As retail increasingly goes mobile, the humble shopping app is evolving into something much smarter and more influential. In 2025, mobile commerce already accounts for the majority of online retail sales globally, and consumers spend nearly 90% of their mobile time in apps rather than on mobile websites. Retailers are recognising that a well-designed app isn’t just a nice-to-have – it’s fast becoming the primary way loyal customers engage with their brand. Now, a new wave of intelligent technologies is redefining what these retail apps can do. Artificial intelligence (AI), personalisation, and predictive commerce are converging to transform retail apps from simple purchase portals into proactive shopping assistants. This article explores where retail apps are heading next, and how AI-driven personalisation, contextual notifications, and predictive technology are shaping the future of shopping. Along the way, we’ll also look at Awayco’s roadmap for adaptive retail experiences as an example of how these trends are being put into practice.

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Why Retail Apps Are Becoming Critical

Mobile apps have become a cornerstone of retail strategy due to the convenience and engagement they offer. Shoppers who download a retailer’s app typically convert to buyers at much higher rates than those browsing a mobile website. Studies have shown retail apps can convert customers at 3–4 times the rate of mobile sites, with significantly higher average order values and far lower cart abandonment. This is largely because apps provide a faster, smoother experience – no repetitive logins or slow-loading pages – and they can leverage smartphone features like cameras, GPS, and stored payment info for seamless shopping. In short, the app is where a brand’s most loyal, engaged customers tend to gravitate.

Crucially, apps also open a direct, persistent channel to the customer. Once on a shopper’s home screen, a retail app isn’t competing for attention in the same way a website is. It can send timely push notifications, use location awareness for in-store prompts, and remain logged-in for instant access. These capabilities translate into real revenue impacts: many retailers report that a relatively small segment of customers who use the app account for a disproportionately large share of sales. For example, shoppers with the app might visit twice as often and spend substantially more per visit than others. It’s clear that retail apps drive higher conversion and frequency – and thus have become critical in cultivating customer lifetime value.

However, simply having an app is no longer enough. The next frontier is making that app intelligent and deeply personalised. Consumers now expect their shopping apps to know them intimately – to remember their preferences, anticipate their needs, and make the experience feel as convenient as having a personal shopper in their pocket. Let’s delve into how AI is enabling this level of personalisation.

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AI-Powered Personalisation: Tailoring Every Experience

Today’s consumers don’t just want a generic catalog on their phone – they want a personalised shopping app experience that feels like it was made just for them. Artificial intelligence is the key to delivering this at scale. AI algorithms can analyse each user’s browsing history, past purchases, wishlists, and even contextual data (like time of day or local trends) to tailor the app’s content in real time. The result is an app that greets users with items and offers that align with their tastes, rather than a one-size-fits-all homepage.

Personalisation in retail apps manifests in many ways. The most familiar example is personalised product recommendations: “You might also like…” suggestions that actually reflect the shopper’s style and current interests. Modern AI recommendation engines go beyond simple “people who bought X also bought Y” logic. They can factor in hundreds of signals – from a user’s recent browsing patterns to what’s trending among similar profiles – to serve up dynamically curated product selections. For the customer, this feels like the app “gets” what they are looking for, often surfacing relevant products they might not have discovered on their own.

Another aspect is personalised content and promotions. Retail apps can present different homepage banners, deals, or even navigation options depending on the user. For instance, a fashion retail app might show a frequent shoe-buyer the latest footwear arrivals prominently, while a user who often shops sale items might see a personalised discount offer first. Loyalty program status, preferred brands, and size preferences can all be used to customise what the app displays. The goal is to make each user’s journey unique and frictionless – removing anything that isn’t relevant and highlighting what is. When done right, this boosts engagement and conversion because customers spend less time searching and more time finding things they love.

Importantly, personalisation builds an emotional connection. Shoppers tend to feel valued and understood when an app consistently shows them suitable options or remembers their birthday with a special offer. This drives loyalty and repeat usage. Surveys back this up: in a 2024 Deloitte study, 80% of consumers said they are more likely to purchase from brands that offer personalised experiences, and those shoppers end up spending up to 50% more on average. Yet many retailers are still catching up to these expectations – a significant portion of consumers feel that current retail apps don’t personalise enough. This gap represents an opportunity for forward-thinking retailers to differentiate.

The future is heading toward hyper-personalisation, where AI uses real-time data to treat each customer as a “segment of one”. Rather than broad customer segments, hyper-personalised apps might adjust on the fly for each individual – for example, recognising that a particular user tends to buy skincare every three months and proactively suggesting a replenishment (with perhaps a complementary product) right when it’s needed. Achieving this level of granularity requires sophisticated data integration behind the scenes, but it can yield incredible loyalty by making the customer feel the app truly anticipates their needs.

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Contextual Notifications and Smart Engagement

One of the greatest advantages of a mobile app is the ability to communicate proactively with users through push notifications and other contextual messages. In the next generation of retail apps, these notifications are becoming smarter, more personalised, and perfectly timed to maximise engagement.

Contextual notifications mean the app uses situational data about the user to send relevant alerts. For example, consider a scenario where a shopper adds items to their cart but doesn’t check out – the app might send a gentle reminder or even a limited-time free shipping offer via push notification to entice them to complete the purchase. If a user is physically near one of the retailer’s stores, the app could trigger a notification about an in-store event or a special discount available at that location (“Hey, you’re near our Sydney store – pop in today for 20% off new arrivals!”). This kind of location-based personalisation uses GPS or beacons to merge the online and offline experience seamlessly.

Timing and relevance are everything. AI plays a role here by learning when a particular user is most likely to engage. If the data shows you tend to browse the app in the evenings, the AI can schedule promotional notifications to arrive just at that window. Or it might observe that you respond to notifications about certain product categories and tailor the content accordingly. The aim is to avoid the pitfall of spammy, generic push messages (which users often ignore or disable) and instead send fewer, high-value pings that feel helpful.

The effectiveness of well-crafted app notifications is striking. Industry metrics show that push notifications on retail apps can achieve open rates as high as 70–90%, vastly outperforming email marketing. About 40% of users will engage with a push message (such as by tapping it) within an hour of receiving it – indicating that these alerts can prompt almost immediate action, whether it’s a flash sale purchase or a reminder to check out a saved cart. Retailers also find that users who enable and receive regular personalised push updates tend to have much higher retention rates, meaning they keep using the app month after month. In essence, smart notifications keep the brand on the customer’s radar and drive them back into the app (or store) with relevant calls-to-action.

Examples of contextual app engagement are growing more innovative. Some beauty retailers’ apps, for instance, can send a notification with a skincare tip of the day along with a product suggestion, creating a content angle that isn’t purely transactional. Others use contextual triggers like weather – a sporting goods retailer’s app might recommend “rainy day workout gear” when it’s pouring, delivered as a timely alert. As AI evolves, these notifications will likely draw on even richer context (calendar events, social media trends, personal milestones, etc.) to engage customers in a highly personalised dialogue.

The key is to always enhance the user’s experience rather than interrupt it. When a customer feels that an app’s notification is genuinely useful – saving them money, reminding them of something they care about, or introducing them to something they actually want – it strengthens their connection to the brand. Retailers that master contextual and AI-driven notification strategies will enjoy superior engagement and loyalty from their app user base.

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Predictive Commerce: From Personalised to Proactive

Personalisation as described above is largely about responding to known customer preferences and behaviours. Predictive commerce takes it a step further – it’s about anticipating what the customer will want or need next, sometimes even before the customer is consciously aware of it. This concept, enabled by advanced AI and analytics, is poised to redefine shopping by making it more proactive and frictionless.

Imagine opening your retail app and seeing a suggested shopping list generated just for you: it knows you’re likely running low on a staple item (based on past purchase timing), it’s noticed a trending product in your favourite category, and it’s learned from your browsing that you’ve been eyeing a certain gadget. Instead of you searching and filtering, the app surfaces these items with a friendly note like, “We thought you might need these soon.” This is predictive commerce in action – using data and AI to predict a customer’s needs.

One emerging example is the idea of “zero-click” purchases or automated re-ordering. If an app is confident you will want a monthly refill of pet food or a skincare product, it could proactively place it in your cart or even auto-ship it unless you cancel. While fully automated commerce still requires customer trust and opt-in, we’re moving in that direction with subscription models and auto-replenishment options becoming popular. Predictive retail technology can also monitor external factors like price changes or inventory levels to benefit the customer. For instance, an intelligent app could alert you, “That jacket you liked is low in stock – grab it now before it’s gone,” or, “The camera you viewed last week just went on sale, tap to purchase at 15% off.” This turns the app into a personal shopping assistant working on the user’s behalf.

Major tech players are investing in these predictive capabilities. We’ve seen developments like AI chatbots that can guide users through shopping decisions conversationally, and systems that track price drops to automatically notify or even complete a purchase if the user has pre-approved the parameters. Google’s recent initiatives with predictive shopping AI, for example, hint at a future where an AI can handle tasks like phoning a local store to check an item’s availability or finalising a purchase when a desired item’s price meets the customer’s target. All of this underscores the push toward anticipatory retail – shifting from reactive customer service to proactive customer delight.

From the retailer’s perspective, predictive commerce is powerful not only for sales but also for operations. Predictive analytics can forecast demand for products, helping ensure that the items the AI might recommend are actually in stock when needed. This minimises those disappointing “out of stock” moments and keeps the customer’s trust. In fact, AI-driven demand forecasting has been shown to significantly reduce stockouts and overstock situations, directly improving the shopping experience with better product availability. When the right product is suggested at the right time and it’s available right where the customer wants it – that’s the ideal intersection of predictive intelligence and customer satisfaction.

It’s worth noting that with great power comes great responsibility. Predictive commerce requires careful handling of data privacy and accuracy. Consumers will only embrace these predictive features if they feel their data is secure and the suggestions truly add value (and aren’t just aimed at pushing more sales indiscriminately). The winning retail apps will be those that can strike the balance – leveraging intimate knowledge of the customer to help them, not to spook or annoy them. When done right, predictive commerce can feel like magic: the retailer seems almost clairvoyant, saving you time and effort in your shopping journey.

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Blending Digital and Physical: A Unified Experience

The future of retail apps isn’t happening in isolation from stores – in fact, the line between online and offline is blurring. The best retail apps of tomorrow will serve as the connective tissue between a retailer’s digital presence and their physical storefronts, creating one continuous unified commerce experience. AI and personalisation play a role here too, ensuring that customers get consistent, contextual service no matter where they interact.

Consider walking into a brick-and-mortar store with your app in hand. The app might transform into an in-store companion mode: it could greet you by name, pull up your online wishlist or past online purchases for reference, and even guide you through the aisles to the items you saved. Some retailers are already experimenting with contextual store mode features like store maps, product locator functions, or the ability to scan a barcode with the app to see extended product information (such as reviews or additional stock online). This bridges the convenience of online data with the tactile in-store experience.

Personalisation extends here as well. If the app knows you have an appointment with a personal shopper or you’re a loyalty VIP, it can alert store staff of your arrival so they can provide white-glove service. We’re not far from a scenario where a sales associate, equipped with a tablet (or their own version of the app), can access your preferences and purchase history (with your permission) to give truly personalised recommendations face-to-face. This concept, often called clienteling, turns store visits into tailored experiences powered by the same data fueling the app.

Then there’s the checkout experience. The traditional queue at the register is being challenged by mobile technology. Mobile POS systems – essentially the ability to complete a purchase on a tablet or smartphone – are allowing store staff to check out customers anywhere in the shop. Imagine you’ve tried on an item and an associate can scan it and take payment for you on the spot via a mobile app, emailing your receipt – no lining up necessary. Or perhaps you find an item is out of stock in store; a well-integrated retail app can let you order it online right then and there for home delivery, effectively turning the store into an “endless aisle.” These examples show how a unified app-platform can make shopping truly flexible: shop online, pick up in store; shop in store, get it shipped; browse in app, buy in app or in store – every path is seamless.

AI underpins much of this by synchronising data in real time across channels. It ensures that whether you’re on the app or in a physical outlet, the system “knows” your context and provides continuity. For instance, if you add something to your cart on the app at home, a store associate could see that and proactively ask if you need help with that item when you arrive in store. This level of integration creates what’s called an adaptive retail experience – one that adapts to how and where the customer is shopping, rather than forcing the customer to adapt to siloed channels.

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Awayco’s Roadmap for Adaptive Retail Experiences

Innovative companies like Awayco are at the forefront of enabling these next-generation retail app capabilities. Awayco’s own roadmap is focused on helping retailers deliver intelligent, adaptive experiences through an integrated suite of solutions. Three main pillars define their approach:

• AI Platform for Retailers: “Make smarter decisions, automatically.” Awayco’s AI Platform provides real-time forecasting, personalised recommendations, and actionable insights to retail businesses. In practice, this means a retailer can leverage AI to do things like optimise inventory (forecast demand so the right products are in stock at the right time), automatically surface product recommendations to each app user, and even adjust pricing or promotions dynamically based on data. By improving margin and inventory health, the AI Platform ensures that the personalisation the customer sees is backed by intelligent operational decisions. Retailers using this platform can more confidently deliver features like predictive recommendations and know that their supply chain can support them.
• Mobile App Studio: “Build your customer-facing app without the heavy lifting.” Recognising that not every retailer has a big development team to create a high-performance app from scratch, Awayco’s Mobile App Studio is a toolkit for rapidly building modern shopping apps. It enables personalised, high-speed mobile experiences out of the box – meaning retailers can quickly launch an app that already incorporates best practices in UX and personalisation. Crucially, these apps are designed to boost loyalty, engagement, and conversion, by including features like easy login, saved preferences, and integration with loyalty programs. Essentially, the Mobile App Studio lowers the barrier to entry for retailers to offer their own branded app that competes with the best in the industry.
• Mobile POS: “Fast, flexible checkout anywhere in the store.” Awayco’s Mobile POS system equips retailers with a modern point-of-sale that isn’t tethered to a cash register. It supports scenarios like queue-busting (associates can check out customers on a handheld device to reduce lines), endless aisle (selling products that are not physically in that store via online inventory), and unified order management across online and offline. For the shopper, this means the buying process in store becomes as convenient as using an app – no waiting, more payment options, and continuity with their online shopping cart or loyalty credits. From the retailer’s side, Mobile POS ties together the data from stores and the app, so everything stays in sync. This is a key component of delivering the unified experiences discussed earlier.

By combining these three elements – a powerful AI backend, an easy-to-deploy personalised app front-end, and seamless in-store integration – Awayco is positioning retailers to meet customers wherever they are with a consistent, intelligent experience. It’s a roadmap that acknowledges retailers need both the brains (AI and data) and the channels (app and in-store tech) to succeed in this new era. The end goal is an adaptive retail ecosystem where every interaction, whether on a phone or on the shop floor, informs and enhances the next. With platforms like Awayco’s making this more accessible, even traditionally brick-and-mortar retailers can leapfrog into offering the kind of smart, predictive, and personalised shopping journeys that consumers will increasingly expect.

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Key Stats and Trends (2025)

• Mobile Dominance: Approximately 59% of all retail e-commerce sales worldwide now come from mobile devices. Consumers also spend about 88% of their mobile time in apps (vs. only ~12% in web browsers), underscoring the importance of dedicated retail apps for engagement.
• App Conversion Uplift: Retail apps convert shoppers to buyers at 3–4 times the rate of mobile websites. For instance, one study found conversion rates around 18% in-app versus 4% on mobile web for the same retailer. Apps also yield about 15% higher average order values and significantly lower cart abandonment (around 20% abandonment in apps vs. 85% on mobile web).
• App Users = Loyal Customers: Customers who download and use a retailer’s app tend to visit stores (or the app) about 2× more often and spend around 40% more annually compared to those who only shop via the mobile site. In fact, a large portion of many retailers’ sales now comes from app users and loyalty program members (for example, roughly 60% of Starbucks’ U.S. sales are through their app-based loyalty members).
• Demand for Personalisation: 80% of consumers prefer to shop with brands that offer personalised experiences, according to Deloitte research, and they typically spend more with those brands. Yet, only about 48% of consumers feel that retailers are currently doing a good job at personalisation – indicating room for improvement. Meanwhile, a McKinsey report found 71% of customers expect personalisation, and 76% get frustrated when it’s lacking.
• Push Notification Engagement: Push notifications from retail apps can achieve open rates of 70–90%, far higher than traditional email marketing. Approximately 4 in 10 users engage with a push message within an hour. Shoppers who receive periodic, relevant push alerts show substantially higher retention and repeat purchase rates than those not opted in to notifications.
• AI and Predictive Benefits: Retailers implementing AI-driven forecasting and personalization see tangible results. For example, AI-based demand forecasting can reduce out-of-stock situations and lost sales by over 50–60% by improving inventory accuracy. Additionally, next-gen AI shopping assistants and predictive recommendations are projected to influence a growing share of orders – industry analysts estimate that hundreds of billions of dollars in orders will be influenced by AI agents in the coming years as predictive commerce takes hold.

Retail apps are on the cusp of a transformation. What started as a convenient add-on to the shopping experience is rapidly becoming the central hub of customer interaction, powered by AI, rich personal data, and seamless omnichannel integration. The future of retail apps lies in making every customer feel like the experience was tailor-made for them, and even proactively helping them find what they want before they have to ask. Brands that embrace AI-driven personalisation and predictive commerce will not only delight their users with convenience and relevance – they will also gain a competitive edge in loyalty and lifetime value.

In this dynamic landscape, success belongs to those who stay ahead of the curve. It’s a time for retailers to reimagine their mobile strategy, investing in intelligent platforms and partnerships that can turn their apps into truly smart shopping companions. The technology is here, and shopper expectations are higher than ever. The retailers that blend human insight with AI’s analytical power, and merge digital agility with physical presence, will define the next era of commerce.

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Discover how Awayco is building the next generation of intelligent retail apps.

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