As Amazon continues its takeover of the retail sector, the scale at which it operates continues to impress.
Today’s infographic continues along that same thread, except this time focusing on Amazon from more of an operational perspective.
Amazon: At a Glance
Amazon has more than 304 million users, and 3 billion products selling on their 11 marketplaces – and every day, 1.3 million new products are added.
The company has a 43.5% market share of U.S. ecommerce spending. It’s no surprise then, that the average customer spends $700 per year with Amazon, and that 34.7 items are shipped every single second.
Shipping and Logistics
Amazon has 45,000 warehouse robots that work in the company’s 77 million square feet of warehouse space. This is equivalent to the size of 1,336 football fields.
The biggest single warehouse is in Schertz, TX, just outside of San Antonia, which alone measures 1,264,200 square feet. Warehouses this size can ship up to 1 million items per day during the holiday rush.
Amazon Prime and Partners
A whopping 64% of U.S. households have Amazon Prime, which has proven to be a lucrative model for Amazon since those subscribers spend $1,300 per year on the site. Impressively, there are 40 million items eligible for Prime, and 8,000 cities where same-day shipping is a possibility.
Amazon Partners also play a big role in the ecosystem. There are 2 million sellers on Amazon, and 70,000 of them have sales of $100,000 or more per year using Amazon as a selling platform.
Why do sellers use Amazon? About 47% of sellers say it’s because it gives them access to new customers, while 65% say it’s to increase sales.
The top five categories for Amazon sellers: Clothing, Shoes & Jewelry, Electronics, Home & Kitchen, Sports & Outdoors, and Books.
The trend toward the experiential is the main driver of successful retail in the future. From now on, there is no day off, no downtime; consumers are on all the time, and so should retailers be. Retailers are not just competing with other retailers and other brands. They’re competing for what consumers want to spend money on. The U.S. apparel industry last year grew 3%, while the U.S. domestic travel industry grew 5%, and there are seven million more cruise passengers today than there were 10 years ago.
Last year, for the first time, American consumers spent more at bars and restaurants than on groceries. People are spending more on experiences. We know they want to look good when they’re taking all those selfies – so how can retailers be there?
We expect winners to emerge from retailers that can find innovative ways to deliver goods. Free shipping is ubiquitous, but is also one of the largest P&L expenses from an e-commerce standpoint. It’s just not sustainable having UPS and FedEx be the core delivery system. Amazon has done great stuff around drones and developing pick-up spots. We see that trend increasing.
Providing an omnichannel customer experience requires companies to become more flexible and responsive. Although consumers have quickly adopted digital channels for both service and sales, they aren’t abandoning traditional retail stores and call centers in their interactions with companies. Increasingly, customers expect omnichannel convenience that allows them to start a journey in one channel (say, a mobile app) and end it in another, by picking up the purchase in a store.
For companies, the challenge is to provide high-quality service from end to end, regardless of where the ends might be. That was the case for a regional bank that sensed that too many customers were falling into gaps between channels. Mapping its customers’ journeys confirmed the suspicions. Four out of five potential loan customers visited the bank’s website, but from there, their paths diverged as they sought different ways to have their questions answered. About 20 percent stayed online, another 20 percent phoned a call center, and 15 percent visited a branch, with the remainder leaving the process.
The channels’ differing performance pointed to specific problems. Ultimately, more than one-fifth of customers who visited a branch ended up getting loans. But in the online channel, less than 1 percent got a loan after almost 80 percent dropped out rather than fill in a registration form. Finally, in call centers, a mere one-tenth of 1 percent of customers received a loan—perhaps not surprising, since only 2 percent even requested an offer.
To integrate digital and traditional channels more effectively, the bank had to become more agile, with the understanding that its one-size-fits-most processes would no longer work. Complex registration forms were simplified and tailored to different types of customers. Revised policies clarified which channel took the lead when customers moved between channels. And new links between the website and the call centers enabled agents to follow up when online customers left a form incomplete. Together, these types of changes helped increase sales of current-account and personal-loan products by more than 25 percent across all channels.
The secret is in customization: dynamic-pricing solutions must be tailored to a retailer’s business context, objectives, and ways of working. When it comes to dynamic pricing, Amazon is still the retailer to beat. Other retailers continue to marvel at—and attempt to emulate—the e-commerce giant’s ability to rapidly and frequently change prices on millions of items. Amazon continually burnishes its low-price reputation by undercutting competitors on top-selling, high-visibility products, while protecting margins by charging more for less price-sensitive items. Indeed, the success of Amazon and a handful of other leading online players has made clear that dynamic pricing is a critical capability for competing in e-commerce, omnichannel, and even brick-and-mortar retail to drive revenue and margin growth.
But as retailers have begun to invest in dynamic-pricing solutions—whether off-the-shelf or custom-built by third-party providers—they’ve often run into the “black box” problem: none of the end users actually understand the math or logic behind the algorithms. The tools somehow crunch data and spit out pricing recommendations, which are sometimes much higher or lower than current retail prices. The pricing staff thus ends up rejecting them entirely because they don’t trust the recommendations.
Overcoming that trust barrier requires customizing every part of the solution, including the implementation. In our experience, a dynamic-pricing solution should be optimized for use by category managers and pricing managers. These end users should be involved in developing, refining, and rolling out the tool and be able to override the pricing recommendations. Only when this happens can businesses expect to capture significant and sustained impact—typically, sales growth of 2 to 5 percent and increases of 5 to 10 percent in margins, along with higher levels of customer satisfaction through improved price perception on the most competitive items.
Dynamic pricing plays a crucial role in boosting both consumer price perception and retailer profitability. Many retailers sell about one-fifth of their assortment at very low prices to shape their price image and remain competitive. These key value items (KVIs) are usually top sellers, traffic generators, or highly-searched SKUs whose prices consumers tend to remember. Key-value categories can account for up to 80 percent of an average retailer’s revenue but only half of its profit. The retailer therefore needs to make up margins in the rest of the assortment—the “long tail” items. However, identifying KVIs isn’t as easy as it sounds, and setting and validating prices for long-tail items is difficult precisely because of the sparse historical data on such items.
Dynamic-pricing solutions help retailers overcome both of these challenges. Generally speaking, a robust dynamic-pricing solution should consist of five modules, all working in parallel to generate price recommendations for every SKU in the assortment . The long-tail module helps a retailer set the introductory price for new or long-tail items through intelligent product matching—that is, the module determines which data-rich products are comparable to new items (which have no history) or long-tail items (which, as mentioned, have limited historical data). The elasticity module uses time-series methods and big data analytics to calculate how a product’s price affects demand, accounting for a wide variety of factors including seasonality, cannibalization, and competitive moves.
The KVI module estimates how much each product affects consumer price perception, using actual market data rather than consumer surveys. This enables the module to automatically detect changes as to which items consumers perceive as KVIs. While a best-in-class solution includes all five modules, retailers can often begin with only the KVI and competitive-response modules. These help retailers nimbly respond to competitive moves on key items. Retailers can then add the rest of the modules over time.
Developing a world-class dynamic-pricing solution starts with a thorough understanding of the retailer’s business context and objectives, and then translating those into mathematical “recipes” that can be executed repeatedly. Careful selection of the inputs, as well as the sophistication of the underlying analytics, will largely determine the accuracy of each module’s calculations. The tool needs to be flexible and adaptable enough for businesses to customize the inputs and features based on their particular objectives and existing capabilities, which greatly increases confidence in the outputs. And of course, whether category managers and pricing managers will ultimately use the solution in their daily work depends partly on how intuitive the interface is and how easily it integrates into the retailer’s existing systems and work flows.
To build a case for dynamic pricing, a retailer could first quantify the potential of introducing dynamic pricing into the organization—for instance, by systematically comparing the retailer’s price levels to those of its chief competitors, assessing how frequently competitors change their prices, and studying how competitors react to the retailer’s own price changes. The findings from such an exercise will almost certainly spur the retailer to take action on dynamic pricing.
The next logical step would be to conduct a pilot in a handful of categories for concept design and testing. Done right, the pilot—and the subsequent rollout of dynamic pricing across all product categories—will yield meaningful improvements in revenue, profit, and customer price perception.
The following examples illustrate how retailers can tailor dynamic-pricing modules to their particular business needs and objectives. In each case, the retailer collected massive amounts of granular data, used advanced analytics, and made sure that category managers and pricing managers participated in developing and testing the solution.
A US-based general retailer with more than two million SKUs in its assortment had two high-priority business objectives that required frequent trade-offs: to maximize absolute revenues and to increase productivity. The objective functions of the algorithms in each of its modules therefore had to be adjusted accordingly—a level of customization that wouldn’t have been feasible with an off-the-shelf solution.
To build its long-tail module, the retailer assembled a rich set of data, including daily sales data for its 100,000 top-selling SKUs, competitor prices (gathered via web scraping) for those SKUs, data on customer browsing and purchasing behavior, product attributes and descriptions, and online metrics such as impressions and search rankings. With algorithm-design experts and analysts working alongside category managers, the team codified a set of product-association rules specific to the retailer, using factor analysis to assign “attribute similarity scores” that indicated which products its customers find comparable. The retailer set simple ground rules for its product groupings—for example, a grouping should have minimum sales of 20 units a week, or all products in a grouping should be in the same life-cycle stage. The algorithms also helped the retailer understand which of its product prices should move in concert so as to avoid cannibalization effects.
In just eight weeks, the team built working prototypes of both the long-tail module and a competitive-response module. Both were designed and tested with pricing managers to integrate simply into the retailer’s regular pricing processes and cadence. The impact: up to 3 percent increases in both revenue and margins in the pilot categories.
A leading Asian e-commerce player aspired to develop an item-level pricing strategy that could optimize for both profit and gross merchandise value (GMV). To that end, the company knew it needed to be able not only to change prices frequently, but also to take many more factors into consideration when setting or changing prices.
As part of a broader dynamic-pricing effort, the company built an elasticity module. At its core was a multifactor algorithm that drew on data from approximately ten terabytes of the retailer’s transaction records. Data for each product included the price of the product, the price of a viable substitute product, promotions, inventory levels, seasonality, and estimates of competitors’ sales volumes—creating a custom module unique to the retailer’s available data and pricing strategy. The module then generated pricing recommendations, taking into account both of the retailer’s business objectives.
Recommendations were displayed on an easy-to-read dashboard that category managers helped design and test. Category managers, who on any given day would be weighing other important trade-offs with regard to, say, a product’s growth potential or expected additional inventory, could then accept or reject the pricing recommendations. The retailer felt strongly that category managers should have the final word on pricing decisions. After only a few months of using the module, the company saw a 10 percent rise in gross margin and a 3 percent improvement in GMV in the pilot categories.
Looking to stand out from competitors, a leading European nonfood retailer sought to identify and prioritize the KVIs in its assortment. It built a tailored KVI module that could statistically score each item’s importance to consumer price perception on a scale of 0 to 100. The module generated this “KVI index” by analyzing granular internal and external data, including shipping costs, return rates, search volume, number of competitors carrying the product, and competitor pricing. It also identified which other retailers were the true competitors for that specific item. Importantly, the module was flexible enough that category managers could adjust the weighting of each parameter.
The module defined the price range, or the upper and lower price bounds, for each item Each product’s exact price position within the range would then be based on its score in the KVI index. But a KVI index to help set the base price was only the first step. Via the competitor-matching module, the retailer also programmed into its dynamic-pricing solution a set of business rules that would trigger pricing changes. For instance, if inventory levels for a certain SKU were high or if a competitor reduced the price of that SKU, the solution might recommend a price drop for the SKU. These rules would all feed into the solution’s recommended price, which the category manager could either accept or reject based on additional indicators and considerations.
At the end of a three-month pilot, the retailer saw a 4.7 percent improvement in earnings before income and taxes in the pilot categories and identified a 3 percent improvement potential in overall return on sales. And it had a trusted solution that category managers could incorporate into their work flows.
In each of these examples, the retailer custom-built the algorithms and invested time and effort to ensure that the tool was adopted by end users. A test-and-learn approach, beginning with a pilot in a few categories, will help produce a solution that builds trust and yields market-proven, statistically sound results. Just as important, the testing process can pinpoint how best to embed the solution into end users’ existing work flows.
Each of the three retailers invested in detailed documentation and thorough training to strengthen the organization’s skill base and capabilities in dynamic pricing. One of the retailers even established a certification program for dynamic pricing, creating a pipeline of employees who would be qualified to manage and continually improve the pricing process.
In light of the explosive growth of e-commerce, dynamic pricing is fast becoming a must-have capability to drive growth while sustaining margins. By understanding how to move quickly and customize solutions, retailers can build this capability into a significant competitive advantage.
In Supply Chain 4.0, supply-chain management applies Industry 4.0 innovations—the Internet of Things, advanced robotics, analytics, and big data—to jump-start performance, and customer satisfaction.
Over the last 30 years, supply chain has undergone a tremendous change. What was once a purely operational logistics function that reported to sales or manufacturing and focused on ensuring supply of production lines and delivery to customers has become an independent supply-chain management function that in some companies is already being led by a CSO—a chief supply-chain officer. The focus of the supply-chain management function has shifted to advanced planning processes, such as analytical demand planning or integrated sales and operations planning (S&OP), which have become established business processes in many companies, while operational logistics has often been outsourced to third-party logistics providers. The supply-chain function ensures that operations are well-integrated, from suppliers through to customers, with decisions on cost, inventory, and customer service made from an end-to-end perspective rather than by each function in isolation.
Digitization creates a disruption and requires companies to rethink the way they design their supply chain. At the same time, customer expectations are growing: recent online trends have led to growing service expectations combined with much more detailed orders. Also, a definite trend toward further individualization and customization is driving strong growth of and constant changes in the SKU portfolio. The online-enabled transparency and easy access to a multitude of options regarding where to shop and what to buy drive the competition of supply chains.
To build on these trends, cope with changed requirements, and enable a wide range of new technologies, supply chains need to become much faster and much more precise.
The digitization of the supply chain enables companies to address the new requirements of customers, the challenges on the supply side, and the remaining expectations in efficiency improvement. Digitization leads to a Supply Chain 4.0, which becomes faster. New approaches to product distribution can reduce the delivery time of fast runners to few hours. How? Advanced forecasting approaches, such as predictive analytics of internal data (e.g., demand) and external data (e.g., market trends, weather, school vacation, construction indices), when combined with machine-status data for spare-parts demand, provide a much more precise forecast of customer demand. What once were monthly forecasts instead become weekly—and, for the very fastest-moving products, daily. In the future, we will even see predictive shipping, for which Amazon holds a patent: Products are shipped before the customer places an order. The customer order is later matched with a shipment that is already in the logistics network, and the shipment is rerouted to the exact customer destination.
Supply Chain 4.0’s ad hoc, real-time planning allows companies to respond flexibly to changes in demand or supply, minimizing planning cycles and frozen periods. Planning becomes a continuous process that is able to react dynamically to changing requirements or constraints (e.g., real-time production-capacity feedback from machines). Even after products are sent, agile delivery processes let customers reroute shipments to the most convenient destination.
New business models increase the supply-chain organization’s flexibility. Rather than maintaining resources and capabilities in-house, companies can buy individual supply-chain functions as a service on a by-usage basis. Service providers’ greater specialization creates economies of scale and scope, increasing the potential for attractive outsourcing opportunities.
An Uberization of transport—crowdsourced, flexible transport capacity—will significantly increase agility in distribution networks as well. Manufacturers may therefore see new direct-to-consumer opportunities in what once was a playing field only for retailers.
With customers looking for more and more individualization in the products they buy, companies must manage demand at a much more granular level, through techniques such as microsegmentation, mass customization, and more-sophisticated scheduling practices. Innovative distribution concepts, including drone delivery, will allow companies to manage the last mile more efficiently for single-piece and high-value, dense packages—fulfilling customers’ customization needs while delivering their orders even faster than is possible today with mass-market, standard products.
Next-generation performance management systems provide real-time, end-to-end transparency throughout the supply chain. The span of information reaches from synthesized top-level key performance indicators, such as overall service level, to very granular process data, such as the exact position of trucks in the network. The integration of that data from suppliers, service providers, and others in a “supply chain cloud” ensures that all stakeholders in the supply chain steer and decide based on the same facts.
In digital performance-management systems, clean-sheet models for warehousing, transport, or inventory set targets automatically. To keep performance-management aspirations in focus even if supply-chain disruptions occur, the systems will automatically adjust targets that can no longer be achieved to more realistic aspiration levels.
We will see performance-management systems that “learn” to automatically identify risks or exceptions, and that change supply-chain variables to mitigate harm. These capabilities enable the automatic performance-management control tower to handle a broad spectrum of exceptions without human involvement, engaging human planners only for disruptive, unplanned events. The resulting continuous-improvement cycle will push the supply chains closer to its efficient frontier.
The automation of both physical tasks and planning boosts supply-chain efficiency. Robots handle the material (pallets or boxes as well as single pieces), completely automatically the warehouse process from receiving/unloading, to putting away, to picking, packing, and shipping. Autonomous trucks transport the products within the network.
To optimize truck utilization and increase transport flexibility, companies share capacity through cross-company transport optimization. The network setup itself is continuously optimized to ensure an optimal fit to business requirements.
To create an ideal workload in the supply chain, the system leverages the high degree of transparency and dynamic planning approaches to drive advanced demand-shaping activities, such as special offers for delivery time slots with low truck utilization.
Supply Chain 4.0 will affect all areas of supply-chain management. Supply-chain planning will benefit tremendously from big data and advanced analytics, as well as from the automation of knowledge work. A few major consumer-goods players are already using predictive analytics in demand planning to analyze hundreds to thousands of internal and external demand-influencing variables (e.g., weather, trends from social networks, sensor data), using machine-learning approaches to model complex relationships and derive an accurate demand plan. Forecasting errors often fall by 30 to 50 percent.
Heavily automated, fully integrated demand and supply planning breaks traditional boundaries between the different planning steps and transforms planning into a flexible, continuous process. Instead of using fixed safety stocks, each replenishment-planning exercise reconsiders the expected demand probability distribution. Consequently, the implicit safety stocks are different with every single reorder. Prices can then be dynamically adapted to optimize profit and minimize inventories at the same time.
In the consumer-goods industry, several of the most prominent global conglomerates are leveraging advanced planning approaches, and a strong interest in broader application can be observed.
Logistics will take a huge step forward through better connectivity, advanced analytics, additive manufacturing, and advanced automation, upending traditional warehousing and inventory-management strategies. Easy-to-use interfaces such as wearables already enable location-based instructions to workers, guiding picking processes. Advanced robotics and exoskeletons could have equally dramatic effects on human productivity in warehouses.
Autonomous and smart vehicles will lead to significant operating-cost reduction in transportation and product handling, while at the same time reducing lead times and environmental costs. Linking warehouses to production loading points may even enable entire processes to be carried out with only minimal manual intervention. Finally, as production facilities start to rely more on 3-D printing, the role of the warehouse may change fundamentally.
Performance management also is changing tremendously, with several major food companies taking a lead in making detailed, continually updated, easily customizable dashboards available throughout their organizations. Gone are the days when generating dashboards was a major task and performance indicators were available only at aggregated levels. Instead, performance management is becoming a truly operational process geared to real-time exception handling and continuous improvement, rather than a retrospective exercise on a monthly or quarterly basis.
Using data-mining and machine-learning techniques, this type of revamped performance-management system can identify an exception’s root causes by comparing it with a predefined set of underlying indicators or by conducting big data analyses. The system can then automatically trigger countermeasures, such as by activating a replenishment order or changing safety-stock or other parameter settings in the planning systems.
Order management is improved through a pair of measures: no-touch order processing integrates the ordering system to the available-to-promise (ATP) process, and real-time replanning enables order-date confirmations through instantaneous, in-memory rebuilding of the production schedule and replenishment needs in consideration of all constraints. The net result is reduced costs (via increased automation), improved reliability (via granular feedback), and better customer experience (via immediate and reliable responses).
The supply-chain cloud forms the next level of collaboration in the supply chain. Supply-chain clouds are joint supply-chain platforms between customers, the company, and suppliers, providing a shared logistics infrastructure or even joint planning solutions. Especially in noncompetitive relationships, partners can decide to tackle supply-chain tasks together to save administrative costs and learn from each other.
One leading consumer conglomerate has already found that collaboration along the value chain allows for much lower inventories through an exchange of reliable planning data. It also slashes lead times, thanks to instantaneous information provision throughout the entire chain, while providing an early-warning system and the ability to react fast to disruptions anywhere.
Following the need for further individualization and customization of the supply chain, supply-chain setups adopt many more segments. To excel in this setting, supply chains need to master microsegmentation. A dynamic, big data approach allows for the mass customization of supply-chain offerings by separating the supply chain into hundreds of individual supply-chain segments, each based on customer requirements and the company’s own capabilities. Tailored products provide optimal value for the customer and help minimize costs and inventory in the supply chain.
Eliminating today’s digital waste and adopting new technologies together form a major lever to increase the operational effectiveness of supply chains. The potential impact of Supply Chain 4.0 in the next two to three years is huge. Expectations include up to 30 percent lower operational costs, 75 percent fewer lost sales, and a decrease in inventories of up to 75 percent. At the same time, the agility of the supply chains should increase significantly.
How did we calculate these numbers? They are based on our experience with numerous studies and quantitative calculations. The three performance indicators are highly correlated; for example, an improved inventory profile will lead to improved service level and lower cost.
Supply-chain service/lost sales. When customer service is poor, the driver is either a wrong promise to the customer (e.g., unrealistic lead times), a wrong inventory profile (ordered products are not available), and/or an unreliable delivery of parts. Lost sales in addition occur if the required products are not available on the shelf or in the system; customers will decide to switch to another brand. This is true for both B2C and B2B environments.
Service level will increase dramatically when the supply chain significantly improves interactions with the customer, leverages all available point-of-sale data and market intelligence, improves the forecast quality significantly (up to more than 90 percent in the relevant level, e.g., SKU), and applies methods of demand shaping in combination with demand sensing to account for systematic changes and trends. With the resulting service improvement, lost sales will decrease significantly.
Supply-chain costs. Driven by transportation, warehouse, and the setup of the overall network, the costs can be reduced by up to 30 percent. Roughly 50 percent of this improvement can be reached by applying advanced methods to calculate the clean-sheet costs (bottom-up calculation of the “true” costs of the service) of transport and warehousing and by optimizing the network. The goal should always be to have minimal touch points and minimal kilometers driven while still meeting the required service level of the customer. In combination with smart automation and productivity improvement in warehousing, onboard units in transportation, etc., these efforts can achieve the savings potential.
The remaining 15 percent cost reduction can be reached by leveraging approaches of dynamic routing, Uberization of transport, use of autonomous vehicles, and—where possible—3-D printing.
Supply-chain planning. The planning tasks such as demand planning, preparation of S&OP process, aggregated production planning, and supply planning are often time intensive and conducted mainly manually. With advanced system support, 80 to 90 percent of all planning tasks can be automated and still ensure better quality compared with tasks conducted manually. The S&OP process will move to a weekly rhythm, and the decision process will be built on scenarios that can be updated in real time. This combination of accuracy, granularity, and speed has implications for the other elements, such as service, supply-chain costs, and inventory. Systems will be able to detect the exception where a planner needs to jump in to decide.
Inventory. Inventory is used to decouple demand and supply, to buffer variability in demand and supply. Implementing new planning algorithms will significantly reduce the uncertainty (the standard deviation of the demand/supply or forecast error), making safety stock unnecessary. The other important variable to drive inventory is the replenishment lead time: with more production of lot size 1 and fast changeovers, the lead time will be reduced significantly. Also, long transport time—say, from Asia to the European Union or the United States—will be reduced, due to a significant increase in local-for-local production. In addition, 3-D printing will reduce the required inventory. We would expect an overall inventory reduction of 50 to 80 percent.
The transformation into a digital supply chain requires three key enablers: a clear definition, new capabilities, and a supportive environment. Defining the digital supply chain starts with an understanding of the current operation’s digital waste. Capabilities regarding digitization then need to be built; typically they require targeted recruiting of specialist profiles. The final prerequisite is the implementation of a two-speed architecture/organization. This means that the establishment of the organization and IT landscape must be accompanied by creation of an innovation environment with a start-up culture.
This incubator needs to provide a high degree of organizational freedom and flexibility as well as state-of-the-art IT systems (two-speed architecture independent of existing legacy systems) to enable rapid cycles of development, testing, and implementation of solutions. Fast realization of pilots is essential to get immediate business feedback on suitability and impact of the solutions, to create excitement and trust in innovations (e.g., new planning algorithms), and to steer next development cycles. The incubator is the seed of Supply Chain 4.0 in the organization—fast, flexible, and efficient.
Amazon has expressed a mission to take over the retail world, and it seems to be working. While the company’s chief executive officer, Jeff Bezos, was criticized years ago for plowing profits back into the digital platform, that strategy has given the company the ability to sell virtually anything that can be shipped anywhere.
Their model is that the product is almost a commodity. They can control those products, but what they’re differentiating on is the retail experience and technology. So, they take out all the pain points in shopping, and they lock you in. Amazon Prime is the perfect example. Prime, a subscription service that offers free or low-cost shipping to members, creates incredible loyalty among customers who prefer the ease and convenience.
Another way Amazon builds customer loyalty is through pricing. Its staggering assortment of goods enables it to offer everyday low prices, so shoppers don’t have to watch for a sale. They take out the cognitive dissonance that’s increasingly infecting department store customers who shop and wonder whether today’s the day they should buy something because tomorrow or the next day they might see it at a lower price. There’s a persistent belief that when it comes to clothing, customers prefer to try, touch and feel the garments before they purchase. But more and more consumers are willing to forgo that option for Amazon’s ease.
The old thought is, retail is detail, and they are practicing that at an extraordinary high level. They’re killing legacy retailers who basically have been dropping the ball for years on the basis of poor assortment, poor price strategy and terrible presentation. I think customers, for the most part, will want to touch and feel and try things, but the experience in stores is so abhorrent to so many customers that they’d just as soon not.
The outliers in retail are stores that have managed to stay successful by hyper-focusing on improved customer relations. Nordstrom is one such retailer; Costco is another. If customers are going to spend precious time trudging through a store, they want the process to be pleasant and the sales associates to be attentive.
That’s simply not the case in most brick-and-mortar stores anymore. Too many of our legacy retailers are full of dispirited employees. The stores don’t look crisp and clean, and they’re not well-merchandised, so the malaise that is expanding is really frightening.
More than a dozen clothing retailers that have traditionally populated the American mall landscape have announced bankruptcy, shuttered locations or closed down completely in the last several years, including Macy’s, The Limited, Wet Seal, Bebe, Guess, Payless ShoeSource, BCBG Max Azria, Abercrombie and Fitch — the list goes on. Those who haven’t shut down or scaled back are stagnant, such as Target and Kohl’s.
The legacy retail business is in terrible shape. For big players like Macy’s — who are not in imminent danger of bankruptcy but, frankly, don’t have a strategy to go forward — this is breakage that is just starting to reveal itself. We’re looking at a paradigm shift that’s just getting started. But it’s a fool’s game for these retailers to try to compete with Amazon at this point. They need to chart their own path and figure out what will work.
If you are not willing to change, you are going to lose in this market. For these legacy retailers, it’s very hard for them to change because they’ve had so much infrastructure, they’re so big and they’ve had this established way of doing things that it’s hard for their systems to change, it’s hard for their people to change. Some smaller boutiques and retail clothiers have enjoyed success because they offer their products online, have good customer service and appeal to certain types of shoppers. These stores are green shoots and disruptors.
It’s a myth that all the lost retail jobs will be replaced. While it is true that Amazon is adding a significant number of positions, the assumption that those jobs will be filled by laid-off retail employees is not. Amazon’s fulfillment centers typically are not in major metropolitan areas with a large population, and laid-off retail employees may not be able to pick up and move to where these centers are. The nature of the work at fulfillment centers is also very different than the customer-centric jobs that traditional store employees are used to. In addition, there are few job-retraining programs available. The retail industry is shedding jobs a lot faster than e-commerce like Amazon is adding them. There are going to be far more retail workers out of work than, for example, coal miners.
It’s fair to say that Amazon now provides the largest cloud computing service in the world. The company is continuously investing in technology and infrastructure, including its fleet of delivery jets, trucks and drones. Amazon is also refining its formidable ability to collect and aggregate data about customer preferences.
This is an outfit that doesn’t consider any boundaries as insurmountable. We recently peeked at Amazon’s first grocery store in Seattle, in which customers can walk in and out with nothing, but all their purchases are uploaded to their account and delivered. Amazon’s goal is to lock you into their universe. They can recommend such personalized products that you’ll never want to leave.
At the Rebecca Minkoff store in New York’s Soho, “smart” digital walls and mirrors let you tap for a different clothing size or color — as well as a free glass of champagne. At the Warby Parker store near Hollywood, you and your friends can create your own 15-second shareable video in a “green room” furnished with props and backdrops. At Jungle Jim’s International Market near Cincinnati, bizarre animatronic figures entertain you while you browse unusual gourmet foods. And at Pirch’s luxury home appliance stores, you can try out the appliances before buying them, including shower heads (just bring your own swimsuit.)
Other brick-and-mortar retailers offer cooking classes, celebrity appearances, personalized makeup advice, wine tastings: the list goes on and on. Much of this activity, of course, is intended to combat the juggernaut of online ordering via Amazon and other sites.
“The customer can get all of their clothing without ever leaving their bed,” says Stacey Bendet, CEO and creative director of designer clothing company Alice + Olivia. “So the experience in-store has to become more VIP, more exciting.”
But are these in-store “experiences” worth the effort and money that retailers are pouring into them?
“You can’t just [look at] … what’s the ROI on a certain thing in the store, like short-term, immediate impact,” says Denise Dahlhoff, research director at Wharton’s Jay H. Baker Retailing Center. While various measurements are possible — comparing test stores to control stores, measuring differences in amount of revenue or number of new customers — she recommends thinking bigger-picture. Store experiences should be considered holistically, “part of your branding and marketing in general.”
Barbara Kahn, a Wharton marketing professor and director of the Baker Retailing Center, cautions that not all in-store experiences are created equal. For example, simply installing a photo booth in your store probably isn’t enough to get people to come in and shop. Rather, retailers should “create something that’s of value … an experience that people would go out of their way to take part in. Not just incidental experiential trappings.”
She talks about “drop culture” as a successful example. Urban clothing brands such as Supreme create specially-timed launches — “drops” — of unique new apparel that actually draw crowds. The scenario is similar to people camping out outside an Apple store to get the newest iPhone. With Supreme’s drops, she says, you can only get that cool thing if you’re in the right place at the right time. The customer is essentially purchasing excitement, a crowd experience, a social experience in addition to the clothing itself.
Another experiential success, in Kahn’s view, is Eataly, a chain of Italian marketplaces that combines restaurants, grocery stores and cooking schools. It capitalizes on the appeal of Italian culture and sophistication. “It all works together like a little universe,” she says. “There’s a nice synergy there; you can taste the foods in the restaurant … you might then go to the grocery store to buy it so you can make it at home.”
Beauty products, too, lend themselves well to in-person experiences, says Kahn. She says makeup is about “trying on, learning, a little bit of instruction about … what will look good on me particularly; talking to other people.” Physical stores such as Sephora and Ulta are doing well as a result. While cosmetics are of course sold online, too, says Kahn, those transactions are missing that experiential, social piece.
Dahlhoff notes that successful in-store experiences, “because they often involve human interaction, and services, and unique kinds of things … create stickiness and loyalty.” And there are other benefits. For instance, customers engaged in an experience tend to think less about how much things cost. “In this retail environment where everybody’s so focused on promotions and discounts, and nobody buys at full price anymore, adding an experience … diverts your attention from the price,” she says.
Generating in-store excitement is also a way for a store whose offerings don’t change very much to keep customers interested, says Dahlhoff. She references J. Crew, which has held special themed weekends. On one weekend, customers learned how to create their own fresh flower bouquets. Another featured a career theme: Store associates gave advice on career-boosting outfits, and shoppers who spent over a certain amount received a premium LinkedIn account.
If someone has an interesting experience in a store, they may well share it on social media, Dahlhoff says. Wharton marketing professor David Bell agrees and considers this a key benefit of today’s in-store events. He comments that 20 years ago, if 100 people visited your store, maybe 10 of them would tell someone else about the experience. But today, “when you have a physical footprint somewhere, 100 people come and maybe 20,000 learn about it because whatever goes on in there can be amplified through digital.” Your audience has an audience, he says.
“The sales per square foot of someone like a Warby Parker, or an Away luggage … those folks are definitely doing very, very well from the offline retail,” adds Bell.
Less Buzz, More Data?
Wharton marketing professor Peter Fader isn’t quite as convinced of the value of in-store experiences. While acknowledging that they do have a role in the marketing mix, he notes that competing to create ever-more-stimulating retail environments could lead to an “arms race,” similar to the way online retailers have competed to reduce shipping costs and have basically trained customers to expect free shipping. What’s more, it’s easy for businesses to copy in-store event ideas from each other so they no longer serve as differentiators.
Moreover, efforts to create dynamic in-store experiences don’t necessarily insulate retailers from the problems currently plaguing the industry – for example, J. Crew has struggled with debt woes and the recent departures of longtime CEO Mickey Drexler and creative director Jenna Lyons amid declining sales.
Plus, Fader says, with many of the things we shop for, we’re not really looking for a fun and engaging experiences. If you’re just buying underwear or a dish drainer, you probably want to get in and out as quickly as possible. Of the Rebecca Minkoff store experience, he comments, “As if that is going to break them away from the pack. As if that is going to keep people from going to Amazon: ‘Oh, I can get champagne.’”
In fact, he characterizes many of the initiatives that brick-and-mortar retailers engage in — at a time when many companies are forced to close their doors — as merely “rearranging deck chairs on the Titanic.”
For Fader, a better strategy for retailers is to focus on data and analytics: to understand their customers’ lifetime value and find ways to make the in-store experience better for individuals who are more valuable to the business. Essentially, the idea is to treat different shoppers differently and solve important customers’ pain points on the spot, rather than trying to “wow” everyone en masse. “Someone walks into your store and you should know right away, through a mobile app or some kind of status indicator, whether they’re worth lavishing attention on or not.”
But in his experience, retailers are resistant to the idea, unlike their colleagues in industries such as banking, packaged goods, gaming and pharmaceuticals. “I build a lot of these predictive models and teach a variety of companies how to build appropriate strategies around them. And there might be no sector … less willing to embrace it than retailers.” He says that as a group, retailers are risk-averse, afraid of data, and too set in their ways — and he believes they are making a mistake.
“A magic [dressing room] mirror, anyone can have,” says Fader. “But a deep understanding of your customers … you just can’t buy that.”
Online-offline Retail Hybrids
Tying together their online and offline operations – seamlessly — is a goal for many retailers, according to the experts. And it’s evident that online and offline retailers each recognize the value of each other’s business. Bell points out that digital native Amazon recently bought Whole Foods, and brick-and-mortar king Walmart has been buying up a spate of small online companies.
Bell notes that the jewelry, fashion apparel, and home goods sectors have had good success with the “buy online, pick up in store” model. When the customer visits the store to pick up, they may start browsing other merchandise, too. Kahn describes another big benefit of this model: it’s cheaper for the retailer. “The most expensive part of delivery is the last mile to the house. If you can get the customer to go into the store and pick it up instead of having it trucked to you, that reduces costs.”
Alice + Olivia’s Bendet notes that over the past few years, a major focus and challenge for her business has been integrating wholesale, retail and e-commerce “to create a more seamless shopping experience” and to build back-end technology for her company’s future. “I think the best experience is a hybrid one,” she says.
Bell believes that brick-and-mortar retail’s future lies in developing a white-hot focus on customer service and divesting itself from carrying inventory. “What’s wrong with a traditional store in my view is that you’re trying to accomplish too many things. You’re trying to hold inventory, which is a real pain the neck, [dealing with] how many blue blazers you need to have on the floor and how many in the back…. Trying to provide excellent service and manage inventory all at once is completely inefficient.”
He foresees physical stores which showcase only a few key items and offer personalized customer interactions. Purchases are then completed online and the items are shipped to the customer.
He says a similar model can be seen at the online men’s fashion retailer Bonobos (one of the companies recently purchased by Walmart), which runs physical stores called Bonobos Guide Shops. A customer makes a reservation to meet with an associate, who shares style pointers and personalized recommendations. The store holds a limited amount of stock, but has swatches of all the colors available. The shopper makes his purchase on an iPad, and the items are shipped to him the next day. “Separating out the inventory holding from the experience is doing what a store’s really good at, which is giving someone potentially a great experience with the brand,” Bell notes.
Bendet echoes this type of approach to the physical store: “Our salespeople are trained to be stylists, creating a more exclusive and personalized shopping experience.”
Whatever form the experts project physical stores would take in the future, none predict they would disappear entirely. Kahn, while stating that the U.S. is “over-stored” and that many stores are closing or being turned into distribution centers, says, “People do have a need for enjoying their day, for something that’s fun to do.”
She compares in-person shopping to going to the movies — something that pundits were predicting “forever and ever” would go away. “Some people want to go out on a Friday night. They want to do something; they don’t want to hang out in their house the whole time.”
Building hybrid retail is a new challenge that requires new guiding principles. Digital retail relies on the winner-take-all dynamic underpinned by zero marginal costs, network effects, access to data, and user convenience. But hybrid retail cannot succeed through technology and scale alone. Operating in the physical realm means dealing with the messiness of hardware. Hybrid retail tends to have a narrower scope because they require deep domain knowledge and dense business relationships. They tend to grow more slowly both because of physical constraints and because the prerequisites of growth can often be acquired only through experience and commitment. Therefore, hybrid ecosystems will likely not achieve the breadth and scale of purely digital ecosystems.
Consider Toyota, which orchestrates a hybrid ecosystem of suppliers, innovators, and collaborators to enable not just superior manufacturing but constant innovation.6 Toyota’s super-ecosystem is a prototypical hybrid ecosystem, in that it simultaneously involves a dense network of traditional physical suppliers and a newer digital ecosystem that can explore capabilities like fleet management, ride sharing, and autonomous driving. Toyota has built this ecosystem over decades, investing in its partners and their capabilities. Even so, Toyota is just one of the many players in the auto-manufacturing space, with less than 20% share globally.
The difference between hybrid ecosystems and those that are purely digital is reminiscent of a pattern in ecological succession. Pioneer species (so-called r species) in a new niche succeed by prioritizing growth. They quickly conquer the niche by reproducing and dispersing quickly. However, as the ecosystem matures and the environment becomes more diverse, new strategies emerge. In later stages, species optimized for competition in higher-density environments (K species) tend to thrive. Their strategy involves more parental investment in fewer offspring. The strategy shifts are a natural consequence of the changing ecological environment. In the same way, different types of business environments favor different managerial approaches. The management of hybrid ecosystems requires major shifts in approach.
Technology and Relationships. Technological edge is clearly a key success factor for digital ecosystems; delivering a strong digital product, scaling it, and making it possible for stakeholders to seamlessly build on it are all primarily technical challenges. Digital ecosystem orchestrators tend to focus only on certain relationships, neglecting others. For example, Amazon is known for its customer obsession—and for being less focused on its third-party vendors. Hybrid ecosystems face high technological requirements, but in the physical world, where business success often depends on customization, consulting, or enablement, relationships are also important. For an ecosystem to reach critical mass, the orchestrator must deeply understand and shape the real-world behaviors of the people and the enterprises in it. This requires building strong relationships with multiple actors and often developing specific capabilities. Creating value in hybrid ecosystems requires not only transactions but also change management.
Depth and Breadth. The breadth of successful digital ecosystems is one of their chief characteristics. For example, the vast majority of consumers in developed countries use the services of near digital monopolies like Google and Facebook. But these ecosystems tend not to be deep; they are not optimized for specific niches or particular modes of use but instead fulfill the “common denominator” use cases. Hybrid ecosystems need to develop sufficient breadth to reach critical mass but, at the same time, must have a deep focus on particular problems and deploy the relevant domain expertise to address them. Their business model relies on creating greater value by solving specific problems rather than populating a large open niche. Only in this way will they be able to fully satisfy customer needs.
Creation of New Ecosystems and Rejuvenation of Old. Digital ecosystems mostly occupy completely new niches, and the occupiers are mostly upstarts. They have the luxury of starting from scratch and writing the rules as they build out new markets. Hybrid ecosystems will mostly involve existing niches, with existing capabilities and existing competitors. Therefore, hybrid ecosystems must balance between creating entirely new capabilities and taking advantage of or actively reshaping existing ones. They must be able both to create something new and to rejuvenate something old.
It’s natural to ask whether any company has successfully translated these guiding principles into action. Let’s turn to a company that has done so serially and has demonstrated a robust repeatable formula.
Recruit is currently one of Japan’s most successful large companies, with close to 20% annual growth in the past five years in a sluggish economy. The company’s approach to management: to build multiple digital-physical ecosystems in areas as diverse as tourism, dining, education, used-car sales, and recruiting. In each area, Recruit aims to be the orchestrator of a tight, vertically focused hybrid ecosystem of digital and physical players.
For example, Recruit has built an ecosystem of restaurants in Japan by bringing together hundreds of thousands of restaurants and various service providers and developers onto a single platform. Through this platform, AirREGI, restaurants are able to access not only a variety of Recruit’s own services but also dozens of others in areas such as advertising, accounting, work force management, procurement, payment processing, and even cutting-edge recommendation engines powered by machine learning. Although Recruit’s role was to build the digital platform, the company was able to penetrate the physical restaurant market only because it had a capable field force that already had relationships with restaurants and vendors.
Recruit has been successful in building hybrid ecosystems in part because the company underscores the importance of coming up with generalizable formulas (called “kata”). In fact, when it chooses employees for its company-wide innovation award, it explicitly looks for innovations that have the potential to be applied in multiple contexts. Recruit calls its formula for building and managing successful hybrid ecosystems the Ribbon Model. The following five imperatives are some of the key elements of this model.
Create a culture of serial entrepreneurship. Given their tight vertical focus, hybrid ecosystems tend to lack the scale that broad digital ecosystems enjoy. This implies that companies that seek to become hybrid-ecosystem orchestrators must create multiple ecosystems in order to maintain their growth trajectory. In fact, this is how Recruit approaches growth: it has invested in more than a dozen ecosystems.
This means that Recruit must hire and train entrepreneurial talent, and it does so by building an entrepreneurial ecosystem within the company. Recruit acts as seed accelerator, venture capitalist, advisor, back-office service provider, and recruiter for would-be entrepreneurs within the company. Recruit is able to systematically identify, encourage, and nurture entrepreneurial talent through programs that allow any employee, potentially in collaboration with outside stakeholders, to apply to start new businesses, which could develop into new ecosystems. One such program, New Ring, receives more than 1,000 proposals every year and has given birth to some of Recruit’s core ecosystems.
Combine domain and technology experts. The competitive edge of digital ecosystems often lies in their superior technology, and the orchestrators unsurprisingly obsess over technological talent: strong engineers and designers are crucial for scalability and creating a superior product experience. However, in hybrid ecosystems, domain experts are just as necessary to uncover and address deep customer needs. Therefore, the crucial capability of hybrid ecosystem orchestrators is to recruit both domain and technology experts and to bring them together to solve challenges collaboratively.
Recruit facilitates collaboration between such experts by allowing some of its top talent to roam freely across business domains. For example, Recruit was able to vastly reduce the labor cost associated with processing online customer reviews by connecting a content management expert and a machine learning expert, who technically resided in separate companies. Such successes are shared in an enterprise-wide platform to motivate other technologists and domain experts to seek opportunities to collaborate.
Balance all sides of the ecosystem, including the supply side. Within an ecosystem, there is often a particular group of stakeholders that generates revenue (consumers, advertisers, or business clients, for example). It’s tempting to focus on catering to the needs of the revenue-generating side. In digital ecosystems, this group is often end consumers, and suppliers and innovation partners in these ecosystems often feel neglected or squeezed. Hybrid ecosystems, more so than other ecosystems, depend on all participants, and their orchestrators must make sure that each stakeholder group gets its fair share of attention.
Recruit has built this notion into its model for ecosystem management, which places Recruit as the glue between consumers and suppliers, with many explicit milestones between first contact and value creation. For example, the milestones on the consumer side of the used-car-sales ecosystem include number of views, number of inquiries, number of in-person contacts, and number of sales. These milestones are then translated into and measured as KPIs, along with indicators that track the overall health of the ecosystem.
Experiment and co-evolve with other stakeholders. In order to thrive in the long run, hybrid ecosystems must adapt and learn continuously. The orchestrator, which is in the position to set the ecosystem’s direction, must drive this process. But rather than setting the agenda by dictum, the orchestrator should act as an antenna that picks up on learning opportunities as well as an enabler to help guide other partners to address those opportunities. Orchestrators must have the humility to experiment and learn with other members.
At the heart of Recruit’s ecosystem innovation strategy is its strong field force and its emphasis on co-evolution. Recruit’s field force is trained not only to generate revenue and hit targets, but also to build deep relationships with clients so that it’s able to learn about latent opportunities in the market and mobilize stakeholders around them. For example, Recruit coordinated a large campaign called Yuki Maji! 19 to revitalize winter sports in Japan. This campaign, which provided free ski-lift tickets to 19-year-olds, initially experienced strong pushback from resort owners. However, by working with advertisers, government agencies, resorts, and industry organizations, Recruit was ultimately able to persuade hundreds of resorts to participate in the effort, which was a commercial success.
Have the courage to re-engineer old ecosystems. Every successful company faces a dilemma born from its very success: it is often difficult to change what has been working. This is the familiar story of Kodak and Blockbuster, who had extremely profitable business models and ample resources, but still failed to respond adequately to highly visible disruptions. This is a persistent threat for both physical incumbents and digital natives. To win the battle for new hybrid niches, companies must be willing to cannibalize their existing business models, whether digital or physical. Often, this requires creating a sense of urgency to stir up energy for self-disruption.
Recruit experienced this in the early 2000s, when it transformed its magazine-based tourism ecosystem (called Jalan) into an online travel ecosystem. Jalan, which sold advertisement slots in its magazines, faced revenue declines of 5% a year because of increasing competition. Jalan’s online platform had no booking functionality and could not compete with more advanced online travel agents. In the 2000s, Jalan went through a painful transformation, replacing its old business model with a new, untested online model. This required both decisiveness and persistence: Recruit placed new managers (from outside the travel industry) in the business to signal a new direction, and it invested in the new model for seven years before reaching profitability. The approach was like gene augmentation therapy—the company pulled in completely new “genes” to endow an old ecosystem with new capabilities.
Recruit’s experience demonstrates how to be successful in the realm of digital-physical ecosystems. But purely physical incumbents can also be competitive in this realm. Emerging opportunities at the intersection of the digital and the physical give incumbents a new opportunity to build valuable ecosystems.
To make the most of these opportunities, incumbents should recognize that they are no longer playing catch-up but are pioneering a new game—one in which they have the potential to define new niches and new rules. Leaders of physical businesses can take a few lessons from Recruit to aid in their journey.
First, they must reinforce strategic ambidexterity: the ability to explore the new and exploit the old at the same time. Managing conflicting goals is crucial for all business leaders today, but it is doubly so for those attempting to build hybrid ecosystems. That task will be about both technology and relationships, breadth and depth, and creation and rejuvenation.
Second, they must have the courage and the ability to envision and shape new spaces and orchestrate corporate and noncorporate stakeholders. These “shaping capabilities” are often more strongly associated with digital giants than with physical incumbents. It is thus all the more urgent that physical incumbents start investing in the management skills essential for ecosystem building and management.
Finally, they must treat hybrid ecosystems as repeated rather than one-off games. Unlike in digital ecosystems, where winning once was sufficient, hybrid ecosystems will often involve multiple wins and a system for achieving this. Companies may end up as orchestrators in one ecosystem and participants in others. This dynamic will favor plays that invest in a repeatable formula and prioritize trustworthiness and collaboration.
The digital ecosystem is one of the most successful business model innovations we have ever seen. Nevertheless, it is only one of many possible models of collaboration in business. As digital economies grow and proliferate, they will inevitably create new opportunities for digital-physical ecosystems. Incumbents should start preparing today.