TABLE OF CONTENTS
- Digital Experience (DX) Defines the AI-Powered Shopper Journey ........ 3
- The New Retail Reality: From Omnichannel to Unified Commerce ........ 5
- AWARENESS: Discovery in the Age of AI ................................................ 8
- CONSIDERATION: Intelligent Navigation and Personalized Curation .... 12
- EVALUATION: AI-Assisted Decision Making ......................................... 16
- PURCHASE: Frictionless, Intelligent Conversion .................................... 19
- RE-ENGAGEMENT: Predictive Personalization ...................................... 21
- REFERRAL: Amplifying Authentic Advocacy ......................................... 24
- Building the Foundation: Information Architecture for AI Commerce .... 26
SECTION 1: DIGITAL EXPERIENCE DEFINES THE AI-POWERED SHOPPER JOURNEY
The Transformation Is Complete
The digital transformation of retail that began in earnest a decade ago has reached maturity. Today's consumers don't distinguish between "digital" and "physical" shopping—they simply shop. The channel is irrelevant; the experience is everything.
What has changed dramatically is the intelligence behind that experience. Artificial intelligence, machine learning, and advanced information architecture now orchestrate journeys that adapt in real-time to individual context, intent, and behavior. Generative AI creates personalized content at scale. Computer vision enables visual search and virtual try-on. Natural language processing powers conversational commerce. Predictive analytics anticipate needs before shoppers articulate them.
The Modern Shopper Journey
The shopper journey still moves through recognizable stages—AWARENESS → CONSIDERATION → EVALUATION → PURCHASE → RE-ENGAGEMENT → REFERRAL—but the path is no longer linear. Shoppers bounce between stages, jump channels mid-journey, and expect seamless continuity across every touchpoint.
Modern shoppers are:
- Hyper-informed – Product research happens across TikTok, Reddit, Instagram, YouTube, brand sites, review platforms, and AI shopping assistants
- Context-driven – Shopping moments are triggered by life events, social feeds, influencer content, or AI-surfaced recommendations
- Privacy-conscious – Expect personalization but demand control over their data
- Values-aligned – Sustainability, ethics, and brand values matter as much as product features
- Impatient – Expect instant answers, same-day delivery, and real-time inventory visibility
The AI-Commerce Imperative
There is a need to satisfy the immediacy of each moment with alluring, relevant experiences that feel both personalized and authentic.
This requires:
- AI-powered personalization that works at scale across millions of shoppers
- Intelligent product discovery that surfaces the right products at the right moment
- Conversational interfaces that understand natural language and shopping intent
- Visual AI that enables search by image, style matching, and virtual try-on
- Predictive systems that anticipate needs and proactively engage
- Unified data architecture that breaks down silos between systems and channels
THE NEW SHOPPER JOURNEY OFFERS RETAILERS BOTH UNPRECEDENTED OPPORTUNITY AND EXISTENTIAL CHALLENGE
SECTION 2: THE NEW RETAIL REALITY—FROM OMNICHANNEL TO UNIFIED COMMERCE
Beyond Omnichannel
The omnichannel promise of the 2010s—that shoppers could move seamlessly between channels—has evolved into unified commerce: a single, real-time view of inventory, customer data, and commerce operations that powers intelligent experiences across every touchpoint.
Unified commerce means:
- Real-time inventory visibility across stores, warehouses, drop-ship partners, and marketplaces
- Unified customer profiles that recognize shoppers regardless of channel
- Consistent pricing and promotions orchestrated by AI based on demand, inventory, and competitive dynamics
- Flexible fulfillment with buy online/pick up in-store (BOPIS), curbside, same-day delivery, ship-from-store
- Connected associate experiences where store teams have the same tools and data as e-commerce
The New Business Model
Retail organizations must be guided by strategic and measurable goals:
- Customer lifetime value (CLV) over transaction revenue
- First-party data richness over third-party cookie dependence
- Conversion rate optimization powered by AI testing at scale
- Average order value (AOV) growth through intelligent bundling and upsell
- Return rate reduction via better product data, sizing guidance, and AR try-on
- Supply chain intelligence with demand sensing and dynamic allocation
The Science Behind the Art
Most retailers struggle to keep up with their own product changes across touchpoints, let alone shopper context.The proliferation of SKUs, channels, digital assets, and content variations has overwhelmed manual merchandising processes.
Success in 2025 requires applying AI and information architecture as a science to the art of creating captivating shopper experiences. This means:
- Structured product data that AI systems can understand and reason over
- Rich metadata and taxonomies that enable intelligent search and recommendations
- Governed content operations that ensure accuracy and consistency
- Real-time decisioning engines that personalize every interaction
- Agentic AI systems that automate merchandising decisions while maintaining brand control
Let's follow Sarah as she moves through her AI-powered shopping journey in 2025.
SECTION 3: AWARENESS—DISCOVERY IN THE AGE OF AI
The Discovery Moment
At this stage, the shopper may not even know they're looking for something. Awareness emerges from social feeds, AI recommendations, influencer content, or life events. The goal is to be present—with the right message, product, and creative—when inspiration strikes.
Sarah's Journey Begins:
Sarah scrolls through TikTok and sees a "get ready with me" video from a home décor influencer she follows. The room setup catches her eye—it feels exactly like the aesthetic she's been craving. She taps the video to reveal shoppable product tags powered by TikTok Shop. The sofa, rug, pillows, and lighting are all instantly shoppable.
Sarah adds the sofa to her "Favorites" but doesn't buy yet. She's inspired but wants to explore options. Behind the scenes, the retailer's AI captures this signal: Sarah is in AWARENESS for "modern coastal living room furniture."
The AI-Powered Social Commerce Stack
Social commerce in 2025 is dramatically different from the early Pinterest and Instagram shopping experiments:
TikTok Shop & Live Commerce:
- Short-form video dominates product discovery
- Live shopping events create urgency and entertainment
- AI-powered product tagging makes every video shoppable
- Creator marketplaces connect brands with nano- and micro-influencers
Instagram & Facebook Shopping:
- Integrated checkout removes friction
- AR try-on for fashion, beauty, furniture, and accessories
- AI-curated shops based on browsing and purchase history
YouTube Shopping:
- Long-form product reviews drive considered purchases
- Shoppable moments in videos and livestreams
- Creator storefronts with affiliate attribution
Reddit & Community Commerce:
- Authentic peer recommendations in subreddits
- Brand participation requires authenticity and expertise
- Community-vetted product lists and buying guides
The Information Architecture Challenge
Keeping product data social-commerce ready requires discipline:
Social platforms demand structured product data:
- GTIN/UPC for product identification
- Standardized attributes (color, size, material, brand)
- Dynamic pricing that updates across platforms
- Real-time inventory to prevent selling out-of-stock items
- Rich media assets optimized for each platform (square, vertical, carousel)
- Platform-specific metadata (TikTok categories, Instagram collections, Pinterest boards)
The "shelf life" of social content varies wildly:
- TikTok: Minutes to hours
- Instagram Stories: 24 hours
- Instagram Feed: Days to weeks
- Pinterest: Months to seasons
- YouTube: Years
To keep assets current and maximize reach, retailers need:
- Product Information Management (PIM) as the single source of truth
- Digital Asset Management (DAM) with automated resizing and formatting
- AI-powered tagging that applies metadata at scale
- Syndication engines that push updates to all platforms in real-time
- Social listening tools that capture engagement signals
Intelligence in Action
When Sarah adds the sofa to her TikTok Favorites, the retailer's customer data platform (CDP) creates a profile:
- Engagement signal: AWARENESS stage
- Style preference: Modern coastal aesthetic
- Product interest: Living room furniture
- Channel behavior: Social discovery, mobile-first
- Influencer affinity: Home décor creators
This intelligence informs what Sarah sees next—in retargeting ads, email, on the website, and even in-store if she visits.
RETAILERS REQUIRE INTELLIGENT INFORMATION ARCHITECTURE FOR AI-POWERED COMMERCE ACROSS SOCIAL, SEARCH, AND OWNED CHANNELS
SECTION 4: CONSIDERATION—INTELLIGENT NAVIGATION AND PERSONALIZED CURATION
The Exploration Phase
Consideration is where shoppers actively explore options, compare alternatives, and narrow their choices. They want to see similar products, read reviews, understand pricing, and visualize how products fit their needs.
Sarah's Journey Continues:
The next day, Sarah receives a personalized email: "We noticed you loved this sofa—here are other styles you might like." She clicks through to the retailer's website, which recognizes her from the TikTok interaction and immediately shows a curated landing page with coastal-style living room furniture.
Sarah uses the site's AI-powered visual search to upload a screenshot from the TikTok video. The system recognizes not just the sofa, but the entire room aesthetic, and surfaces similar complete room packages. She browses by style ("coastal modern"), price range, and delivery timeframe.
A chatbot appears: "Hi Sarah! I see you're interested in coastal living room furniture. Would you like help finding pieces that work together?" Sarah opts in and tells the AI assistant about her space dimensions and existing décor.
Intelligent Product Discovery
Modern product discovery goes far beyond basic search and navigation:
AI-Powered Search:
- Natural language understanding interprets queries like "affordable coastal sofa that fits a small space"
- Visual search finds products from images shoppers upload
- Voice search enables hands-free shopping
- Search personalization ranks results based on individual preferences and history
Dynamic Navigation:
- Adaptive taxonomies that reorganize based on shopper behavior
- Faceted search with AI-suggested filters based on common shopper paths
- Related categories that surface adjacent shopping needs
- Seasonal and trend-based merchandising that evolves automatically
Recommendation Engines:
- Collaborative filtering ("shoppers like you also bought...")
- Content-based recommendations (similar style, features, price)
- Complete-the-look suggestions powered by visual AI
- Contextual recommendations based on time, location, weather, or events
The Metadata Architecture
To enable intelligent discovery, products must be enriched with layered metadata:
Product Attributes (Structured Data):
- Category, subcategory, brand
- Price, SKU, GTIN
- Color, size, material, dimensions
- Care instructions, origin, certifications
Descriptive Attributes (AI-Enhanced):
- Style tags (coastal, modern, minimalist)
- Lifestyle associations (family-friendly, pet-friendly, apartment-living)
- Use cases (entertaining, relaxing, working from home)
- Emotional attributes (cozy, sophisticated, playful)
Behavioral Metadata:
- Frequently purchased together
- High return rate indicators
- Popular with specific demographics
- Seasonal demand patterns
Channel-Specific Metadata:
- Best sellers on mobile vs. desktop
- High converters from social
- Better in-store or online
- Requires expert sales assistance
Dynamic Personalization
As Sarah explores, the system adapts in real-time:
Immediate Signals:
- Clicked on budget-friendly options → Adjusts price range shown
- Spent time on "easy care" fabrics → Prioritizes performance fabrics
- Viewed complete room sets → Surfaces bundled packages
Historical Context:
- Previous purchases from this retailer (if any)
- Browsing behavior from other sessions
- Email engagement patterns
- Social media interactions
Predictive Intelligence:
- Likelihood to convert this session
- Preferred communication channel
- Optimal discount threshold
- Risk of cart abandonment
TAXONOMY AND METADATA DESIGN ORCHESTRATES DYNAMIC PERSONALIZATION ACROSS CHANNEL, DEVICE, SEGMENT, CONTEXT, AND REAL-TIME INTENT
SECTION 5: EVALUATION—AI-ASSISTED DECISION MAKING
The Decision Phase
At this stage, Sarah has narrowed her choices and is weighing final options. She wants reassurance through reviews, comparisons, expert guidance, and if possible, a way to visualize the product in her actual space.
Sarah's Journey:
Sarah has three sofas saved. She opens the AR visualization tool and uses her phone camera to see how each would look in her living room. The AI assistant suggests: "Based on your room dimensions, the Madison sofa in Sea Salt linen would fit best. It also pairs perfectly with the coastal rug you favorited."
Sarah clicks to see detailed comparisons: frame construction, fill materials, fabric durability ratings, delivery timelines, and warranty terms. She reads reviews filtered by "pet owners with light-colored furniture" (the AI noticed she follows pet accounts on social media).
She sees a live chat option: "Our design specialists are available now." She connects with an associate who has access to Sarah's browsing history and can see the exact products she's considering.
AI-Enhanced Decision Support
Modern evaluation tools leverage AI to build confidence:
Augmented Reality (AR):
- Room visualization using smartphone cameras
- Scale accuracy that warns if items are too large/small
- Style coordination showing how pieces work together
- Lighting simulation showing products in different conditions
Virtual Try-On:
- Fashion and accessories rendered on shoppers' photos
- AI body measurements from photos for accurate sizing
- Multiple angle views and movement simulation
Intelligent Comparisons:
- Side-by-side specs with AI-highlighted key differences
- Value analysis showing price-per-feature breakdowns
- Sustainability scores comparing environmental impact
- Total cost of ownership including maintenance and longevity
Contextual Reviews & Ratings:
- AI-filtered reviews relevant to shopper's context (e.g., "pet owners")
- Sentiment analysis surfacing common themes
- Question clustering answering FAQs before they're asked
- Verified purchase badges and helpful vote ranking
Expert Guidance:
- AI chatbots for instant product knowledge
- Video consultations with specialists
- Store associate connections via chat or video
- Guided shopping tools for complex purchases
The Information Architecture of Confidence
Effective evaluation requires rich, accurate, and trustworthy product information:
Technical Specifications:
- Complete, accurate, and consistent across channels
- Standardized measurement units
- Clear feature explanations for non-experts
Lifestyle Context:
- Room setting images showing scale and style
- "How-to" content for styling and care
- Customer photos in real environments (user-generated content)
Social Proof:
- Authentic reviews with response from brands
- Influencer testimonials with disclosure
- Expert recommendations and awards
- Community discussions and Q&A
Assurance Information:
- Return policies clearly stated
- Warranty terms and service options
- Environmental and ethical certifications
- Availability and delivery precision
Intelligence in Action
The AI assistant recognizes Sarah is in EVALUATION and adjusts its approach:
- Provides detailed product information vs. broad discovery
- Highlights reviews from similar customers
- Offers immediate specialist connection
- Shows time-sensitive incentives (e.g., free design consultation with purchase today)
The system also predicts Sarah's likelihood to convert and deploys appropriate engagement:
- High likelihood → Gentle nudge with delivery timeline
- Medium likelihood → Specialist connection offer
- Low likelihood → Save for later prompt and follow-up email sequence
PROGRESS FROM 'PRODUCT INFORMING' TO 'STORYTELLING' AND FINALLY TO AI-ASSISTED 'STORY-SELLING'
SECTION 6: PURCHASE—FRICTIONLESS, INTELLIGENT CONVERSION
The Conversion Moment
Once the decision is made, checkout must be effortless. Any friction—confusing process, limited payment options, unexpected costs, or delivery concerns—risks cart abandonment.
Sarah's Journey:
Sarah decides on the Madison sofa in Sea Salt linen. She adds it to her cart along with the coordinating rug the AI suggested. At checkout, she's offered:
- One-click checkout with saved payment (Apple Pay or Shop Pay) - Flexible payment options including buy now/pay later - Delivery options: Standard (7-10 days), Expedited (3-5 days), or White Glove with assembly (choose your date) - AI-powered size reminder: "This rug is 8x10. Your room is 12x15. Consider upgrading to 9x12 for better proportions."
Sarah appreciates the suggestion and upgrades the rug. She selects White Glove delivery for next Saturday. Order confirmation arrives via text with a calendar invite for delivery. She receives a personalized thank-you email with care instructions and styling tips.
Frictionless Checkout Architecture
Modern checkout removes every possible point of friction:
Accelerated Checkout:
- One-click purchase for returning customers
- Guest checkout without account creation
- Digital wallet integration (Apple Pay, Google Pay, Shop Pay, PayPal)
- Autofill for address and payment data
Flexible Payment:
- Buy now, pay later (Affirm, Klarna, Afterpay)
- Subscription options where relevant
- Gift cards and store credit application
- Dynamic currency conversion for international shoppers
Intelligent Delivery:
- Real-time inventory with accurate availability
- Multiple delivery options with cost transparency
- AI-optimized delivery date suggestions
- Ship-to-store and BOPIS options
- Sustainable shipping options
Cart Intelligence:
- Cross-sell optimization without feeling pushy
- Bundle suggestions that genuinely add value
- Abandoned cart recovery with AI-personalized messages
- Inventory alerts if items are low stock
Post-Purchase Orchestration
Confirmation is just the beginning:
Immediate:
- Order confirmation via preferred channel (email, text, app notification)
- Calendar integration for delivery appointments
- Access to order tracking
- Easy modification options (change delivery date, add items)
Pre-Delivery:
- Proactive updates on order status
- Preparation tips (e.g., measure doorways for furniture)
- Related product recommendations for arrival
- Easy customer service access
Delivery:
- Day-of tracking with delivery windows
- SMS updates on driver location
- Contactless delivery options
- Immediate feedback request post-delivery
Cross-Functional Alignment
Frictionless purchasing requires frictionless operations:
Retailers must align stakeholders across:
- Merchandising (product assortment, pricing)
- Operations (inventory, fulfillment, delivery)
- Customer Service (issue resolution, returns)
- Marketing (messaging, promotions, loyalty)
- Technology (systems integration, data flows)
Unified commerce platforms integrate these functions with real-time data visibility, enabling every team and channel to access the same truth about inventory, orders, customers, and products.
SECTION 7: RE-ENGAGEMENT—PREDICTIVE PERSONALIZATION
The Loyalty Phase
Post-purchase is when brand loyalty is built or broken. The experience must feel personalized, valuable, and authentic—not spammy or transactional.
Sarah's Journey:
Two days after delivery, Sarah receives a text: "How do you love your new sofa? Share a photo and get entered to win a $500 shopping spree!" She snaps a photo of her styled room and shares it on Instagram, tagging the retailer.
A week later, she gets an email from the same sales associate who helped her: "Sarah, I found the perfect accent chairs for your space. They're on sale this week, and I've set aside two in a fabric that coordinates with your rug. Want to see them?"
The email includes an AR link—Sarah can visualize the chairs in her space instantly. She's impressed by the thoughtfulness and places the order. The retailer's AI predicted Sarah would be interested in completing the room based on purchase patterns of similar customers.
Predictive Engagement Models
AI enables hyper-personalized post-purchase journeys:
Next Product Prediction:
- Purchase history analysis across customer base
- "Frequently bought together" patterns
- Natural product lifecycle triggers (replace, upgrade, complement)
- Seasonal or event-based suggestions
Optimal Engagement Timing:
- When to send follow-up messages
- Best channel for each customer (email, SMS, app, retargeting)
- Frequency that maximizes engagement without annoyance
- Moment of peak purchase intent
Personalized Content:
- How-to guides for products purchased
- Styling or use case ideas
- Complementary product recommendations
- Exclusive loyalty member offers
Proactive Service:
- Early identification of potential issues (delivery delays, product concerns)
- Warranty and service reminders
- Reorder prompts for consumables
- Trade-in or upgrade programs
The Shopper Event Attribute Model
To enable intelligent re-engagement, retailers need structured metadata for customer lifecycle events:
Purchase Events:
- First-time buyer
- Repeat customer
- High-value purchase
- Gift purchase vs. personal use
- Seasonal purchase (holiday, back-to-school)
Engagement Events:
- Email opens and clicks
- Website return visits
- Social media interactions
- Review submissions
- Customer service contacts
Lifecycle Events:
- Product arrival
- 30/60/90-day ownership milestones
- Warranty expiration approaching
- Predicted replacement timing
- Trade-in eligibility
Contextual Events:
- Moving house
- Life stage changes (new baby, college, retirement)
- Seasonal triggers (summer, holidays)
- Local events or weather
This event taxonomy feeds AI models that trigger the right message, offer, or touchpoint at the optimal moment.
A SHOPPER EVENT ATTRIBUTE MODEL EXTENDS TO POST-SALE SCENARIOS TO HARVEST LONG-TERM CUSTOMER LOYALTY
SECTION 8: REFERRAL—AMPLIFYING AUTHENTIC ADVOCACY
The Advocacy Phase
Satisfied customers become brand advocates, sharing their experiences organically through social media, reviews, and word-of-mouth. Smart retailers make advocacy easy and rewarding.
Sarah's Journey:
Sarah is thrilled with her new living room. She posts a photo on Instagram with the caption: "Obsessed with my new space! 🛋️✨ Everything from @retailername—best shopping experience ever!"
The retailer's social monitoring AI flags Sarah as a potential brand advocate. She receives a DM: "Sarah, we love your space! Would you be interested in joining our Home Style Collective? Share your decorating journey and earn rewards for inspiring others."
Sarah joins and becomes part of a community of brand fans. She receives early access to sales, exclusive content, and earns store credit when people buy products she features. The retailer gains authentic user-generated content and a network of micro-influencers.
Modern Advocacy Programs
Referral and advocacy have evolved beyond simple "share and save" programs:
User-Generated Content (UGC):
- Social listening to identify organic brand mentions
- Automated requests for permission to repost
- Galleries of customer photos on product pages
- Hashtag campaigns with incentives
Community Building:
- Private social groups or apps for brand fans
- Early access to new products or sales
- Exclusive content and behind-the-scenes looks
- Member-to-member advice and inspiration
Influencer & Creator Partnerships:
- Affiliate programs with transparent tracking
- Nano- and micro-influencer networks
- Creator marketplaces for authentic partnerships
- Co-created content and product lines
Referral Programs Reimagined:
- Personalized referral links with custom messaging
- Tiered rewards (more referrals = better perks)
- "Give $X, Get $X" mutual incentives
- Integration with loyalty programs
Review & Rating Incentives:
- Post-purchase review requests at optimal timing
- Points or discounts for verified reviews
- Q&A engagement rewards
- Photo/video review bonuses
Monitoring & Response Architecture
To capitalize on advocacy and address potential churn:
Social Monitoring:
- AI-powered sentiment analysis across platforms
- Real-time alerts for brand mentions
- Trend identification (emerging complaints or praise)
- Competitor mention tracking
Engagement Automation:
- Automated "thank you" for positive mentions
- Priority escalation for negative sentiment
- Influencer identification and outreach
- Community moderation assistance
Insight Extraction:
- Product feedback loops to merchandising teams
- Feature requests and improvement ideas
- Trend forecasting from social signals
- Customer language analysis for marketing copy
RETAILERS SHOULD MONITOR, HARVEST INSIGHTS, CAPITALIZE ON TRENDS, REWARD INFLUENCERS, AND ADDRESS MOMENTS OF CHURN
SECTION 9: BUILDING THE FOUNDATION—INFORMATION ARCHITECTURE FOR AI COMMERCE
The Integration Challenge
The bar has been set impossibly high, and retail organizations must step up their digital merchandising execution with considerations for how people, systems, processes, content, and context intersect.
An intelligent approach, design, and deployment of Digital Information Architecture bridges traditionally fragmented organizations:
- Product operations
- Merchandising
- Marketing
- E-commerce
- Store operations
- Customer service
- Supply chain
This enables greater collaboration and choreographs a delightful, consistent customer experience across every touchpoint.
The Modern Commerce Technology Stack
To realize an AI-powered digital commerce vision requires integrating:
Core Commerce Systems:
- E-commerce platform (Shopify, Adobe Commerce, Salesforce Commerce Cloud, BigCommerce)
- Point of Sale (POS) for unified commerce
- Order Management System (OMS) for fulfillment orchestration
- Inventory Management with real-time visibility
Product & Content Systems:
- Product Information Management (PIM) as the single source of truth
- Digital Asset Management (DAM) for images, videos, and rich media
- Content Management System (CMS) for editorial and marketing content
- Translation Management for global commerce
Customer & Personalization:
- Customer Data Platform (CDP) for unified customer profiles
- Personalization engines for real-time decisioning
- Marketing automation for lifecycle campaigns
- Loyalty platforms for rewards and engagement
AI & Intelligence Layer:
- Product recommendations (Dynamic Yield, Nosto, Bloomreach)
- Search & discovery (Algolia, Coveo, Constructor)
- Visual AI (Syte, ViSenze) for image search and recognition
- Conversational AI (Ada, Certainly) for chatbots and virtual assistants
- Predictive analytics for demand forecasting and pricing
Operations & Fulfillment:
- Warehouse Management (WMS)
- Transportation Management (TMS)
- Returns Management
- Distributed Order Management (DOM)
The Information Architecture Framework
Harmonizing, modeling, and analyzing data across these systems requires a unified information architecture:
Product Data Model:
- Standardized attributes across categories
- Extensible for category-specific attributes
- Support for variants, bundles, and configurations
- Multi-language and multi-currency support
Taxonomy & Classification:
- Hierarchical product categorization
- Faceted navigation attributes
- Style, lifestyle, and use-case tagging
- Seasonal and trend classifications
Metadata Strategy:
- Product-level metadata (SKU attributes)
- Asset-level metadata (image context, usage rights)
- Content metadata (audience, channel, lifecycle stage)
- Shopper event metadata (journey stage, context)
Content Governance:
- Roles and responsibilities for data stewardship
- Workflows for content creation and approval
- Versioning and lifecycle management
- Quality assurance and validation rules
Syndication & Distribution:
- Rules engines for channel-specific formatting
- Automated publishing workflows
- Real-time updates across touchpoints
- Marketplace and social platform syndication
The Path Forward
Winning in today's competitive marketplace is defined by a retailer's ability to differentiate the digital experience in order to drive greater loyalty.
Differentiation can only be achieved by:
- Creating personalized and contextualized shopping experiences powered by AI
- Showcasing product lines in innovative and informative ways
- Providing seamless navigation through a relevant shopper journey
- Delivering authentic, frictionless service that sustains loyalty
A winning digital experience takes the art of digital merchandising to a new level, reached and sustained through the science of intelligent Information Architecture—producing effectively choreographed product information, digital assets, and content marketing from the supply chain through to every channel.
SATISFY THE IMMEDIACY OF THE MOMENT WITH ALLURING, RELEVANT EXPERIENCES POWERED BY AI, AND DELIVER ON AUTHENTIC, SEAMLESS SERVICE THAT SUSTAINS LOYALTY
GLOSSARY
Augmented Reality (AR)
Technology that overlays digital information onto the real world through smartphone cameras or AR glasses, enabling virtual try-on and room visualization.
Buy Now, Pay Later (BNPL)
Flexible payment options allowing customers to split purchases into installments without traditional credit cards.
Customer Data Platform (CDP)
A unified database that creates persistent customer profiles by combining data from multiple sources and making it available to other systems.
Digital Asset Management (DAM)
A system for storing, organizing, and distributing digital content like images, videos, and documents with rich metadata.
Product Information Management (PIM)
A centralized system that serves as the single source of truth for all product data across channels.
Unified Commerce
An integrated retail approach where all channels share real-time data about inventory, customers, and orders—beyond omnichannel.
User-Generated Content (UGC)
Content created by customers (photos, reviews, videos) that brands can leverage for authenticity and social proof.
Visual Search
AI-powered technology that allows shoppers to search for products using images instead of text queries.
ABOUT EARLEY INFORMATION SCIENCE
For over 25 years, Earley Information Science has helped market-leading brands succeed at the speed of digital agility. We design and implement the information architecture, taxonomy, and data governance foundations that make AI-powered commerce possible.
Our Retail Expertise:
- Product Information Management (PIM) strategy and implementation
- Taxonomy and metadata design for intelligent search and discovery
- AI readiness assessments for personalization and automation
- Content operations and governance
- Omnichannel and unified commerce architecture
We help retailers:
- Scale personalization across millions of products and customers
- Enable AI-powered search, recommendations, and content generation
- Break down silos between merchandising, marketing, and operations
- Create seamless experiences across digital and physical channels