A recent volume co-authored by the Director of the Oxford Institute of Retail Management at Saïd Business School, Richard Cuthbertson asks the question: do we know how to innovate successfully in a consumer driven society?

The leading challenge for both business and society today is that future economic growth is increasingly based on services rather than manufacturing products. In our advanced consumer society, services dominate and are provided through digital as well as physical channels by local, national and global firms: service and knowledge-based economies make up about 70% of GDP.

In order to encourage growth, innovation must be understood within this context. However, the Organisation for Economic Cooperation and Development (OECD) reports a considerable knowledge gap when it comes to service innovation.

In their book Innovation in an advanced consumer society, Richard and his fellow author, Peder Inge Furseth, identify the ways in which value can be increased for all stakeholders through both incremental and disruptive innovation, by developing an innovation theory focussed on today’s consumer society: Value Driven Service Innovation. This can be put into practice via a methodology also introduced in the book: the Service Innovation Triangle.

Innovation in a service-dominant, globally-reliant, digitally-enabled, consumer society means that “how” is as important as “what”. Invention can happen in isolation; innovation in today’s world requires collaboration, co-ordination, and integration. Firms need to align their business model, service system, and values with customer experiences that resonate not only with customers, but also with the firm’s owners and suppliers.

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.


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