HCL Commerce v9.1 release saw a major change in features, functionality, and technology. This blog series will focus on each of these components separately. Some examples of these changes include HCL Commerce Search, which is powered by Elasticsearch, a modern storefront that uses Next.js, containerized cloud native architecture, modern business user tooling, and provides support for new integrations and companion software.

Part 2 of this blog series will focus on the coexistence of the Next.js Ruby & Aurora Storefronts.

Background

A client had multiple e-sites running on the HCL Commerce v9 using the Aurora JSP-based storefront. The client wanted to migrate to the Next.js Ruby storefront and take advantage of the modern headless store, including server-side rendering (SSR) for page optimization. The client wanted a cost-effective solution to drive ROI through built-in SEO capabilities, improved page site performance (increase Google Core Web Vitals), and improved end-user experience.

A migration of multiple e-sites to the Next.js Ruby storefront with HCL Commerce Search using Elasticsearch and the client-specific customizations can be a large rewrite.  Perficient worked with the client to find a cost-effective solution and identified the home page and the product details page (PDP) to migrate to the Next.js Ruby storefront.  This also allowed the client the ability to evaluate the storefront and capabilities before migrating the remaining pages to the Next.js Ruby storefront.

Pros & Cons of the Hybrid Approach

The hybrid approach has several pros and cons and can vary based on each client and the business requirements. This client used many e-marketing spots throughout the site, and it was challenging to maintain duplicate content to support both storefronts. Since the content syntax is different between storefronts, any changes to the common header and footer navigation will need to be maintained for both storefronts. Another consideration is implementing third-party integrations and ensuring compatibility with both storefronts. For example, Segment was used for Analytics tracking, and our team had to ensure that events were triggering successfully with the correct data on both storefront pages. One of the most critical components of a hybrid approach is correctly identifying and routing requests so that pages are rendered correctly between the Aurora and the Next.js Ruby storefronts. The client had PDP URLs with a unique SEO pattern allowing the Perficient team to create rules to route requests so they can be rendered by the correct storefront container. Post migration, the client immediately started seeing the advantages of the Next.js Ruby storefront’s features and capabilities. The client saw improvements in page load times and on Core Web Vitals for the migrated pages.

Conclusion

The hybrid approach allowed the client to take advantage of the newer technology and realize the ROI on the migrated pages. The site benefited from the Core Web Vitals score increase, enhanced SEO capabilities, and improved page performance. The hybrid approach allowed the technical and marketing teams to familiarize themselves with the features and capabilities of the Next.js Ruby storefront and deploy it to the most impactful areas of the site. As a next step, the client is migrating the remaining pages to the Next.js Ruby storefront to fully take advantage of HCL’s continued enhancements.

To obtain further information from our award-winning team, please visit https://www.perficient.com/who-we-are/partners/hcl.

Other Blogs in the Series

HCL Commerce V9.1 – The Power of the Next.js Ruby Storefront

A subscription business model is a recurring revenue model in which customers pay a fee periodically, such as monthly or yearly, in exchange for products and services. This model allows you to leverage your customer relationships to create a steady income stream.
In addition to the discounted nature of the product, replenishment subscriptions are also appealing to customers for their convenience, delivering commodity items on a recurring basis.

Setting up Subscriptions on Shopify

Enabling subscriptions on Shopify is very simple through their supported subscription apps, which merchants can enable, customize, and configure from the Shopify admin.
A subscription policy must be created once a subscription app is configured from the App Store. After this, choose the required products that need a subscription and any corresponding discounts. Also, it is possible to edit the information shown in Checkout, like the subscription frequency. In the Payment step, we can have a message to confirm the customer’s understanding of the payment frequency.
Some of the key features that can be used are:

1. Access and Membership Subscription

Subscribers pay to receive exclusive products, content, or discounts like Netflix or Amazon Prime.

2. Curated Subscriptions

Involves personalized products for different customer segments based on their purchase history/ profile.

3. Replenishment Subscription

Customers subscribe to receiving the same product regularly without the risk of running out of stock.

4. Subscription-based Pricing

Custom, dynamic, tiered, per-user, or gift-based pricing based on product bundle or customer segment.

5. Loyalty and Reward Points Subscription

Offer Loyalty points based on every purchase or customer segment, which can be redeemed by the user at a later point in time.

The subscription business model helps engage more with customers on a recurring basis due to the nature of the purchase. It also helps have a predictable revenue model since we know the subscription and payment frequency. It also provides an easy entry point to enable the customer to buy in small quantities, but over a long period of time, it turns out to be a large purchase.

Looking for Shopify Commerce Expertise?

Our team of top-tier Shopify commerce professionals specializes in tailoring solutions to meet your unique objectives, whether you operate in the B2B, B2C, or enterprise space. With our expertise, you can leverage cutting-edge architectures such as Headless, Composable, or Microservices to build robust platforms that meet the demands of today’s digital landscape. From initial consultation to post-launch support, our experts are committed to ensuring smooth and successful implementation every step of the way.

Ready to Transform Your Commerce Experience?

Do not let outdated commerce solutions hold your business back. Partner with Perficient today and unlock Shopify’s full potential for your online sales channels. Contact us now to schedule a consultation and take the first step towards commerce excellence. Let us turn your vision into reality together.

Can a Product Information Management (PIM) System Make Your Data AI-Ready?

Yes, a PIM system can help get your data ready for AI—but only if it’s set up the right way.

If you’re managing product info across channels, you already know that bad data means bad results. And if you’re thinking of adding AI—like auto-tagging, personalization, or predictive tools—your product data needs to be spot-on.

This post breaks down what “AI-ready data” actually means, why messy product data kills your AI plans, and how a PIM system fits into fixing it.

What Does “AI-Ready” Data Mean?

AI-ready data is clean, complete, consistent, and structured to match what the AI needs to do. If any part of that is missing, the results from your AI model will be wrong or useless.

Gartner outlines five key steps to make data AI-ready:

1. Assess the data needed for each AI use case. You can’t just throw all your product data into an AI tool and expect magic. You need to know what the AI is supposed to do—recommend products, tag images, write descriptions—and check if the data supports that.

2. Align your data with the AI’s goals. Let’s say your goal is to personalize search results. That means every product needs the right tags, images, and categories. If that info’s missing or inconsistent, AI can’t deliver what you want.

3. Set clear rules for data governance. This includes naming standards, formatting rules, and tracking changes. AI systems rely on patterns. Without strong data governance, the AI can’t recognize patterns well enough to learn or predict accurately.

4. Use metadata to give your data context. Metadata helps AI understand what each piece of data means. It’s how you tell a machine the difference between a color and a size, or between an image and a feature.

5. Make data everyone’s job. If only IT or product teams handle data cleanup, you’ll never scale. You need marketing, content, and sales to be part of the process. That cross-team input helps AI models learn faster and smarter.

Without these steps, AI tools waste time trying to clean or guess data—and that leads to mistakes.

Common Product Data Problems That Hurt AI Outcomes

AI depends on structured, reliable data. When product data is messy or incomplete, AI tools can’t learn correctly or make accurate decisions.

Here are the most common issues that mess up AI results:

1. Missing values. If your product descriptions don’t always include size, color, or materials, the AI can’t group or recommend items correctly.

2. Inconsistent formats. “Red”, “RED”, and “#FF0000” might mean the same thing to people—but not to machines. AI models treat each format as different unless the data is standardized.

3. Duplicate entries. Two versions of the same product can confuse the AI. It might see them as separate products and deliver incorrect suggestions or analytics.

4. Unstructured content. If your product titles are crammed with keywords but no pattern, AI can’t extract useful meaning. Structured data is easier for models to work with.

5. Lack of metadata. AI models need more than just the product image or title. Without tags, category labels, and usage context, the model can’t learn how to connect products.

6. Outdated info. AI training requires current, real-world data. If product details change often but don’t get updated fast enough, the AI works off bad inputs and gives wrong outputs.

Each of these issues reduces the accuracy of your AI’s predictions, recommendations, or automations.

How PIM Systems Solve These Problems

A PIM system helps fix the data issues that stop AI from working well. It brings structure, control, and context to your product data—all of which AI needs to deliver value.

Here’s how PIM lines up with the five AI-readiness steps from Gartner:

1. Data aligned with use case: In a PIM, you define which attributes are required for each product category. If your AI needs color, size, and material to personalize product recommendations, PIM ensures that data is there—before the product is published.

2. Data normalization: PIM tools standardize formats. “Blue” won’t show up as “BLU” or “navy blueish” in different listings. The system enforces data rules, so your AI can trust the inputs.

3. Data governance: PIM systems let you set validation rules, version tracking, and user permissions. This means every change is tracked, and only approved data moves forward—key for AI systems that depend on clean histories.

4. Metadata management: PIM systems store and manage metadata like categories, usage tags, and even SEO terms. This extra layer helps AI models understand context—whether it’s matching a product to a search or choosing the best image.

5. Cross-team collaboration: With a PIM, marketing, product, and eCommerce teams work from the same source. This reduces errors, speeds up updates, and gives AI a steady flow of reliable product information.

By solving these issues at the source, a PIM platform creates the clean, structured, and well-governed data foundation that AI tools need to do their job right.

Can PIM Alone Get You There?

A PIM system solves the data problems—but it doesn’t replace the AI stack. Think of PIM as the prep kitchen. It gets everything clean, sorted, and ready to go. But you still need the right tools to cook.

Here’s what PIM does well:

• Cleans up product attributes

• Standardizes formats and values

• Adds missing metadata

• Makes data accessible across teams

But once the data is ready, you still need AI platforms to do the heavy lifting. That includes:

• Machine learning models to drive personalization

• Predictive tools to forecast demand or returns

• Agentic AI tools that take action (like re-tagging or alerting on gaps)

• Analytics platforms to visualize outcomes

So no, a PIM alone won’t give you full AI capabilities. But without a PIM, your AI tools will spend most of their time cleaning up your mess instead of giving you results.

PIM Is Your First Step to Smarter AI

AI can only work well when the data behind it is complete, consistent, and structured. A PIM system lays that foundation. It organizes your product information, enforces data standards, and adds the context that AI tools need to operate accurately.

Without clean data, AI models deliver flawed results. But with a strong PIM in place, you give AI the best chance to succeed—whether it’s automating product tagging, powering recommendations, or optimizing digital experiences.

Need help setting up a PIM or making your product data AI-ready?
Connect with us today—We help businesses use the right mix of PIM and AI to get real results faster. Whether you’re starting fresh or upgrading what you’ve got, we’ll make sure your data is ready for the next step.

The HCL Commerce v9.1 release saw major features, functionality, and technology changes. This blog series will focus on each of these components separately. Some examples of these changes include HCL Commerce Search, which is powered by Elasticsearch, a modern storefront that uses Next.js, containerized cloud-native architecture, modern business user tooling, and support for new integrations and companion software.

Part 1 of this blog series will focus on the HCL Commerce Next.js-based Ruby storefront.

Benefits of the Next.js Ruby Storefront

The Ruby Storefront is an HCL Commerce-provided Next.js-based B2B & B2C starter store that exploits the powerful features and capabilities of the HCL Commerce platform. It is a fully headless store utilizing REST services to interact with the HCL Commerce logic framework to drive the features and capabilities of the platform. The store uses server-side rendering (SSR), which helps drive improvements in initial page load times, Google Core Web Vitals, performance, and overall page optimizations. The store also provides a generic data layer for Google Analytics (GA4) and has built-in SEO capabilities, which are crucial for digital marketing. The storefront has prebuilt components, is CDN optimized, and supports the mobile-first approach that allows business owners a faster time to market.

Template-based Layouts in the Storefront

The storefront utilizes a template-based layout for each page, such as the home page and the product detail page (PDP). Having separate layouts allows customers to render each page differently based on the business requirements. These layouts support e-marketing spots and segmentation to drive a more personalized experience in the targeted area of the layout. There is also support for category and product-specific pages, which allow business users more control. Our team has taken advantage of the template-based approach to help incrementally migrate existing customers and leverage the benefits of the Next.js Ruby storefront with a hybrid migration approach.

Hybrid Approach

A complete migration to the Next.js Ruby storefront can be costly and time-consuming. As a result, the Perficient team has developed a solution that allows customers to migrate to the Next.js storefront using a hybrid approach. The solution enables the legacy Java Server Pages (JSP) based Aurora Storefronts pages to run in parallel with the new modern Next.js Ruby storefront pages. Additionally, as of HCL Commerce 9.1.15, HCL has provided the ability to use Elasticsearch or SOLR as the back-end search engine, which functions seamlessly with the Next.js Ruby storefront. This hybrid approach can be a cost-effective solution that helps drive ROI for pages where it is most needed.

Conclusion

HCL Commerce Next.js Ruby Storefront is a feature-packed headless storefront built using one of the latest and most popular technologies. The storefront can leverage either Elasticsearch or SOLR search as the back-end search engine. This serves as the foundation for efficient collaboration with our clients to migrate incrementally and cost-effectively from the legacy JSP Aurora store to the Next.js Ruby storefront.

To obtain further information from our award-winning team, please visit https://www.perficient.com/who-we-are/partners/hcl.

Other Blogs in the Series

HCL Commerce V9.1 – Coexistence of the Headless Next.js Ruby & Aurora Storefronts

Perficient continues to be recognized in the industry as a leader in commerce strategy and implementation, achieving awards from partners and industry organizations as well as inclusions in key analyst reports. Our experts’ insights on distributed and dynamic commerce strategies, including distributed channels such as social media, influencer content, shoppable media, and GenAI as a catalyst for dynamic interactions, have been fueling conversations both with our clients and on the show floor of major industry tradeshows.

Most recently, Perficient was interviewed by Forrester for insights into the landscape of commerce in the United States. We believe our inclusion in The Future of Commerce (US), Distributed And Dynamic Commerce Strategies Pave The Way To Intelligent Commerce by Chuck Gahun reflects Perficient’s commitment to innovation within an ever-changing commerce landscape, as well as our ability to construct high-impact solutions that evolve as AI becomes more prevalent in commerce strategies.

Our Key Takeaways: Consumers Demand Smarter Commerce

While much has changed since the inception of ecommerce, some core truths still stand the test of time. Consumer confidence remains a crucial factor throughout the shift from in-store to online shopping. Brand value significantly contributes to consumer confidence and has grown in importance, especially regarding customer lifetime value and affinity. To keep pace with changes, there is an increased demand for AI and intelligent commerce.

Intelligent commerce is defined in two ways:

1. Commerce that will inherently evolve from assisted and anticipatory to agentic.
2. Consumer emotion is included as part of the equation.

Intelligent commerce interactions of this type will find ways for agentic models to understand emotion, sentiment, and dialect to make recommendations, truly bringing immersive shopping to life in a channel-less fashion.

“The shift to agentic commerce will introduce emotional elements at every interaction with consumers, thus deepening their connection to these brands,” says Justin Racine, Principal, Commerce Strategy.

Companies are finding ways to incorporate AI into experiences throughout the customer journey. We’re beginning to see what’s now known as “phygital,” a blending of traditional screens for shopping and their incorporation into daily life.

Omnichannel for Overall Better Commerce

Omnichannel initiatives can be costly and time-consuming. At Perficient, we’ve helped many clients navigate this complex maze by creating seamless omnichannel experiences, from sentiment analysis to contact center and chatbot creation and training.  However, the Forrester report states, “Only 21% of B2C business and technology professionals report their organization is prioritizing efforts to improve omnichannel initiatives, but consumer expectations for omnichannel experiences are higher than ever — across all consumer cohorts.” This dissonance between prioritization and consumer expectation will lead to major needs for increased commerce innovations in the future, especially if customer experience is to be at the forefront to nurture lasting relationships with audiences at every touchpoint.

Brands are looking for ways to connect more with customers and create experiences that resonate across channels, depending on where customers are in the process. With hyper-personalization and AI, customer data points around behavior can be synthesized through AI to create experiences that resonate with those customers.

Explore how to leverage customer data and utilize generative AI to help guide your omnichannel solutions.

Access the report here (available to Forrester subscribers or for purchase).

Our trusted Unified Commerce Platform partner, Kibo, is gearing up to host the Kibo Connect Client Summit from May 7th to 9th at the Loews Downtown Chicago Hotel. Since the start of our partnership in 2021, Kibo has consistently delivered success through innovative commerce and delivery models, a dynamic omnichannel pricing and promotions engine, and robust delivery options seamlessly integrated into its user-friendly interface. This upcoming summit promises to bring together industry leaders, innovators, and experts to exchange valuable insights, strategies, and success stories from the world of commerce.

What to Expect at the Summit

The event offers plenty of networking opportunities, with more than 200 executives and industry experts in attendance. Key figures like CTOs and SVPs from renowned businesses such as Total Wine & More, Forrester, and Ace Hardware will be among those contributing thought leadership on stage. Adding to the lineup, our very own Zach Zalowitz, Principal of Order Management and Product Information Management, and Kim Glasscock, Director of Order Management, will represent us at the summit.

Expect a wealth of strategic discussions on the latest practices in commerce, order management, and customer experience. One must-attend session is the panel ‘The Future of Commerce: Navigating Disruption and Driving Innovation,’ featuring Zach Zalowitz alongside prominent leaders from ODP and Proactiv. The panel discussion takes place on Thursday, May 8th, at 2:00 PM.

Additionally, attendees will have the chance to gain actionable insights by participating in various workshops and sessions led by global brands and technology providers.

Join Us at Kibo Connect 2025!

We’re thrilled to be part of the Kibo Connect Client Summit and look forward to seeing you there. Attendees can expect some exciting surprises from us at the event, whether it’s inspiration from the main stage or insightful conversations at our partner table. As we countdown to May, stay tuned for more updates and information.

Explore our commerce expertise in and in your industry as we prepare to connect, and contact us if you’re ready to schedule time for a discussion at the event.

A ticketing system, such as a Dynamic Tracking Tool, can be a powerful tool for MSO support teams, providing a centralized and efficient way to manage incidents and service requests. Here are some more details on the benefits.

1. Organize and triage cases: With a ticketing system, MSO support teams can easily prioritize cases based on their priority, status, and other relevant information. This allows them to quickly identify and resolve critical issues before they become major problems.
2. Automate distribution and assignment: A ticketing system can automate the distribution and assignment of incidents to the right department staff member. This ensures that incidents are quickly and efficiently handled by the most qualified support team members.
3. Increase collaboration: A ticketing system can increase collaboration between customer service teams and other stakeholders. It allows for easy and quick ticket assignment, collaboration in resolving issues, and real-time changes.
4. Consolidate support needs: Using a ticketing system consolidates all support needs in one place, providing a record of customer interactions stored in the system. This allows support teams to quickly and easily access customer history, track communication, and resolve issues more effectively.
5. Dynamics Tracking Tool: This shows various reports, such as the Real-Time Tracking Report and Historical Data Report, which are provided to monitor and analyze tracking data efficiently.

Overall, a ticketing system can help MSO support teams to be more organized, efficient, and effective in managing incidents and service requests.

Benefits of a Dynamic Ticketing Management System

1. Prioritization: A ticketing system efficiently prioritizes incidents based on their impact on the business and their urgency. This ensures critical issues are resolved quickly, minimizing downtime and maximizing productivity.
2. Efficiency: A ticketing system streamlines the incident management process, reducing the time and effort required to handle incidents. It allows support teams to focus on resolving issues rather than spending time on administrative tasks such as logging incidents and updating users.
3. Collaboration: A ticketing system enables collaboration between support teams, allowing them to share information and expertise to resolve incidents more efficiently. It also enables users to collaborate with support teams, providing real-time updates and feedback on the status of their incidents.
4. Tracking & Reporting: A ticketing system provides detailed monitoring and reporting capabilities, allowing businesses to analyze incident data and identify trends and patterns. This information can be used to identify recurring issues, develop strategies to prevent incidents from occurring, and improve the overall quality of support services.
5. Professionalism: A ticketing system provides a professional and consistent approach to incident management, ensuring that all incidents are handled promptly and efficiently. This helps to enhance the reputation of the support team and the business as a whole.
6. Transparency: A ticketing system provides transparency in the incident management process, allowing users to track the status of their incidents in real time. It also provides visibility into the actions taken by support teams, enabling users to understand how incidents are being resolved.
7. Continuity: A ticketing system provides continuity in the incident management process, ensuring that incidents are handled consistently and effectively across the organization. It also ensures that incident data is captured and stored in a centralized location, providing a comprehensive view of the incident management process.

A Support System Orbits Around 3-Tiered Support

Tier 1

Tier 1 tech support is typically the first level of technical support in a multi-tiered technical support model. It is responsible for handling basic customer issues and providing initial diagnosis and resolution of technical problems.

A Tier 1 specialist’s primary responsibility is to gather customer information and analyze the symptoms to determine the underlying problem. They may use pre-determined scripts or workflows to troubleshoot common technical issues and provide basic solutions.

If the issue is beyond their expertise, they may escalate it to the appropriate Tier 2 or Tier 3 support team for further investigation and resolution.

Overall, Tier 1 tech support is critical for providing initial assistance to customers and ensuring that technical issues are addressed promptly and efficiently.

Tier 2

Tier 2 support is the second level of technical support in a multi-tiered technical support model, and it typically involves more specialized technical knowledge and skills than Tier 2 support.

Tier 2 support is staffed by technicians with in-depth technical knowledge and experience troubleshooting complex technical issues. These technicians are responsible for providing more advanced technical assistance to customers, and they may use more specialized tools or equipment to diagnose and resolve technical problems.

Tier 2 support is critical for resolving complex technical issues and ensuring that customers receive high-quality technical assistance.

Tier 3

Support typically involves highly specialized technical knowledge and skills, and technicians at this level are often subject matter experts in their respective areas. They may be responsible for developing new solutions or workarounds for complex technical issues and providing training and guidance to Tier 1 and Tier 2 support teams.

In some cases, Tier 3 support may be provided by the product or service vendor, while in other cases, it may be provided by a third-party provider. The goal of Tier 3 support is to ensure that the most complex technical issues are resolved as quickly and efficiently as possible, minimizing downtime and ensuring customer satisfaction.

Overall, Tier 3 support is critical in providing advanced technical assistance and ensuring that the most complex technical problems are resolved effectively.

Determine The Importance of Tickets/Incidents/Issues/Cases

The first step in a support ticketing system is to determine the incident’s importance. This involves assessing the incident’s impact on the user and the business and assigning a priority level based on the severity of the issue.

1. Receiving: The step is to receive the incident report from the user. This can be done through various channels, such as email, phone, or a web-based form.
2. Validating: This step involves validating the incident and verifying that it is a valid issue that needs to be addressed by the Support team.
3. Logging: Once the incident has been validated, it is logged into an incident application, which is used to track and manage it throughout the process.
4. Screening: The next step is to screen the incident and determine the user’s symptoms. This involves asking questions to gather more information about the issue and to identify any patterns or trends that may help resolve the incident.
5. Prioritizing: Once the symptoms have been identified, the next step is to prioritize the incident based on its impact on the user and the business.
6. Assigning: After the incident has been prioritized, it is assigned to a support team that will handle it. If the support team cannot handle the incident, it is escalated to a higher-level tier.
7. Escalating: If the incident requires more advanced expertise or resources, it is escalated to a higher-level tier where it can be resolved more effectively.
8. Resolving: The support team or higher-level tier works on resolving the incident and provides updates to the user until the issue is resolved.
9. Closing: Once the incident has been resolved, the ticket is closed by logging the resolution and changing the ticket status to indicate that the incident has been successfully resolved.

Summary

Ticketing systems are essential for businesses that want to manage customer service requests efficiently. These systems allow customers to submit service requests, track the progress of their requests, and receive updates when their requests are resolved. The ticketing system also enables businesses to assign service requests to the appropriate employees or teams and prioritize them based on urgency or severity. This helps streamline workflow and ensure service requests are addressed promptly and efficiently. Additionally, ticketing systems can provide valuable insights into customer behavior, allowing businesses to identify areas where they can improve their products or services.

Perficient’s experts recently attended Shoptalk Spring in Las Vegas, immersing themselves in three days of meetings and networking with brands and partners amidst the lively atmosphere of smoke-filled hallways, pulsating music, and dazzling lasers. Justin Racine, Principal of Commerce, shared his insights with CMSWire, and we’ve highlighted some of his key takeaways below.

The Golden Age of Retail

Retail and customer experience are about to enter a transformative era—the Golden Age of retail. From the advent of department stores to the rise of shopping malls, consumers and brands are now shifting focus toward people over products. Businesses are increasingly prioritizing human connections, bringing joy and excitement back into shopping. Retail will serve as a medium for inspiring consumers to explore who they are, express their identity, and connect with the world around them.

Creating Customer Connections at Gap

Justin had the opportunity to hear from Gap CEO Richard Dickson, who underscored the importance of fostering meaningful connections between brands and their consumers. According to Dickson, Gap’s mission is to create products that empower customers to express their individuality. “We pride ourselves on giving customers the ability to make Gap their own—to wear it the way they want,” Dickson explained. He emphasized that while price and affordability matter, customers are willing to invest in experiences and products that elevate their sense of self.

Gap has successfully cultivated generational loyalty by creating memorable experiences for families. Parents shop at Gap for their kids, and those children grow up wearing the brand, forming a deep emotional connection. These cherished memories are often captured in photos, further embedding the brand into customers’ lives. By facilitating connections on a deeper, emotional level, Gap builds lasting generational impact and loyalty.

AI in Advertisements: A Conversation with Meta

While Shoptalk Spring emphasized the human side of consumer behavior, discussions around AI inevitably arose. Clara Shih, VP of Business AI at Meta explored the future of branding through AI, focusing on Meta’s Advantage+ toolset. This suite enables businesses to deliver targeted media and content across various channels. Shih showcased new features, including location-based ads on Facebook that integrate maps directing customers to nearby stores. Another demo highlighted AI-powered live chat within ads, allowing consumers to engage with brands directly in their active channel. These innovative features fulfill customers’ desire for seamless interaction and enhance their ability to connect with brands on a humanistic level.

A Conversation with Liza Lefkowski of Wayfair

Wayfair is also deepening its understanding of customers through the integration of data and experience. Liza Lefkowski, Chief Merchant and VP of Stores at Wayfair, discussed the brand’s expansion into physical retail and its aim to inspire and excite consumers. During her session, Lefkowski explained how store associates provide personalized guidance, bridging the gap left by an exclusively online presence. This approach fosters emotional connections between customers and the brand. “Stores are designed to stand on their own but also integrate seamlessly into the overall customer experience—it’s the immersive manifestation of Wayfair,” she said.

Retail Should Spark Emotion, Not Just Transactions

This spring marked Justin’s first time attending Shoptalk Spring, but the themes from the event echoed those from Shoptalk Fall last year: retail must delight, surprise, and connect with customers. While technology and AI are crucial, human connections remain the cornerstone of retail success. By inspiring customers to be the best versions of themselves, brands can create genuine, personal relationships that drive loyalty and satisfaction.

For more insights, visit Perficient’s retail and commerce expertise page.

To read Justin’s full article, head over to CMSWire.

Adobe Summit 2025 is officially a wrap, and I have pages and pages of notes to go through with my team to plan out our year! The two terms that appear the most in my notes are Performance and Personalization. Customer expectations continue to rise, and the demand is higher than ever for lightning-fast sites that keep shoppers engaged, with hyper-personalization that lets them know brands care about them, leading to loyalty and customer retention. These themes were consistent throughout the sessions that I attended throughout the week.

ADOBE COMMERCE AS A CLOUD SERVICE

This summer, Adobe Commerce as a Cloud Service will officially be available. This high-speed storefront is a fully SaaS solution that can be provisioned in just a few minutes. It is version-less and maintained by Adobe, eliminating the maintenance costs that come along with patches and upgrades. Catalog Service, Intelligent Merchandising, Payment Services, Product Asset Management, and Developer Tools are added on top of the existing Commerce Foundation.

The results are super-fast experiences delivered from the edge, perfect Lighthouse scores, boosts to search engine rankings, more organic traffic, and higher conversions. Add in generative AI to rapidly build personalized variations of content, alongside analysis of shopper behavior and sales data for that next level personalization that keeps customers coming back.

ADOBE COMMERCE OPTIMIZER

Available at the same time, Adobe Commerce Optimizer offers the same features as Cloud Service but allows customers to keep their existing commerce back-end. The experience layer provides the storefront, catalog and merchandising tools, allowing for quick wins with ROI and modernization. Pre-built APIs and Connectors can be utilized and tweaked as needed, to get to launch as quickly as possible. Then, if desired, the commerce back-end can be migrated later, at the merchant’s pace to realize the full set of benefits that come with Cloud Service. Adobe offers migration tools to make the transition smoother.

KEY FEATURES

The new Storefront is powered by Edge Delivery, resulting in four times faster page loads, fifteen percent or higher increase in organic traffic, improved search engine rankings, and ninety or higher Lighthouse scores. Separating the front-end from Commerce Foundation in a composable fashion, and loading data in, increases stability and security, and decreases total cost of ownership.

Authoring options include drag and drop visual, and document based via SharePoint or Google. Content creation powered by generative AI helps create personalized experiences from content variations, which can then be validated with built-in A/B testing. This helps merchandisers find the right content to display to the right customer at the right time.

With integrated digital asset management powered by AEM Assets, gone are the days of uploading product images and other media directly into Commerce. Through the power of AI and integration with Firefly and Express, product image variations can be generated in bulk and stored in the DAM. The images are easily linked to SKUs making them immediately available for product pages, once they are published. Enhanced experiences are possible with the product variations, 3D models, and augmented reality.

Intelligent Merchandising can increase conversion rates and order values by tailoring search results, category pages, and product recommendations based on customer behavior, account history data, and business goals.

SCALABILITY AND REDUCED TCO

Catalog Service can handle 250 million SKUs with 30K prices each, syndicated across multiple channels and audiences without any duplication. By using a single catalog, 10K products and 50K prices can be ingested per minute from integrated systems.

The cloud-native platform scales dynamically for increased traffic and order volumes. 330 data centers and API orchestration on the edge allow for 99.9% availability and over 10K requests per minute with ease.

Continuous updates managed by Adobe, and instant access to new features turned on via toggle when ready, remove hours and hours of maintenance. Boilerplate themes and drop-in components allow for sites to be built rapidly with less development needed. Starter kits, APIs, events, web-hooks, and marketplace apps speed up integrations and time to launch. This reduction in operating costs allows resources to use the freed-up time for fine tuning strategies for rapid, continued growth.

OMNI-CHANNEL SUPPORT

B2B buyers are expecting the same personalized experiences at work, that they receive through personal interactions with brands in their daily lives. Over 60% of B2B buyers develop selection criteria or finalize a vendor list based solely on content available to them. Adobe makes this achievable by supporting B2C, B2B, and B2B2X all one the same platform. Toggle-able features designed for B2B users include parent-child account model with support for multi-buyer assignments, a storefront context switcher, standing quotes, requisition lists, and contract pricing. All that needs to change is access to them on the storefront, driven by the customer’s account type. The new Storefront has boilerplates for both B2C and B2B.

Experiences on other channels like social media are just as important, providing additional touch points for a brand to interact with customers. Adobe has partnered with Meta (Facebook and Instagram) and TikTok to release apps for seamless integration. Product catalogs, ratings and reviews, and cart rules can be sent from Commerce to the corresponding social platform. Direct integrations with ad accounts allow this data to be used for product promotions. The customer can then use social shops, or link directly to a brand’s website to purchase products, with all orders automatically flowing through Commerce.

The well-known Extension Marketplace is also receiving some updates and becoming the Apps Marketplace. Pre-built apps from third parties can be acquired to enhance native features or provide features that might be specific to only certain industries. These apps are built on API Mesh and App Builder, which can also be used by brands to build their own apps to solve their unique business needs.

ADVANCED ADOBE EXPERIENCE CLOUD FEATURES

Adobe Commerce data, including storefront clicks, back-office fulfillment information, and customer profiles can be shared with AEP for use with other Adobe applications. CDP can be used to analyze data and build audiences for advertisements, merchandising, and abandoned cart campaigns. AJO can be used for personalized journeys and offers. Marketo can be used for automated marketing campaigns, account nurturing, interactive webinars, and dynamic chat. These are just some of the possibilities!

AEM Sites Optimizer uses an AI agent to identify issues and propose resolutions, showing immediate value. The entire funnel is optimized, from acquisition, to brand engagement, and ultimately conversion. The agent identifies issues, projects lost traffic and revenue and suggests resolutions for review and deployment with a few clicks.

It is estimated that 39% of consumers already use AI for online shopping, and over half of Fortune 500 companies will adopt experience agents to match that growing expectation. Adobe Brand Concierge is a virtual assistant, that uses first party data to deliver true personalization. Built on AEM, CJA, AJO and AEP, it is connected to assets, insights, profiles, and campaign orchestration. Customers can interact with Brand Concierge through welcome or purchase confirmation emails, or once they log into their account again after onboarding. Using account history and profile preferences, Brand Concierge interacts with customers using buttons, and text or voice prompts. Curated search results, and product recommendations can be shared, and easily purchased, making it the ultimate shopper assistant.

WHAT’S NEXT?

Adobe remains committed to their customers and will continue supporting the existing on-premises and PaaS versions of Commerce. The nearly 9 million lines of Commerce Foundation code remains unmodified, and the business logic will continue to be available and supported. If you would like to learn more about any of these solutions, or plan out an incremental approach to implementation, Perficient is here to help. Please contact us to set up time to review some of our success stories and discuss your future customer experience and commerce strategy. Talk to you soon!

In this blog, we will explore the various Containers, their functionalities, and how they interact to create a seamless customer shopping experience.

HCL Commerce Containers provide a modular and scalable approach to managing ecommerce applications.

Benefits of HCL Commerce Containers

• Improved Performance: The system becomes faster and more responsive by caching frequent requests and optimizing search queries.
• Scalability: Each Container can be scaled independently based on demand, ensuring the system can handle high traffic.
• Manageability: Containers are designed to perform specific tasks, making the system easier to monitor, debug, and maintain.

 HCL Commerce Containers are individual components that work together to deliver a complete e-commerce solution.

Different Commerce Containers

1. Cache app: This app implements caching mechanisms to store frequently accessed data in memory, reducing latency and improving response times for repeated requests.
2. Nextjs-app: This app utilizes the Next.js framework to build server-side rendered (SSR) and statically generated (SSG) React applications. It dynamically interfaces with backend services like store-web or ts-web to fetch and display product data.
3. Query-app: Acts as a middleware for handling search queries. It leverages Elasticsearch for full-text search capabilities and integrates with the cache app to enhance search performance by caching query results.
4. Store-web: It handles the user interface and shopping experience, including browsing products, adding items to the cart, and checking out.
5. Ts-app, Ts-web, Ts-utils:
   • Ts-app: Manages background processes such as order processing, user authentication, and other backend services.
   • Ts-web: This container is for the administrative tools. It supports tasks like cataloging, marketing, promotions, and order management, providing administrators and business users the necessary tools.
   • Ts-utils: Contains utility scripts and tools for automating routine tasks and maintenance operations.
6. Ingest-app, Nifi-app:
   • Ingest-app: Handles the ingestion of product and catalog data into Elasticsearch, ensuring that the search index is current.
   • Nifi-app: This app utilizes Apache NiFi for orchestrating data flow pipelines. It automates the extraction, transformation, and loading (ETL) processes, ensuring data consistency and integrity across systems.
7. Registry app: This app implements a service registry to maintain a directory of all microservices and their instances (Containers). It facilitates service discovery and load balancing within the microservices architecture.
8. Tooling-web: Provides a suite of monitoring and debugging tools for developers and administrators. It includes dashboards for tracking system performance, logs, and metrics to aid in troubleshooting and maintaining system health.

HCL Commerce containers

Conclusion

This blog explored the various HCL Commerce Containers, their functionalities, and how they work together to create a robust e-commerce solution. By understanding and implementing these Containers, you can enhance the performance and scalability of your e-commerce platform.

Please go through the link to learn about Deploying HCL commerce elasticsearch and solrbased solutions”  https://blogs.perficient.com/2024/12/11/deploying-hcl-commerce-elasticsearch-and-solr-based-solutions/

Shoptalk Spring 2025 in Las Vegas is only a few weeks away, and our experts cannot wait to reconnect with brands and discuss where the industry is headed. They have been busy scheduling meetings, packing their mics, and making predictions about which technologies and strategies retailers and ecommerce brands will be placing their bets on this year.  What do they believe is a safe bet?

Will you be there? Let’s schedule some time to chat over a coffee, tea, or cocktail.

An Agenda that Centers On the Customer

Nothing tells the story of ecommerce and retail this year quite like the topics making up the conference’s agenda. Across the agenda, key themes rise to the top like innovation in retail media, customer centricity, the concept of hospitality within retail, and the transformation of customer journeys using new technologies. Justin Racine, Principal of Commerce Strategy, points out that he is most looking forward to hearing from Liza Lefkowski, Chief Merchant and VP of Stores for Wayfair. She will discuss Muse, their new AI-powered tool for personalized home shopping and how it will lead to better customer experience. Another keynote that our senior strategist, Timm Henderlight, is excited about is “Re-Imagining Gap for a New Golden Age,” presented by Richard Dickson, President and CEO of Gap Inc. While this session focuses on unified commerce, there will be additional sessions on ecommerce personalization and retail media strategies.

Our team is eager to listen in and be a part of the conversation about what it means to be customer-centric and how brands and consumers need to come together to humanize the shopping experience. More than ever, brands are embracing connection with their customers and creating a personal experience rather than a transactional one.

Will Brands Take Risks or Play It Safe?

Our experts are going all-in on several bets that retailers might be taking, including mobile apps, augmented reality, and increased AI usage. The house doesn’t always win, however, and Timm suggests that a few capabilities parallel each other. There’s going to be continued discussion about AI and how it’s going to change the industry, but ample opportunities for personalization at scale are often missed by retailers. We’ll continue to see disconnected experiences, whether customers are searching or browsing, usually due to retailers using different technologies. Customer identity resolution and customer data platforms will continue to play a role and should be part of everyone’s personalization strategy.

As more companies move towards personalization, our team recommends brands keep these key points in mind when deciding on a solution:

1. Primarily ensure you are not creating friction in the experience.
2. Be able to identify the customer both online and offline through previous order history and customer affinities.
3. Track intersession data when a customer is not logged in and augment the experience based on how the customer was attracted to the website.
4. Represent the customer journey from a mobile ad or paid advertisement throughout the entire experience, augmenting based on real-time signals from the customer.

Staying relevant in retail is an ongoing challenge, and it means understanding the customer across all channels. It requires having a clear omnichannel strategy and understanding how the journey inside the store touches the digital footprint. In the future, there’s the potential for beacon technology being implemented in physical retail environments, which will track your movements between departments to give in-store associates access to your previous behaviors. This will help guide the store associates and yourself to purchases you’re most likely to make based on where you’ve been. Of course, there’s so much more beyond this, and our experts can’t wait to meet with brands and discuss new opportunities during the event.

For now, watch your step: the future has a lot in store. Stay up-to-date with all the new innovations in commerce, retail + distribution, and consumer goods.

Will you be there? Let’s schedule some time to chat over a coffee, tea, or cocktail.

Website performance is crucial for user satisfaction and overall business success. Slow-loading pages, unresponsive features, and delayed database queries can lead to frustrated users, decreased conversions, and a poor user experience. One key to improving site performance is identifying bottlenecks in your database interactions, and that’s where SQL Server Profiler comes in.

SQL Server Profiler is a tool provided by Microsoft SQL Server to help database administrators, developers, and support teams monitor, trace, and troubleshoot SQL Server activity in real-time. It captures and analyzes SQL Server events such as queries, stored procedures, locks, and performance issues.

How SQL Server Profiler Helps Improve Website Performance

1. Identify Slow Queries: One of the most common causes of slow website performance is inefficient database queries. SQL Server Profiler allows you to capture query execution times and identify which queries take too long to execute. Once identified, these queries can be optimized through various methods such as indexing, query refactoring, or adjusting database schema.
2. Monitor Server Load: SQL Server Profiler can show how your server responds under load, such as which operations consume the most CPU and memory resources. You can monitor the server’s performance over time to ensure it’s scaling appropriately or identify when you need to upgrade your hardware or optimize your server configuration.
3. Track Deadlocks and Blocking: Deadlocks and blocking can significantly affect your site’s performance by causing delays in query execution. SQL Server Profiler helps you identify deadlock situations where two or more queries are waiting for each other to release resources and blocked queries waiting on locks. You can optimize your database’s locking strategy to reduce contention and improve performance by tracking these.
4. Optimize Indexing: Poor indexing is another common culprit behind slow database performance. SQL Server Profiler captures queries that could benefit from better indexing. With this information, you can identify which columns are frequently accessed and may need new or optimized indexes to speed up query execution.
5. Analyze Execution Plans: SQL Server Profiler can capture and analyze the execution plans used by SQL Server for queries. Reviewing these plans lets you identify inefficient operations like full table scans, missing indexes, and redundant joins. Analyzing and improving execution plans is critical in optimizing SQL Server performance.
6. Tuning SQL Server Configuration: SQL Server Profiler helps you identify issues in SQL Server configuration that may affect your site’s performance. For example, you might locate memory-related issues, insufficient buffer sizes, or improper settings causing slowdowns. These can be fixed through configuration changes to improve performance.

How to Create a SQL Trace in SQL Server Management Studio

You need to capture the events that will help you identify slow queries and stored procedures. In this blog, we will discuss one of the events provided by SQL Server Profiler.

RPC: Completed – This event will capture the execution details of stored procedures that are called remotely.

1. In SQL Server Management Studio, open the Tools menu, then select SQL Server Profiler.
   

2. Profiler will then ask you to log into the SQL Server instance you want to run the trace on. After logging in, a new Trace Properties window will automatically open. Here, you must provide a name for the new trace and select a trace template.
   

3. Next, click on the Events Selection tab. This tab allows you to select what SQL Server events you want the trace to capture and apply filters to block out the events you do not want to capture. The default trace captures events for all instances’ databases and user accounts. Check the Show All Events and Show All Columns check box at the bottom right to view all events and the respective columns.
   

          Columns to Include:

          Ensure the following columns are selected to track performance and identify slow Stored Procedures:

• • Duration – Time taken to execute the query or stored procedure.
  • TextData – The SQL query or stored procedure that was executed.
  • ApplicationName – To identify which application made the request.
  • LoginName – The user who executed the query.
  • CPU – The CPU time used for the query.
  • Reads – The number of logical reads (I/O) for the query.
  • Writes – The number of writes during query execution.

4. If you want to see all the events for a specific database, apply a filter by database names that are “like” or “not like.”
   

Running a Trace

Running trace-capturing events for the database. We can stop and start the trace and clear all the events in the trace using the toolbar. If you want to start a whole new trace, you can also do this using the toolbar.

Start a new trace, then load the webpage from which you want to capture data. Once the page has finished loading, stop the trace and review all the events captured.

After stopping the trace, you can analyze the captured data:

• Sort the data by Duration to identify which stored procedures took the longest to execute (Refer to the image below for the duration it takes for the stored procedure to execute).
• Look for patterns, such as repeated calls to a specific stored procedure or unusually long execution times.

Conclusion

SQL Server Profiler is an invaluable tool for boosting your website’s performance. By identifying slow queries, analyzing execution plans, and tracking server activity, you can pinpoint and resolve performance bottlenecks in your database interactions. Whether you’re dealing with slow queries, deadlocks, or server configuration issues, SQL Server Profiler provides the insights you need to make informed decisions and optimize your website’s performance.

Reference

https://learn.microsoft.com/en-us/sql/tools/sql-server-profiler/sql-server-profiler?view=sql-server-ver16