Assortment planning in the Amazon era

Voor de 'Insights Scientist van het jaar' award zijn de artikelen gebundeld in de publicatie MOA Topic of the Year 2019: Surviving the Retail Revolution. Robert P. Rooderkerk gaat in zijn bijdrage in op de omni-channel assortiments planning om de klantervaring over alle consumenten touchpoints te optimaliseren.

Challenges on the Marketing-Operations Interface

Robert P. Rooderkerk1

Abstract

New technologies have enabled customers to visit multiple channels in rapid succession, or even at the same time, throughout their journey to their next purchase. For instance, customers can use a smartphone to check prices on Amazon while visiting a physical store. The resulting omni-channel customer journeys are characterized by multiple touchpoints that are dispersed across different (offline and online) channels that may not all be under the firm’s control. The profound changes in customer behavior give rise to the need for omni-channel assortment planning. This study describes this process and highlights the most important challenges.

Introduction

The customer journey consists of a sequence of consumer decision making stages that starts with the recognition of a need and ends with an evaluation of the journey. Figure 1 depicts what customer journeys look like in today’s omni-channel world. As opposed to the traditional clear, linear, single-channel path, omni-channel customer journeys look much messier, a convolution of different touch-points spread across different channels. With the rise of new competitors and business models such as Amazon and Alibaba these channels may not be under full firm control. A few aspects are worth highlighting.

Consumers increasingly visit online channels as part of their journey. Related, goods are also purchased more online. This means that the traditional ‘Purchase’ stage is now separated into an ‘Order’ and ‘Fulfillment’ stage, recognizing that goods do not necessarily change hands at the time an order is placed. Figure 1 also explicitly recognizes the act of returning an item, something much more common for online purchases. The ‘Fulfillment’ and ‘Return’ stages may involve varying levels of customer participation. The explicit recognition of these two stages underscores the fact that operational touchpoints (e.g., home delivery, pickup as part of click-and-collect, return to store) are becoming increasingly important. In fact, consumers no longer just buy a good but a product- delivery/pickup-return policy bundle. Consequently, most challenges that firms face in omni-channel environments are located on the Marketing-Operations interface. A lot of these challenges are part of the process of omni-channel assortment planning, which will be defined in the next section.

Omni-channel customer journeys are characterized by a lot of switching between channels, both within and across decision making stages. Two types of switching between offline and online channels dominate the academic and management literature (Verhoef et al., 2007; Bell et al. 2014). Showrooming describes offline search, followed by online ordering (Sevitt & Samuel, 2013). Webrooming, the more popular of the two, refers to the opposite behavior, in which the orientation phase is done online, followed by ordering in store (Sevitt & Samuel, 2013). Reasons to showroom include lower online prices, poor service, the convenience of not having to carry items home, and the offline unavailability of an item. In turn, webrooming is fueled by consumers’ desire to touch and feel the product and the desire for instant gratification.

Topic3 fig1

Omni-Channel Assortment Planning

Effective assortment planning should enable seamless customer journeys that are at the heart of a successful omni-channel proposition. With this goal in mind Rooderkerk and Kök (2019) define omni-channel assortment planning as the process of coordinating all aspects of the assortment across channels to facilitate a seamless customer experience across all consumer touchpoints.

Assortment planning in the traditional, single channel, sense is already very hard and relatively understudied in the academic marketing and operations literature (Rooderkerk, 2007; Kök et al., 2008). Omni-channel assortment planning complicates this process even more by requiring coordination across channels and firm functions, most notably the marketing and operations functions. This results in several challenges, summarized in Figure 2, which will be briefly discussed in the next sections. In this research brief we focus on the strategic and tactical challenges.

ChallengesFig2

Strategic Challenges

In an omni-channel world firms are faced with new channels they could expand to. Being present in multiple channels does require more coordination. I discuss channel expansion and channel coordination in turn.

Channel expansion. Facing the threat of showrooming, traditional players with a physical presence only, feel the need to expand into online channels. However, successfully doing so has turned out to be very tough for many traditional firms. In The Netherlands alone this has resulted in several bankruptcies, including Free Record Shop, The Phone House, and the warehouse institute V&D (the list is long and still growing!). Other strategies that physical players have followed to expand their presence in the online domain include (a) partnering with a large technological provider, exemplified by Walmart’s strategic collaboration with Microsoft, (b) selling through a platform, a strategy pursued by German holding MediaMarktSaturn Retail Group, which runs the German webshops of its subsi- diaries MediaMarkt and Saturn on the eBay platform, and (c) the acquisition of (pure) online players, such as Walmart’s acquisition of Jet.com.

At the same time digital native companies such as Alibaba and Amazon, and closer to home Coolblue, witnessed the negative consequences of webrooming and felt the need to expand their presence to, and currently in, the physical domain. For instance, Amazon opened up Amazon Books stores, and more recently, its Amazon 4-star stores, where everything sold is rated 4 stars or higher on Amazon.com. In addition, Amazon acquired the WholeFoods grocery chain. The stores these digital native firms open often excel on at least one of the 3 e’s; experience, expertise, and efficiency. For instance, Coolblue provides a lot of experience in its XXL stores, with a dedicated room allowing consumers to listen to soundbars, and vacuum cleaners that can be tested on several surfaces in the store. They also have a lot of trained staff on hand to help consumers choose with their expertise. Amazon on the other hand introduced automatic checkout in its Amazon Go stores to allow for more efficient shopping trips. Stores such as Coolblue’s XXL format facilitate both showrooming and webrooming within an omni-channel ecosystem. In an extreme version of allowing for showrooming, Bonobos (US, men’s fashion) introduced the so-called zero-inventory showroom, which is an exact representation of the website; each item is available in every size to allow for fitting, but there is no inventory on hand. All purchases are effectively made online during the physical shopping trip and sent home.

It seems easier for digital native companies to open up stores than for physical natives to travel the reverse route. To illustrate this, in the same week Mattress Firm filed for bankruptcy and announced it would close its 3,000 stores in the US, mattress-in-a-box startup Casper announced it would be opening up 200 stores across America. Casper’s first and till that point only store is very expe- riential, allowing consumers to interact with the product in several ways. In general, experience-oriented stores reserve more space per product. Moreover, stores that are part of an omni-channel ecosystem typically also fulfill other functions such as click-and-collect, returns, and repairs. Consequently, omni-channel stores are expected to have smaller assortments, making assortment planning even more complicated. An interesting development in that respect is the so-called blurring of offline and online assortments into so-called phygital assortments. As an example, the Tmall x Intersport collaboration in Beijing uses so-called cloud shelf technology (i.e., large swipable screens) to connect store visitors to the online assortment.

Channel coordination. With assortments offered through different (for simplicity offline and online) channels, coordination is important to maximize cross-channel profitability. The most important issue that has to be decided on in this respect is the nature and level of assortment integration; the degree to which the off- and online assortments overlap. It seems obvious to assume that online assortments always include their offline counterparts and more. Reasons include the scarcity of offline shelf space, avoiding customer confusion, and the long-tail effect that can be catered to more efficiently online. However, there may also be reasons to carry products in store, but not online. Exclusive access, for instance through a pop-up format, may lead to sales hypes. Moreover, certain products may require explanation best given in person. If these products were to be bought online this could lead to high return rates, which are very costly to deal with. For instance, online, retailers experience relatively high rates for certain tv’s. One of the reasons is that consumers choose the wrong size (typically too large) or complain about the poor sound. The right size is easier to determine in a physical store, whereas the sound of a tv often does not meet the expectation. In store, the latter could be explained by the retailer’s salespeople, with an accompanying advice to invest a soundbar. This avoids a return and, in fact, converts it to a cross-sell.

To deal with showrooming retailers need to ensure that shoppers in their physical stores stay within their own channels when moving online. This can be done in multiple ways. For instance, German DIY retailer Hornbach offers free wi-fi, with a login page that displays all of Hornbach’s online channels (online store, app, YouTube channel). Another example is Amazon, which accompanied products in their recent Barbie pop-up store with QR codes for ordering on Amazon.com. Facilitating webrooming, on the other hand, pushes the retailer to provide accurate offline product presence and inventory information. Spanish shoe brand Camper invested in an enterprise system that would allow them to present accurate store-level inventory information at the level of an individual model-size combination. As a side benefit this also allowed them to engage in ship-from-store, which greatly increased their online sales.

Tactical Challenges

The two important tactical challenges in assortment planning are deciding on the composition and the layout of the assortment. In an omni-channel setting firms could make these decisions per channel. However, some coordination is in order. Moreover, learnings should be transferred between channels. Finally, online channels offer a lot of possibilities for assortment personalization (‘customi- zing assortment dimensions to the individual customer’) and contextualization (‘adjusting assortment dimensions based on contextual factors such as time and weather’).

Assortment composition. In the era of Amazon, the ‘everything store’, it is easy to believe that restrictions to what to offer do not exist online. However, even online, space is not free. There is a cost associated with each individual product page that has to be made. In addition, when holding one’s own inventory like Coolblue, online product proliferation could easily lead to explosive growth of inventory costs at the distribution center(s). Moreover, to protect their customers from overchoice, retailers may fulfill the role of curator like Coolblue, limiting the size of the assortment. Methodology designed to optimize the assortment composition is typically calibrated on transaction data (aggregated or individual level). In other words, existing methods focus on data pertaining to the ‘Order’ stage of the customer journey. However, online channels also provide a wealth of data on consumer preferences in the ‘Search’ stage (what products do people view, what search terms do they use, etc.), the ‘Alternative evaluation’ stage (e.g., use of comparison matrices, attribute filters), and the ‘Post-journey evaluation’ stage (customer reviews). The insights from these data, in addition to those following from product returns, should be integrated in the optimization of the online assortment composition, but can also be transferred to the offline channels.

Online channels allow for a lot of personalization and contextualization of the assortment. However, the degree to which this is implemented is still fairly limited, with notable exceptions such as Alibaba, which used 11 million different websites on the most recent Singles Day. In the physical world new technologies can be deployed to offer similar customized experiences. For example, Acure introduced a new type of vending machine on Tokyo’s railway stations that is equipped with a large touch screen, a camera, and facial recognition software. Based on a ‘guestimate’ of the gender and age of a person standing in front of it (personal factors) and weather information and time of the day (contextual factors) a different set of products would be shown on the first screen (hot vs cold drinks, alcoholic vs. non-alcoholic, sugar-rich vs. sugar-low). It is interesting to note that assortment customization may not only serve marketing-benefits. When McDonald’s announced its $300 mio. acquisition of omni-channel personalization company Dynamic Yield, the first application it mentioned was recommending items from the menu that are easier to make in times when the kitchen is busy, leading to operational benefits as well.

Assortment layout. The layout of an assortment represents an external categorization of the products in the category. An effective layout matches as good as possible with the internal categorizations of consumers, their own representati- on of the category (Rooderkerk and Lehmann 2019). The challenge is that internal representations vary across consumers. Offline, typically only a single layout can be implemented. The dominant practice to deal with this challenge is to first summarize the internal categorizations by a single consumer decision tree (CDT), which assumes a hierarchical way of decision making by consumers (e.g., first brand, then pack size, next flavor) and next to match the layout. CDTs are commonly constructed by applying some form of cluster analysis to the outcomes of simulated shopping tasks. However, Rooderkerk and Lehmann (2019) show that consumers’ internal categorizations are much fuzzier instead of revealing a clear hierarchy, and that they vary considerably across customers. This means that aggregation bias is a serious concern when using the CDT approach. They present alternatives that directly consider the variation in internal categorizations to optimize the assortment layout.

Compared to their offline counterparts, online layouts are quite unstructured. Retailers probably argue that their online channels provide all sorts of filter and sorting options, which allows the user to customize his or her layout. However, this places unnecessary burden on the customer and fails to utilize the potential for online assortment layout personalization and contextualization. Online, retailers could use browsing and clickstream data harvested at previous visits to the same or similar categories, by the focal customer, to pro-actively organize the assortment. For instance, if earlier a customer sorted on color when browsing t- shirts, the layout of the sweater category could already be presented by color to him or her on first encounter. In addition, retailers could infer the processing style from customers, for example more analytic (focusing on details) or more holistic (looking at the big picture) and adjust their layout accordingly. Custo- mers with a holistic processing style may want to see a larger number of product alternatives per page with less information per product compared to their more analytical counterparts. There is also ample room to contextualize the assort- ment layout, for instance adjusting it to the device that is being used, less pro- ducts and less details when the website is visited on a smartphone.

There are opportunities for transferring learnings with respect to assortment lay- out across off- and online channels. For instance, data on online filtering and sorting behavior can be leveraged to aid in the construction of offline layouts. However, firms should also consider the channel idiosyncrasies. For example, customers may like seeing tv's organized by brand online, because that allows them to compare different screen sizes within a given brand/product line on price, while preferring them organized by screen size in a physical store, because that allows for a comparison of the image quality across brands. Finally, the advent of showroom-type stores results in very different store layouts, where products receive a lot more space, while being surrounded by digital displays with product information and reviews. This looks more like a product-by-product display than a category.

Operational Challenges

Superior operational performance has become the centerpiece of winning in an omni-channel world. Providing relevant and accurate information and designing efficient decision aids will greatly help in this respect. Moreover, inventory and returns management become more important and more complex in an omni-channel environment. Next. I briefly discuss these challenges.

Information provision and decision aids. Providing consistent information across channels is very important in omni-channel retailing. Retailers could use in-store kiosks or other digital solutions to familiarize in-store shoppers with their online assortment, and vice versa, they should offer online visitors with accurate information regarding offline availability and inventory levels. Consumers also use a lot of information in their decision making that is not offered by the firm like consumer reviews from an external source (the Vivino wine app for instance provides customer reviews when feeding it with a picture of a wine label). Firms should investigate what information consumers are seeking and think of ways to provide this information in their channels. This especially holds for offline channels. An example would be to provide real-time customer review scores trough electric shelf tags in physical stores. Data augmentation is another interesting avenue. For instance, the Italian supermarket chain COOP Italia presents all sorts of product information in monitors above the products that can be activated by a simple hand movement towards a product. The information includes source of the product, nutrients, and allergens.

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It seems odd that choosing from a store assortment, which is large but considerably smaller than its online equivalent, can feel so much harder than choosing from very large online assortments. However, that is because online customers can use all sorts of decision aids (e.g., filters, comparison matrices) to make the task easier. There is a lot of potential for firms to introduce such “online” decision aids in offline settings. A great example is the “Wijnwijzer’ designed by DOBIT, shown in Figure 3. This digital wine advisor installed in a store in Oud-Turnhout, Belgium consists of a large digital screen on which customers can use filters such as country of origin or dish the wine has to pair with. After entering all the filters led lights installed on the shelves indicate what products satisfy the filters. This allows customers to choose from a large assortment of wines, in a physical store, but with the same ease as online.

Inventory management. With next-day delivery as new standard and a push for same-day delivery (bol.com in The Netherlands and Amazon in the US) inventory planning becomes even more crucial to omni-channel success. Moreover, new fulfillment models such as click-and-collect and ship-from-store make inventory planning even more complex; the question is not only how much inventory to hold, but where to hold it as well. The current trend in industry seems to push more inventory to the stores, which improves service levels in the stores, but decreases inventory consolidation, which means the retailer benefits less from demand/inventory pooling. However, in combination with a ship-from-store policy this may not be a problem.

Return management. Returns have skyrocketed in online channels, putting pressure on free return policies. Even companies like Zalando, which marketed its online channel by highlighting the hassle-free return policy, has begun to realize this business model is not viable. The main reason for higher online returns is the inability to touch and feel the product. This increases the uncertainty to what degree the product fits with the customer’s preferences. Besides opening up showrooms that facilitate this look and feel, retailers are also looking at technology to reduce preference uncertainty. For example, many online retailers selling fashion invest in product videos, 360-degree pictures, and elaborate sizing tools. Retailer also use incentives to reduce returns, such as providing discounts when an x number of orders is not returned. Research by Gallino et al. (2019) shows that most consumers prefer to return in store, another reason for pure online players to open stores. Moreover, return handling in store is typically cheaper than by mail.

Managerial Implications

Customer behavior continues to evolve in today’s omni-channel environment. Omni-channel customer journeys have become much more complex due to the fact that both information flows (product information, feedback to sellers, and other customers) and physical product flows (fulfillment, returns) occur across channels with no predetermined schedule. From an assortment planning perspective this leads to a lot of new challenges. This study has highlighted the most important challenges and some of the best practices in dealing with them.

Addressing these challenges requires firms to (a) better coordinate its activities across different firm functions (most notably marketing and operations), (b) integrate product and information flows across different channels, including those not owned by the firm, and (c) introduce new services, technologies, and even business models to better serve the omni-channel customer. Firms cannot afford to wait or move too slow. The new retail world forces them to be in a constant “fail-forward” mode, allowing for quick experimentation with new ways of delivering superior experiences to ever demanding customers that are used to switch between channels in a heatbeat.

1. This contribution is based on (1) the book chapter “Omni-Channel Assortment Planning”, co-authored with Gürhan Kök, which is forthcoming in the book “Operations in an Omni-Channel World,” Springer Series on Supply Chain Management, eds. Santiago Gallino and Antonio Moreno-Garcia, and (2) the article “New Product Development in an Omni-Channel World”, co-authored with Santiago Gallino, which is under review at California Management Review.

References

Bell, D. R, Gallino, S., and Moreno, A. (2014). How To Win in an Omnichannel World. MIT Sloan Management Review, 56(1), 45-53.

Bijmolt, T. H. A., Broekhuis, M., De Leeuw, S., Hirche, C., Rooderkerk, R. P., Sousa, R., and Zhu, S. (2019). Challenges on the Marketing-Operations Interface in Omni-Channel Environments. Working Paper.

Gallino, S., Moreno, A., and Rooderkerk, R. P. (2019). Omni-Channel Fulfillment Dilemmas: Customer Preferences and Manager Perceptions. Working Paper.

Kök, A. G., Fisher, M. L., and Vaidyanathan, R. (2008). Assortment Planning: Review of Literature and Industry Practice. In Retail Supply Chain Management, (pp. 99–153). Springer.

Rooderkerk, R. P. (2007). Optimizing Product Lines and Assortments, Dissertation, Tilburg University, Tilburg School of Economics and Management.

Rooderkerk, R. P. and Kök, A. G. (2019). Omni-Channel Assortment Planning. Forthcoming in “Operations in an Omni-Channel World,” Springer Series on Sup- ply Chain Management, eds. Santiago Gallino and Antonio Moreno-Garcia.

Rooderkerk, R. P. and Lehmann, D. (2019). The Effect of Congruency between Individuals’ Product Categorizations and Store Assortment Layout on Purchasing Behavior,” Working Paper.

Sevitt, D., and Samuel, A. (2013). How Pinterest Puts People in Stores. Harvard Business Review, 91(7), 26–27.

Verhoef, P. C., Neslin, S. A., and Vroomen, B. (2007). Multichannel Customer Management: Understanding the Research-Shopper Phenomenon. International Journal of Research in Marketing, 24(2), 129–148.

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