Retailers have spent years competing on convenience: fast delivery, flexible returns, same-day fulfillment. And it’s worked, customers have never had more options, or indeed higher expectations.
McKinsey research finds that consumer tolerance for friction will continue to decrease while expectations for speed and service will only rise.
Delivering this convenience, however, is not free — every fulfillment promise comes with a price tag. Yet many retailers can’t tell you exactly what that is.
Revenue numbers rarely tell the full story
Different fullfilment channels have different cost profiles. Home delivery isn’t click-and-collect, while a same-day order doesn’t look like a scheduled weekly run. When transportation costs, labor, inventory handling, delivery mileage, and returns data all live in disconnected systems, the true cost of each order stays invisible.
Compounding the challenge, the business logic used to allocate and interpret those costs is often trapped in legacy systems, manual processes, and with domain experts. The rules that determine how transportation costs are assigned, how returns are attributed, or how customer profitability is measured may exist in a number of places, making it hard to build a consistent view of cost-to-serve across the organization.
The result? Decisions get made on incomplete information.
A customer segment that looks highly profitable in a revenue report might be generating expensive last-mile routes that quietly destroy margin. A product category that seems to be performing could be driving disproportionate returns.
Gartner has warned that many organizations overlook factors that distort customer and product profitability.
This challenge is one that automation and AI are well positioned to solve — that is, if the underlying data can be trusted.
The urgency is real, and it’s building
Cost pressure in retail is not a new problem, but the stakes are higher.
Nearly all retail executives anticipate higher costs, finds Deloitte, yet 82% are still forecasting better margins for 2026. As cost pressures mount, understanding cost-to-serve at a deeper level becomes essential for protecting profitability.
KPMG has gone further, naming cost-to-serve the #1 priority for supply chain leaders, and explicitly calling on organizations to “get very granular” about what it costs to serve each customer, product, and channel.
The message is consistent: broad-brush profitability analysis is no longer adequate; the margin is in the detail.
How a retailer identifies unproductive SKUs and saves $50M
Consulting firm Kearney put this into practice with a leading retailer that lacked visibility into the true impact of each product on their bottom line. Using Alteryx, Kearney built a cost-to-serve model that mapped costs at every step of the supply chain for over 100,000 SKUs in the retailer’s assortment, cutting data processing time by more than 70% compared to their previous Excel-based approach.
The results were significant. The analysis identified 13,000 unproductive SKUs and delivered $50 million in working capital savings, plus an additional $10 million in annual transportation cost reductions.
As Matt Feeley, of Kearney, put it: “Cost-to-serve is the backbone to understanding your supply chain performance. It feeds into so many other analyses.”
Visibility is a growth strategy
The most successful retailers understand that cost-to-serve visibility isn’t simply about cutting costs.
When you know the true economics behind every order, you can confidently decide which fulfillment options to invest in, how to price across channels, which customer segments to grow, and where to renegotiate.
The organizations pulling ahead aren’t running cost-to-serve analysis as a one-time exercise. Rather they are embedding profitability insight into every planning cycle, every fulfillment decision, every pricing conversation. That requires automation, trusted data, and a way to consistently apply the business logic.
AI is only as good as the data feeding it
As retailers turn to AI to solve these challenges, a new problem emerges: the data feeding those models is often the same fragmented, incomplete data that created the visibility gap in the first place.
The scale of that readiness gap is striking. Alteryx’s 2026 State of the Data Analyst Report makes this tension clear. While nearly all (96%) of analysts are now using AI tools and 85% say AI influences business-critical decisions, 47% of failed AI and analytics projects are still attributed to poor data quality or governance.
AI ambition is outpacing data readiness. In retail operations, where cost-to-serve depends on connecting fulfilment, transportation, inventory, and financial data across disconnected systems, that gap is where margin evaporates.
Alongside data quality, the business rules, cost allocation models, and operational knowledge behind cost-to-serve must be captured, standardized, and applied consistently. Without that, organizations struggle to build a reliable view of profitability at scale.
As a result, retailers seeking a true understanding of cost-to-serve must create a trusted, repeatable framework that combines high-quality data with standardized business logic, enabling consistent profitability analysis across customers, products, and channels.