How Lovesac and Other CPGs Are Embracing DTC in Their Data Analytics Strategies

As consumer tastes and habits evolve, DTC strategies are becoming increasingly popular among consumer goods companies. Offering the opportunity to gather valuable first-party data, personalize the shopper journey, and drive value while remaining compliant with privacy laws, the direct-to-consumer model forms a small but mighty part of the overall engagement strategy for savvy brands today. In this edited webinar transcript, Khurram Moiz, lead principal, retail customer success at BlueConic and CGT's editor-in-chief, Lisa Johnston touch on some of the findings of CGT's latest DTC report before diving into three case studies that break down where leading brands are currently finding success in the direct-to-consumer model. This includes how how Lovesac has unified online and offline data to form a robust omnichannel strategy, and how a leading bricks-and-clicks retailer uncovered new customer insights and delivered personalized experiences to members of its loyalty program. Read on to discover how brands are navigating the challenges and maximizing the opportunities of DTC. Note: This transcript shows an edited segment of the conversation. Lisa Johnston: Hi everyone. Welcome to “Case Study Success Stories: 3 Consumer Goods Companies Building DTC into Their Data Strategy.” My name is Lisa Johnson, editor-in-chief of CGT. This webinar is an extension of CGT’s recent special report, “DTC's Shifting Fit in the Consumer Insights Puzzle.” However, this webinar will not simply rehash the report – who would want that? Today, Khurram Moiz of BlueConic joins us to discuss three of the biggest trends from the report, and how they translate to the larger industry. We will focus on three case studies that exemplify the challenges and opportunities of DTC. Khurram, it’s great to have you, we're thrilled you could join us. May you tell us a little bit about yourself and about BlueConic, then we can jump into our discussion. Khurram Moiz: Absolutely. At BlueConic, my role is to be the domain expert – to provide strategic direction to all of our customers, specifically when it comes to first-party data and the customer data platform. I come from an extensive retail and consumer goods background, so I own that vertical here at BlueConic. There’s a lot of great information to share today, both on current trends, as well as how BlueConic has helped our customers in general. I’d like to address a few major trends, and then consumer-behavior aspects that we've been seeing in the market. Three key trends that a lot of our customers, and folks like yourself, are grappling with are major factors that are continuing to be top of mind, and have been the status quo for about the last 10 years. First is the shift away from third-party data to first-party data. Of course, this is driven by Google's deprecation of third-party cookies, Apple's changes to their ecosystem, and ongoing issues with third-party data and privacy concerns. All of those indicators point to the need to liberate first-party data and leverage it in the best way possible. Second is agility. Ninety-two percent of C-level executives say that they would absolutely need agility to be competitive in this space or in their industries, but only 27% feel like they're actually agile. This is quite telling, and probably something that we're familiar with in our own organizations, which drives the need for capabilities like BlueConic. Finally, 87% of business leaders say that they need digitization and customer-centricity in order to scale. Customer-centricity has been a concept for a while. Forrester has been calling it “the age of the consumer” for quite some time. Understanding that this is a priority and key focus is important as we think about how to leverage capabilities, such as BlueConic, from a DTC perspective. Some key consumer-behavior metrics that I wanted to share, specifically where there were lenses on retail and CPG, and things we've uncovered in the pandemic: 47% of retailers have seen an increased interest from new customers as they explored new products from home, returned to the physical space, or the physical conversion space. This rings true when it comes to experience and providing the best product. 64% of consumers prefer delivery, but 25% prefer to order online and pick up in-store. The concept of omnichannel is back. Earlier this week, Office Depot launched a 20-minute BOPIS delivery, which makes sense for that industry and that vertical, but this is absolutely a key area of focus. From a physical perspective, 5,100 new store openings were announced in 2022, which is more than two times the number of closings. Consumers are back in physical retail, it's an important channel, and being mindful of this is important from a retail/CPG perspective, and especially from a DTC perspective. Two aspects that are directly from the report are: 1. 82% of consumers share personal data for a better customer experience. There is a normalization of sharing data happening. 2. 76% of consumers get frustrated if they don't receive a personalized experience. Understanding the ability to leverage first-party data, which customers are willing to give, while also providing personalized experiences that are relevant and mindful is important. As we think about some of these metrics, one of the key pieces to remember – from a report that McKinsey recently released – is that consumers are starting to think about privacy. The entrusted ability of brands to manage data in an ethical manner is almost as important when thinking about pricing and quality. Let’s not start thinking about how to balance or calibrate pricing and quality with ethics, trusted reputation, and brand. But we need to understand that this is extremely important to consumers – 82% of which are willing to share their data. This becomes one of those things that drives customer-centricity with the right lens on the brand and the brand's reputation in the market. Something to keep in mind from a DTC business-growth perspective. Finding balance in DTC amongst these trends and consumer-behavior shifts, requires finding capabilities that arms non-technical teams with the right data and tools to create personalized experiences, as well as drive customers to an experience or business outcome that is mutually beneficial for both. This can involve things like building a first-party data asset, maintaining standardization control, and consumer privacy. Governance on how to manage this data. Then, of course, making sure that marketing teams can experiment, execute, and analyze with the right technology in place. Often, we're stuck in queues with the BI team, data-science team, or IT team in order to do something simple. Having the right capabilities in place to harness the power of first-party data, as well as turnaround for activation or experience it for customers that make sense, is key. BlueConic is an industry-leading, pure-play, customer-data platform. We power customer-centricity by leveraging unified profiles for DTC. Our capability drives things like multidimensional segmentation, modeling and analytics, and lifecycle orchestration – all within the lens of economic and consumer-behavior pressures that are happening and evolving extremely quickly. Johnston: Thank you for setting the stage so nicely. Let’s dive into the three trends. As I mentioned earlier, this webinar is an extension of our special report, “DTC's Shifting Fit in the Consumer Insights Puzzle.” Once upon a time, DTC was a very small subset of e-commerce. While that remains the case for many companies, others are expanding operations, while some digital natives are moving into brick-and-mortar. DTC is no longer a replacement for other commerce channels, but a smaller and important part of a bigger picture. The first trend is direct-to-consumer being part of the bigger, connected commerce puzzle. The most interesting to our team were the stats from eMarketer, which show that established brands are the ones fueling sales growth, accounting for more than three-quarters of US DTC sales in 2022. For many of these companies, DTC is serving as a testing ground. Mohamed Al Lawati of Bimbo Canada shares that for his company, DTC primarily acts as an incubator and isn't meant to scale. Of course, we see this opinion completely on the other end of the spectrum for other companies that are trying to scale their DTC business. Khurram, we're going to dig into a case study from your work at BlueConic that supports this trend and that is Lovesac. Can you first tell us what kind of company Lovesac is? Moiz: Lovesac is an American furniture retailer, specializing in patented, modular-furniture systems called Sactionals that work extremely well. They have custom fit covers, associate accessories. They also sell sacks or beanbag seats with a proprietary foam mixture. It is absolutely worth the investment – they’re super comfortable. Lovesac has a significant brick-and-mortar presence, and of course, an online presence and customer service channels – from a DTC, retail, CPG perspective. That's a little bit about Lovesac. The company is facing a number of challenges that folks on this call may also be dealing with. Johnston: Let's dig into those. What were the challenges that drove them to decide to make this investment? Moiz: Lovesac was in a situation where they had a growing number of showrooms, and a strong e-commerce presence. They were looking to address cleanliness or hygiene around data across disparate online and in-person systems. This is not atypical from a lot of customers. How do we integrate data across multiple systems in a way that allows for a unified profile? Once we have a unified profile, how do we aspire to create relevant and personalized experiences across all channels? Today, customers aren't thinking about whether they’re dealing with Lovesac in the physical channel, online channel, or through customer service – they think of it as a brand. That's where ensuring touchpoints are consistent, and experiences are consistent with the first-party data driving that was key. Johnston: One of the biggest benefits of having access to this first-party data is to be able to personalize consumer experiences. For Lovesac, what does that look like? How do they define personalization? Moiz: Sue Beckett, VP of digital marketing e-commerce at Lovesac said it best, "Without a single customer view of all the interactions that are happening, you could actually undermine truly personalized experiences and also undermine the brand." Listen to the definition of personalization – it varies across customers and the depth of it – but this underscores what the value of personalization is. Consumers are comfortable providing data, their personal data. They also get frustrated if they don't get the personalized experience. Balancing both of these in order to provide an experience that makes a lot of sense, or from a personalization perspective, but also protect the brand is key. The journey across channels – brick-and-mortar, online, customer service – tying all of that together to engage in a mindful, relevant way is critical. That's a little bit of the definition of personalization. Johnston: Consumers are expecting a personalized experience at this point. You only stand to lose if you're providing generic experiences. For Lovesac, what are some of the data sources that they're leveraging in order to differentiate themselves? Moiz: Broadly, they’re looking at product-preference behavior through listening, hearing what consumers are telling you as they come to your channels, and analyzing that to define things, such as: top categories, engagement time, or whether they're abandoning cart or browse – there is a wealth of information that should be leveraged. Getting into more novel areas, loyalty programs inform personalization and help customers move along the loyalty journey. Pulling that data in as part of that unified profile allows for an understanding of where each audience or segment resides within that journey and the right touchpoints to move them along. More novel than that, offline data such as in-person events are being leveraged via QR codes and UTM tracking to inform how those events are performing, understand who those individuals are, and what their preferences are. Then, to engage them in digital channels to complete that flywheel. It’s very interesting, we’ve found a lot of success there. The final piece is exploring partnerships with adjacent companies through clean rooms. There’s a lot of conversation around this today with customers. This is an area where an adjacent organization company can share first-party data or second-party data in order for both to get a richer understanding of how consumers are behaving across organizations. Johnston: You mentioned earlier that this is a journey – it's a marathon, not a sprint. There's always challenges, there's always those hills when you're going through the marathon that you need some help climbing up. What were some of the challenges that Lovesac encountered and how did you work through them together? Moiz: Their use case was built around cleanliness of the data across multiple channels. In order to get to that point, they need to be able to integrate that data in an easy format and an easy method that allows them to post-process the data, validate it very quickly, and get to an understanding of what that data is telling them. That was a challenge and a solution that we were unable to provide through our capability. Going back to relevant and personalized experiences – we have an extensive martech stack. Being able to pull in and connect into each one of these is extremely important. We're in the second age of the martech landscape where it's more about ecosystems and less about point solutions. Integrating across the various martech stacks was key. A lot of that comes native to our platform and acts like oxygen to the data that helps provide a rich, unified profile that can then be used for personalized experiences. Johnston: For other companies like this, what are some things that you think they should be thinking about? Moiz: I could go through a laundry list, but I’ll touch on what makes sense and where a lot of these conversations are positioned to think about value-based outcomes, as well as what that means from our perspective. We have a framework around this and it seems to work well. An organization – from leadership down to practitioners, all the way across various functional organizational aspects – has various business initiatives or strategic outcomes that they're expecting. Things they've promised to the board. There's no reason that building personalized experiences should not tie directly back to those strategic-business objectives. We call these value-based outcomes. The conversation on broader alignment across the organization, on what those priority use cases are, and what those strategic business outcomes are can be happening now. As you're thinking about these capabilities, start thinking about where to prioritize use cases. What are the outcomes we want to measure, and how do they ladder up to what we want to present to the board of shareholders, or the market by the end of the year? All of this is helpful to ensure that personalized experiences work well for customers, but then also align with business outcomes. Johnston: That probably rings familiar for some people who are listening, but it definitely bears worth repeating because it's critical to success. Moiz: There’s also an opportunity in this space, for partnerships with various capabilities that can come in and help onboard capabilities such as BlueConic, or even looking into service packages that organizations provide, in order to ensure you get the time to value quickly. Johnston: In the report, first- and zero-party data allows brands to get as close to the consumer as possible. For many legacy consumer-goods companies, they previously had to rely on retailers for these insights. Now, being able to get that close to the consumer has been a big win. Eighty-two percent of consumers would share personal data for a better customer experience. We know that they're not only willing to do it, but they're now expecting it, as we've said. The closer brands can get to the voice of the consumer, the better the quality of the data. Khurram, for our next case study, you're going to talk about an omnichannel retailer that has had success with this, which is a bit different from Lovesac. We're not going to use the name, but can you tell us a little bit about the company to set the context? Moiz: Absolutely, we've worked with the company for quite a bit, it’s a great partner of ours. The brick-and-click retailer was founded more than 50 years ago and originally served a niche set of loyal customers. Since then, they've grown to about a billion dollars in annual revenue, opening store locations across the United States, and offering a global e-commerce capability. They've expanded the customer base to include multiple new segments and even beyond their original core audience – it's a great success story. Several years ago, they launched a loyalty program, which has quickly grown to more than 20 million members. It’s a phenomenal story. Of course, the big question becomes how do we quickly take action against this first-party data, especially when it comes down to 20 million members within the loyalty program? It’s a huge footprint. Johnston: When we're talking about pain points and why they came to BlueConic – were they looking to take action on loyalty data or were there other things they wanted to put in motion along with this? Moiz: The broad theme with this retailer was to action more quickly based on consumer insights. Marketing teams needed to be much more efficient in terms of acting on those customer insights. There’s a ton of data accessible to us. A lot of it defines things, including the next purchase, price sensitivity, new product launches based on behavior – there’s a lot that can be actioned on these customer insights for a retailer that is trying to scale, and has been pretty quickly. From a customer insight-to-action perspective, the more high-risk customers have a higher propensity for churn – which is key. They’re in the loyalty program, and you want to keep them there. If there are triggers or red flags that define a high risk, then quickly take action against that rather than waiting for somebody to crunch the data and come up with a perspective that may or may not work. In order to be able to test programs that target and influence at-risk customers, these key points need to be launched, measured, and analyzed in a very short timeframe. There is a much larger customer life cycle marketing program that the retailer is going to launch very soon. Urgency was absolutely the case, and this defines the pain points, the reason why they came to BlueConic. Johnston: You mentioned a few interesting use cases. Could you share a bit more, for example, some of the ways brands are upping the game when it comes to engagement and why this is important. Moiz: I'll get to the important thing in a moment. There are some tangible results, with this customer, that make a lot of sense. In the end, upping the game requires thinking about the 82% that are willing to share data, and the 76% that get frustrated when they don't get a personalized experience. What is the personalized experience offering them? What is the value exchange for them to provide that information? Remember, it doesn't necessarily have to be an offer. There can be advanced emails or notifications on product updates, or be the first to hear about various categories, partnerships, etc. In some cases, advanced features as customers come in and are able to dive into various capabilities within your offering. A value exchange helps customers in various brands up the game for engagement. What we want is to be able to capture that data in a mindful way. That allows for more relevant conversations with all of this additional information, information that might be missing. Of course, thinking about this from a privacy-first approach is top of mind. Upping the game with that lens in mind, allows us to do things like engage with the brand. When customers engage with the brand and find what they're looking for at the right time in the right place – that’s valuable. In this case specifically, the results are: six times higher customer engagement or four times the number of purchases pre- and post-onboarding the capability. Getting this value exchange, privacy, and consistent experiences across channels right drives outcomes that align with business priorities and make it very valuable. Johnston: Going back a little bit to the marathon up the hill. What was the challenge for this company when it came to collecting data? Moiz: I'd love to dive into a ton of challenges because there are many to discuss. This is a fun question. First, let's go back to cleanliness. Data hygiene is key. The platform has the ability to engage with pre- and post-processors, and validation in order to address those without the need for highly technical skill sets. Let's talk a bit more about consumer behavior and experience, as well. Challenges around things like householding and merges that define a profile across multiple channels, as well as being able to unify them, is key. The ability in the platform to be flexible about merge rules and then also manage an individual privacy, allows the ability to look at, not just the individual customer or a broader segment, but to start thinking about householding. Think about how you want to engage when it comes to a DTC perspective. Within the household there may be consistencies and the ability to scale up average transaction value. Johnston: When we were talking about this, this was a company that had developed a “SWAT team,” right? Can you explain what a SWAT team is for those who might not be familiar in this context, and why it was valuable for them? Moiz: Think of a SWAT team as more of a Center of Excellence, or a group of engaged individuals with the capability to understand business priorities as well. We're really moving away from point solutions, and more into an ecosystem play in the second wave of martech. Flexibility is a key aspect of any capability and integration is driving a lot of that. A SWAT team that is engaged at a core level, both in terms of implementation as well as understanding where leadership's head is at, is critical. They can help create an alignment, drive prioritization, and help influence. A lot of that is two or three individuals that are able to get things going, and start showing value in this capability. They don't have to be highly specialized teams. Johnston: Are you seeing more of these teams? Are you seeing this across the broader industry? Moiz: We're seeing a lot of marketing managers jumping into capabilities like this, getting trained on BlueConic, and getting empowered to start building quickly – whether it’s a personalized experience directly on the site, or integrating with various aspects of the martech stack. What's great is that you start having a group of folks that defines what gets implemented, but based on a broader strategy. It becomes one place to go ask all the questions as opposed to everybody trying to figure out who the decision-maker is. Of course, a strong executive sponsor is helpful, but we've seen a lot of this. Johnston: That's exciting. I can see how that structure would carry multiple benefits. Moiz: 100%. Johnston: Let’s dig into the third trend, which is particularly exciting. Brands should look to leverage data and feedback into product development and marketing efforts. When we talk about this in the report, we are looking at companies that are leveraging consumer insights all the way back into product development. For instance, Traeger Grills, who leveraged consumer feedback from customer-service operations to reconfigure a part of the grill based on complaints that they were receiving which turned out to be a common user error. Based on these insights, they're able to redesign that piece of the grill and have improved the customer experience. This is from Unilever, who uses consumer feedback on a failed new-flavor formulation. It failed to resonate with consumers in a big way, and they use these insights to refine the product. It was also used in a humorous marketing campaign that included a museum of failure with the criticism. Johnston: Consumer behavior is shifting more quickly now, especially within the last two years. Honing in on product development again, what are some of the biggest unlocks when it comes to leveraging consumer insights and product development? Moiz: The biggest unlock is not having to rely on highly-specialized teams – the BI team or data-science team – to give an idea of how consumer behavior is influencing or driving conversion and business outcomes. Non-technical teams that can quickly create experiences, test them, validate, and scale based on good, deep consumer insights is key. That is critical when it comes to product development. What ends up happening is understanding preferences for an attentive audience and realizing you should be thinking about products in a different way, with some sort of bundle, different pricing, etc. Understanding high-value segments when it comes to product development is key. This matters across product lines and high-value segments where one may not be a high-value segment in another. When thinking about these high-value segments – whichever the definition is for your organization, for your company – being able to understand preferences and engage with them via a poll, or understand product information. A value exchange is key. Having the right level of granularity in terms of high-value segments is key. Johnston: These are all great reasons to do this, so then why aren't more companies doing it? Again, going back to that marathon, that uphill, what's the hardest thing about doing this? Moiz: This question is funny because a lot of companies are moving in this direction, it's top of mind, or it's an area that investments are starting to grow in very quickly. There was a great study done by CGT last year that defined, or showed, that both CGs and retailers are thinking about things like customer-data platforms and customer-experience platforms in terms of the next level of investment, after they've already got some of the fundamentals done. That's great to see. The challenge ends up being internal alignment. Getting executive sponsors to drive direction, as well as ensuring that the customer experience is always going to change. The economy's always going to be in flux, there’s always going to be a bright, shiny object. Ensuring that we're focusing on fundamentals and staying tight to what we're trying to achieve over the long run is critical. Those are a bit of the challenges. Johnston: I would agree that this is going to become the norm. We published a story last week about Nike, a leader in using consumer insights for benefits, who announced they had developed footwear using consumer insights for member polling. Then, they’re going to market it exclusively to these members and create full-circle experiences. It'll be exciting to see other companies as they start doing the same thing. Moiz: 100%. Johnston: Can you make some predictions? I'd love to get some predictions on what we can expect when it comes to the potential of consumer insights. All these great things we've been talking about, but what else is out there? Moiz: I can make a couple predictions, but don't hold me to them completely. The big topic right now is AI and the significant opportunities that it can provide throughout the customer journey. Automation and insight-driven activation become key areas to optimize every dollar with AI. We'll see a ton of that within the next year – we’re already beginning to see some of that, and BlueConic is extremely well positioned to execute from that lens. Back to the second wave of marketing and the ecosystem play, there will be a lot more integrations and consolidation in the market. As decisioning is handled in a more automated way, less technical resources need to be required to do the things we're talking about. Once again, that's another area that BlueConic is involved in and where we're helping customers. Hopefully those help and ring true. Johnston: Along the same line, there’s a question regarding online virtual concierge. What are your thoughts on how to progress on real-time analytics and intervention to detect, direct, and close sales while in the buying process? Moiz: There’s a lot of work to be done here. These are often driven by ML models that have to learn behavior. Having the depth of data, a capability that can listen, and then quickly digest and drive insights to be tested is key. A little bit of learning has to be required here. It’s definitely an area that folks are moving along, but I would not be surprised to see that in the very near future. Johnston: Are there any warning signals out there? Not necessarily specific to this use case, but in general, that you don't think companies are paying enough attention to? Moiz: I alluded to this earlier, shifting priorities internally is something that we encounter at times, and at times just show up as a surprise. Strong executive sponsors, making sure we're sticking to the fundamentals, business objectives that are driving use-case development – these are some of the key areas to continue to focus on to drive impactful change. There's always going to be flux and evolution capabilities, but staying true to these aspects is key. It's something that I don't think companies necessarily know is coming. They know it's happening, but are not set up to buckle down and stay the course in this direction. BlueConic is here to help do that. Johnston: This question seems to be from someone early in the journey: Is it possible to know your customer and personalize their experience from the first time they visit your website? Or is it required to have data collected before you can personalize these experiences? Moiz: Even an unknown visitor to your site can have a personalized experience. That could be something as simple as listening to the behavior that an individual is conducting on your site, or it could be they spent 90 seconds on a product description page, but then decided to leave the site – maybe they went to the top left to close the browser. In that case, absolutely. For example, BlueConic can pop up a dialogue on your site for an offer to sign up for the newsletter, or whatever it is. “Sign up for the newsletter for new-product information, or it looks like you are interested in this product category, would you like to know more?” There's a ton of personalization that's possible with BlueConic with the flexibility that allows you to create engaging experiences for consumers that you don't know yet, and for those that you don't have any data on yet. Johnston: Almost at the other end of the spectrum: Does your team or you have any pro tips for managing ROI conversations around DTC, data infrastructure investment at legacy organizations? Moiz: Absolutely, we offer many great tips, but I'll focus on two specific areas. The first is ROI from a performance perspective. How are marketing campaigns, activation, and personalized experiences performing? Now that you have a first-party customer data platform (like BlueConic) performing versus how they were before? Of course, we all understand that. The second lens to this is operational efficiencies to segment faster and activate quicker. We were able to activate against a very specific segment in a faster fashion. Another aspect of that – and this is a great example that isn’t a use case we presented today – BlueConic allows the capability to think about other platforms in the martech stack and redundancies there. Can you be doing that in BlueConic and offer yourself the opportunity to sunset some of those capabilities and remove redundancies? When thinking about the EOI, it's not just about performance and speed to value, but also the ability to lower overall investment in martech by consolidating some of those capabilities in BlueConic. Johnston: We are almost out of time, may you leave us any final thoughts? You gave us a whole bunch of great ones, but any final thoughts or best practices to leave the audience with? Moiz: First, I’m happy to have had this conversation. Consumer behavior is constantly going to evolve, we know that, it's happening very quickly. Having the right capabilities in play that allow teams to get to time to value very quickly, non-technical teams is critical. Integrating across your martech stack is critical with a privacy-first lens. We're here to help do that, and hopefully some of these use cases helped define how that's possible. Johnston: Khurram, thank you so much for joining me today. I'm afraid that is all the time that we have. Thank you Khurram, and to BlueConic for sponsoring today's webinar. Thank you, everyone, for joining.

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