How do you predict product success when every retailer speaks a different data language?
In Part 3, we break down the practical framework CRSTA uses to solve this challenge—starting with store-level data collection and ending with scalable AI-driven predictions. Learn how brands can build a retailer network that gets smarter with every store added.
Retail marketing doesn’t have to be a guessing game. Over two years, thousands of stores became testbeds for grassroots strategies designed to get new products on shelves—without relying on restricted consumer advertising. From in-store ambassadors to pop-ups, trade shows, and direct mail, these experiments revealed what actually drives retail adoption. Learnings from these tests helped shape a more scalable, data-driven approach to breaking into retail.
Pop-ups still work—when they’re done right. They boost sales, build stronger retail relationships, and turn shoppers into loyal customers. This post breaks down the real value of in-store events and how CRSTA helps brands run them smarter, faster, and more profitably.
Marketing alternative CPG products is a minefield of restrictions, fragmented retail data, and limited budgets—making product launches a gamble. With millions in marketing dollars going unspent or wasted, how can brands break through? Let’s dive into the challenges and what’s holding the industry back.
Small retailers are losing money on dead stock while big brands use AI to stay ahead. CRSTBL’s AI-driven approach gives smaller businesses the same power—without the massive IT budget. See how AI can transform inventory management.
Most AI automates tasks—CRSTBL builds AI that makes decisions. Instead of simple workflow bots, our AI agent, CRSTA, thinks like an experienced operator—adapting to context, weighing tradeoffs, and making judgment calls that drive real business outcomes.
