The way people search for information online has changed. With tools like ChatGPT and Google AI Overviews now providing direct answers to questions, it’s no longer enough for your content to simply rank high. Your content needs to be part of these AI-generated answers to be truly visible. Answer engine optimization (AEO) helps with that. AEO ensures your content is optimized not just for search engines but also for AI tools, reshaping how users get answers.

Choosing the right agency to guide your AEO strategy is key to staying relevant in this AI era. In this article, we’ll highlight the top AEO agencies around the world – those that can help optimize your content for AI visibility and keep your brand front and center.

What is an AEO agency?

An AEO agency helps businesses optimize their content for AI-driven tools like ChatGPT and Google AI Overviews by focusing on clarity, structure, and schema markup. Unlike traditional SEO, AEO ensures content is easily understood and reused by AI, increasing the chances of being featured in AI-generated answers or summaries. 

AEO agencies support eCommerce, B2B, and any content-rich brands. They identify key entities, improve on-page formatting, and build off-site authority, making sure your content stays visible and relevant as AI search continues to grow.

What to look for in a top AEO agency

• Understands how AI models behave and uses prompt testing to see how content is interpreted and summarized by tools like ChatGPT, Gemini, Perplexity, etc.
• Focuses on entities (people, products, concepts) instead of just keywords to make content more recognizable and usable by AI systems.
• Applies advanced schema markup that accurately reflects on-page content and enhances AI understanding of context and relationships.
• Has proven success getting content featured in AI-generated results across platforms.
• Offers specialized AEO audits, testing tools, and real case studies that show clear impact on AI visibility.

Also read: successful AEO use cases

• AEO Makes Novatours #1 Travel Agency in AI Search Across the Baltics
• AI Search Optimization Helps Kouboo Break Into the Top 3 AI Answers
• How AEO Helped Enviropack Become a Top AI Pick for Sustainable Packaging

Best AEO agencies for post-SERP success

1. scandiweb

A leader in AI search optimization, scandiweb stands out for its deep integration of SEO and AEO, especially for eCommerce and B2B businesses. Their team focuses on making content AI-friendly, ensuring it gets picked up by tools like ChatGPT, Google AI, and Perplexity. By optimizing for structure, clarity, and relevance, scandiweb helps brands appear in AI-generated results and drive meaningful traffic. Their AEO strategies are designed for visibility and increasing on-site conversions and revenue.

2. BetterAnswer

BetterAnswer focuses on positioning brands as trusted sources within AI-powered search environments. Their SEO and AEO experts work to improve visibility across primary AI tools by structuring content for maximum clarity and authority. With a strong emphasis on measurable results, they aim to turn AI exposure into real business growth.

3. Arc Intermedia

Arc Intermedia specializes in creating AI-friendly and user-focused content and offers tailored strategies to improve AI visibility across platforms.

4. OMR Digital

OMR Digital offers AEO services to ensure content performs well across AI tools and search engines. They focus on increasing content visibility within AI-driven answers to help brands gain recognition and authority in AI-generated answers.

5. Gorilla Web Tactics

Gorilla Web Tactics specializes in AEO for legal and healthcare clients, helping them adapt content for AI platforms. With 15+ years of experience, the agency aligns niche business content with how AI tools process and deliver information to improve visibility in AI-generated responses.

6. Perrill 

Perrill offers generative engine optimization (GEO) services to help businesses optimize their content for generative AI tools. Their strategy ensures content is structured and clear, allowing it to be easily extracted and used by AI models like ChatGPT and others.

7. ThatWare

ThatWare leverages advanced AI and automation to optimize content for AI-driven search platforms. Their approach combines semantic engineering, NLP, and proprietary algorithms to enhance content visibility and relevance in AI-generated answers.

8. Dreikon

Dreikon focuses on optimizing content for ChatGPT, ensuring that it’s structured to be fully compatible with AI assistants. They offer tailored strategies that help businesses stay competitive in AI-driven search.

How to choose the right AEO agency

Internal readiness: audit, CMS flexibility, content team

Before choosing an AEO agency, assess your internal capabilities. Ensure your website is ready for optimization with a flexible CMS that can handle schema updates and content restructuring. Also, evaluate your content team’s ability to collaborate with the agency on refining and producing AI-friendly content.

Tailored approach for different industries

When choosing an AEO agency, consider the specific needs of your industry. eCommerce brands often require a strong focus on optimizing product content for AI-driven shopping assistants and recommendations, while service-based industries or B2B businesses may need to prioritize optimizing technical content and thought leadership for AI-generated responses in industry-specific queries.

Short-term testing vs. long-term structured strategy

Some agencies may focus on short-term testing and quick wins, while others offer comprehensive, long-term AEO strategies. If you’re looking for sustainable results, choose an agency that can provide a structured, ongoing approach to AEO, ensuring your content stays relevant and visible as AI tools evolve.

Budget considerations

When selecting an AEO agency, align your budget with the scope of services you need. Some agencies offer lower rates of basic optimization for AI tools, while others provide more comprehensive, long-term strategies with ongoing audits and optimization efforts. Assess the level of service required for your business and choose an agency that offers a clear, transparent pricing structure based on your needs.

Conclusion

As more people turn to AI tools like ChatGPT, Gemini, and Google AI Overviews for quick, reliable answers, visibility in AI platforms has become essential. Optimizing your content for these tools helps you guarantee that your brand shows up where users are already searching today.

AEO goes beyond trend-following to ensure your content is structured, discoverable, and trusted by AI. If staying visible in a changing search environment is your priority, partnering with the right AEO agency can make all the difference.

Ready to take the next step? Explore scandiweb’s AEO services, and let’s connect and start building a stronger presence in the AI-powered search experience.

The post Best AEO Agencies to Help You Get Mentioned in AI-Powered Results appeared first on scandiweb.

If you operate in multiple markets with separate platforms, managing content, product data, and customer flows likely becomes harder with every update. In a similar case, we collaborated with BK-Group to bring two markets under a single Magento 2 store built with Hyvä, integrated with their ERP and PIM systems.

About

BK-Group is a private company specializing in electronic alarm systems and equipment distribution. As part of the Hikvision family, BK-Group provides high-quality security tech across the Baltics and Poland. Founded in 1994, they’ve earned a strong reputation for reliability and innovation in the security technology sector.

Project goals

The main goal of this project was to consolidate BK-Group’s fragmented eCommerce infrastructure and build a strong foundation for future expansion. This involved:

• Merging two sites – Latvia (WordPress + WooCommerce) and Estonia (Magento 2) into a single Magento 2 instance, hosted on ReadyMage
• Storefront redesign using Hyvä
• Building a fast, secure B2B store with tailored workflows
• Integrating Pimcore and the client’s ERP system to sync and manage product and customer data.

Also read:
Magento B2B Case Study: Building a B2B eCommerce Ecosystem for an IT Supplier

Challenges

Working closely with BK Group’s ERP team required flexibility and multiple testing iterations to finalize integration logic.

Additionally, the production database was hosted on the client’s servers, limiting access and requiring custom deployment workflows with strict DevOps coordination. 

Real-time pricing and stock visibility varied by customer and market (fetched from Pimcore), and the registration/approval process had to align with internal rules and sync properly between Magento, ERP, and other systems.

Approach

We rebuilt BK-Group’s online presence for Latvia and Estonia using Magento 2 with the Hyvä frontend, replacing both legacy setups with a single store. At the time of redesign and development, Hyvä was already widely trusted across scandiweb projects as a reliable frontend choice. 

A core part of the project was connecting Magento 2 with BK Group’s internal systems. Product data, pricing, and customer access rules were all synced via Pimcore and ERP. Magento communicates with Pimcore via APIs, while Pimcore itself acts as middleware between Magento and ERP, allowing product and pricing updates to sync dynamically across storeviews.

Custom B2B features and integrations

BK-Group required a customized B2B solution to ensure that every customer only sees the pricing, inventory, and content relevant to them. To support market-specific workflows, we implemented:

• Real-time pricing per customer, synced from Pimcore
• Customer-specific stock visibility
• Custom registration and approval flow integrated with ERP.

Pricing logic

The pricing logic works as follows: ERP pushes updated B2B prices to Pimcore every few minutes, using the customer code and SAP material code. When a logged-in customer browses product listings or product detail pages, Magento sends a request to Pimcore with those same identifiers to fetch their customer-specific pricing in real time.

Stock is checked at two points: once when a customer views a product detail page, and again during checkout – each via a direct API call to ERP to confirm current availability.

Customer approval process

A customer submits the registration form on the site. After admin approval, Magento checks whether the customer already exists in ERP using the company registration number or email. If not, Magento sends the customer record to ERP to create a new entry. If a match is found, ERP returns the full customer data to Magento.

Magento handles four store views: LV (LV), LV (EN), EE (EE), and EE (EN). Pimcore pushes localized data like pricing and attributes per storeview, while some content, such as brand translations, is maintained directly in Magento admin.

Because the production environment was hosted directly by the client, we worked closely with their DevOps team to define a deployment and access strategy, including limited external access, custom deployment scripts, and secure handling of testing and go-live conditions.

Results

BK Group now operates a single, centralized Magento 2 store for the Latvian and Estonian markets. The unified setup provides a strong foundation for Lithuanian and Polish sites and other markets.

The custom-built B2B logic gives each business customer access to real-time pricing pulled from Pimcore, stock availability tailored to their account, and a registration and approval process fully synced with ERP. 

ERP and Pimcore integrations have streamlined how product, pricing, and customer data flow between systems. With automation in place, internal teams can now easily manage customer-specific logic.

The new store is hosted on ReadyMage and runs on Hyvä, providing a fast and flexible frontend that can grow with the business.

Core Web Vitals and overall frontend performance have already improved and are expected to improve further following the next Hyvä update.

The platform is set up to continue evolving under scandiweb’s Service Cloud support:

• Expanding Pimcore usage across more websites
• Improved performance in pricing sync and loading speed
• Broader use of Pimcore for managing user roles and access control.

Planning a similar migration or facing complex B2B requirements? Scandiweb is the official Platinum Partner of Hyvä and Pimcore. Reach out to us, and we’ll help build the right setup for your business.

The post Case Study: Merging Two Markets on Magento+Hyvä for a Multinational B2B Supplier appeared first on scandiweb.

You really went the extra mile, especially during these last hectic 1-2 weeks. We had an awesome first day at the convention today, and the website was a great help for the team to show people products and prices. You guys delivered, and we are grateful for it!

Marvin, CEO of ATX Fitness USA

Our client, ATX Fitness, saw an opportunity to grow in the U.S. market and moved fast to make it happen. We planned to launch a fully transactional Magento 2 store with the Hyvä theme by June 2025 – just in time for the Home Gym Expo. 

About

ATX Fitness is a European brand specializing in professional equipment for gyms, semi-professionals, and home users. ATX products are distributed by Fitness Seller, a company active in the Benelux region and other European markets. This project marks the launch of ATX’s first direct-to-consumer eCommerce store in the U.S.

The U.S. Home Gym market is one of the largest globally, with high demand for durable, space-efficient equipment suited for hybrid training habits. ATX aimed to stand out with quality, performance, and a full-featured eCommerce experience ahead of Home Gym Expo, their first major U.S. industry event.

Project goals

• Launch a U.S.-specific eCommerce store under the ATX brand
• Build a custom-designed, optimized storefront on Magento 2 using Hyvä Commerce
• Prepare the platform for long-term growth, including ERP integration and a U.S.-based sales and customer support team.

Read more scandiweb x Hyvä case studies here.

Approach

We kicked off the project with a focused UI and design sprint, aligning quickly with the client to avoid rework later. Once design approval was secured, we moved into development without delay, starting from global elements and core templates, then layering in features across product, cart, and checkout flows.

This was our first Hyvä Commerce implementation at scandiweb. As one of the earliest adopters doing hands-on exploration, it introduced technical risk, but the payoff was worth it. Using Hyvä CMS, ATX Fitness got improved site maintainability, a cleaner content editing experience, and more flexibility for marketing pages, while staying within Hyvä’s high-performance frontend environment.

We also implemented Hyvä Checkout, Hyvä’s lightweight alternative to Magento’s Luma checkout, to give the store a faster and more responsive checkout experience.

We covered a full end-to-end Magento 2 build, starting from base setup and extending through every core page and user interaction. The scandiweb team designed and developed all global and market-specific elements, including the homepage, PLP, PDP, cart, Hyvä checkout, account, blog, and CMS content pages. 

Performance optimization, accessibility compliance, technical SEO setup, GA/GTM tracking, transactional emails, and shipping/delivery logic were also part of the scope from day one. Given the pace of the project, we delivered many of these tasks in parallel with design alignment and development sprints and documented deferred requirements for the future roadmap.

Extensions integration

Throughout the project, we worked closely with the client to evaluate extensions for SEO, product feeds, analytics, and custom product logic. We also supported ongoing comparisons between potential alternatives to ensure that every third-party module aligns with business goals and performance requirements.

Key integrations

• Yotpo Reviews, Magefan Blog, Amasty Gift Card, Amasty Product Labels, Amasty Elastic Search, and Mageworx SEO Suite Ultimate for marketing and engagement
• Xtento Product Feed Export to handle product feeds
• Simple Bundle Product extension along with Grouped Product Custom Module, Google reCAPTCHA, and Amasty Rewards for supporting product configuration, security, and user retention.

In addition to the core build, we handled a set of custom requirements identified during the project:

• Product data import and attribute migration
• Store selector, Q&A sections on PDP/PLP, and PLP layout adjustments
• Wishlist customization and custom PDP image roles.

Results

In just 2.5 months, the new ATX Fitness U.S. store went live in a custom design, with full feature coverage, fully optimized, ready for demos:

• Magento 2 + Hyvä store launched on time and within scope
• First Hyvä CMS implementation successfully delivered
• All green Core Web Vitals
• 93–100% PageSpeed scores for accessibility across key pages, to be further optimized following the next Hyvä update
• 15+ additional features implemented before launch
• Product, checkout, and content flows ready to support ATX Fitness’ U.S. entry.

Interested in launching a fast, scalable Magento store with Hyvä Commerce? As the Hyvä Platinum Partner, scandiweb is among the world’s top agencies working with Hyvä. Let’s connect and discuss building the right setup for your next market.

The post Hyvä Commerce Case Study: A High-Performance Store for ATX Fitness’ U.S. Launch appeared first on scandiweb.

Creating high-quality LinkedIn content regularly takes time—especially when aiming for posts that are research-backed, visually aligned with your brand, and consistent in tone. We wanted to eliminate the manual work and make the entire process operate from a simple Slack message.

How it works

This week, we built an AI Agent that automates LinkedIn post creation from a Slack channel using n8n to orchestrate the workflow. The agent combines:

• Research generation via Sonar (Perplexity API) for accurate topic context
• Post copy crafted by GPT-4o in brand voice and LinkedIn tone
• Visual assets created using GPT-Image-1 for on-brand representation
• Slack integration to deliver and iterate via chat

All of this happens from a single prompt inside Slack. The AI agent handles everything—from research to visuals—then sends back a ready-to-publish LinkedIn post, directly in the thread.

Processing and development

• n8n orchestrates API calls between Slack, Sonar, GPT-4o, and GPT-Image-1
• Sonar (Perplexity API) runs deep web research to extract current insights
• GPT-4o agents format the insights into a concise, engaging LinkedIn post and coordinate the Slack chat
• GPT-Image-1 generates visuals tailored to the post topic and tone
• Slack API provides chat interface for initiating and reviewing output

Example walkthrough

Impact

This AI agent turns a multi-hour content creation process into a 60-second Slack interaction:

• Eliminates manual research and design
• Delivers LinkedIn-ready content from Slack
• Maintains consistency in voice and branding
• Frees up creative teams for higher-level strategy

For teams seeking to scale their thought leadership without burning resources, this setup is a game-changer.

Built with

• n8n – AI automation and API workflow builder
• Sonar (Perplexity API) – web research
• GPT-4o – post copy generation
• GPT-Image-1 – image generation
• Slack API – prompt input and result delivery

Complexity

Medium

Looking to leverage modern AI tools within your company? Get in touch and explore next steps.

The post AI Agent Bites #9: AnswerRank LinkedIn post and image agent appeared first on scandiweb.

You don’t need a website visit to make a sale anymore.

More and more, shopping starts with a message: a quick question about sizing, a request for a gift recommendation, a reply to a product suggestion sent last week, or a follow-up from a customer wondering if something’s back in stock. And when the conversation feels natural and helpful, it often ends with a purchase.

That’s conversational commerce. 

It brings the buying experience into chat on any platform through assistants that engage in real time, respond with context, and guide customers toward what they’re likely to buy. Conversational commerce means dynamic conversations shaped by customer behaviour, preferences, and past interactions and overall context. 

Brands adopting this approach will reduce friction and convert interest into revenue without forcing users through long browsing sessions. Leading brands worldwide are already testing it – you can launch it! Let’s look closer at how conversational commerce works in practice, where it’s making the biggest impact, and what to consider if you’re considering launching your own.

Key takeaways

• Conversational commerce brings the shopping experience into real-time chat, helping customers find and buy products through natural 1:1 conversation.
• Success depends on clean product data, clear use cases, strong brand voice, real-time integrations, and continuous optimization.
• With the proper setup, brands can shorten buying journeys, increase AOV, and reduce support pressure without sacrificing user experience.
• scandiweb built an AI assistant for a leading Nordic DIY retailer to guide project-based shopping, leading to 95% higher AOV and a 68% increase in conversions.

What is conversational commerce, really?

Conversational commerce is a new way of selling through real-time dialogue. Instead of browsing a site, clicking, scrolling, and navigating between tabs, customers get help through a conversation. No less than one that feels personal and focused on what they actually need.

It can happen anywhere your customers already are: on your website, in a native app, over chat platforms, or even via SMS. What matters more than the channel is the experience. Your customer asks a question, gets a response, and moves forward with clarity.

Unlike traditional chatbots, modern conversational commerce tools don’t rely on scripts. Instead, they draw on customer data about past interactions with your brand and product details to guide the conversation. This makes the assistant more useful and part of the shopping journey. Done right, it’s fast, helpful, and completely integrated with how shoppers already prefer to communicate.

Why brands are investing in conversational commerce

91% of consumers globally say they’re more likely to shop with brands that offer real-time assistance. And according to industry experts, messaging will be the fastest-growing commerce channel by 2026.

Customers have grown used to getting fast, tailored responses across every platform they use. That expectation doesn’t stop at eCommerce. When someone is ready to buy or has a question holding them back, they want clear answers immediately without switching channels.

Conversational commerce shortens the time between interest and purchase and reduces drop-offs, especially for returning users. It also creates more opportunities to re-engage customers based on real data. Leading brands recognize that this means moving closer to the customer decision point without adding complexity to internal teams.

What makes it work (and what doesn’t)

AI-powered chat can increase conversion rates by up to 30% when used for guided selling. For conversational commerce to deliver such results, here’s what needs to be in place:

• The assistant has access to clean, connected product data and up-to-date info on inventory, pricing, and attributes; if it can’t answer basic questions or suggest available alternatives, the conversation breaks.
• Clear use cases and goals, like recommending products, handling repeat questions, or guiding through gift selection; otherwise, a broad, unfocused assistant often leads to more confusion.
• Assistants that respond based on real-time behavior, recent views, and purchases feel more helpful – context is everything.
• The assistant should speak in a voice that fits your brand and sound natural and human. 
• The experience has to feel instant, like chatting with someone who knows the answer before you finish asking and can suggest things you didn’t even think to ask.

Where things fall apart are generic bots with no access to product or user data and overcomplicated flows that trap the user between decisions.

Conversational commerce in practice

At scandiweb, we’ve built Concierge AI: a conversational digital assistant that feels like a boutique-style concierge. These are examples of how we see it working across different industries, based on real customer needs and typical customer profiles and shopping behaviors.

Curated product discovery

A digital beauty assistant curates full routines beyond answering your customer’s questions. After identifying the customer’s current preferences and routines, it can recommend contour, blush, lip, brush, and highlighter products that match tone and texture. Product education woven into every suggestion guides the customer through a feel-good, ultra-personalized routine, like chatting with a knowledgeable in-store rep, only faster.

Retention & gifting

When a returning customer asks for gift recommendations in his favorite watch store, the assistant instantly can reference previous orders and suggest alternatives, highlighting subtle style differences, showing product images, and offering advice on what to wear for a big event.

Multi-customer logic

A sports store customer chats in to refresh her son’s training gear. The assistant recalls previous purchases, adapts to sizing needs, and recommends tennis-appropriate shoes, even when faced with stock limitations. If a favorite item is out of stock, it can offer brand alternatives instead, tell about pricing options, and style-matching picks for mother and child, creating a helpful shopping experience (in any language, may we add).

Case study: scaling DIY sales with AI-powered guidance

Our client, a leading Nordic home improvement and DIY retailer with over 200 stores, over $1B in annual revenue, and a fast-growing eCommerce channel, serves customers across Sweden, Norway, Finland, and Denmark. 

They want to help customers who come in with a project idea, like building a deck or updating a patio, but don’t know what products, sizes, or quantities they actually need. 

Here’s the problem. 

Around 70% of annual revenue is concentrated between May and September. To support this seasonal spike, our client expands its workforce by up to 10x, primarily hiring temporary staff who need to quickly learn key product knowledge, how to talk to customers about their projects, and how to answer common in-store questions.

The training process was too slow and inconsistent to keep up. So we helped them introduce an AI-powered assistant trained on common DIY projects and linked to their full product catalog.

Project goal

Many customers walk in with a project idea but no clear understanding of what materials are required. They know what they want to build, but not the SKUs, sizes, or product combinations they need. To serve these customers better and increase basket size, this retailer needed a scalable solution to guide customers through their projects and toward purchase.

Solution

We built an AI-based ShopBot, accessible on the website and through in-store kiosks. It’s trained on common DIY projects and connected to the retailer’s full product catalog. The assistant can:

• Advise on how to complete specific projects, step by step
• Recommend exact SKUs, sizes, and quantities for the task
• Process uploaded images to understand the construction area and suggest materials
• Provide consistent, 24/7 guidance without human staff.

Results

Since launch, users interacting with the assistant spent 5x longer engaged than on standard project content. Early results from the pilot:

• +68% conversion rate from users who engaged with the assistant
• +95% average order value
• Projected 50% reduction in seasonal staffing needs once fully rolled out.

This case is a perfect example of how conversational commerce can guide the entire decision-making process, even for complex purchases.

How to get started

You don’t need to launch on every platform or build a fully autonomous AI from day one. But if you want conversational commerce to drive results, it needs a solid foundation.

Here’s what the process looks like:

1. Prepare your data foundation for AI

Clean product data, enriched attributes, and organized customer behavior logs give your assistant something to work with. The better the input, the more useful the conversation.

2. Build your custom conversational agent and create the interface

The interface should fit naturally into your existing flow – on-site, in-app, or embedded in chat, and be able to handle your real use cases.

3. Train your assistant to sound human and on-brand

Scripted or AI-generated, your assistant needs to reflect your tone in how it responds, how much detail it gives, and so on.

4. Connect the agent with your eCommerce & other platforms

To stay helpful, the assistant needs access to current inventory, pricing, customer profiles, and order history. Integrations with your eCommerce platform, CDP, and analytics tools make this possible.

5. Track interactions to fine-tune offers and timing

Monitor how users engage with the assistant. Where do they drop off? What prompts convert? This feedback loop lets you adjust conversation flows, tweak recommendations, and improve overall performance over time.

Start small. Test a focused use case. Then scale once the assistant proves it can make the experience faster and more helpful.

Before you know it, the best salesperson will be in your customer’s pocket.

If you’re curious how conversational commerce could work for your brand, let’s schedule a free consultation! We’ll build a tailored demo with your products and typical customer profiles to show what’s possible.

The post Conversational Commerce Explained: Be Among the First Adopters appeared first on scandiweb.

A luxury fashion house in New York partnered with scandiweb to rebuild its customer data infrastructure and improve how it personalized experiences across channels. With boutiques, outlet locations, international stores, and a high-touch clienteling model, the brand needed a system that could unify fragmented data, support calculated traits, and power smarter segmentation. 

Across the 5 years of using a customer data platform (CDP), they’ve doubled revenue, automated hundreds of hours of reporting work, and now drive over 40% of online revenue through targeted email and SMS, reducing reliance on paid media, strengthening loyalty from high-value customers, and saving money while gaining complete control over their in-store and digital customer data.

About

Our client is a New York City-based fashion house that specializes in women’s apparel, footwear, and accessories, offering a range of modern and sophisticated designs. They sell in physical boutiques and online with an eCommerce store powered by Magento. In the boutiques, stylists and store clerks enhance the shopping experience by offering personalized recommendations based on customer preferences.

Their operations rely heavily on a CDP to support personalization and data-driven decision-making across channels.

Also read:
Webinar Recap: How to Increase Your eCommerce Revenue 2-3x with CDPs

Challenges

The brand’s data ecosystem was highly complex. With operations spanning flagship boutiques, outlet and international stores, stylists providing 1:1 guidance, and an eCommerce store powered by Magento, customer interactions were scattered across many systems. Each customer could be represented by multiple identifiers – email, phone number, loyalty account, purchase history, and in-store interactions, making it difficult to form a unified profile.

At the start of our collaboration, the client was already using an advanced customer data platform to support its personalization and marketing efforts. Over time, however, specific challenges, such as limited flexibility in managing identity resolution rules and increasing demands on reporting, led them to explore new solutions. With a focus on scalability, internal control, and deeper integration across its systems, we chose to migrate to Salesforce Data Cloud.

• Moving to a new platform without disrupting existing workflows
  A major concern during the migration was ensuring that existing workflows remained uninterrupted. The brand faced a strict deadline before their Acquia contract expired, which would have required committing to another year of service. Within this tight timeframe, we had to complete the migration to Salesforce Data Cloud, ensure all systems were integrated, and thoroughly test that data processes continued running smoothly without disruptions.
• Reporting inefficiencies and missed revenue opportunities
  The fashion brand needed deeper insights into customer purchasing behavior, but limited reporting tools required extensive manual effort to compile and analyze data. Since reports could not be generated automatically, teams had to manually export raw data, transfer files via SFTP (Secure File Transfer Protocol), and piece together information using pivot tables and VLOOKUP functions. This time-consuming work diverted resources away from tasks with higher ROI, like more time spent identifying upselling and cross-selling opportunities.
• Identity resolution limitations
  While the team had visibility into why customer profiles merged or didn’t merge, they could not modify the matching logic without support from Acquia. It was difficult to react quickly to data discrepancies or evolve identity resolution rules as business needs changed. 
• Limitations in digital marketing insights and customer acquisition tracking
  Acquia lacked the ability to link website visits, product interactions, and purchase intent to individual customer profiles, leaving marketing teams without clear visibility into customer journeys. Additionally, the platform did not support direct integration with advertising networks, meaning customer data could not be synced with Google Ads or Meta. Without this connection, the company could not run targeted ad campaigns or measure how ads influenced purchases. The transition to Salesforce needed to bridge this gap by integrating web event tracking and aligning customer interactions with marketing performance.
• Scalability concerns and long-term growth
  The company needed a system that could evolve with their customer data strategies without incurring high implementation costs. Already using Salesforce Marketing Cloud, they benefited from good data exchange within the Salesforce ecosystem, making Salesforce Data Cloud a future-proof choice for expanding capabilities and maintaining operational efficiency over time. 

Approach

scandiweb implemented a flexible, scalable solution to migrate to Salesforce Data Cloud, enabling improved customer segmentation and reduced reliance on external services.

Transitioning from Acquia to Salesforce Data Cloud

The previous system was deeply integrated into the brand’s data workflows, making it critical to preserve existing functionality during the migration. scandiweb implemented Salesforce Data Cloud to give the internal team greater control over data processes and simplify future changes.

A key part of the migration involved integrating Salesforce Data Cloud with their existing technology stack, including Magento, in-store customer relationship tools, and external data sources. 

Data validation and integrity checks were utilized throughout the migration to prevent discrepancies and ensure a clean dataset. We developed customized automation rules, which helped map Acquia’s existing data structures into Salesforce without data loss or corruption. 

The transition process included initial user training to help internal teams get up to speed with the new system. With the most technical onboarding still underway, the brand is building toward full in-house autonomy. The migration to Salesforce Data Cloud laid the groundwork for a scalable and future-ready customer data infrastructure.

New data infrastructure with AWS

As the project progressed, we encountered limitations in data ingestion and transformation capabilities within the initial setup. To address it, we explored external cloud solutions and expanded the existing AWS environment to support more flexible and scalable data operations.

The following AWS services were integrated into the new setup:

• AWS S3
  • Used to store most ingested input data sources
  • Also serves as the storage for processed files ready for ingestion into the Salesforce Data Cloud
• AWS Glue
  • Handles data transformations such as blacklisting, standardization, and API-based tasks
  • Combined visual ETL configuration for simpler workflows with custom code via the Script Editor for more complex processes
• AWS Glue DataBrew
  • Enables visual data profiling and initial data exploration to identify key issues and edge cases
  • Also used to validate outputs during pipeline quality assurance
• AWS Lambda
  • Used for real-time processing jobs triggered by specific events
• AWS Redshift Serverless
  • Implemented as a data warehouse to support structured data access and analysis without infrastructure management overhead.

Identity resolution system update

Identity resolution is the process of matching and unifying customer data across multiple touchpoints to create a single, accurate customer profile. Each individual is recognized consistently across online, in-store, and marketing interactions. Without a proper identity resolution system, businesses face issues like duplicate records and a fragmented view of their audience.

To support unified customer profiles, scandiweb configured Salesforce Data Cloud’s identity resolution framework to fit the existing data logic and structure. Our setup included tools for testing and validating match rules in a controlled environment, ensuring confidence in profile merges before any changes were applied. The migration process also included knowledge transfer and onboarding for internal teams to take ownership of the identity resolution logic moving forward.

Calculated traits and segmentation

With unified customer profiles in Salesforce Data Cloud, the team gained the ability to work with calculated traits – derived attributes that go beyond standard fields from source systems. They gave a more detailed view of customer behavior and preferences.

Examples of calculated traits

• Order metrics: lifetime order count, time since first and most recent purchase, purchase frequency by period (last 12 months, 13–24 months, 24+ months)
• Revenue patterns: lifetime revenue, decile rankings, trend segments
• Engagement: email and SMS sends, clicks, opens, CTR by timeframe
• Web activity: total sessions, last session date
• Preferences: sizes, colours, product types, styles, channels, preferred stylist or store.

Segmentation strategies based on enriched customer profiles

• Full-price vs off-price buyers
• Lifecycle stages (new, inactive, lapsed, reactivated, unidentified)
• Revenue trend groups
• Location-based segments for in-store events
• Product-based segments for tailored communications.

Automated analytics with Tableau

With the implementation of Tableau as the primary business intelligence tool, we created an automated analytics framework. Tableau’s integration with Salesforce Data Cloud enabled the company to automate reporting processes and centralize performance tracking across teams.

Enriching customer segmentation with external data

Customer segmentation plays a key role in our client’s personalization efforts. The previous platform made integrating new data sources difficult, limiting the segmentation depth. With the new system, adding third-party data became significantly easier, enabling more refined and flexible audience segmentation.

With Salesforce Data Cloud, we ensured they gained the ability to ingest and unify external data sources to support more advanced customer segmentation. For example, Census Data enriched segmentation efforts by adding socioeconomic indicators.

Results

1. 85% of revenue generated by re-engaging existing customers

With better visibility into customer history and preferences, the brand focused more on retention and lifecycle-based engagement. Personalized campaigns, proactive churn prevention, and curated in-store experiences helped make the most of their existing customer base.

2. Highly personalized in-store customer experience

The brand had existing customer profile visibility in their clienteling tool, which stylists use to enhance the in-store experience. scandiweb supported migrating this data to Salesforce Data Cloud and preserved the export process to Proximity. We built a new dashboard in Tableau, offering greater flexibility to access and customize customer insights. Unlike frontend changes in the previous clienteling tool, Tableau dashboards could be quickly updated internally to expand available data to stylists without relying on external development.

scandiweb’s implementation of Salesforce Data Cloud enabled them to deliver an even more personalized in-store experience. Staff can access detailed profiles before appointments, including transaction history and preferred product categories, to tailor recommendations more precisely, create a curated shopping experience, and anticipate customer needs.

3. Effective email and SMS marketing communication

Email and personalized SMS marketing contribute over 40% of online sales. By contrast, brands without a CDP often generate just 1–3% of revenue from these channels.

The brand leveraged our consolidated customer data to implement more precise email and SMS marketing segmentation. To engage customers, personalized offers and targeted messaging were sent based on purchase history, browsing behavior, and engagement patterns, delivering promotions aligned with their shopping habits and preferences. 

4. Enhanced identity resolution

Salesforce Data Cloud allows them to test, compare, and deploy identity resolution rules. Instead of relying on predefined, unchangeable rules, they can now adjust identity resolution settings based on their specific needs, giving them greater control in managing customer data. 

5. Targeted advertising spend and ROAS improvements

With customer data more accessible and organized in Salesforce Data Cloud, we improved how audience segments were activated within Google Ads and Meta Ads. Building on its existing personalization efforts, they can deliver more personalized ads to existing customers and high-intent prospects, potentially reducing wasted ad spend and increasing ROAS. Additionally, third-party data enrichment enhanced customer lifetime value modeling, helping the team better prioritize high-value audiences.

6. Advanced business reporting and BI solutions

Our integration of Salesforce Data Cloud and Tableau enabled a centralized and automated business intelligence system. Teams can now track KPIs automatically, measure the effectiveness of customer segments, and analyze user journeys.

Interactive dashboards allow deeper customer data exploration, with Tableau’s drill-down capabilities for reviewing behavior by segment, transaction history, or web engagement. This has reduced reporting time and improved the speed and accuracy of business decision-making. With access to more reliable data, our client can plan long-term strategies based on customer behavior.

Looking to transform your customer data management? scandiweb can help you leverage Salesforce Data Cloud or any suitable platform to streamline reporting, optimize marketing, and enhance customer engagement. Get in touch today to learn how we can help!

The post Luxury Retail Meets Data-Driven Personalization: Salesforce Data Cloud Success Story appeared first on scandiweb.

When product information is scattered across platforms, it slows everything down, especially at scale. For Rocket Industrial, managing tens of thousands of SKUs across ERP and Magento 2 created inefficiencies and gaps. See how we helped them bring it all together in one place, creating a clean, organized system their teams can rely on every day.

About

Rocket Industrial is a US-based packaging distributor with roots dating back to 1957, when it began as a family-owned packaging tape company. Over the decades, it has evolved into a nationwide operation that helps businesses solve complex packaging challenges across various industries.

Rocket Industrial serves a diverse range of B2B clients, offering a comprehensive suite of solutions that includes packaging materials and automation systems. Their eCommerce presence is a key part of their operations, supporting thousands of SKUs and an expanding catalog powered by reliable digital tools.

Project goals

Our mission was to build a single source of truth for Rocket Industrial’s large product catalog and make product management faster, more accurate, and enjoyable. We defined the following goals:

• Establish a single source of truth for product data across internal systems
• Centralize SKU management to eliminate inconsistencies
• Reduce manual data entry and associated errors through automation
• Build a structured, scalable product catalog capable of supporting 80K+ SKUs
• Improve the internal UX for managing product attributes and relationships.

Approach

We implemented Pimcore as their product information management system, connecting and consolidating product data from ERP and Magento 2. Our focus was data unification, custom import development, and building a clean foundation for efficient product publishing and management.

Integrating product data with ERP and Magento 2

We started with a full audit of product data across both systems to identify mismatches and incomplete records in SKUs. Since SKUs weren’t always reliable, we created matching rules based on additional product attributes to identify and merge records correctly. 

We also fixed the broken parent-child product relationships. This regrouped related products correctly under one structure.

Custom import logic development for handling scale and limitations

The client’s existing system (AP) limited access to only recently updated products. To work with the full 70,000-item product catalog, we exported the complete dataset from their ERP via CSV and built a dedicated import tool directly within Pimcore.

This workflow found and combined duplicate product entries. It also reconciled records from both the ERP system and Magento 2. As a result, Rocket Industrial has a clean, unified product catalog with 81,000 structured entries managed directly in Pimcore.

Controlled publishing workflow creation for Magento 2

The central Pimcore catalog also held products not intended for display on the Magento 2 storefront. To manage this, we built a custom publishing workflow. This gives internal teams control over product visibility. 

Teams can now review items, mark them, and publish only those that meet the specific business criteria. 

Also read: 

• Case Study: The Power of Centralized Product Data Management in eCommerce
• Revolutionizing Travel with Novaturas: A Pimcore-Powered Innovation
• Pimcore PIM Powers Online Vehicle Sales Microservice Platform

Results

Pimcore implementation delivered structural improvements to Rocket Industrial’s product management processes:

• The unified product catalog grew from 70,000 to 81,000 complete records
• Duplicate entries from ERP and Magento 2 were identified and merged
• SKU inconsistencies were resolved across systems
• Broken parent-child relationships were successfully rebuilt
• Manual data entry was significantly reduced through automated imports
• Products are now published to Magento 2 through a controlled workflow
• Internal teams can effectively manage and filter product data within Pimcore.

Need a better way to manage product data across platforms? scandiweb is a certified Pimcore Platinum Partner with a team of experts ready to tackle your product data challenges. Reach out to us, and see how we can help you solve any challenges with PIM.

The post Case Study: Centralizing Rocket Industrial’s 80K+ Products with Pimcore appeared first on scandiweb.

Managing content across a multi-region, multi-language eCommerce platform requires structure. It’s the only way to scale. See how we created a reliable system to manage our client’s global content without slowing down regional teams or creating new dependencies.

About

OM System is a digital imaging and audio brand operated by OM Digital Solutions Corporation. Their global Adobe Commerce platform powers eCommerce for a wide range of high-end cameras, lenses, and accessories. With 70 store views and customers in over 20 languages, content consistency and control were becoming more complicated to manage.

Project goals

• Integrate a headless CMS to manage content delivery across 20 languages
• Replace scattered content logic with structured, reusable blocks
• Support a parallel frontend migration to the Hyvä theme
• Enable API-driven dynamic rendering inside Magento (Adobe Commerce)
• Improve content publishing workflows
• Set up an editor-friendly CMS studio for regional teams.

Approach

OM System’s platform is anything but simple. With three Adobe Commerce installations, 35 live store views, and another 35 in staging, plus content in 20 languages, their content architecture needed a clear structure. 

We chose Sanity CMS because of its headless, API-first approach, built to handle exactly this kind of multilingual, multi-site complexity. Unlike traditional CMS platforms, Sanity gave us the flexibility to define and scale content independently from the frontend.

The first step was defining and structuring all content blocks. We began by working closely with the client to map all design-approved content blocks before implementation across regions, categories, and page types. Some of them were simple, like banners or callouts, while others involved more complex logic and variations based on language, device, or campaign. In total, we built over 55 modular blocks, replacing at least four different methods the OM System’s content team previously used to create tiles in Magento Page Builder.

Dynamic rendering logic

On the backend, we developed custom logic in Magento (Adobe Commerce) to dynamically fetch and render Sanity content, block by block, across all 70 store views. A single structured content source could now power every site variant, whether for the UK, Japan, or the US, while respecting region-specific language and layout differences. This setup also supports flexible scheduling and localization, with editors able to control which content appears where and when, without touching the codebase or involving developers.

Sanity Studio

We configured the Sanity Studio interface to mirror real editorial workflows. This has reduced content publishing time from hours to minutes, while improving consistency across markets. Instead of a cluttered CMS with unnecessary fields and scattered logic, editors now have a clear, clean interface. They can:

• Update content across 20 languages in one session
• Reuse structured blocks across campaigns and countries
• Preview changes in context
• Push updates live without waiting on dev cycles

Collaboration and documentation

This project ran in parallel with a Hyvä frontend migration, while other teams worked on support, SEO, and analytics, with regular internal syncs.

For SEO, we added custom fields in Sanity for meta titles, descriptions, hreflang, canonical tags, and OG tags. These are fully manageable by editors and backed by automatic fallback logic to Mirasvit SEO rules when no custom value is set. 

After go-live, we held several workshops and supported OM System’s internal teams with how-to documentation and user guides, giving them confidence in the system from day one.

Also read:
Cervera’s Successful Digital Turnaround with Sanity CMS

Results

With 70 store views connected to a single CMS instance, this project stands among the largest Sanity implementations built to date. The system now supports 20 languages, 55+ reusable content blocks, and fully API-driven content rendering across Magento storefronts. Editorial workflows are significantly faster through an intuitive Sanity Studio setup. 

Key outcomes

• 70 store views managed under one CMS (35 production + 35 staging)
• 20 languages supported
• 55+ reusable content blocks implemented
• Content updates that previously took hours now take minutes
• Previously fragmented Magento content creation logic unified under a single CMS
• Delivered in 5 months from design sign-off to go-live.

Need to come up with an efficient way to manage and scale your content? Get in touch with us today to plan your CMS transformation.

The post Case Study: One of the World’s Biggest Sanity CMS Builds appeared first on scandiweb.

Every click, product view, purchase, or support request reveals something about your customer. That is, if you know where to look. In eCommerce, customer data platforms (CDPs) are the power tools for understanding customer behavior and personalizing marketing, meanwhile improving overall customer experience.

Research shows that companies using CDPs achieve 2.9x greater YoY revenue growth compared to those that don’t. However, to achieve those results, you need to fully understand the topic. In our latest webinar, we broke down what a CDP is, what it does, how it works, and what it takes to implement one. Here’s what you need to know.

Watch the full webinar recording above, or read more for all the essentials on how CDPs can help eCommerce retailers grow their revenue.

What are CDPs and why should you care?

A customer data platform (CDP) is software that unifies customer data from various channels, such as eCommerce, mobile apps, POS, live chat, SEO, PPC, and more, into a single, centralized customer profile.

Unlike customer relationship management (CRM), data management platforms (DMP), or data warehouses, a CDP brings together customer data from all sources into one clear profile, online and offline, that stays updated over time. This makes it easier to personalize in real time and connect across channels.

It offers stronger identity resolution than CRMs (which focus on known contacts) and DMPs (which rely on anonymous data), and unlike data warehouses, CDPs are built for analysis and action.

Essentially, CDPs are built specifically for identity resolution and real-time personalization, which most CRMs and DMPs are not.

CDP core values

CDPs bring structure to messy customer data and turn it into something you can use. Here’s what they do best:

• Connect and combine data from all platforms (eCom, CRM, POS, ads, etc.)
• Merge customer info into one clear profile
• Build smart audience segments, including AI-driven ones
• Send personalized messages through email, ads, and more
• Track customer behavior, predict churn, and show segment performance.

Why CDPs are so valuable for customer retention and personalization

Businesses that use CDP show up to 90% reduction in time spent on segmentation, customer journeys, and reporting tasks. 

CDPs also unify data across channels so teams can personalize in real time and act on insights without manual effort. They increase customer loyalty and lifetime value, reduce churn, support data privacy compliance, as well as save time through effective data management, all of which contribute to higher revenue and marketing performance.

Also read:
What is CDP? Guide to Customer Data Platforms for eCommerce

CDPs and AI

AI goes beyond being a trendy term in the context of CDPs; it serves as a practical engine driving better decision-making and more precise targeting. Predictive features, such as anticipating user churn and tailoring messages to be more timely, are some of the ways AI helps CDPs make better use of data.

Here’s how the top platforms today use AI for customer insights, making them more precise and actionable.

Predictive audiences and analytics

Various AI models can help businesses forecast the following customer behavior:

• Who is likely to make a purchase soon
• Who is at risk of churning
• Which customers are likely to respond to a campaign
• How much a customer might spend over their lifetime
• When a customer is most likely to buy again.

Some CDPs with these features are Segment (Twilio Segment), Salesforce Data Cloud (Einstein AI), Bloomreach, and Treasure Data.

Real-time customer data unification and identity resolution

Real-time data unification means bringing scattered customer data into one clear profile automatically and instantly.

AI connects the dots between different devices, email addresses, purchase histories, and more to recognize the same person across platforms. This gives you a complete, up-to-date view of each customer without manual cleanup.

Some of the platforms that have these features are BlueConic and Salesforce Data Cloud.

Automated personalization

Automated personalization uses AI to adapt every interaction to each customer without manual setup. It adjusts everything from messages and website content to product recommendations based on what users engage with the most. 

Platforms like Salesforce Data Cloud, Bloomreach, and BlueConic use this to make personalization faster and more relevant.

Data cleansing and enrichment

Data cleansing and enrichment keep your customer data accurate and complete, so that it’s ready to use. In this case, AI takes care of routine cleanup by removing duplicates and fixing inconsistent and missing fields, so your team doesn’t have to. 

Tools like Treasure Data and BlueConic’s AI Workbench help maintain high data quality without manual effort.

Dynamic segmentation

Dynamic segmentation uses AI to automatically group customers based on how they behave and what they’re likely to do next. It updates segments in real time, so you’re always targeting the right people with the right message. 

Bloomreach, Segment, and Treasure Data make this possible with no constant manual updates.

Customer journey optimization

Customer journey optimization uses AI to track how people move across channels and fine-tune their experience as it happens. It spots drop-off points, adjusts timing, and delivers the right message at the right step, whether that’s on your site, in an email, or in-store. 

Bloomreach, BlueConic, and Salesforce Data Cloud are some of the tools that can handle this in real-time.

Case study: how a New York retailer increased revenue with CDP

A luxury New York fashion retailer struggled with fragmented customer data across online, offline, and loyalty channels, making personalization difficult. Without a unified view, insights like preferences and purchase history had to be manually calculated, limiting effective segmentation and targeted campaigns.

After implementing a CDP, these were the outcomes: 

• 2x revenue growth in 5 years
• 85% of revenue from re-engaged customers
• Over 40% of online sales driven by email and SMS
• Personalized customer service and in-store experience, knowing customer preferences
• Automated business reporting and insights, saving hundreds of hours of manual time
• In-depth customer knowledge, driving smarter product and marketing strategies across teams.

What it takes to implement a CDP platform

Implementing a CDP requires the right team and a solid tech stack. When you’re thinking about the team setup, these are the people to consider:

• Marketing, CRM, and eCommerce managers who define use cases and activation goals
• People who know the customer and the data
• Tech specialists: a CDP specialist, a data (analytics) engineer, and a BI specialist.

For a tech stack, you’ll need:

• A data warehouse to store and organize clean customer data
• A traditional or composable CDP 
• Data transformation tools to clean and transform data if not handled inside the CDP
• BI tools (e.g., Tableau, Power BI, or Looker) for visualization and reporting.

Our scandiweb team has the expertise to manage every aspect of CDP implementation, from defining use cases and activation goals to handling the entire tech stack. Our team of specialists will ensure your data is organized and ready for action, giving you the best results.

How to choose the right CDP platform

Before choosing the right platform, you have to keep in mind that there is no “one size fits all” solution. Here’s what you need to consider when choosing the right tool for your business.

Types of CDPs

The two main categories, traditional and composable, offer different levels of flexibility, control, and ease of use.

Traditional CDPs are packaged, all-in-one platforms that come with built-in tools for data unification, identity resolution, and activation. They’re quicker to set up and easier to manage, making them a good fit for teams that want a ready-to-use solution without heavy internal resources. However, they offer less flexibility when it comes to customization.

Whereas composable CDPs are modular and built on top of your existing data warehouse. Instead of an all-in-one tool, you combine best-in-class tools for each function. This gives you full control and avoids vendor lock-in, but it requires a strong internal data team to manage setup and ongoing operations.

Factors to consider when choosing the right CDP for you

When you’re about to choose a CDP, there are many variables to consider, which is why we here at scandiweb work closely with the client and run an in-depth discovery to evaluate everything properly. These are the key factors to consider:

• Business goals you want to achieve (data unification, personalized marketing, retargeting in ads, or better customer insights)
• Pricing models based on tracked users, event volume, or data records
• Privacy compliance requirements like GDPR or CCPA
• Data source compatibility, as some CDPs offer native integrations, while others require external connectors or workarounds
• Data transformation needs, as some CDPs expect cleaned, structured data before ingestion
• Activation targets like email platforms or ad tools
• Identity resolution flexibility 
• Documentation quality and access to onboarding resources.

Some of the top CDP platforms

1. Salesforce Data Cloud offers strong integration with Salesforce tools and flexible data modeling, but has complex pricing, limited documentation, and non-persistent unified IDs.
2. Bloomreach is user-friendly, cost-effective for small and medium-sized businesses, with solid support and documentation, though it has a learning curve and less intuitive data validation.
3. Segment excels in integrations and data governance, with native messaging and AI features, but lacks built-in reporting and can become costly with scale.
4. Insider CDP includes built-in automation and a strong UI, but it offers fewer integrations and has pricing and stability issues.
5. Adobe Experience Cloud is powerful but best suited for companies already using the full Adobe stack.
6. Pimcore is open-source and flexible, ideal for custom setups, but has fewer ready-made connectors.
7. RudderStack is warehouse-native, giving full data ownership and flexibility, but requires technical expertise for setup and maintenance.

Summary: why a CDP is a must for any eCommerce store

Retailers often struggle with disconnected systems and scattered customer data. This limits the ability to satisfy customers and slows down personalization efforts and business decisions. Bringing online and offline customer data together solves a key challenge for businesses. CDPs provide the central system needed for this.

Take, for example, the New York luxury fashion retailer. With the help of our CDP experts and all the right tools, we were able to increase their revenue more than twice. In-depth customer knowledge is one of the driving factors for creating smarter product and marketing strategies.

With the rising role of AI in marketing, CDPs are becoming even more effective for delivering true data-driven insights and actions. Therefore, retailers that don’t use CDPs in their eCommerce are losing to their competitors. 

Want a clearer view of your customers? Get in touch with us and see how unified customer data leads to more effective marketing and stronger customer loyalty, producing better business results.

The post Webinar Recap: How to Increase Your eCommerce Revenue 2-3x with CDPs appeared first on scandiweb.

Search behavior is rapidly shifting—people now expect AI-driven queries rather than manual filters and browsing. Brands must be discoverable by AI, accessible directly in assistants like ChatGPT. Offering a custom GPT like SpoGPT is a crucial step toward being present and effective in this emerging “AI search” landscape.

How it works

This week, we’ve built SpoGPT, a ChatGPT Custom GPT for Sportland, a leading sports apparel and equipment retailer. Leveraging OpenAI’s Custom GPTs feature with actions, SpoGPT combines:

• General GPT knowledge (product trends, use cases, web search)
• Live access to Sportland’s Magento catalog via custom OpenAPI‑based actions

These “actions” let SpoGPT fetch real-time details—inventory, pricing, sizes—on demand, blending structured API data with conversational expertise

Processing and development

• Defined robust OpenAPI schemas covering key Magento endpoints
• Configured secure authentication using API-key auth
• Uploaded action schemas and configured them via the Custom GPT builder
• Crafted prompt flows that trigger API calls only when needed, keeping conversations coherent

Example walkthrough

Impact

SpoGPT positions Sportland as AI-ready:

• Shows up in AI-led search via ChatGPT
• Offers real-time accuracy on price, stock, size
• Delivers expert guidance on fit, terrain, materials
• Simplifies UX with natural language—no discipline switching needed

This demonstrates how brands can embed themselves into the future of AI-powered shopping.

Built with

• OpenAI Custom GPTs – leveraging the Actions feature for API integration
• Magento API – source of real-time product data

Complexity

Medium

Looking to leverage modern AI tools within your company? Get in touch and explore next steps.

The post AI Agent Bites #8: SpoGPT, the Sportland AI Agent appeared first on scandiweb.