Customer-Driven Digital Transformation: Commerce Playbook (2026)

Customer expectations continue to move quickly across channels and buying experiences. PwC recently found that 70% of executives say customer expectations are evolving faster than their company can respond. In the same report, 29% of consumers said they’d stopped buying from a brand after a poor customer experience, online or in-store.

That pressure has changed how some commerce teams approach transformation planning. Customer behavior data can help organizations prioritize investments, sequence migrations, and evaluate platform requirements before implementation begins.

This article examines how customer signals can shape transformation initiatives, which behavioral patterns matter most during commerce project implementations, and how brands can apply customer-driven strategies to platform migrations, checkout redesigns, personalization initiatives, and omnichannel experiences.

How customer behavior should drive digital transformation models 

Digital transformation programs can start with different priorities. Some organizations center projects around infrastructure modernization or platform consolidation. Others use consulting-led operating models to define implementation roadmaps.

Businesses can use customer behavior data to shape transformation priorities early in the process. Metrics like repeat purchase rate, return frequency, channel switching, cart abandonment, and customer lifetime value (CLV) give commerce teams a clearer view into where operational friction affects conversion or retention.

That approach changes how transformation projects are scoped. Instead of defining initiatives around internal systems first, commerce teams can prioritize the customer journeys tied to retention, repeat purchases, and conversion performance—metrics that speak directly to a retail brand’s potential to grow.

Defining customer-driven vs. customer-centric models

Customer-centric transformation and customer-driven transformation rely on different decision-making models. Customer-centric programs focus on designing better experiences for buyers, while businesses undertaking customer-driven transformation use measurable customer behavior to determine which operational and technology changes happen first. 

Why customer-centric transformation is largely a design philosophy

Customer-centric transformation begins with experience-design goals. A clothing retailer, for example, might work with a commerce vendor or consulting partner to redesign their storefront around new browsing features, personalized merchandising, or updated loyalty experiences tied to a broad definition of “modern customer expectations.”

That project improves the visual experience, but can leave operational problems unresolved. If the retailer never analyzes product availability issues, fulfillment delays, or return behavior, the same friction points continue affecting conversion and retention after launch, regardless of how nice the new storefront looks. The experience changes, but the underlying customer signals never reshape the roadmap.

How customer-driven transformation relies on a feedback loop that rewrites the roadmap

Customer-driven transformation starts with measurable buyer behavior. During a commerce platform migration, a retailer might analyze sales seasonality, channel usage, buyer geography, return patterns, repeat purchase rates, and fulfillment performance before deciding how to structure their storefront, loyalty program, checkout architecture, or omnichannel operations.

Those signals influence which systems get prioritized and which capabilities will continue to need more flexibility after launch. A composable commerce architecture can support that model by allowing teams to introduce new functionality over time as customer behavior changes or new operational needs emerge.

Customer-driven transformation depends on measurable buyer signals to determine which technology investments move forward and in what sequence. The commerce platform becomes central to that process because it connects storefront activity, checkout behavior, order management, point-of-sale (POS) transactions, wholesale operations, and B2B purchasing data in a single operational layer.

That visibility gives commerce teams a unified view of customer behavior across channels, which can then influence decisions such as merchandising, fulfillment strategy, loyalty operations, and platform architecture.

How Molson Coors used DTC commerce to build direct customer relationships 

Molson Coors expanded into direct-to-consumer (DTC) commerce after COVID-19 restrictions changed how customers purchased alcohol. The company saw brands in adjacent categories launch online ordering with local delivery and curbside pickup, then partnered with Shopify to rapidly create and launch Ship and Sip, a branded storefront connected to their Toronto brewery retail operation.

Customers could place orders online using Shopify Payments for home delivery or brewery pickup. Molson Coors also supported in-person transactions at the brewery through Shopify POS, giving the company visibility across ecommerce and retail purchases.

The launch gave Molson Coors a direct relationship with customers for the first time in a historically wholesale-driven business. The company could now test products, distribute samples, collect feedback, and analyze purchasing behavior without relying entirely on retail intermediaries.

“By building our direct-to-consumer service, we’re able to grow sales of our iconic brands. But it’s a lot more about the direct relationship that we now have with our customers and being able to adapt to their needs as we learn,” said Joy Ghosh, North American brand director at Molson Coors Beverage Company.

The project shows how customer-driven transformation can emerge from measurable shifts in buyer behavior. Molson Coors responded to changes in purchasing patterns during COVID-19, then used the resulting customer data to shape how the channel evolved over time.

Why commerce businesses need to make the shift now

Customer expectations are fragmenting across channels, devices, and buying models. McKinsey research found that Gen Z and millennial consumers interact with nearly twice as many brands as baby boomers, increasing pressure on commerce businesses to deliver connected experiences across every touchpoint.

That shift also creates opportunity. McKinsey also found that 78% of consumers are more likely to make repeat purchases when experiences feel personalized. Personalization depends on customer data, which in turn depends on the systems behind it. Commerce teams need visibility into the signals customers generate before deciding which technology investments support retention, conversion, and long-term growth.

The five customer signals that should shape your technology agenda 

A retailer implementing a customer-driven digital transformation can start with these key customer behaviors as a basis for their strategy.

1. Channel-switching behavior

McKinsey reports that more than half of consumers interact with three to five channels during a single purchase journey. A customer might discover a product through social media, compare options on mobile, visit a physical store, then complete checkout later through email or a marketplace link.

Those movements can expose gaps between commerce systems. If inventory, customer profiles, loyalty programs, or checkout experiences fail to stay connected across channels, businesses can use that friction to prioritize omnichannel investments and close those gaps with platform unification projects.

2. DTC buyer performance

DTC purchasing behavior can reveal where commerce experiences support retention and where they create friction. Metrics like repeat purchase rate, customer lifetime value (CLV), average order value (AOV), subscription retention, and cart abandonment help teams identify which customer journeys deserve additional investment.

Low repeat purchase rates, for example, can point to poor post-purchase experiences, weak loyalty functionality, inconsistent fulfillment, or disconnected customer accounts. Commerce platforms that unify customer data, checkout, subscriptions, and order history can give teams more flexibility to improve those journeys over time.

3. Wholesale reorder frequency

Wholesale reorder patterns can reveal operational friction in account management, inventory visibility, and purchasing workflows. If wholesale buyers place inconsistent orders, rely on manual sales support, or delay reorders during stock changes, those behaviors can point to gaps in self-serve B2B ordering systems or account experiences.

Commerce platforms like Shopify can centralize wholesale catalogs, self-service ordering, inventory visibility, and account management in a single environment, providing commerce teams clearer visibility into reorder behavior across accounts.

4. Returns and fulfillment patterns

Return behavior and fulfillment preferences can expose operational issues that could be impacting customer retention and margin. High return rates tied to specific products, shipping methods, or regions can point to problems with product information, inventory placement, or delivery expectations.

Fulfillment data can also shape technology decisions around inventory routing, local delivery, order management, and adding fulfillment options like ship-from-store and buy online, pick up in-store (BOPIS). Commerce platforms that connect fulfillment operations with customer data can provide teams visibility into where those issues occur and how they affect repeat purchases.

Customer-driven initiatives can also create opportunities to gather more valuable customer data. For example, if products purchased online can also be returned in person through buy online, return in-store (BORIS), that gives brands an additional face-to-face touchpoint and opportunity to get information from the customer regarding what led to the return. 

5. Checkout abandonment and payment behavior

Checkout behavior gives commerce teams direct visibility into conversion friction. Cart abandonment rates, failed transactions, abandoned product categories, and preferred payment methods can reveal where checkout experiences interrupt purchasing intent.

Those signals can inform investments in accelerated checkout, payment flexibility, mobile optimization, and cart recovery workflows. Features like Shop Pay, saved payment methods, and automated cart reminders can help reduce friction for returning customers while giving teams more visibility into checkout performance over time.

How Who Is Elijah used platform consolidation to support B2B and DTC growth

Perfume retailer Who Is Elijah originally focused on DTC ecommerce through Salesforce. As their wholesale business expanded, the company needed infrastructure that could support different pricing, catalogs, and buying experiences across both DTC and B2B customers.

Their existing platform struggled to support wholesale segmentation, international expansion, and growing order volumes across markets. The business also needed more flexibility to localize pricing, taxes, and storefront experiences for customers in different regions.

Who Is Elijah consolidated their B2B and DTC operations on Shopify, launching localized storefronts in the UK, US, and New Zealand alongside dedicated B2B expansion stores for wholesale customers. Shopify allowed the company to manage regional pricing, customized catalogs, and customer-specific purchasing experiences through a unified back end.

“One of the reasons we needed custom pricing for our wholesale customers was that many of them fall into different B2B categories; some have hard margins, and some we can control. The custom catalog capabilities in B2B on Shopify meant we could set individual pricing categories and attach them to the various types of B2B customers we have so they get a more personalized experience,” said technical leader Brylee Lonesborough.

The migration reflects how customer and wholesale purchasing behavior can shape platform decisions. As Who Is Elijah expanded across regions and account types, those operational requirements informed how the company approached consolidation, localization, and B2B commerce infrastructure.

The unified commerce platform as transformation engine

The commerce platform sits at the center of customer-driven transformation because it connects every revenue-generating interaction across the business. Storefront activity, checkout behavior, order management, point-of-sale (POS) transactions, wholesale purchasing, and B2B account activity all generate customer signals that can shape decisions across operations, merchandising, fulfillment, and growth strategy.

When those systems operate on separate platforms, customer intelligence stays fragmented across teams and channels. Platforms like Shopify give commerce businesses a way to unify their storefront, checkout, order management system (OMS), POS, and B2B data in a single operational environment, making it easier to connect customer behavior across DTC, retail, wholesale, and international markets.

That visibility creates compounding benefits over time. Customer signals gathered in one channel can improve decisions in another, helping commerce teams identify operational friction, improve conversion, refine inventory planning, personalize experiences, and support higher CLV across the business.

Why siloed systems prevent customer-driven transformation

Customer-driven transformation depends on connected customer intelligence across every buying channel. When ecommerce, retail, wholesale, fulfillment, and marketing systems operate separately, it becomes difficult to connect how customer behavior in one channel affects performance in another.

Commerce businesses can manually combine data from disconnected systems during an initial migration or transformation project. But ongoing optimization becomes more difficult when customer signals remain fragmented across platforms, reporting tools, and operational teams.

The commerce platform as system of record for customer intelligence

Commerce platforms that unify ecommerce, retail, wholesale, and operational data can create a central foundation for customer-driven transformation. Platforms like Shopify connect storefront activity, checkout behavior, order management, POS transactions, and B2B purchasing data into a shared operational layer that commerce teams can analyze in one place.

That visibility helps businesses identify operational friction and opportunities to capitalize on it as customer behavior changes. A wholesale business, for example, might notice repeat customers manually rebuilding the same orders each season through their B2B portal. Reorder patterns like that can inform changes to the purchasing experience, like implementing one-click reordering, saved wholesale carts, or account-specific product catalogs.

The same data can also shape marketing workflows. Seasonal reorder behavior from wholesale buyers can help sales and marketing teams coordinate outreach around actual customer activity instead of fixed campaign schedules.

How Parks Project used DTC cohort data to reshape their commerce strategy

Outdoor apparel brand Parks Project has donated more than $2.2 million to parks through sales of outdoor-inspired clothing and gear. As the company expanded, Parks Project used direct-to-consumer customer data to identify which products and purchasing behaviors supported more consistent long-term growth.

The brand saw stronger repeat purchase behavior around replenishable products like candles inspired by national parks, giving the company a product category customers returned to purchase multiple times. Parks Project also found that customers responded strongly to distinct merchandise designs, leading them to collaborate with local artists on limited collections and original artwork.

Customer and sales data also informed operational planning. Using Shopify’s inventory and commerce tools, Parks Project improved inventory forecasting during peak seasonal periods, helping the business manage holiday demand without overstocking or inventory shortages.

“We did a couple of events early on, and there were big spikes [in sales]. If you have a big spike in your business, you're gonna churn a lot of resources, including your own well-being, because you're all in, on a big high. And then you're going to fall and build it again,” said Keith Eshelman.

The company’s approach reflects how DTC cohort data can shape decisions beyond marketing. Repeat purchase behavior, seasonal demand patterns, and product performance informed how Parks Project approached merchandising, inventory planning, and long-term growth strategy.

A customer-driven digital transformation framework for commerce

Customer-driven digital transformation strategy uses customer behavior data to inform operational and technology decisions across the business. Commerce teams can use a structured framework to connect customer signals to platform capabilities, then translate those signals into roadmaps, workflows, and investment priorities.

Each stage of the framework is defined by one of three areas: the customer signal being measured, the commerce systems surfacing that signal, and the operational decisions informed by the data. Unified customer signals can help commerce organizations identify friction across channels, evaluate technology gaps, and prioritize future investments.

Stage 1: Signal unification

The first stage focuses on connecting customer data across DTC, retail, wholesale, and B2B channels. Customer signals sit across disconnected ecommerce, POS, fulfillment, and customer relationship management (CRM) systems, making it difficult to identify how behavior changes across channels.

During larger transformation projects like a commerce platform migration, businesses can manually consolidate data to establish a baseline view of customer behavior. Once a unified commerce platform is in place, future transformation opportunities become easier to identify because storefront, checkout, order, wholesale, and retail data exist in the same operational environment.

Stage 2: Insight translation

Once customer signals are unified, the next step is translating behavioral patterns into operational and technology priorities. The goal is to identify where customer behavior exposes friction in existing systems, workflows, or buying experiences.

A business with growing wholesale demand, for example, may notice internal teams creating manual pricing adjustments or account workarounds inside systems originally designed for DTC customers. The behavior not only signals that B2B systems are needed, but also provides valuable guidance on what those systems should look like. Reorder frequency, purchasing seasonality, and account behavior can inform how the business designs its B2B portals, self-service ordering, customer-specific catalogs, or wholesale account management capabilities.

Stage 3: Organizational alignment

Customer-driven transformation also affects workflows outside the commerce team. Cart abandonment trends, fulfillment preferences, channel-switching behavior, and customer support patterns can all influence how sales, marketing, fulfillment, and operations teams prioritize work across the organization.

If customers regularly move between retail, ecommerce, and wholesale channels before completing purchases, those signals can prompt improvements to inventory visibility, loyalty workflows, fulfillment routing, customer service processes, or campaign timing across departments.

Stage 4: Outcome measurement

After transformation initiatives launch, commerce teams can measure operational and customer impact over time. Tracking before-and-after performance helps businesses evaluate whether platform investments improved customer experience, operational efficiency, or revenue performance.

Common KPIs include:

• Gross merchandise value (GMV)
• Customer lifetime value (CLV)
• Repeat purchase rate
• Average order value (AOV)
• Checkout conversion rate
• Cart abandonment rate
• Wholesale reorder frequency
• Fulfillment speed
• Return rate by product or region
• Cost-to-serve
• Inventory turnover
• Multichannel customer retention

Anchoring transformation efforts to metrics like GMV, CLV, and cost-to-serve helps commerce teams evaluate future investments against measurable business outcomes instead of isolated platform features or redesign initiatives.

What staying fragmented costs commerce businesses

Fragmented commerce systems can slow down both operational changes and customer experience improvements. Teams can end up spending more time connecting data across platforms, managing manual workflows, or maintaining custom integrations than responding to customer behavior directly.

That fragmentation can also delay smaller improvements that affect conversion and retention. Initiatives like adding new payment methods, improving checkout flows, launching loyalty functionality, or supporting new fulfillment options can require complex changes and extensive manual labor across disconnected systems before teams can deploy them.

Without unified customer data, it becomes more difficult to identify how behavior changes across DTC, retail, wholesale, and B2B channels. Commerce teams struggle to connect purchasing trends, fulfillment patterns, repeat purchase behavior, and channel-switching activity into a single operational view that informs future investments.

Those delays can affect how quickly businesses respond to customer expectations, launch new experiences, or expand into new channels and markets compared to competitors operating on more unified commerce infrastructure.

Let your customers write the technology brief with Shopify

Customer-driven transformation changes how commerce businesses approach platform decisions. Instead of organizing transformation projects around broad modernization initiatives with high-level views of customer experience, a customer-driven digital transformation makes customer behavior the source of operational direction across ecommerce, retail, wholesale, fulfillment, and B2B channels.

Brands can use customer signals to identify where existing systems create friction, where operational gaps affect growth, and where platform flexibility makes the biggest impact. Repeat purchases, reorder behavior, checkout activity, fulfillment preferences, and channel-switching patterns all create signals that inform how businesses can approach commerce transformation.

That process becomes more difficult when customer data remains fragmented across storefronts, POS systems, B2B portals, checkout tools, and operational platforms. Commerce platforms like Shopify give businesses a way to unify customer signals from DTC, retail, wholesale, and B2B operations while supporting ongoing modernization across storefront, checkout, order management, and international expansion.

As customer expectations continue changing across channels and markets, commerce teams need infrastructure that allows them to respond quickly as buyer behavior evolves. Talk to a Shopify commerce expert about unifying your DTC, retail, wholesale, and B2B channels on a single platform.