7 outstanding examples of omnichannel marketing

In this article, we cover the omnichannel strategies working for brands right now, including:

Omnichannel marketing connects a brand's channels, such as social media, email, retail stores, websites, and mobile apps, into one consistent, personalized customer experience. The goal is a unified brand message that lets customers move between channels without losing context. That consistency strengthens satisfaction and loyalty while driving revenue.

Momentum behind omnichannel has accelerated in recent years. Omnichannel customers show a meaningfully higher lifetime value than those who stick to a single channel. That gap is why brands across every sector are working to close the divide between their online and offline experiences.

Here are seven examples of omnichannel marketing strategies that have driven real customer engagement and sales growth. Each one points to a piece of what it takes to build a stronger strategy of your own.

Buy online, return in store (BORIS) is a growing omnichannel practice. It lets customers use digital channels to buy and physical ones to return an item that did not work out. German ecommerce company Zalando runs one of the more established BORIS programs in the industry.

Customers can return purchases at a nearby store at no cost, within an extended window after purchase. For a category like shoes or clothing, where fit is often the deciding factor, that removes a major source of purchase hesitation.

Fintech and logistics companies are moving into omnichannel territory too. PayPal built Happy Returns into a box-free, label-free returns model, then sold the business to UPS in a deal that closed in 2023. UPS has since grown the Return Bar network to more than 10,000 US locations, with retail partners including Ulta Beauty and The UPS Store.

The model removes the need for customers to visit a separate website or app to start a return. Instead, the retailer's own checkout generates a QR code. Customers take that code, along with the item, to a nearby Return Bar location. They don't need a box or label, and the refund lands as soon as staff scan the code.

Airlines have run frequent-flyer loyalty programs for decades, but some have moved past legacy points systems to build a genuinely frictionless support experience. JetBlue partnered with AI platform ASAPP to strengthen its digital and phone support channels. Customers can now message the airline the way they would text a friend, instead of waiting on hold. ASAPP's real-time voice transcription and analytics also surface the next best response for JetBlue's contact centers during a live conversation.

Customer-service AI like this is the early version of a bigger shift already underway. More of the shopping and support journey is moving to AI agents acting on a customer's behalf. Brands that expose their offers and loyalty benefits in a machine-readable way, not just through a human-staffed channel, will be the ones agents can actually act on. That kind of readiness starts with preparing for agentic commerce well before agents make up a meaningful share of traffic.

Self-checkout lets customers complete a purchase without waiting for a staffed register. It is becoming one of the more visible ways retailers modernize a point-of-sale system built for a single channel.

French sporting goods retailer Decathlon has installed self-checkout units across nearly all of its stores. Customers place the item at the counter, tap a card or phone, and the transaction is done. Dutch retailer C&A has rolled out similar units. Its in-store checkout ties back into the same promotions and loyalty rules customers see online.

Retailers making this shift are usually solving a deeper problem than checkout speed. Most point-of-sale systems started as closed, monolithic platforms. That makes it hard to run the same offer consistently between the register and the app. It's why more retailers are moving toward headless POS systems instead.

Amazon blends its online and in-store experience with several fulfillment innovations, Amazon Hub among them. Shoppers who order online can choose to collect their package from a Hub Counter or a Hub Locker instead of waiting for home delivery.

Hub Counter is a staffed pickup point where an associate hands the customer their package. Hub Locker is a self-service station where customers collect a package on their own schedule. Locker locations span markets including the US, UK, and Germany. Either way, the online order and the offline pickup become one continuous transaction, not two separate experiences.

Hyper-personalization takes personalization further. It pulls from real-time and behavioral data to shape a message or experience for a single customer rather than a segment. AI, natural language processing (NLP), and machine learning (ML) are what make that level of individual tailoring possible at scale.

Netflix's viewing experience is one of the clearest examples. Switch devices, and the experience carries over without interruption, with AI and ML surfacing content suited to that specific viewer.

In a CX Network interview, Saki Takeda, director of product management at Netflix, pointed to a common trap in how brands chase this kind of personalization. Expanding into new channels just to claim an omnichannel presence can fragment the customer experience rather than improve it. The same goes for splitting support teams into channel-specific silos. Getting the underlying data and journey right matters more than the number of channels a brand shows up in.

Retailers that get this right tend to sequence their personalization investment carefully, rather than chasing every channel at once.

Machine learning is well suited to designing an individualized customer journey, and Starbucks has built its ordering experience around exactly that. The Starbucks app uses a recommendation engine to generate order suggestions tailored to a customer's past behavior and current store inventory. The same customer sees a different suggestion depending on when and where they open the app.

Starbucks has paired that with a blockchain-based traceability feature. Customers can see where farmers grew their coffee beans, how the company supports those farmers, and when it roasted the beans. Digital menu displays extend that same personalized, always-current pricing and promotions to in-store customers and drive-thru shoppers alike.

Turning real-time behavioral signals like these into an actual incentive at the moment of purchase is where a lot of brands stall. The ones that pull it off usually pair incentives with signals in real time, instead of treating personalization and promotions as separate systems.

Omnichannel execution is what actually closes the gap between a brand's online and offline channels. Done well, it gives customers a shopping experience that is consistent everywhere they show up. It also gives the brand a single, cross-channel view of how customers behave.

Promotions and loyalty are as much a part of that experience as a website, app, or store layout. They are usually the last piece brands get around to unifying. The Italian fashion brand Twinset is a case in point.

It needed a promotions platform that would integrate across its existing tech stack, rather than force a rebuild. Talon.One brought its retail and ecommerce teams onto a shared promotions playbook. Campaigns that used to run separately online and in stores now execute from the same rules, across physical stores, the website, and outlet locations.

Talon.One's enterprise loyalty management platform gives brands that same kind of unified execution. One rule set for promotions and loyalty runs consistently across web, app, and point of sale in real time, instead of separate logic maintained channel by channel. If your promotions and loyalty program still behave like two different products depending on the channel, that inconsistency is usually the one worth fixing first.