While the company had strong relationships with its top-tier customers, understanding the behavior of lower-frequency buyers was a challenge. Data lived across Salesforce, Snowflake, and internal systems — making it difficult to see when smaller accounts might reorder or churn. As a result, sales teams often relied on intuition rather than insight, leading to missed opportunities and delayed responses.

The goal was to create a smarter way to predict buying patterns and help the sales team engage at the right time, with the right message.

To address these challenges, Redtag proposed a Salesforce-based data and AI solution centered around Salesforce Data Cloud and Einstein 1 Studio (Model Builder).

Here’s how it worked:

• Unified customer view: Data from Salesforce and Snowflake was connected through Data Cloud’s zero-copy approach, giving teams a real-time, unified view of every customer without duplicating information.
  
• Predictive AI models: Using Einstein 1 Studio, the client’s team could easily build no-code models to predict reorders, detect churn, and identify high-probability sales opportunities

• Smart automation: Predictions were tied to Sales Cloud actions — automatically creating tasks, alerts, and outreach reminders so sales reps could focus on timing their interactions perfectly.‍
• Future-ready architecture: The setup also prepared the client for deeper automation in future phases through Agentforce.

This connected architecture transformed data into action, equipping every sales rep with the insights they need to prioritize accounts, predict demand, and personalize outreach.

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