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  • 1. How Customer Insights Transform Merchandising 2013  Prospective  View Nikki Baird, Managing Partner Edited by Steve Rowen June 2013 Sponsor:
  • 2. Being Customer Centric While retailers have been focused on dealing with a convergence among channels - feeling the pressure to respond to consumers' desire to engage with retailers as one brand regardless of channel - there has also been a convergence between strategies. What started out as a crosschannel endeavor has run headlong into the need to become more customer-centric. A focus on product is no longer enough to succeed in retail. If retailers want to win, they must become customer-centric: instead of looking at which products sell, retailers must understand and anticipate customer needs. It's about a highly personalized, relevant shopping experience, of which product is only part. The easy work has been done on the marketing front - retailers increasingly feel that, even if they haven't done everything they want to do to present one face to the customer, they at least know everything they need to do. The hard part remains: bringing customer-centric principles and practices to the rest of the retail operation. This means the people who don't necessarily interact with customers everyday, and yet play a critical role in defining the customer experience - from the product, to how the product is displayed, to how and who it is merchandised to, delivered, and what happens after the sale. And the changes that are expected of these groups are at least as disruptive as the changes still being felt in customer-facing organizations. On the road to cross-channel, the time has come when product-oriented organizations can no longer ignore the customer revolution bearing down on them. A Prospective View This report will focus on the future of merchandising and how customer insights are transforming merchandising processes and practices. It is part of an occasional series of reports that RSR produces to more deeply explore specific industry topics. RSR's Prospective View methodology leverages multiple research benchmarks to dig deeper into issues that cut across retail disciplines. Prospective Views bring together these disparate data trends to provide not only a current-state perspective on a topic, but also to bring a more forward-looking viewpoint to the industry. For a list of the reports referenced in this research, please see Appendix A. What Merchandising Wants To be fair, many in the merchandising and supply chain organizations recognize that the power shift has happened - the customer is in charge, and it's up to the retailer to respond. In RSR's research, we've seen customer analytics move from "Why would I need that?" to a capability at the top of Merchandising's list (Figure 1). Alongside customer analytics, localized assortments have moved from a "nice to have" capability to something much more important. While localization appears on the surface to have more of a geographic, rather than customer, component to it, this definition of localization is changing. Geography has historically been a proxy for customers. But as customers increasingly cross channels, geography is becoming muddled - a less relevant proxy than going straight to customer behavior. 1
  • 3. Figure 1: Customer Analytics How Important are the Following to your Retail Success? Extremely Important Somewhat Important Retail Forecasting (demand forecasting, forecasting for replenishment, etc.) Not Very Important 75% Customer Analytics 25% 71% 25% 4% Localized assortments 48% 42% 10% An optimized, end-to-end merchandising lifecycle 48% 45% 6% Unified pricing, promotion and assortment modeling 47% Lifecycle price optimization 29% 42% 62% 11% 9% Source: RSR Research, Optimizing Assortments to Invigorate Retail Benchmark 2012 But what do merchants want with these capabilities? How do customer analytics get incorporated into merchandising beyond "nice to know" perspectives on customer segments? Retailers' experience getting to know customer behavior much more deeply, thanks to the deep analytics available through digital channels, is changing their expectations for how those insights can be used far beyond marketing. In the early days of incorporating customer insights into merchandising, retailers are focusing on two initial opportunities: 1. Customer Segments & Assortment The primary objective in bringing customer insights to assortment planning is to ensure that the product assortment meets a comprehensive set of customer needs. For example, an electronics retailer may wish to target a strategic customer segment that it's not currently strong in: moms with young children. In order to ensure that the retailer is attractive to the mom segment, it's important to not only make sure that stores have kid-friendly shopping carts, but to also ensure that there is an assortment of products that meets mom-shopper needs. In assortment planning, the merchandiser responsible for cameras might not only plan the assortment according to other, standard attributes, she might also look at the assortment from a customer segment perspective and balance it out. Are there enough cameras at different price points to appeal to the mom segment? Is there a strong representation among specifications that are popular with moms? In this case, the analytical work of customer segments must already be done - the retailer must know both of the customer segments it's already strong in, as well as the customer segments it may not have - but wish to attract. The output of the analytical work - the customer segments becomes input into the assortment planning process, as an attribute assigned to products. Retail 2
  • 4. merchants must then understand whether they have an assortment that will provide balance against the retailer's customer segment goals. Post-season, understanding whether products that were expected to do well with certain customer segments is just as important as deciding which products to target to which segments in the first place. In this case, the electronics buyer might be evaluated as to whether she grew the category among the moms segment, and whether that segment bought the cameras she had targeted against that segment - or whether assumptions about that segment need to be adjusted. 2. Responding to Customer Sentiment in Planning and Replenishment Retailers understand that there is value in monitoring and understanding customer sentiment via social media channels. The immediate value was in customer service - identifying and responding to unhappy customers who had taken to social media to complain. But as retailers grow more comfortable with the data and historical data accumulates, they are starting to turn to customer sentiment for more predictive analytics - and product planning and replenishment are prime areas of opportunity. Theoretically, social media can be used to ask a nearly unlimited number of interesting questions about products: • • • • What happens to a product's sales when reviews bump it from a 4-star item to a 5-star item? What about if it moves from 5 stars to 4? Or fewer? Does the overall portfolio of product ratings have any bearing on a category's sales? For example, if a category is too heavy in 1-star rated products, does that help or hurt category sales? Do commonly used phrases in reviews have an impact on sales? For example, if reviewers say that a size runs small or that a color or pattern isn't true to the picture, does that hurt an item's sales? Can reviews of online-only products be used to predict how a product might do in store? Can the rate of growth in Facebook "Likes" for a product be used to predict demand? If so, can social media be used pre-season to test demand for seasonal goods or for new product introductions? These questions can be applied during the overall category or season planning process, where last year's results are potentially used alongside pre-season testing results. They can also be applied in-season, to impact replenishment decisions, potentially even before sales results start rolling in. Why Merchandising Wants It There are few cases in RSR's research where Winning Retailers and laggards generally agree, but customer analytics as a part of merchandising's future is one of them. Out of the varied list of opportunities for customer analytics to play a role, the top two are merchandising related: better what-if capabilities for demand modeling, and more intelligent space and product allocations. Third on the list is the first marketing application - retaining customers (Figure 2). 3
  • 5. Figure 2: A Rare Consensus Top 3 BI Business Opportunities Winners Others Better “what if” modeling capabilities for matching demand with assortment, price, and promos 71% 74% 57% 50% 46% 52% More intelligent space and product allocations Higher customer retention Labor and Store Operations efficiencies 29% 14% 29% Higher average transaction value Increased shopping frequency 17% 43% 25% 25% 26% Better reaction to supply chain shocks Improved IT responsiveness & better system performance 11% 19% Source: RSR Research, Retail Business Intelligence: A Work in Progress Retailers' objectives are two-fold. First, they want to delve more deeply into customer preferences; they want to better understand the "why" behind the "buy". But, according to our merchandising benchmark, retailers have a second, much more interesting objective: they want to become more experimental and innovative in merchandising. Increasingly we find that it is difficult for retailers to maintain differentiation based on product alone. Products are too-easily copied, and in a global environment, local tastes can find a much larger market thanks to the internet. Localization of products based on the geography where those products are made can actually be difficult to maintain in the long run. Differentiation based on customer insights and providing relevancy in merchandising and marketing is much more sustainable. When retailers make the shift to customer-centric as their focus, their differentiation comes from knowing their customers, not from having unique products. Or, alternately, think of it this way: a retailer's unique products are wildly successful because the company knows its customers. And insight-based differentiation is not easily copied, because your unique customer base can't be replicated. Further, the value derived from your interactions with that customer base is not easily copied, because your competitors won't have the same insights - even if the exact same shoppers frequent two direct competitors, those shoppers may show different personalities, preferences, and ultimately, buying behavior, to those two competing retailers. It would ultimately lead each retailer to develop a different - but each equally valid - understanding of the why behind the buy. Getting There 4
  • 6. Cultural and technical challenges prevent merchandising from immediately making the most of customer insights. Cultural Challenges As much as the merchandising team recognizes that their processes and systems need to transform to be able to take on the customer dimension of data, this does not prevent the merchandising organization from becoming one of the biggest barriers to change. Buyers become buyers because they like products - if they wanted to deal with customer insights, they would have become marketers. However, the biggest source of cultural resistance to change is financial. No one wants to risk their bonus by experimenting with customer data. And no one really wants to sign up to be accountable for customer-oriented financial goals when they have little insight into past performance or what to except in the future. Retailers need to overcome this barrier by creating a "safe zone" for experimentation with customer insights in merchandising along with setting objectives aligned with the customercentric goals. Merchants should be rewarded for finding innovative ways to use customer insights to drive business results, rather than penalized for trying to learn a new way of doing things. On the flip side, marketing is not always willing or able to be forthcoming about customer insights. You might have a merchandising organization hungry for such insights, but challenged to get them because marketing has for so long been responsible for "protecting" customer data and insights that they are resistant to the idea of spreading those insights around. Or marketing might have previously tried to reach out to merchandising with customer insights only to be rebuffed and reluctant to try again. The way through this barrier is through the customer segments themselves. While marketing may have insights into who makes up each strategic segment, they may not have much visibility into which products each segment buys, or which product attributes most appeal to them, or even which discounts or offers get the greatest response. In that sense, their segmentations are incomplete. Insight sharing can often begin over a cross-functional effort to build out everything that the entire company knows about each segment, so that marketing is no longer the sole arbiter of that information, but becomes the facilitator for sharing that information. However, marketing may not share insights well not because the organization doesn't want to, but because it doesn't know how to - and that leads to technical challenges. Technical Challenges One of the biggest challenges marketing departments face these days is reconnecting to enterprise IT. Over the last several years the pace of customer technology change has increased tremendously. Retailers have coped with that change by setting marketing free to invest, however it needed, in order to respond. But now all of those investments need to be integrated with the rest of the company, and the primary driver for this is the need to share customer insights. The challenge isn't limited to marketing, either (Figure 3). Retailers face pressure from all sides of the business to make customer insights an asset that can be leveraged across the enterprise. 5
  • 7. Figure 3: Pressure From All Around Please identify the top three (3) OPERATIONAL challenges you face that create interest in using near-real-time BI in your company: Merchants don’t get information fast enough today to react to differences between what they thought would happen vs. what is actually 61% Information is siloed 43% Can’t support customer cross-channel activities very well (eg. In-store return of a web-based customer order) 39% We can’t act quickly enough on the information we receive 39% Can’t identify our best customers to offer special incentives to them while they are shopping 37% Marketing doesn’t know what customer sentiment is until we can see it in sales 37% 26% We struggle to match inventory to demand Logistics managers don’t get information fast enough to minimize the impact of supply chain problems 14% Source: RSR Research, Retail Business Intelligence: A Work in Progress However, even if retailers could wave a magic wand and solve all of their information-sharing challenges, customer data would pose unique problems in its own right. Customer data is a moving target - a target that moves as fast as customer technology evolves. CRM systems and customer databases of ten years ago didn't imagine a future where you tracked a customer's Facebook credentials or social graph, where you tracked their social participation alongside their transaction history. Or the tags or hashtags they use when talking about your products or brand. And retailers have hardly scratched the surface of the customer data they have been tracking for much longer than ten years. According to RSR's benchmark on Marketing, only 45% of survey respondents track customers' email responses as part of their behavioral analytics. Only 35% track behavioral data from their own website, and 31% track social media behavior. And for the ultimate hottest priority for retailers - consumer mobile phones - only 25% use mobile behavior as part of their customer analytics. So while the organizational issue associated with using customer insights in merchandising can seem substantial, there may well be a more immediate need on the technical side. Customers and customer centricity have disrupted retail - but that wave of change is truly only getting started. 6
  • 8. Appendix A: Relevant RSR Research Optimizing Assortments to Invigorate Retail: Benchmark Report, RSR Research, August 2012 http://www.rsrresearch.com/2012/08/08/optimizing-assortments-to-invogorateretail-benchmark-report-2012/ Retail Business Intelligence: A Work in Progress, RSR Research, October 2012 http://www.rsrresearch.com/2012/10/11/retail-business-intelligence-a-work-inprogress/ a
  • 9. Appendix B: About Our Sponsor Symphony EYC delivers ROI for retailers and manufacturers by using customer insights to drive execution through software and services that enable a better customer experience and optimized operations. The solutions optimize multi-channel customer engagement strategies, assortment, merchandising, marketing, inventory fulfillment, store operations and supply chain execution ensuring multi-channel connectivity of both the customer experience and retail operations. Deployed in over 70 countries, Symphony EYC is a strategic partner to leading retailers and manufacturers worldwide enabling them to differentiate against the competition, improve productivity and deliver value to the bottom line. More at www.eyc.com and follow us on twitter @SymphonyEYC. b
  • 10. Appendix C: About RSR Research Retail Systems Research (“RSR”) is the only research company run by retailers for the retail industry. RSR provides insight into business and technology challenges facing the extended retail industry, providing thought leadership and advice on navigating these challenges for specific companies and the industry at large. We do this by: • Identifying information that helps retailers and their trading partners to build more efficient and profitable businesses; • Identifying industry issues that solutions providers must address to be relevant in the extended retail industry; • Providing insight and analysis about a broad spectrum of issues and trends in the Extended Retail Industry. Copyright© 2013 by Retail Systems Research LLC • All rights reserved. No part of the contents of this document may be reproduced or transmitted in any form or by any means without the permission of the publisher. Contact research@rsrresearch.com for more information. c