Cross-Device & Cross-Channel Shopping

Having the right products is only half the challenge; customers also want a consistent, frictionless experience across every device and channel. A 2024 Forrester Research survey found 60% of U.S. consumers say they would stop shopping with a company that set different prices for the same products in different channels.

Not only must retailers provide consistency online and in stores, but across their mobile websites and apps. A global study showed the average person now uses 3.6 devices, and Americans even more. Research from Google and the Harvard Business Review shows that 73% of retail consumers are “omnichannel” shoppers—those who move between devices and channels during their buying journeys. These customers are also 1.7 times more likely to make a purchase than single-channel shoppers and tend to spend more overall.

When customers start browsing on a smartphone, research products on a laptop, and complete purchases on a tablet, disconnected systems often treat them as three separate individuals. The failure to preserve context across channels erodes trust, as disappearing cart contents and inconsistent personalization send the message that brands don’t recognize or value their customers.

To solve this, retailers are turning to three interdependent AI–powered technologies: identity resolution, journey stitching, and cross-channel personalization.

Identity resolution links fragmented customer data across platforms into unified profiles. Advanced systems combine deterministic matching—using exact identifiers like email addresses—with probabilistic algorithms that infer identities through behavioral patterns and device characteristics. Probabilistic techniques such as device fingerprinting and cross-device graphing allow customer recognition even without logins or cookies.

Journey stitching then reconstructs fragmented interactions into coherent narratives, ensuring that person identifiers are available across authenticated and unauthenticated sessions. Graph-based systems use identity graphs to connect events across marketing, ecommerce, and customer service platforms, enabling brands to see everyone’s full path from awareness to purchase.

Cross-channel personalization is the last step, activating unified profiles in real time. These AI-driven engines maintain context as customers switch channels, continuously predicting next-best actions and tailoring content accordingly. The most advanced systems apply “multi-zone” identity resolution—using deterministic data for transactions while applying probabilistic matching for advertising and engagement—to balance precision and reach.

Implementing these systems is not simple. Compliance with the European Union’s General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) demands strict consent and data transparency. Technical barriers such as poor data quality and fragmented marketing technology stacks add further complexity. Companies must also train marketing, IT, and customer service teams to interpret AI-driven insights and act on them responsibly.

Retailers that succeed demonstrate measurable impact. Swedish fashion brand NA-KD increased customer lifetime value by 25% in just one year by integrating customer data from its website, app, and digital marketing channels. Danish retailer Ganni reported that 9% of its store sales came from clienteling after deploying mobile point-of-sale systems that connected in-store and online data. Target has become a model for omnichannel execution—more than 80% of its online orders are fulfilled directly from stores, enabling faster delivery and curbside pickup.

Aggregate industry data reinforces these examples. Analysis by customer engagement platform Braze found that combining in-product messaging (while the customer is engaged with a company’s website or app) and out-of- product messaging (when they are not engaged) increased purchase frequency by 25% per user. 70% of marketers report higher returns on investment from omnichannel strategies, according to a 2024 Forrester report. Additionally, 47% of consumers using buy-online pick-up-in-store (BOPIS) services make extra purchases when they collect their orders.

The market spans three primary solution types: warehouse-native platforms that operate directly within cloud data warehouses, traditional customer data platforms (CDPs), and identity graph providers focused on privacy-compliant data matching.

When selecting a solution, organizations must prioritize integration, scalability, and compliance. Essential features include match-confidence scoring, data clean rooms for privacy-safe collaboration, and prebuilding connections with marketing and advertising platforms. Privacy capabilities are now nonnegotiable; leading platforms incorporate consent management and built-in data governance frameworks to ensure compliance by design. 121 2.2 Sell (Conversion & Revenue Growth)