Decoding Consumer Insights: Leveraging Analytics for Enhanced Experiences and Business Growth

What do retailers and consumer goods companies today need to achieve customer experience utopia? One thing is for certain: businesses that can build a strong data foundation will be best positioned to delight customers, optimize pricing, personalization and marketing strategies, and anticipate shopper needs. In this edited webinar transcript, RIS's editor-in-chief, Jamie Grill-Goodman and Irene Sibaja, industry principal and global alliances lead, Treasure Data touch on the results of the 2023 RIS and CGT report on analytics entitled ‘The Analytics Advantage,’ using key stats and findings as a jumping-off point into a broader conversation about using data to create exceptional customer experiences. Maia Jenkins: Welcome, everybody to today’s "Harnessing the Power of Customer Data Analytics to Improve Experience and Drive Growth" webinar, which is hosted by RIS and presented in partnership with Treasure Data. My name is Maia Jenkins, I'm an editor at RIS and am grateful to everybody for joining us. The conversation today is going to touch on the results of the "2023 Retail and Consumer Goods Analytics Study: The Analytics Advantage". CGT and RIS News have commissioned the 11th annual analytics study to benchmark the state of analytics across consumer goods and retail, highlighting current and future investment plans. The survey was fielded in the spring of this year (February and March), and includes large segments of consumer goods and retail leaders, all of whom hold director or above job titles. Broadly speaking, the report shows that retailers and CGs are facing numerous challenges including resource constraints, unclear objectives, and limited tools — many of which will be familiar to those in attendance today. Despite this, companies across the board remain firmly focused on the horizon, the future, and what is to come. There's a sense of optimism in the report about the state of analytics, but also a sense of fragmentation and siloed working conditions. The findings show that both retailers and CGs understand and recognize that analytics is a strategic imperative — a key driver of growth and success not only now, but also looking to the future. Today, we'll cover the state of play of retailers and CGs, seeing where they converge, where there are discrepancies, and touch on things like personalization, analytics, maturity levels, empowering the workforce, and how industry leaders are using analytics and data to expand, innovate, and grow. Without further ado, joining us today to talk about these topics and more are: Irene Sibaja and Jamie Grill-Goodman. Irene and Jamie, may you introduce yourselves and share a bit about what you do at your respective companies? Irene Sibaja: Hi, my name is Irene Sibaja. I was most recently the retail industry principle for Treasure Data, which entailed helping to inform the sales team around trends in retail and what people are thinking about retail. I have 25-plus years of experience in strategic consulting and/or working in and around retail, and recently moved into a role at Treasure Data leading the global alliance partnership with Amazon Web Services, which hosts our platform. Jamie Grill-Goodman: Hi, I'm Jamie Grill-Goodman, the editor-in-chief of RIS News. I've been editor-in-chief for about the last year, but I've been with RIS for the last eight years. I also do a lot of work with Consumer Goods Technology, our sister publication which is also under the EnsembleIQ brand. Prior to my position here, I worked in private label magazines for more than five years, and have a long history in the consumer goods retail space all around. I'm very excited to talk about analytics today. Jenkins: Thank you both. Jamie, what is one theme of the report that resonated with you and why? Grill-Goodman: As I was working on the report, which was interesting, one of the main things that we look at is what are the top areas of focus that retailers and consumer goods manufacturers are focusing on this year for analytics. For retailers, consumer insights moved into the top five areas on the report this year, while personalization slid far down the list — it wasn't even in the top five at 16%. You can interpret the data any way you want, but this signifies that retailers are thinking about taking a step back from personalization to understand the customer better before beginning to craft messages to them and such. Then, when it comes to consumer goods companies, one-quarter of them say that retail partners share no customer behavior data with them. When retailers were asked the same question, nearly half said they don't share any customer behavior data with their CG partners. The big takeaway here is: if the industry, as a whole, wants to start conquering personalization in a meaningful way, then the industry, as a whole, is going to need to start collaborating on customer data and insights as well. Half of CGs (48%) name demand forecasting their top area of focus, but personalization and demand go hand-in-hand. Knowing the customer's needs and where they're going to be in the next six months, based on consumer insights, plays fully into demand forecasting. When it comes to analytics and data, especially customer data, there's a tight linkage across many areas of the business. It's important for retailers and consumer goods companies to take note of that. They need to be asking themselves what their holistic strategy is, how to disseminate information across departments across CG and retail, what steps should be taken to make sure decisions are linked, and look at the answers from all angles of the organization Jenkins: As I was reading the report, I also noticed that demand forecasting was an area of strategic priority, and it's very important to note that demand forecasting and personalization do go hand-in-hand. Irene, what theme resonated with you and how are you seeing this impact in the industry? Sibaja: Stepping back, last year and this year, both are saying analytics, analytics, analytics — the most important thing. Yet they struggle, like Jamie talked about, with a holistic approach. There is no holistic approach in most organizations and across retail and CPG right now. It's ironic. Last year, people were talking about centralizing analytics into a hub and spoke about some type of structure. This year, there seems to be less interest in that than there was the prior year, which says to me that there's even more of a struggle around how to organize to get the work done. Whether it's personalization, customer insights, or pricing optimization, organizations are continuing to be inundated by data — there's more and more data every single day. The strategy for how to organize ourselves around that data, draw insights from that data, and use it to drive business forward continues to elude us as an organization. You can see that in retail and at CPG. Jenkins: That is surprising. Like you say, given this ongoing acknowledgement and recognition that data strategy is essential to drive business success, it is odd to see the seeming lack of investment in this holistic approach to a data strategy. It connects to Jamie's point about looking at different areas of focus, how to bring those together, and how they're interconnected rather than thinking in more siloed terms. Sibaja: To be fair, IT budgets are not growing, but the number of things to spend the IT budget on is growing. A lot of the larger CG and retail companies are battling trying to modernize very old tech stacks and bring them up to date, with a lot of the analytic tools that they need to have that holistic strategy. It's not a lack of wanting that strategy, it's more of where do we start? We'll get into staffing, analytics, and having the resources a bit later, but not having the resources is part of the foundational problem of not having a holistic strategy. Jenkins: Jamie, what did you notice in the study about the retailers and CGs analytics maturity when it comes to personalization? Grill-Goodman: Nineteen percent of retailers said personalization is lagging, and 26% of CGs said the same. It's a competitive environment. If three-fourths of the company is saying that they have better personalization than your company, that signals the need to be changing something and thinking differently. Additionally, 35% of retailers said personalization is advanced, while 16% of CGs said this. However, when asked about customer insights, the script flips. Only 16% of retailers said insights are advanced, and 29% of CGs said the same. How is that happening? It's so interesting. Someone at the Analytics Unite Summit last month said, "Personalization isn't a content problem, it's an analytics problem." How true is this? Personalization is a makeup of what action we're taking with the data that we have — whether it's marketing, technology buys, e-commerce, in-store, inventory — it's important to know who your customer is, and to ask what you are doing with that knowledge throughout the organization, whether the shopper's online or in-store. How are you using this customer data? Simply having this data isn’t doing anything for you, it's an analytics problem. It needs to be put to work. For example, if we're thinking about how to use analytics throughout the organization, make it very simple. Take one thing about the customer — let’s say they always shop on Fridays, maybe they go to lots of different stores, but Friday is the day they shop. This is a simple piece of POS data that you might know, but if you take that one simple thing and text or email them on Thursday night, now you're going to be top of mind when they're coming into the store that Friday. This is a really small thing, but these are the things that drive growth. Then, you add one more layer to that. You know that they buy a certain yogurt, now you send them a promotion on Thursday for that yogurt. Now you are the personalization expert. They are definitely going to come to your store because they're going to get that yogurt because they were planning to go to the store the next day anyway. Now you've captured that customer, and the possibility of them doing all their shopping with you that day is much more likely than it was before you sent that coupon. This is a very simplistic explanation, but it gives a good example of how little things that we can do can drive real growth in the business. One thing to keep in mind for personalization is on a personal level: think of that tired, overwhelmed mother who can't even think, let alone go through sales ads. Knowing that she needs yogurt and giving her a solution, is making it easier for her to make the decision. That is what's going to tie convenience and personalization together for shoppers. Convenience sometimes really means not having to think or work out the logistics. Shoppers are willing to share data if you're going to make their lives easier. Another example of personalization I love to share: one holiday I got a text message from a certain footwear brand, "Here's half off hiking boots." I bought them immediately. Completely seamlessly through the text message. I had no intention of buying hiking boots. I had no intention of shopping with this retailer, but because they answered the question I had at that moment (what am I going to get my husband for the holidays?), they made it simple. It came to my phone, I was able to purchase it, I did. Honestly, it ended up being one of the best purchases I've ever made, and it completely changed my loyalty and feelings about that particular brand in such an easy, quick, one message way. We need to look at how we're taking the data and answer these precise questions: What are your shoppers needing right this second? How can I give it to them? Jenkins: Thank you for sharing those examples, they illustrate the importance of bringing data to life. Irene, what did you notice in the study about both groups' analytics maturity when it comes to personalization? Sibaja: Before I answer that question, a little bit on what Jamie said, because in both examples there are two brands. One is the retailer and one is the company that owns the product getting sent to you. To your earlier point, at the very beginning, the need for collaboration between the retailer and the product company is critical to optimizing that personalization. There are different strategies around knowing someone is going to buy the yogurt anyway, maybe I see what other people who buy yogurt are also putting in their basket. I can target that because as a retailer, what I want is for the customer not just to buy the yogurt, but to buy one more thing than she planned on that trip. This is where a retailer can take all of the data that they have from that POS and deduce that people who buy yogurt also buy cream cheese, non-fat milk, or graham crackers — who knows, but something that’s not already in the basket. What I have seen with retailers who have an incredible amount of data — one in particular that I've worked for — is that I am now a customer of "personalized emails," except they aren’t anything I would buy, which tells me that they're not looking at my data at all. It’s remarkable and feels like such a miss. Back to the original question, I have so much passion around this topic, I could talk about this forever. What’s interesting about the study is that last year 43% of retailers thought they were better at personalization], and this year it’s only 19%. At the same time, CGs stayed exactly the same at 26%. This year we have a huge gap in how prepared they think they are, which is fascinating. Then there was also a significant drop in the number, which makes sense, who think that they have it all figured out. What does that mean? The study shows that they're not as focused on personalization this year as they were in other years. Maybe there's not as much attention going to it and they're not getting the results they wanted, so they feel like they're not as good. The reality is, the difference between retail and CPG, when it comes to personalization, is that retailers own the first-party data — they have it, it's theirs. The CGs have to work a lot harder to get that first-party data — they have to create loyalty programs and figure out how to leverage retail media networks to advertise their brands and what's moving. That's the way they're collecting that first-party data. It's no surprise that retailers would be further ahead than a CPG company when it comes to personalization, which is why this year's results were a little bit surprising in that regard. Jenkins: Jamie, moving to marketing spend. As we talk about analytics maturity, what did the study tell us about marketing spend? Grill-Goodman: CG manufacturers said that they're the most analytically advanced in marketing spend. About 48% said they're advanced, 3% said they're transformational. Retailers weren't terribly far behind: 32% advanced, 10% transformational. The study found that half of retailers named data visualization and dashboards as an area of focus for technology upgrades. If we're talking about marketing spend, having easier data visualization can only improve outcomes for retailers. It makes sense that they're focusing on that this year, and that should be a big focus. If you can see the data, it's going to be a lot better than trying to explain it without helpful visualizations. Adding that will also help personalization and loyalty. To Irene's point about getting the email — having somebody send you an email they think is doing personalization, but it's not hitting the mark with you. That's almost a negative in loyalty, because it gets written off as the brand doesn't know you at all. It’s almost as if it would be better if they did nothing, maybe. When we mentioned earlier about them pulling back, it might be that they are trying personalization, but it's not working for them because they're not getting it right. Things like dashboards and such will make it easier to hit the mark on what you should be doing with the data. Jenkins: It's an area where we're seeing a little bit of alignment between CGs and retailers, and more of an interest in increasing visibility into that data through these investments. I know some of the things I get through email make me wonder where they got that information from. It does seem negative in a way. I think twice about opening an email from that brand or company once they receive something that just completely misses the mark. It's an interesting point. Irene, any comments about improving analytics around marketing spend? How retailers can approach this? Sibaja: Before we talk about even improving the analytics around the spend, we need to talk about how they're spending the money, how they decide where they're going to spend, and what they're going to spend. This can bring us back to the data strategy and data being in silos. It's very hard to think about improving the analytics around your marketing spend until the customer data is centralized in one location and you're leveraging it in a more holistic, efficient way through the customer insights that you have. The way you improve analytics is by making sure you're asking the right questions, staying focused on asking those questions, and making sure you either have the tools or are able to get the tools and the resources to make those calculations. Then, leverage those calculations around what the next best action to market to a consumer is, as well as where you're going to market it (online, in-store, a magazine, or the mini channels you have). Unless you are bringing all of the data together in one place, there will be no analytic improvement. Grill-Goodman: I agree that having all the data in one place is absolutely imperative. Jenkins: We talked about customer experience, now move onto empowering teams. Jamie, what about the associate experience when it comes to analytics and data gathering? Grill-Goodman: Irene touched on this. CG manufacturers and retailers are facing similar challenges. Both groups indicate that they don't have the right staff in place to lead their analytics strategy. Research constraints are widespread as companies struggle to expand analytics in the face of these types of budgets, unclear initiatives, and limited tools. Half of CG manufacturers and 75% of retailers have fewer than 10 internal employees dedicated to business analytics — that’s a grain of salt from this study, which could have something to do with company size that answered the surveys. Looking forward, and you can say this for most companies right now, resources are stretched thin. You run into problems when you stretch your resources too thin, and you're not going to be able to innovate in the way that you would like to. Manufacturers and retailers know where they want to focus analytic efforts, it’s not that they're completely unsure. They do know what they want, which the study also showed, but the obstacles we've mentioned can keep them from reaching full potential. Retailers surveyed are more satisfied with current analytics execution strategy versus manufacturers — they have fewer gaps between their current and ideas. But manufacturers indicated that business analytics should ideally be spearheaded by a shared analytics or centralized department, rather than by IT or a separated, siloed department. Although it’s easier said than done, establishing a strong analytics leadership team will help companies address key challenges, such as ambiguous strategies and constrained budgets. It's important to get that alignment. Staying attuned to analytic trends also allows business professionals to become better educated on how to manage the internal analytics pipeline. There’s got to be a focus on that in organizations going forward. The study found that retailers are quicker to adopt artificial intelligence and machine learning to address their analytic needs than CG manufacturers. But, as we look to the future, this is going to benefit everybody. CGs should be hearing that and thinking about how they’re going to use AI and machine learning to unlock future success. As much as every company wants to beef up the analytics staff, it's not going to be possible across the board. This is one solution, at least, that they can look into. Jenkins: In the report we asked retailers and CGs to provide one word or phrase that they would use to describe the future of their analytics strategy. Retailers overwhelmingly answered with artificial intelligence. On the CG side, it was words like evolving and transformation that they chose to describe how they viewed analytics in their organization in the future. It's an interesting take in light of what you just mentioned. Irene, any comments on any of those stats about work, associates, and empowering teams? Sibaja: The other word retailers said was critical. Some of what Jamie mentioned about the difference between retail and CG, again, goes back to the first-party data situation. If you don't have the customer data, it's very hard to feel equipped to get the right answer. A lot of CG companies are — especially now, with the deprecation of the third-party cookie at nauseam — but that's going away. It’s one little tunnel into first-party data which is disappearing. They've got to figure out how else to get that information. That's where zero-party data comes in. If they can do surveys, quizzes, or what have you on their websites, then they can get that information and have it to know more about shopping. The thing about the associate experience though is also about the data consolidation, not to sound like a broken record. Analyzing and talking about data is going to be better if the person you’re working with or communicating to is looking at the same set of data. Then we can have a conversation. I have worked on both the retail side and the analytics side. One of the retailers I worked with started monetizing data to their CG companies. That way, when they went to joint business planning, they were both looking at the same set of numbers and having a conversation as opposed to spending hours aligning what the right set of numbers was. When talking about the associate experience, you must provide the tools that are needed to do the job for people to do the job. If you don’t, they can’t be judged for not doing the job if they’re ill-equipped. To the AI question, a CG company may use AI more around innovation. A retail company may use it to iterate around past purchases to better predict future behavior, or to more effectively adjust assortment, demand forecasting, price sensitivity, or price optimization. AI has many different applications. The impact that it's going to have on retail and CG — the crystal ball's a little fuzzy — but it's coming. Jenkins: I like the distinction you drew between the way CGs may be using AI and the way retailers may be using it. It’s a term that's on the top of everyone's minds at the moment, but the ways in which it's being deployed are complex. If we expand out a little bit here, talking hypotheticals, let’s touch on expanding and innovating. Irene, if you're a multinational enterprise with operations and data silos across the globe, how can you get started letting data analytics help make decisions in operations overall? Sibaja: Gosh, I feel like a broken record. Again, you can't do anything when your data lives in silos, unless you want geographically. If you're a multinational company and you want geographic silos, that's one thing. But if you're a multinational company — like Nestlé, Mars, etc. — you want to understand how your brands are performing across the globe. To do that, you have to be able to — within the realm of data privacy and what data can or can’t be shared — pull together as much as you can to understand geographic nuances where this is selling better in one area than another. Looking at apples to apples, you know that's true, but if you're not, then it's hard. That type of analysis can help determine whether or not to expand a brand overseas, or keep it in a set of countries? Should it be priced differently? What's the pricing strategy? What should the pricing strategy be? For a CG company it can help manage quantities of manufacturing, and where to flow. Everybody's looking at costs, specifically where to cut costs. For a consumer goods company, cost of goods is where to cut costs, but for a retailer, supply chain distribution is where to cut costs. How do they leverage that data to know where to send the product and when. This will streamline those costs and optimize them to drive the business so that they don't run out of products in California while having overstock in London. Jenkins: With uncertainty and ongoing volatility across the globe, it's no small feat. Jamie, circling back to this idea of silos, what can the study tell us about working in silos? Grill-Goodman: When we looked at internal data alignment within organizations, more than two-thirds of CGs are working on establishing a shared data model, although just 10% report that they're using the same data source. Retailers are a little further along with 42% already sharing the same data source, but 29% of retailers and 23% of consumer goods manufacturers said that they are still working in those, when asked. That's still a big chunk of people who are not even saying they're moving along to something. Moving to a shared data model in your organization is important. However, sharing customer data is key to personalization across all of the touch points that consumers interact with customer data from multiple sources, silos, etc. The difficulty translating fragmented data into actionable insights are these challenges that retailers are facing when trying to identify customer behavior and create more targeted engagement strategies, marketing messages, and such. We recently did a feature on RIS "Overhauling Your Data Strategy," where we looked at companies that are looking into ways to overcome this. For example, one company was receiving continuously updated customer data in near real-time from its customer data platform, along with AI-driven predictive models. That allows it to hyper-localize inventory and custom-stock stores, which is what Irene is talking about, but on the global level. Another chose a customer data platform to aggregate multiple data points and create a single customer view to create more precise marketing strategies. From inventory to marketing, there are many ways that having shared customer data is a huge benefit. We have to bring it together, break down the silos, and get everybody aligned. What's interesting about having the right tools, is that we have to take out all those hours of things that we can skip over. Once we have the right resources and the right tools, we can get right in and do the work that needs to be done. No matter how good your employee is, they're only going to be as good as the tools and resources that are given to them because we're talking about analytics. You need the resources. Sibaja: The other reality is that every day there is more data, and organizations need to be ruthless about deciding what data really matters and what data doesn't. Instead of saying everything, pouring it all in, and looking at all of it — it's impossible. I read somewhere that the amount of data we see in a day is like 100 newspapers — it’s impossible. You have to be laser-focused on what questions you’re trying to answer, and what the right questions are for now. Those questions might change next quarter, next month, or whatever, but what needs to be answered now to drive the business forward. Answer that question, then iterate, and iterate again. But trying to do everything is part of why there’s so much fragmentation and confusion inside a lot of organizations. Grill-Goodman: This was discussed a lot at Analytics Unite. How to flip the pyramids from,"What data do I need to get to this outcome," to be, "What is the question that I'm trying to answer? What is the decision I'm trying to make?" From there, you can go backwards to find the data needed to answer that. It makes a lot more sense because you're able to focus on the challenges you are looking to address at this moment, as opposed to having a huge amount of data that you'll never be able to get through. Sibaja: At the end of the day, it's about the customer, the shopper. Sadly, the shopper has become more and more unpredictable and fickle, if you will, as time has gone on. If we're not starting with the people who are buying — back to the very beginning of the conversation when we mentioned customer, insights, and customer insights being the driver for personalization — we must look at what the customer is telling or not telling, but also what they're doing, sometimes they'll tell us one thing and do something else. Grill-Goodman: Actions speak louder than words. Sibaja: Exactly. If we’re not starting with that and looking at the shopper, then we can be doing all the analytics in the world, but it’s not going to matter. Grill-Goodman: That is the core of customer centricity: Start with what the customer does. Sibaja: This is customer experience at its core, which is why retail is the most exciting place to be. At the end of the day, the products and services are going to come and go, but there's always going to be something about the shopper, the customer, the experience that's going to be the switch that's going to flip. And we always need to determine that. That's what makes this whole field of study so much fun and different every day. Jenkins: Thank you very much, to you both for that. The words ruthless and consolidation came up a lot. Irene, if you want your teams to be data-driven, but the data tells a different story than what the execs are saying, how can you bridge the gap and get everyone aligned? Sibaja: That's a great question. I just mentioned starting with the customer — that's how you get everyone aligned. I have been in situations as an analyst where a leader asked me to make the data say what they wanted it to say, as opposed to what it said. That's a really uncomfortable position to be in. As the person looking at the data and listening to a leader, you need to try and understand where your leader is coming from and why they feel the way they do. The way you get alignment is by seeking to understand their position, understanding your position, and figuring out if there is a middle ground, and if so, what that is. That's less of an analytic question and more of a, "How do I manage the question?" My approach is to seek to understand my leader and have a conversation around where they're coming from to understand that. Jenkins: There is always that human factor behind the data. You always have to appeal to this idea of what people actually want to get from the data. There is a very human side to it, even though at its core it’s all facts and figures. It still has to tell us human stories. Sibaja: One thing you may find in that situation is that the leader feels this way because he or she was looking at this set of data and I was looking at that set of data, so which set of data do we want to look at and why? Let's align around that because it may be as simple as that, or it may be as complicated as the shareholders want this, it has to say this, and the analyst or the person doing the work is saying it doesn't say that. There are a lot of different scenarios that could take place, but if you want to start with the data, start with what question you’re trying to answer and what data are you answering it with. If we're on the same page there, then peel it one layer further. Jenkins: We all want to provide a frictionless, delightful customer experience for our customers. How does analyzing data get me there? What are the steps to achieving customer experience utopia? Grill-Goodman: That's a tough one, Irene. Sibaja: The way you get to customer experience utopia is to have a customer data platform that allows you to understand your customer journey and tailor the experience to what you know about that customer. Then, using artificial intelligence to help predict that next item that should be recommended. Or, based on everything I know about the customer and the journey they’ve taken so far, what can I anticipate the next action is going to be, and how do I meet them there? How do I take friction out of the steps the customer is going to take, which could be not advertising a product they bought three days ago — we've all had that experience. Jamie, the hiking boots that you bought, how many ads did you get for those hiking boots for the next three weeks after the purchase? That creates friction. How do I make the payment experience seamless, easy? How do I make it fast? Grill-Goodman: The payment, the convenience is definitely an important layer too, especially in the way today's customers or shopping times are strapped, and what they're expecting. Jenkins: Thank you, Irene and Jamie, for sharing your insights today and going through the study with us. Everybody here will agree we've gleaned a lot of interesting insights. Thank you again to our sponsor today, Treasure Data, and to our speakers for joining us. I wish you all a great rest of your day

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