Optimization and analytics for AI-enabled digital commerce

Fast insights and a clean data approach are crucial for discoverability and staying ahead in online marketplaces.

Consumers are fleeing from ‘stare and compare’ browsing online to AI-powered shopping conversations. AI-powered assistants trained on entire product catalogs can now understand increasingly complex customer queries and make robust product recommendations based on detailed product information, attributes (like ingredients, colors, age ranges), review content, and more. Today’s digital shelf not only needs to make sense to shoppers, but also to AI agents curating the experience.

Moreover, suppliers equipped with real-time, streamlined reporting will not only capture and interpret AI-driven shopper signals, but also be positioned to translate them into decisive action. Leveraging the capabilities of AI, they can keep orders on-time and in-full, meeting the demands of this new era, but only if they have a structured, data-driven approach that scales. Our guide explores how to gear up for agentic commerce as we move toward 2026 and beyond.

Gartner predicts that by 2027, 50% of business decisions will be augmented or automated by AI agents

Clean data matters more than ever

Gartner has made a staggering prediction that by 2027, 50% of business decisions will be augmented or automated by AI agents. In retail, an AI-ready data foundation that harmonizes sources, normalizes nomenclature, and leverages a semantic layer adds the structure and business context AI needs to deliver accurate, decision-ready insights. It was only last year, in 2024, that 85% of generative AI projects failed to reach production due to incomplete AI data foundations, incapable of connecting information that could drive real business decisions. The next two years will be critical for consumer goods and retail organizations to establish a single, scalable source of truth for product and sales data. With an AI-ready data foundation in place, teams across supply chain, sales, marketing, finance, and more will reap the benefits of fast, daily insights that can be powered in LLMs of their choice, and technical teams can employ the use of data science-ready AI Blueprints models, for example, to accelerate insights around pressing industry challenges.

From product data to purchase

Beyond analytics, organized product data has become a critical driver of discoverability and sales performance. For AI shopping assistants, let alone the typical digital shopper seeking an efficient experience, comprehensive data makes products discoverable across the marketplace. When a customer asks Amazon’s Rufus, for example, to find the “most popular sparkling water mixers” or “highest-rated retinol cream under $40,” the AI agent will reference product specifications, ratings, and context to offer products as a match. Other leading grocery platforms aim to provide the same concierge-style experience, enabling inquiries beyond “shredded cheese” to “best shredded cheese for my nachos”.

AI shopping agents provide a concierge-style experience, enabling inquiries beyond “shredded cheese” to “best shredded cheese for my nachos”.

Think mobile first: Another way AI is already assisting shoppers every day is by offering predictive search bar recommendations, which can guide up to 80% of mobile shopping experiences – considering tedious typing as a pain point. When a shopper searches for “pizza”, for example, they’ll be met with suggested queries such as “family-sized pizza”, “veggie pizza”, “pizza pockets”, and others. Brands will benefit from exploring their own search paths and incorporating helpful keywords into their product data and listings.

As for what’s ahead, AI-powered master data management (MDM) solutions promise to resolve the endless manual labor of connecting and maintaining detailed product identifiers and attributes. Across SKUs, channels, and retail partners, data is organized and structured into a complete, up-to-date intelligence system instead of a very long spreadsheet. This, in turn, supercharges analytics, making granular insights both accurate and accessible, and establishes the foundation for faster, more reliable AI-powered decision-making.

AI-powered master data management (MDM) solutions promise to resolve the endless manual labor of connecting and maintaining detailed product identifiers and attributes.

Omnichannel analytics by team

From supply chain excellence to marketing precision, Crisp provides fast, accurate analytics so teams can act quickly, optimize performance, and capture opportunities across all channels.

📦 Supply Chain

• Inventory – Track stock levels across all retailers, DCs, and even at the individual store-level in real time to prevent out-of-stocks and missed sales, or overstocks that amount to costly waste.
• Nil-pick reports – Nil-picks are up there with OTIF as a way to measure omnichannel supplier performance. They occur when products ordered for pickup or delivery are not available or identifiable when the order is packed. Nil-pick reporting helps suppliers stay on top of digital demand. 
• Root cause analysis – These Crisp reports pinpoint, as well as address the operational or supply issues causing delays, shortages, or fulfillment errors.

💼 Sales

• Digital sales reports – With Crisp, real-time sales reporting across retailers can be broken down by e-commerce and brick-and-mortar sales – down to the individual SKU- and store-level – to react quickly to shifts in demand. As well as make the case for larger or expanded order quantities to meet increased demand.
• Purchasability – Ensure online sales success with PEAT (Published, Eligible, Available, Transactable) reporting, a one-stop view of product sellability that flags issues blocking online, pick-up, and delivery transactions. 

🌟 Marketing

• When retail and marketing data operate together, campaigns become smarter and more profitable. With Pacvue, Crisp data can flow directly into Target Roundel campaigns, preventing wasted ad spend on out-of-stock products, dynamically reallocating budget toward high-performing SKUs, and optimizing placements based on real-time demand signals.
  
  With Haus, Crisp connects retail performance data to media investments, enabling brands to measure true incremental lift across in-store and online sales.
  
  Leading soda brand poppi, for example, used Crisp retail data to link a targeted TikTok campaign to an 80% in-store sales lift – proving the direct impact of digital strategies on physical retail.

🥫 Category Management

• Performance data – At J.M. Smucker, Target Category Manager Katie Asleson leverages Crisp to analyze SKU-level performance across in-store and digital channels, informing assortments, pricing, and promotional strategy for dog treat brands.
• Category benchmarking – Benchmarking against the category can also help you identify assortment opportunities. Are there certain subcategories where you’re underrepresented? Are there emerging trends that you’re not capitalizing on? By analyzing the market as a whole, you can find the white space where you can expand your assortment and capture a larger share of the market.

At J.M. Smucker Co., Target Category Manager Katie Asleson leverages Crisp to analyze SKU-level performance across in-store and digital channels, informing assortments, pricing, and promotional strategy for pet brands.

AI Blueprints for digital-ready data science

Crisp AI Blueprints are pre-built, customizable data science templates that help teams uncover actionable insights around retail’s most pressing challenges. They’ve been developed to eliminate technical heavy-lifting, and accelerate advanced analytics and AI adoption across everyday workflows.

Here are a few that drive measurable omnichannel outcomes:

• On-Shelf Availability – Monitor stock status across in-store, pickup, and delivery to prevent gaps and lost sales.
• Price Elasticity Estimation – Measure how price changes impact demand to optimize pricing for each channel.
• Store Clustering – Group stores by shared traits to uncover localized trends and tailor omnichannel strategies.

AI Blueprints plug directly into cloud platforms like Databricks or Google Colab, offering instant access to powerful models without the bottlenecks of custom data science.

What’s next: Agentic AI applications can take AI-driven analytics a step further, surfacing insights, sending alerts, and even triggering workflows to make continuous revenue-boosting improvements. Crisp AI Agent studio enables teams across the enterprise to design and deploy their own concierge-style workflows – from custom Monday morning reporting, to promotional optimization, supply chain alerting, and more. And it all starts with an AI-ready retail data foundation.

Conversational commerce? Time to listen up

To succeed in an online environment increasingly dominated by shopping assistants, retailers and CPG brands need data-driven strategies that support both traditional retail optimization and AI-powered discovery. This means committing to unified, comprehensive data that AI can process. Those who prepare for AI-assisted shopping now will see significant advantages as it rapidly becomes standard retail infrastructure.

For a deeper dive, book a demo with Crisp. Let’s discuss your data challenges and map out the fastest path from raw data to real-world retail wins.