of that can be automated through our system in the first place. And then what happens is that data can then be shared and exported and viewed and collaborated upon with colleagues across various different departments. Those colleagues can then say, “Hey, yeah, that looks great.” “There's something wrong with this. Something's not looking right. We need to look into it deeper and fix it.” That allows you to have a one source of truth for your data, which is where you should start for sure. And then from there, you are going to have to standardize a little bit on how you approach a problem. And a lot of that comes from direction, which is what I was talking about earlier from the center of excellence, either your digital officer, your data officer or perhaps your e-commerce officer. Taking a little bit of a hand with a team that says, “Hey, you know, we now agree. We're all in agreement on the data. It looks good. And so now we're going to standardize on a platform. We're going to standardize on a platform because we know that that platform is going to do it the way we want it to. It's going to have the tools, the algorithms, everything necessary for us to move forward and to move in that.” And again, because, Alteryx is a human-centric platform, it gives you the capability of working collaboratively with others from other departments to solve problems. Point in fact, we're hosting virtual hackathons right now for a very large global organization working across the oceans to solve problems with virtual hackathon teams. Be on the lookout on LinkedIn for exciting news on how we're progressing with that, so some really fun things here. There are no borders, no barriers. You can absolutely share and collaborate. Denman: Wonderful. That brings a nice conclusion to our talk. If anyone has any questions, you could always reach out to me or Anne directly. If you have any questions about the survey, I'm available. If you have any questions about Alteryx or just want some advice on where to go from here, Anne is always available. Anne, we definitely thank you for your time and for being here. It was a great conversation. And we thank Alteryx for sponsoring this survey and this webinar.

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