Why made-to-measure items create cascading pricing and operational problems, and how to build a reliable, scalable response.
Selling custom or configuration-based products feels strategic. Customers pay more, conversion lifts, and your assortment becomes differentiated. But the reality is different. When product complexity outgrows your systems, you end up with manual quotes, pricing leak, delayed orders, and frustrated teams. This is not just an efficiency issue, it is a margin and customer experience risk.
Two big shifts in the past few years make this an urgent problem for European retailers and brands.
First, customers increasingly expect personalization. Roughly two thirds of consumers say they prefer brands that tailor offers and experiences, and companies that deliver personalization win higher conversion and loyalty. See the Salesforce State of the Connected Customer for context.
Second, online retail continues to grow across Europe, and more sellers are adding configurable SKUs to capture that demand. Eurostat shows steady growth in e-commerce adoption across EU markets, with online purchasing becoming mainstream for large segments of consumers. As configurable offerings scale, the operational and pricing complexity compounds.
At the same time software options are evolving. Configure, Price, Quote tools and product configurators are more capable than before, but many implementations remain point solutions that are not deeply integrated with ERP, OMS, PIM, and pricing engines. The result is a gap between business ambition and technical reality.
(References: Salesforce State of the Connected Customer, Eurostat e-commerce statistics, Shopify on customizable products.)
There are three root causes that turn configurable products into a systemic problem.
First, combinatorial complexity. A made-to-measure sofa with three seat depths, five fabrics, and four leg options instantly becomes dozens of unique configurations. That is manageable at the quote level, but pricing rules, cost rollups, lead times, and margin targets need to work across every combination. If pricing logic is hard coded in spreadsheets or isolated legacy systems, errors multiply.
Second, disconnects between systems. Product configuration data often lives in the configurator, costs in ERP, list prices in PIM, and special-case discounts in CRM. When these data models are not synchronized, frontline staff manually reconcile them. Manual steps slow time to quote and create opportunities for price leakage or inconsistent offers.
Third, human exception handling. Every unusual configuration invites a manual review. Over time your team accumulates a backlog of bespoke requests, price overrides, and one-off rules. That knowledge resides in people, not in systems. When those employees move on, process knowledge walks out the door and inconsistency grows.
Consequences are measurable. Common outcomes include higher order abandonment due to long lead times, margin erosion from incorrect cost pass-through, increasing RMA rates from specification errors, and lower employee productivity. Imagine a mid-size furniture retailer adding a made-to-measure line. Without clear pricing automation, they either absorb margin volatility to avoid lost sales, or they overprice and miss the market opportunity. Either way the configurable range underdelivers.
Solving this requires both discipline and technology. Think in three phases: classify, automate, govern.
Classify: Start by triaging your assortment. Not every configurable product needs full automation. Create a complexity matrix that scores SKUs by configuration variance, average order value, margin sensitivity, and frequency of exceptions. Focus first on the high-impact quadrant: high AOV, high variance, frequent sales. These are the configurations where automation will return the most value.
Automate: For prioritized SKUs, replace spreadsheets with rule-based automation. There are two technical patterns to consider.
Integrate these engines with your configurator, PIM, ERP, and checkout. Where a full CPQ system is too heavy, a modular pricing engine that exposes APIs will let you keep the configurator intuitive while enforcing pricing consistency.
An important operational point, you still need a controlled exception flow. Automate the majority of quotes, and create a lightweight approval workflow for the small percentage that deviate. Capture the rationale for each override to build an exceptions library that informs future automation.
Govern: Build guardrails and measurement. Define KPIs such as time-to-quote, exception rate, margin variance, and on-time delivery for configurable items. Use dashboards that highlight rising exception clusters. Hold monthly reviews where product managers and pricing owners review exceptions and decide whether to tweak rules or change product design to reduce complexity.
Practical governance also includes redesign. If a configuration option causes chronic exceptions but adds little incremental margin, consider removing it or moving it to a modular accessory. Often the simplest SKU design reduces downstream operational costs more than an awkward price premium.
The best examples of retailers and brands we see is that they treat configurable product pricing as product design, not a sales problem.
They use data to redesign offers. For example, they analyze which combinations actually sell, and prune the long tail of low-demand permutations. They also standardize components where possible, which simplifies cost rollups and supplier sourcing.
They embed pricing logic earlier in the customer journey. That means the configurator provides real-time price feedback, not an estimate. Real-time transparency reduces cart abandonment and prevents surprises at checkout.
They adopt a test-and-learn approach. Small changes to component prices, lead time premiums, or package bundles are tested market by market. Measuring lift in conversion and margin keeps the process evidence-based.
Technically, leaders connect a centralized pricing engine to all touchpoints. This single source of truth allows consistent pricing across direct channels and marketplaces. They also rely on workflow automation to contain exceptions, and machine learning to detect patterns in overrides that should be codified.
Finally, they design contracts and supplier relationships to support configurable offers. Shorter supplier lead times and standardized component kits reduce the operational variability that causes pricing slippage.
Custom products are an attractive growth lever, but only if you manage the complexity deliberately. The real cost of complexity is not the occasional manual quote. It is the systemic friction it creates across pricing, operations, and customer experience. Prioritize, automate, and govern. Design products with downstream operations in mind. Over time, the portfolio of configurable offers will become a margin engine, not a headache.
Disivo provides capabilities that match this approach, focusing on automation of price and margin calculations, end-to-end visibility across configurable orders, and AI-assisted rule suggestions to reduce manual exceptions.
Further reading and sources
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=E-commerce_statistics
https://www.salesforce.com/research/customer-expectations/
https://www.shopify.com/enterprise/customizable-products
