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For at least fifteen years, department stores have been navigating a structural decline. Footfall in city centres has fallen in every measured year since 2009. Spending on fashion, department stores’ core category, has contracted. And the pandemic accelerated a migration to online shopping, compressing a decade of change into two years.

The industry’s initial response was to bolt e-commerce onto the existing model. What started as a modest add-on — often an experiment operated on the same principles as the traditional stores — soon grew into a parallel business with fundamentally different cost structures. Against in-store conversion ratios of 25% or more, online hovered at 1–2%. Against sales per square metre, online invoked profit per transaction. Against expectations of bottom-line profit, online demanded years of loss-funded growth. Department store companies found themselves running two business models simultaneously: one built on real estate and people, the other requiring heavy investment in systems, fulfilment, and digital marketing. Integration proved expensive, profitability remained elusive — even for the pure players — and the admittedly complex model of the traditional department store proved inadequate for a truly omnichannel operation.

The IADS Academy 2021 cohort framed this situation in scientific terms: the existing paradigm can no longer solve the problems. The question is no longer whether to become omnichannel, but how to move beyond integration into something more deliberate, precise, and profitable. The answer lies in four interconnected shifts:

• From channel ubiquity to journey optimisation,
• From channel-based P&Ls to customer-centric financial architecture,
• From uniform store networks to precision-engineered physical assets,
•  From inherited commercial models to purpose-built omnichannel ecosystems.

The article below reviews five years of IADS research, the current state of academic knowledge and the latest business articles on the topic, including research by Dr Christopher Knee, Honorary Advisor of the Association, and Professor Robert Rooderkerk, Academic Advisor of the Association.

From ubiquity to optimisation: steering journeys, not channels

The maturity gap

The term “omnichannel” is often misunderstood. As Robert Rooderkerk, Associate Professor at Erasmus University Rotterdam and academic advisor to the IADS, emphasises, omnichannel is not about being present in every possible place at once, but about being present where it matters, delivering value to both the customer and the business simultaneously.

In contrast to multichannel retail where channels coexist without coordination, omnichannel combines expansion into different channels with integration to facilitate seamless customer journeys. Yet as research argues, the usual tension between the market’s desire to offer a seamless experience and operations’ desire to maximise efficiency is particularly pronounced in omnichannel retail. Balancing the advantages of seamless integration with the cost of seamlessness is a standard operations management trade-off — but one that most department stores have yet to master.

To illustrate this, Rooderkerk has outlined a maturity model that most companies are currently moving through. The journey typically begins in a fragmented, multichannel phase, where online and offline channels are siloed. This setup fell short during the pandemic, when retailers rushed to stand up click-and-collect using manual workarounds and disconnected systems — the “experimentation” stage. As retailers progressed, they moved into a “ramping up” phase — scaling services like same-day click-and-collect, expanding geographic reach, and increasing speed. Most companies today remain stuck here, or in the next stage: “channel integration,” focused on order orchestration through centralised systems that choose the best fulfilment nodes based on stock availability, distance, and efficiency.

Yet many organisations are still held back by internal silos between digital and physical teams, leading to “tribal” conflicts over budgets, authority, and strategy. The move from a channel-led to a customer-led organisation requires what Rooderkerk terms customer-journey segmentation: identifying and investing in the most valuable or frequent journeys based on transaction volume or customer value. Instead of trying to optimise every possible journey, it’s all about focusing on those with the greatest return potential.

From supporting journeys to shaping them

The most advanced retailers do not just support these journeys — they shape them. Such maturity, which Rooderkerk calls “omnichannel optimisation,” is about steering customers toward the channels and touchpoints that improve profitability or customer equity, rather than fixating on perfectly seamless customer journeys that do not generate revenue.

The levers available to retailers are more varied than commonly assumed. Holland & Barrett, for instance, uses market-specific nudges to influence channel selection: in the UK, customers are prompted to choose click-and-collect with a prominent “free” message; in the Netherlands, the highlighted benefit is sustainability. Swatch demonstrates how simple, inventory-aware rules can avoid waste: when the stock of a particular watch is low, the reservation option disappears from the site, preventing high no-show rates that tie up valuable inventory. At Coolblue, a Dutch consumer electronics retailer, the physical placement of click-and-collect points meaningfully affects cross-sell potential. In LATAM, 50% of Falabella’s online orders are collected in-store, and these visits drive a 16% increase in gross sales from cross-sales at pick-up. However, before achieving such results, they went through a learning curve, observing that click-and-collect services located at the store entrance consumed premium square metres without NPS payback and created congestion, also preventing cross-selling opportunities.

Fulfilment as a strategic lever, not an operational afterthought

The taxonomy of omnichannel fulfilment models is richer than most department stores realise. These models span the forward supply chain (buy online, pickup in-store, reserve online, pickup in-store, buy in-store, ship to home), the reverse supply chain (buy online, return in-store, buy in-store, return online), and supporting models (ship-to-store, ship-from-store). Each carries distinct trade-offs between customer benefits and operational complexity, and each represents a potential lever for journey optimisation — not merely a service to be offered.

What makes these decisions strategic rather than merely operational is the downstream impact on customer behaviour. Failing to deliver an online order not only causes direct revenue loss but also reduces future customer spending. A study shows that when missing products were reimbursed, customers delayed their next order by almost 24 hours and spent approximately one euro less. When products were substituted — a strategy most retailers assume softens the blow — the delay nearly doubled. These effects compound over the year: the total revenue loss from fulfilment failures exceeded 12% of annual revenue, nearly double the 7% direct cost of reimbursing for missing items. The strategic implication is clear: fulfilment failures may be operational in nature, but their consequences are deeply strategic.

The incentive gap

Among the levers of optimisation, incentives are key. Rooderkerk identifies a worrying 7% to 10% order-cancellation rate for ship-from-store orders, driven by insufficient inventory data or a lack of store compliance. Without financial incentives, stores may deprioritise e-commerce orders or even cancel them to conserve inventory for walk-in customers. Companies like Adidas have responded with reward-based routing, sending orders to stores with strong compliance and fulfilment performance. Walmart and Target rely on real-time inventory accuracy as a key routing factor — Target, after a $3 billion investment, now fulfils 95% of online orders through nearly 2,000 stores. Optimisation requires aligning technology with human incentives.

For department stores specifically, this represents both a challenge and an opportunity. The department store’s distinctive asset — its large, centrally located, multi-category physical space — becomes a powerful fulfilment and experience node only if its organisation can operate as a unified entity rather than a collection of competing channels.

Measuring differently: the omnichannel P&L and the omni-cluster

Why existing instruments fail

In 2021, the IADS Academy was tasked with addressing this precise problem: what would a truly omnichannel P&L look like, and which KPIs would support it? Their diagnosis was blunt: as long as the business consisted of stores, and even stores with a small separate online channel, the traditional model held up. Since cross-channel and multi-channel started impacting the core business, attractiveness and profitability have plummeted.

A key reason for this failure is the persistence of separate P&Ls for stores and for online. Various efforts have been made in the past to credit online sales to stores, but these have been artificial. The fundamental problem is that employees are assessed on KPIs which discourage an omnichannel approach, while data is unavailable to track customers, inventory, and fulfilment in sufficiently granular form to allow appropriate attribution.

Traditional KPIs are backwards-looking and channel-specific. Traffic, conversion rate, sales per square metre — these tell what happened in a single channel yesterday. They cannot evaluate the complex drivers of customer decisions that span multiple touchpoints over time. The shift from a product-based model to a customer-based model means that customer KPIs must become financial KPIs.

The omni-cluster: a customer-centric financial architecture

The IADS Academy’s central proposal was the concept of the “omni-cluster”: clusters of stores and online based on customers, whatever their channel. An omni-cluster is, at its core, a group of customers. All revenues, returns, and costs associated with a customer belong to that omni-cluster. The only rule is that any customer can belong to only one omni-cluster, even if they occasionally shop, return, or collect in several physical stores.

Dr Christopher Knee from IADS subsequently developed this concept further. In its simplest configuration, an omni-cluster consists of one or more stores and the online customers who reside around those stores — a model favoured by Magasin du Nord, for example. Where customer behaviour is more complex — say, when online customers prefer a distant flagship store — a mixed omni-cluster might include a store, its geographically close online customers, and online customers who are geographically closer to another store. This may occur when stores are clustered by size, as at Manor. For businesses with a large tourist clientèle and franchised operations, still more complex configurations may apply — as might be the case at Galeries Lafayette.

The criteria for omni-cluster membership are flexible and vary according to the company’s history, organisation, and market. The aim is to group revenues and costs into a number of omnichannel P&Ls which cover total operations of a cluster, which can serve to manage operations through related KPIs, and which can be consolidated into a total business P&L. Three key consolidated measures were proposed: Customer Lifetime Value (CLV), which is by definition forward-looking and allows the identification of profitable customers; sales per employee across all employees in a given cluster irrespective of channel; and cost of returns as a proportion of total sales, covering costs incurred by all channels.

An irreversible strategic choice

Once established, an omni-cluster ecosystem can no longer be dismantled and split up into its original component parts. It competes as a whole, not as a multi-channel entity. The data architecture is not a separate workstream — it is the foundation on which the entire omni-cluster edifice rests, which makes it quasi-impossible to spin off the e-commerce part of the business, as HBC has done with Saks Fifth Avenue and Saks.com, with the results we now know. The integrated omnichannel route is a different bet: a smaller omnichannel department store following it will be capitalising on its unique status as what Bain & Company has called a “regional gem.” The commitment is no less risky than the spin-off route — but it is a fundamentally different vision of what a department store is.

Note: The question that remains largely unanswered however is what a transition phase towards omni-cluster P&L would look like. Who should guide the transition?

The store as a precision instrument: format, network, and category logic

Deconstructing the halo effect

The much-discussed “halo effect” — the idea that opening a store lifts nearby online sales — is often exaggerated: researchers found that store openings reduced online sales. Online net revenue declined by approximately 8–10% across both large- and small-format openings. However, the large, experience-centric stores more than compensated for this cannibalisation, generating a net revenue uplift of 27.5% in the longer term. The small, convenience-centric store, by contrast, cannibalised online sales without producing compensating gains.

These results held even when the analysis was restricted to categories, suggesting that it is the store experience — not assortment breadth — that accounts for most of the performance differences. Large experience-centric stores create value through how they sell, not just what they sell. Their immersive environments and expert service effectively activate both new and existing customers — benefits not replicable in the smaller format.

The physical department store, with its large footprint, broad breadth of categories, and tradition of service, is structurally well-suited to the experience-centric model. But this advantage is not automatic — it requires deliberate investment in the quality of in-store experiences, expert staffing, and sensory engagement. A department store that merely occupies a large space without delivering distinctive in-store utility risks the same outcome as the small convenience format: cannibalisation without compensation.

Not all categories are created equal

Perhaps the most consequential finding for department store executives lies at the category level. Research reveals substantial variation in revenue uplift across 22 product categories following a store opening. Some categories more than doubled their sales, while over 40% showed no significant uplift.

The answer lies in what the researchers call “perceived in-store utility“ — the value customers expect from experiencing a product in a physical store rather than online. This utility was measured across three stages of the customer journey: information search (sensory experience and expert advice), fulfilment (instant gratification), and returns (ease of processing). The results are striking: a one-standard-deviation increase in perceived information-search utility is associated with a 23.6-percentage-point increase in net revenue uplift for destination categories. A similar increase in fulfilment utility yields a 14.6-percentage-point rise. Return utility, by contrast, showed no significant effect — suggesting that experience-centric stores create value primarily by providing information and fulfilment benefits, not by serving as efficient return sites.

Crucially, these effects apply only to destination categories — high-involvement products that independently drive store visits. Accessory categories showed no utility-driven uplift, but several experienced substantial revenue gains, likely driven by cross-buying and bundling with their destination anchors.

Asymmetric assortment integration: a framework for cross-channel allocation

The question of which categories to put where is not merely intuitive — it requires “asymmetric integration“18. In the most common configuration, the online assortment contains the offline assortment plus more — reflecting the long-tail effect and the lower marginal cost of carrying additional products online. But other configurations may be strategically preferable: certain products may belong exclusively in the physical store, while others may be online-only (when physical space constraints or low in-store utility make floor presence uneconomic).

For department stores, this framework is particularly powerful because of the breadth and heterogeneity of their assortment. Fashion, beauty, homeware, and technology do not all create the same in-store value, and within these broad domains, subcategories will vary enormously. A haute couture dress and a basic white t-shirt, a perfume counter and a shampoo display, a curated furniture showroom and a commodity storage solution — each carries a different utility profile across information, fulfilment, and returns. Retailers can survey customers on perceived in-store utilities and use the results to guide assortment planning, space allocation, staffing decisions, and the design of experiential elements. Categories with high information utility warrant deeper assortments, immersive sensory experiences, and more expert staffing. Categories that score primarily on fulfilment may not need floor presence at all — they can be stocked in inventory and surfaced through efficient click-and-collect or virtual aisles. Accessories should be strategically positioned near their destination anchors or bundled into cohesive solutions.

The role of each channel in the customer journey must be explicitly acknowledged when planning assortments. A product with low in-store conversion but high showrooming value may be essential to the store even if it scores poorly on sales per square metre. Conversely, a product with many online views but low conversion — and where webrooming can be ruled out — might benefit from more prominent physical presence, where salespeople can address the uncertainty that holds customers back. This kind of cross-channel intelligence requires integrating data across all channels and touchpoints and rethinking metrics designed for a single-channel world.

Rethinking the network: densification and role-based formats

Beyond format and assortment, Rooderkerk proposes a wider framework for store network strategy that includes expansion, downsizing, relocation, and densification. Densification — adding smaller stores closer to high-density customer segments — is becoming a strategic frontier. Smaller formats can serve two distinct models. The first focuses on specific touchpoints in the journey, offering services such as advice, pickup, or returns without maintaining full inventory (e.g., Nordstrom Local, IKEA’s “Plan & Order“ stores). The second model supports the full journey within a smaller footprint (Galeries Lafayette’s store in Nimes), which, although much smaller than the flagship, offers a curated yet comprehensive assortment tailored to local customers.

For department stores, this suggests that the traditional model of a single large flagship, supplemented by near-replicas, may give way to a differentiated network in which each node plays a distinct role in the ecosystem. The flagship remains the experience-centric anchor; smaller formats serve as gateways to the broader offer, equipped with endless-aisle capabilities to access online inventory. But each store should be judged not only on its own sales per square metre, but also on how it supports customer experience, delivery efficiency, and brand visibility across all channels. In this respect, the Bloomie’s experiment in the U.S. conducted by Bloomingdale’s is highly interesting.

Rethinking the commercial architecture: who owns what in an omnichannel ecosystem?

The inherited model under pressure

The shifts described above — journey optimisation, customer-centric P&L, precision-engineered stores — all presuppose a commercial architecture capable of supporting them. Yet the dominant commercial models of the department store were designed for a different era.

Department stores have traditionally operated across a spectrum of commercial relationships. Each model carries different implications for the levers of omnichannel optimisation. In a conventional model, the retailer controls the stock and can therefore orchestrate fulfilment — routing orders from store or warehouse, offering ship-from-store, managing returns centrally. In a concession model, the brand controls its own supply chain all the way into the store, which creates friction when the retailer wants to use that inventory for e-commerce fulfilment or cross-channel services. The question of “who owns the stock” becomes operationally critical when a store is expected to function simultaneously as a selling floor, a fulfilment node, and a data collection point.

The digital extension: marketplace and e-concession

These tensions are amplified in the digital domain. The online equivalent of concession is the marketplace or e-concession model, where brands use the retailer’s platform to sell directly, with fulfilment handled either by the brand (drop-shipping) or by the retailer. The marketplace model allows department stores to dramatically expand their online assortment without taking on inventory risk, but it also dilutes control over the customer experience and, critically, over customer data.

For an omnichannel department store pursuing the omni-cluster model, this raises questions. If a customer’s journey involves a concession-operated in-store experience, a marketplace-fulfilled online purchase, and a return handled by the retailer’s own staff, to which omni-cluster do the revenues and costs belong? Who holds the unified customer data? How are the KPIs — CLV, sales per employee, cost of returns — calculated when the employees, inventory, and fulfilment processes belong to different entities?

Unbundling and rebundling

The IADS has suggested that a degree of “unbundling” will be necessary to evaluate the costs attributable to an omni-cluster. When that exercise is pursued, it becomes possible to find potentially more appropriate, more efficient, or more effective solutions before rebundling these into an omni-cluster ecosystem. This is the essence of the challenge: the department store of the future must decompose its inherited commercial functions — buying, stocking, selling, fulfilling, servicing, and relating to the customer — and reassemble them into configurations that serve omnichannel logic.

This may mean, for example, that conventional buying is retained for destination categories where the retailer’s curation and expertise add value and where control over inventory enables fulfilment optimisation; that marketplace is used for long-tail categories where breadth matters more than in-store experience; and that concession is reserved for brands whose in-store presence and expertise genuinely enhance the customer experience in ways the retailer cannot replicate. Each model would need to be evaluated not only on its margin contribution but on its compatibility with the broader omnichannel ecosystem — its impact on data availability, fulfilment flexibility, and customer journey coherence.

No single commercial model will suit every category, brand, or market. But the current patchwork — where the choice between conventional, consignment, and concession is often the product of historical negotiation rather than strategic design — is poorly suited to the precision required by omnichannel optimisation. Department stores that undertake this unbundling exercise deliberately will have a structural advantage over those that allow their commercial architecture to evolve by inertia.

Conclusion: from omnichannel to ecosystem — and beyond

The trajectory outlined, from channel integration to journey optimisation, from channel-based P&Ls to customer-centric omni-clusters, from uniform store networks to precision-engineered physical assets, from inherited commercial models to purpose-built architectures, points toward a destination that transcends the notion of “omnichannel” altogether. What emerges is the concept of an ecosystem: a retail business in which every component — the store, the digital platform, the data infrastructure, the commercial model, the category strategy — is orchestrated in function of its contribution to customer value and profitability.

The IADS Academy 2021 anticipated this evolution, mapping the industry’s trajectory from the single store, through e-commerce, cross-channel, and omnichannel, toward what it called the “ecosystem” stage. Five years on, the contours of that ecosystem are becoming clearer.

But there is an additional dimension that most omnichannel strategies have yet to address: the upstream impact on product development itself. Research argues that companies thriving in an omnichannel world are rethinking how they identify opportunities, design, test, and launch new products. The omnichannel environment creates both unprecedented access to consumer data — through clickstream analysis, DTC channels, online communities, and co-creation platforms — and new operational constraints that must be embedded early in the NPD process. Procter & Gamble’s Tide Eco-Box, designed from the outset for e-commerce fulfilment (less packaging, less plastic, optimised for delivery trucks and letter boxes), exemplifies a product conceived for the operational realities of a specific channel. Alibaba’s Tmall Innovation Centre partnered with Mars to develop Spicy Snickers based on platform data revealing that chocolate buyers also liked spicy snacks — compressing a 36-month development window into less than a year.

As Rooderkerk has noted, leveraging retailer data and sharing it with manufacturers could lead to much better innovations. This represents a shift from the traditional push model of innovation — where manufacturers develop products and retailers stock them — toward what might be called collaborative product innovation, where the retailer’s data on customer behaviour, channel preferences, and operational constraints feeds directly into the development process. For department stores, which sit at the intersection of thousands of brands and millions of customer interactions, this is a largely untapped strategic asset. The department store of the future may not only sell and fulfil — it may co-create the products it sells, using its omnichannel data as the currency of partnership with brands and manufacturers.

This is what the ecosystem stage ultimately means. It is one where optimising customer journey and reinventing the store network are not parallel projects but interdependent levers of transformation. Where the measurement system reflects the reality of how customers actually behave rather than how channels are organisationally structured. Where each store is judged on its contribution to the whole, each commercial relationship is assessed on its compatibility with an integrated logic, and each product is developed with the full omnichannel journey in mind.

Credits: IADS (Selvane Mohandas)