Tag: Retail Technology

AR in Retail: 5 Success Stories That Prove AR Boosts Sales

Posted on by Jayakrishnan M
AR in retail virtual try-on example

Introduction

The retail landscape has seen a sea of change in recent years, motivated by technological advancement and by the changing expectations of customers. One of the most game-changing innovations in the field is Augmented Reality (AR). Seamlessly blending digital content with the physical world, AR has given retailers new ways to interact with customers, personalize their shopping experience, and thus subsequently make sales.

In this article, we will find five retail success stories that prove how AR bumps up retail sales, increases customer satisfaction, and keeps them agile in an increasingly digital world.

IKEA: AR in Retail Revolutionizes Furniture Shopping

The furniture giant IKEA has been at the forefront of the adoption of AR to enrich customer experience. For instance, the company introduced the IKEA Place app, which could essentially help customers see what furniture would look like in their homes before they purchased it. It offers AR capabilities where customers will be able to see how a different piece of furniture would fit into their space; thus, making better decisions without visiting a store.

The IKEA app provides real-scale, high-resolution 3D models of all products available in the stores for the users to have realistic views of size and design in real-time before making a purchase. This has greatly reduced hesitation from customers about large purchases online, thus highly increasing sales in IKEA’s e-commerce.

IKEA has learned by success how precious it is to develop an immersive experience in shopping: so a customer who trusts himself through an immersive experience creates better conversion rates and lower return rates.

Sephora: AR in Retail Personalizes Beauty with Virtual Try-Ons

The beauty trade is personalization and visual appeal, and for all its latest successes, Sephora has successfully well-positioned its AR technology to maximize the engagement and sales it gets from customers. Using its Virtual Artist app, Sephora lets customers test out various makeup products completely virtually using just a smartphone or, for maximum immersion, through the company’s AR mirrors found in select stores.

This sales strategy, driven by the adoption of AR, can enable the ability of a customer to try various lipstick and eyeshadow looks without even getting up to reach for the testers. Sephora thusly can afford to deliver its modern shopper convenience and personalization – two needs that most modern shoppers have. Therefore, it has recorded increased customer satisfaction, improved sales conversion rate, and in-app engagement time.

The best example of this is the fact that enabling virtual try-ons through AR makes a setting like the customer even more loyal, reduces decision fatigue and enhances product discovery factors towards driving retail sales.

Nike: AR in Retail Ensures Perfect Shoe Fit

The company had, for a long time, been a front-runner for innovation. In using AR for the retail space, it is no exception. Using Nike Fit within its mobile app, the company addressed one of the most common challenges to online shoe shopping—finding the correct size.

This AR-enabled solution scans a customer’s feet and then gives recommendations for the most appropriate shoe size based on their measurements. Nike resolved uncertainty over fitting shoes for customers through its improvement of the online shopping experience, increasing sales, and lowering the incidence of returns that are expensive for the retailer while proving costly for the customer.

Through AR integration, the shopping journey will be personalized, data-driven, and enhanced for shoe buying: thereby giving its customers a bit of assurance when purchasing their product, hence raising conversion rates.

L’Oréal: AR in Retail Enhances Product Discovery

Another that is doing better in beauty using AR is L’Oréal, which has been widely activating the use of augmented reality across various media and touchpoints to discover products more effectively and increase sales. It therefore acquired AR company Modiface and developed apps that permit users to ascertain exactly how they will look with hair colors, skin treatments, and cosmetics before they are bought.

Besides their virtual try-on, L’Oréal also armed in-store experiences with AR that enable customers to scan products for personalized recommendations or more information on related products. It is such immersive experiences that have helped succeed in both in-store and online sales.

In this way, L’Oréal demonstrates how convenience, personalization, and immersive engagement can help empower a firm in the beauty industry to make it easier for customers to make quick, confident choices.

Home Depot: AR in Retail Simplifies DIY Product Visualization

What home improvement and DIY enthusiast wants to know what the product will look like in their environment before they purchase it? That’s when Home Depot realized and therefore developed an AR feature on its mobile app to enable the customer to see how a faucet, appliance, or decoration will look in the homes.

The tool, AR, provides real-time scaling and accurate placement. Thus, customers can easily ensure that the items they choose fit into their space. This has given rise to increased customer satisfaction owing to purchase confidence.

Home Depot has made AR product visualization successful, which reduced product returns and increased overall sales. This supports the argument that even in industries where customers are accustomed to a tactile shopping experience, AR can bridge the gap between digital and physical shopping needs.

Conclusion:

How AR Improves Retail Sales: Through these examples, there is seen a face of change in how retailers will interact with customers and sell. AR in retail immersive experiences bring so many benefits to businesses and consumers, including the following:

Increased Customer Confidence: AR in retail lets shoppers try before buying or see what the product looks like in their space; therefore, no guesswork happens, and customers are more confident about purchasing.

Improve Personalization: AR in retail can be quite a very potent tool for retailers to give customers more personalized shopping experiences. This can greatly lead to increased customer satisfaction and loyalty.

Boosted Engagement: Features on AR in retail , such as virtual try-on or product visualization, come in a fun, interactive experience that keeps the customer engaged for a longer time and therefore boosts sales.

Increase Fewer Returns: By helping customers better understand the fit or look of a product, AR in retail minimizes returns-a real hurdles, particularly in e-commerce.

Increased conversion rates, better customer engagement, and higher sales productivity are some of the benefits gained from retailers that utilize AR in retail.

As cited from these retail success stories, some of the biggest advantages one can acquire from using AR in retail is through improving customer experience as well as the generation of better sales. Whether it’s a virtual try-on, a tailored recommendation, or real-time visualization of products, AR is supporting business needs in the form of evolving demands required by the modern customer.

Codelynks is committed to unlocking business outcomes for companies through AR in retail. We develop customized AR solutions for retailers so they can create immersive and engaging shopping experiences that lead to customer loyalty and revenue growth. Let us help your business grow with AR, so you can dominate the competitive retail landscape.

More Blogs : 7 Game-Changing Examples of How AR and AI Integration is Revolutionizing Industries

AI Personalization in Ecommerce: Why 45% of Conversions Now Depend on It, and What Your Architecture Needs to Deliver

Posted on by Jayakrishnan M
Real-timeAI Personalization in Ecommerce architecture showing streaming data and inference pipeline

Introduction

AI personalization in ecommerce has moved from a competitive advantage to a baseline expectation. In 2026, nearly 45% of online conversions are influenced by AI-driven personalization, according to industry analysis.

Most e-commerce product recommendation engines were built on the same premise: group customers into segments and serve each segment a curated experience. Segment-based personalization drove meaningful gains for a decade. In 2026, the data says it is no longer enough.

This post covers what that shift requires architecturally, where most implementations fall short, and how to evaluate whether your current setup can support genuine individual-level personalization. AI personalization in ecommerce now relies on real-time session data instead of static segmentation.

Why AI Personalization in Ecommerce Has Shifted to Real-Time

From Segments to Sessions: What Has Changed : Segment-based personalization works like this: a user who has previously bought running shoes gets shown running accessories. A user in the 25-34 age bracket sees a different homepage banner than a user in the 45-54 bracket. The model is built offline, updated periodically, and applied at request time by looking up the user’s segment and returning pre-computed recommendations.

Individual-level personalization in 2026 works differently. The model observes the current session: what the user clicked, how long they hovered, what they added and then removed from the cart, and what they searched for. It updates its representation of that user’s intent in real time and adjusts the experience, not just the recommendations but also the layout, pricing display, and promotional offers, based on that updated intent.

The distinction matters architecturally. Segment lookup is a read from a pre-computed table. Real-time intent modeling is an inference operation, often involving a neural network, that must be completed within 100-200 milliseconds to avoid impacting page load performance.

The Five Architecture Decisions That Determine Personalization Performance

1. Where inference runs: The most common personalization failure mode is latency. The recommendation model runs in a central data center, 80-150 ms from the user, and the network round trip erodes the user experience before a single recommendation is served.

The biggest limitation of traditional systems is their inability to support AI personalization in ecommerce at the individual level.

The 2026 pattern that high-performing retailers are moving toward is edge inference. Lightweight recommendation models, typically distilled versions of larger models, run at CDN edge nodes close to the user. Full model updates happen centrally and are pushed to the edge on a schedule. The trade-off is model size: edge inference works well for session-level features but cannot run models that require full purchase history or complex cross-session signals.

Decision point: if your target inference latency is under 50ms, edge inference is worth the architecture complexity. If 100-150ms is acceptable, central inference with a well-placed CDN layer is simpler and usually sufficient.

2. Feature pipeline design: Personalization models are only as good as their features. The feature pipeline is the component that transforms raw behavioral events (clicks, searches, purchases, and hovers) into the numerical representations the model uses.

The two-pipeline pattern is now standard: a batch pipeline that processes historical data and generates user embeddings updated daily or hourly and a streaming pipeline that processes real-time session events and updates the in-session representation. At inference time, the model combines both. Historical context provides the long-range signal; session context provides the intent adjustment.

The most common implementation mistake is running only the batch pipeline and calling it real-time personalization. Batch embeddings updated daily cannot capture within-session intent changes. A user who arrived to browse shoes but then searched for a gift idea is being shown the wrong product three pages into their session.

3. Catalogue embedding and search indexing: Recommendation systems need to match a user representation to products in a large catalog. Naive systems do this with collaborative filtering on interaction matrices. Modern systems embed both users and products in the same vector space and use approximate nearest neighbor (ANN) search to find relevant products in milliseconds.

This requires a vector database. Pinecone, Weaviate, and pgvector (for teams already on PostgreSQL) are the common choices in 2026. The catalogue embedding needs to be updated whenever product attributes, inventory, or pricing changes. Serving recommendations for out-of-stock products or products at the wrong price is a trust problem that is harder to recover from than a lower conversion rate.

4. A/B testing infrastructure: Personalization cannot be validated without proper experimentation infrastructure. The challenge is that standard A/B testing assumes independent assignment: user A sees variant 1, user B sees variant 2, and the two groups do not interact.

In e-commerce, users interact: a recommendation served to one user can influence what another user sees in social contexts, inventory is shared, and pricing changes affect the whole market. Rigorous personalization A/B testing uses holdout groups rather than split tests, ensuring a percentage of users always receive the baseline experience and measurement is against that holdout rather than against a simultaneous variant.

The architecture implication: the consent state must be a first-class signal in the feature pipeline. A user who has opted out of behavioral tracking should receive a degraded but functional experience, not an error. Consent management platforms need to integrate directly with the event collection layer, not as an afterthought in the front end.

Businesses investing in AI personalization in ecommerce are seeing measurable conversion improvements.

Build vs Buy: The 2026 Decision Framework

Managed personalization platforms like Dynamic Yield, Bloomreach, and Nosto have matured significantly. For retailers below $50 million in annual GMV, a managed platform almost always delivers better ROI than a custom build. The engineering cost of building and maintaining a two-pipeline feature system, a vector database, and edge inference infrastructure is significant.

Above $50 million GMV, the calculus shifts. At that scale, the recommendation model is a competitive differentiator. Managed platforms apply the same algorithms to all their clients. A custom model trained on your specific catalog, customer base, and business logic can outperform a generic one meaningfully, and the data to train it well is available.

A hybrid architecture is also common: a managed platform for standard recommendation placements and custom models for the highest-value surfaces like the homepage, checkout, and post-purchase experience.

What the Conversion Data Actually Measures

The 45% of conversions driven by AI personalization figure comes from measuring purchases that followed a personalized recommendation or personalized layout change. It does not measure counterfactual conversions, purchases that would have happened anyway without personalisation.

Realistic lift from implementing individual-level personalization over segment-based systems ranges from 15 to 30% in conversion rate, depending on catalogue size, traffic volume, and the quality of the baseline. Smaller catalogues see smaller lifts because the recommendation space is constrained. Higher-traffic sites see larger lifts because the models have more data to work with.

Average order value lift from personalization is typically 8-15%. The mechanism is product adjacency: a well-trained model surfaces complementary products that the customer would not have found through browse navigation.

Key Takeaway

AI personalization in e-commerce is no longer about segments—it’s about real-time intent modeling at the session level.

To compete in 2026, your architecture must support the following:

  • sub-200ms inference
  • streaming + batch feature pipelines
  • vector-based product retrieval
  • consent-aware data systems

Retailers who invest in this shift are seeing 15–30% conversion lifts and measurable revenue impact. Those who don’t are optimizing a model that the market has already outgrown. AI personalization in e-commerce is no longer about segments—it is about real-time intent modeling at the session level.

Need help with AI personalization architecture for your e-commerce platform? Talk to our engineering team at Codelynks. Contact us

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7 Ways AI-Driven AR Experiences Are Transforming User Interactions

Posted on by Jayakrishnan M
AI-driven augmented reality shopping experience

Introduction

AI-driven AR experiences are transforming how users interact with digital content by creating more immersive, intuitive, and engaging interactions across industries. By combining Artificial Intelligence (AI) with Augmented Reality (AR), businesses can deliver personalized customer engagement, interactive AR shopping experiences, and immersive retail environments that improve user satisfaction and digital experiences.

As a pioneering developer in this field, Codelynks combines AI and AR to provide the most innovative solutions that integrate captivation for users as well as promote their engagement levels. This blog shall witness seven ways in which AI-driven AR redefines user engagements towards changing the future of digital engagements.

AI-Powered Personalization in AR Applications

One of the most important advantages that AI-driven AR experiences have is the fact that they can be personalized based on their users’ preferences and behaviors. AI analyzes user data, modifying an AR experience in such a way that the outcome generated by it is relevant information, suggestions, or features, according to individual interests.

For instance, in the retail domain, AI-prompted AR applications would propose customized products based on purchase history or other browsing. AI will change the AR experiences during learning for a student depending on his learning rate and preference in education. In an endeavor to revolutionize the way an enterprise experience is delivered for its user, Codelynks enhances AR solutions by AI. This helps allow users to create highly personalized experience for enterprises increasing engagement and satisfaction.

Interactive AI-Driven AR for Customer Support

Furthermore, the fusion of AI and AR is revolutionizing the customer support feature by creating a more interactive and much more efficient platform. AR applications assist users in visualizing solutions to problems with instructions or guides placed real-time, while AI assists in understanding and predicting user issues.

For example, when debugging a device, AR can give an immediate view of where and how to fix a problem, which will be driven by AI so no long messages or calls related to support are needed. Codelynks provides companies with AI-powered AR tools that make customer support easier to increase resolution times and enhance customer satisfaction.

How AI-Driven AR Experiences Are Transforming Retail

Retail is one such sector where immersive AR platforms is contributing the most. Due to AI, AR-based apps will now be able to create virtual fitting rooms for the users whereby they can “try on” clothes, accessories, or even furniture from the comfort of homes. These interactive AR systems not only foster greater user interaction but also reduce returns by providing an accurate visual representation of the product.

Based on the preference of a user, AI algorithms analyze it and suggest products to a user, thereby making the experience of shopping more personal and thus providing the right kind of products that suit one’s style. Codelynks partners with retail brands to deliver AI-driven AR experiences that enhance customer engagement, boost conversion rates, and create truly immersive shopping journeys. Retail brands are using AI personalization and augmented reality technologies to create immersive retail experiences and interactive AR shopping journeys that improve customer engagement and conversion rates.

AI and AR in Healthcare

AI-driven AR is thus revolutionizing the face of healthcare by changing how professionals interact with patient data and medical tools. The AR headsets, when combined with AI, offer surgeons a view of critical patient data coupled with real-time images so that the precision medical operations are not inaccurate. It works by showing the AI interpretation of imaging data to diagnose the condition and suggesting possible treatment options.

AI-based AR has also completely transformed the means of medical training. Currently, students are able to interact with 3D anatomical models and to simulate surgeries. Codelynks collaborates with healthcare providers as an attempt to develop AI-based AR to support the provision of better care to patients and more effective educational medicine in interactive and healthier health environments.

AI-Driven AR in Entertainment

From entertainment, AI’s powered AR takes a dramatic shift. Whether it is related to the entertainment industry in forms of games or movies, even digital events, AI propels AR to provide more immersive worlds and response dynamically to any user input through the adaptation of the storyline or gaming environment depending on player behavior and so offering unique and personalized experiences.

Such a level of engagement keeps customers engaged for longer periods of time and helps a designer to build even deeper relationships with content. Here, Codelynks is changing the AR entertainment solution that is driven by AI by developing interactivity, relevance to an individual’s personal life, and excitement in engaging with media users.

AI-Enhanced Learning and Training

AI-powered AR changes the game in education and corporate learning. Introducing the exploratory, hand-in-glove kind of learning environment through AR, while AI customized content adaptation caters to the unique learning requirements of every learner; thus, combining the two enables learners to interact with 3D models, simulations, or real-time problem-solving scenarios that adjust to their performance.

For example, in terms of the study of engineering, AR can visualize working machinery in real-time for learners to engage in explorations of the parts and their roles. AI tracks individual performance and makes recommendations for improvement, thereby ensuring a personalized learning path. Codelynks is intensely collaborating with AI-based AR solutions to rethink education and training in terms of making learning more appealing and effective.

AI-Based AR in Smart Cities and Urban Planning

AI and AR are becoming increasingly significant for urban planning and the development of smart cities. AR lets city planners and architects visualize infrastructure projects that they might prepare, while AI analyzes data to make such designs optimized for needs including traffic flow, population density, and environmental impact.

For the citizens, AI-powered AR are interactive systems for navigation that contain real-time information on services and transportation available in the city, as well as current events happening locally. Codelynks is in tight collaboration with smart city initiatives for the development of AI-augmented AR systems toward improving urban living and easing city planning processes.

Conclusion

AI-Driven AR Experiences – A New Era of User Engagement

Integration of augmented reality solutions is already changing the face of various industries, including improved user interaction and engagement beyond what has been possible for years. Such examples range from personalized retail shopping to immersive environments in education where AI and AR together set a new standard in business and user interactions with digital content.

At Codelynks, we are at the forefront of this technological revolution: cutting-edge AI and AR solutions for businesses to help them deliver next-level experiences. It can be enriching customer service, revolutionizing healthcare, or crating immersive entertainment experience, the expertise of Codelynks combines and transforms how users interact with digital platforms in result by increasing engagement and innovative possibilities.

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6 Powerful Steps for AR Implementation in Retail: Transform Customer Experience

Posted on by Jayakrishnan M
AR Implementation in Retail – customer using augmented reality try-on mirror

Introduction

AR Implementation in Retail is transforming the shopping experience, enabling customers to try products virtually and interact with digital displays. This way, retailers use AR to enable interaction and increase sales with the customer, from virtual try-ons to interactive product displays, enhancing the customer experience, streamlining operations in an increasingly digital world.

In this guideline, we will break down the main steps of setting up AR in retail-the technical, integration approach, and best practices for a successful launch.

Benefits of AR Implementation in Retail

Before going into the nitty-gritty of the implementation, let’s discover the benefits you can anticipate from your retail business. With augmented reality, you get to enjoy several advantages, such as

  1. Engagement of More Customers: A key advantage of AR Implementation in Retail is increased customer engagement and reduced product returns.
  2. Personalization: AR lets retail companies provide recommendations to customers based on what a customer likes and what a customer is doing by using the device.
  3. Lower Return Rate: Virtual try-ons and product visualization tools make customers less likely to return the products because they will see what they are going to look like.
  4. Higher Sales: An engaging, convenient experience will translate to conversion rates and, thus sales.

These advantages will allow you to harmonize your AR strategy with other business goals.

Choosing Technology for AR Implementation in Retail

There are numerous AR technologies in the market today and hence the proper one needs to be chosen for proper utilization. Some of the most common types of AR technologies that retailers use are:

  1. Marker-Based AR: This method applies a marker like QR codes for the creation of digital overlays once scanned using a smartphone or tablet. Interactivity in product display and marketing campaigns is usually created by this technique.
  2. Markerless AR: Also known as location-based AR, the user does not require special markers to interact with AR content. The application is ideal for virtual try-ons, navigating shops, and viewing products.
  3. Projection-based AR: It allows projecting virtual images into real-world surfaces to be perceived by the customers as digital displays in actual environments. The most popular usage would be on interactive store displays and pop-up experiences.
  4. Superimposition-based AR: Superimposition AR automatically replaces part of the user’s view with an augmented image, widely used in virtual fitting rooms and furniture visualization.

The right AR technology will rely upon your business model, the type of customer experience you would like to create and how it meets the needs for your retail environment.

Define Your AR Use Case

One of the key factors in using AR effectively in retail is to define your specific use case. Identify what aspects of your retail operations or customer experience can benefit from AR. Some of the most prevalent use cases in retail include:

  1. Virtual Try-Ons: Through AR, customers are now able to try out on their outfits, accessories, or makeup before purchasing them. This is a very popular concept in the fashion and beauty industries.
  2. Product Visualization: AR allows customers to see furniture, appliances, or home decor in a customer’s space, helping them make informed decisions before they buy.
  3. Interactive In-Store Displays: AR can transform static in-store displays into interactive experiences that may offer product information, reviews, and offers.
  4. Inside-Store Navigation: By implementing AR, vast retail areas can offer a real-time navigation support to customers; that is, they are directed towards the products or the exact areas in the store efficiently.

You can adjust your implementation strategy and the right technology based on your well-defined use case of AR to achieve your desired goals.

Invest in Right Hardware and Software

What determines the success of AR is proper hardware and software selection in a retail environment. For most retail companies, smartphone-based AR might be the closest convenience available, considering that the customers can share AR experiences by accessing the same through the mobile device. Depending on your use case, however, you may also have to invest in more specialized hardware components like:

  1. AR-Enabled Mirrors or Displays: These devices enable the customers to collaborate with AR content in-store, such as trying out clothes or previewing the products.
  2. Smart Eyewear or Headsets : Some retail companies have explored the implementation of AR headsets or smart glasses which can offer customers an even more interactive, hands-free experience with digital content.
  3. Custom AR Applications or Platforms: Developing a custom AR app or using an existing AR platform such as Snapchat’s Lens Studio or Shopify AR enables you to engineer unique experiences particularly well-suited to your brand and products.

Your AR system also must be compatible with your existing retail technology and infrastructure. This is where you would want your current integration process to be smooth.

Development of AR Integration Strategy

AR is best planned and coordinated across all departments- marketing, IT, and customer service. For developing a successful AR integration strategy, here are some of the key steps that lead to an effective integration process:

It demonstrates cross-team collaboration, such that any AR implementation would reflect both customer-experience goals as well as technical capabilities.

  1. Test it, then iterate: Prepare a pilot program for smaller-scale AR to get feedback and improve experience before its larger rollout. This will help identify technical issues or improvements related to customer experience.
  2. Seamless Omnichannel Availability: Ensure that your AR experience is seamlessly integrated with online and in-store retail channels. Customers should find an easy way to access AR features from mobile, kiosk in-store, or even through your website.
  3. Empowering the Front Line Support: Preparing the staff with the appropriate skills and knowledge to support AR-related interactions with customers, for instance, when helping out in interaction at displays inside the store or addressing technical issues in AR apps.

And by doing so, you will be able to achieve the maximum value of your AR implementation for the customers and your business.

Measuring Success of AR Implementation in Retail

After your AR solution is in operation, there ought to exist a mechanism of measuring its effectiveness. Some of the key KPIs tracked include

  1. Customer Engagement: One major benefit of AR Implementation in Retail is higher customer engagement. Track the frequency with which customers engage with your AR experiences, tracking engagement levels by quantifying app usage, time spent, and repeat visits.
  2. Sales Impact: Track conversions, increases in average order value, as well as decrease in product returns that AR experiences deliver.
  3. Customer Satisfaction: Collect customer feedback regarding whether or not AR enhances their shopping experience and if their expectations are met.

By checking on these metrics from time to time, you will be able to refine your AR strategy so that it keeps on delivering value to your business over time.

Conclusion

Businesses that embrace AR Implementation in Retail will stay ahead in customer experience innovation. Retail is one brilliant opportunity to increase consumer experiences, encourage customer engagement, and sell more with AR. You will succeed in the integration of AR into your retail environment by selecting the right technology, defining your use case clearly, and developing a clear plan of integration. As AR continues to advance, retailers and their customers will be well prepared to stand out in a very competitive marketplace and deliver value to your customer sets. Retailers focusing on AR Implementation in Retail will enhance customer experience and drive measurable sales growth.

Related blog : Personalized Shopping with AR: 5 Ways It’s Transforming Retail Experiences

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