Meet Your New Omnichannel Data Analyst: The Brij AI Analytics Agent
For years, brand teams have been drowning in dashboards, toggling between tools, exporting spreadsheets, and trying—often unsuccessfully—to turn fragmented data into clear answers. Retail teams need insights now. Marketers need to know what's working. Operations leaders need to know where to invest next.
But until now, getting those answers required technical skills, BI resources, or hours of digging.
Today, that changes.
Introducing the Brij AI Analytics Agent
Brij’s new AI Analytics Agent is the first natural-language intelligence layer built specifically for omnichannel brands. Think of it as a universal translator for your data—one that understands your brand, your customers, your experiences, and your business model.
Instead of navigating dashboards or learning SQL, you simply ask questions in plain English and receive structured, actionable answers within seconds.
"Which of my experiences are driving the highest registration conversion rates?"
"What are my top-performing SKUs by retailer over the last 90 days?"
"Show me experiences that have scans but no registrations."
The Brij AI Analytics Agent understands your question, determines what data is needed, generates the appropriate queries, retrieves information from Brij's data warehouse, and delivers comprehensive answers that include both visual representations and written insights. No technical knowledge required. No waiting for reports. Just instant intelligence delivered where your team already works—in your Brij dashboard and in Slack.
Request to join the exclusive Beta here.
From Passive Analytics to Active Intelligence
Traditional analytics platforms force marketers into a passive relationship with their data. You log in, review static dashboards, export reports, and hope you're looking at the right metrics. The Brij AI Analytics Agent flips this dynamic entirely.
Instead of passive dashboards, you get active intelligence. Instead of predetermined reports, you get answers to the specific questions driving your business forward today. Instead of waiting hours or days for business intelligence teams to run custom queries, you get instant responses that let you act immediately on emerging opportunities or challenges.
This shift matters because modern omnichannel marketing moves too quickly for weekly reporting cycles. When you're running rebate campaigns across multiple retailers, managing product registrations for thousands of SKUs, and optimizing sweepstakes activations in real-time, you need to understand performance as it happens—not after the campaign ends.
The AI Analytics Agent democratizes data access across your entire team. Marketing coordinators can query campaign performance without involving analysts. Brand managers can explore customer behavior patterns during strategy meetings. CMOs can validate hypotheses on the spot rather than waiting for monthly reports. Everyone gets the insights they need, when they need them.
How Leading Brands Are Using the Brij AI Analytics Agent
The most sophisticated omnichannel marketers are using the AI Analytics Agent to answer questions that traditional analytics make prohibitively time-consuming:
Understanding True Omnichannel Performance
Brands are asking questions like "Which products are most frequently purchased together?" and "What percentage of my customers have shopped at multiple retailers?" to understand how customer behavior spans channels. This intelligence informs everything from product bundling strategies to retailer partnership decisions.
Optimizing Campaign Performance in Real-Time
Rather than waiting for post-campaign reports, teams are asking "Which experiences are trending up or down in engagement over the last 30 days, and what changes correlate with those shifts?" This enables rapid iteration and optimization while campaigns are still running.
For rebate campaigns specifically, marketers are querying "What is our average submission rate for rebate campaigns?" and "How many rebates were approved out of the total submissions?" to identify friction points in the redemption flow and improve conversion rates mid-campaign.
Connecting Offline Conversions to Digital Advertising
The holy grail of omnichannel marketing is understanding how digital advertising drives in-store purchases. Brands are now asking "Can you show me how offline conversions are impacting ROAS on Meta or Google?" and getting immediate answers that prove the retail impact of their digital spend—data that's nearly impossible to access through traditional analytics.
Questions like "Which marketing channels are driving the highest in-store conversions?" and "Which campaigns spiked revenue growth the most, by retailer?" are helping brands optimize ad spend based on true omnichannel performance rather than just digital metrics.
Identifying High-Value Customer Segments
Understanding who your customers are—and where they prefer to shop—unlocks more effective targeting and merchandising strategies. Brands are asking "Who are our top 10% most loyal customers, and where do they shop?" and "Are DTC or retail customers more likely to register their products?" to build more nuanced customer segments.
Proving Value to Retail Partners
Forward-thinking brands are using the AI Analytics Agent to generate insights they can share back with retail partners. By asking "What data would be interesting for me to take back to my retail partners (ex. Target) to share with them?" and "Which stores have the highest engagement per display activation?", brands are strengthening retailer relationships with data-driven collaboration.
This is particularly powerful for brands using Brij's serialization capabilities to connect specific product scans to specific retail locations. Questions like "What retail location is getting the most scans?" help both brands and retailers understand which stores are driving the most engagement and why.
Strategic Guidance: Getting the Most from Brij’s AI Analytics Agent
To maximize the value of the Brij AI Analytics Agent, focus on asking questions that drive specific business decisions rather than general exploration. The most effective queries are specific about time periods, include relevant filters, and seek actionable comparisons.
Strong questions include:
"What are the top 5 performing rebate campaigns at this moment in time?" → Informs resource allocation decisions
"Which experiences are driving the most new-to-file customer profiles for our brand?" → Identifies your most effective customer acquisition strategies
"From which retailer am I getting the most completed registrations?" → Helps optimize retail partnerships and in-store activations
"Given the performance of my experiences so far, what optimizations do you recommend to increase scans, registrations, etc?" → Surfaces proactive recommendations based on your data patterns
Frame questions around business outcomes:
Instead of "Show me registration data," ask "What is our registration rate quarter over quarter?"
Instead of "Tell me about customers," ask "What is the main demographic of people interacting with my Brij experiences?"
Instead of "Look at revenue," ask "If my experiences keep performing how they are today, what incremental revenue can I expect for the total year?"
The AI Analytics Agent understands context and can follow up on previous questions, so don't hesitate to ask clarifying questions or drill deeper into interesting findings. If an answer reveals an unexpected pattern, immediately ask "What factors contributed to this?" or "How does this compare to our top-performing campaigns?"
Why the AI Analytics Agent Represents the Future of Omnichannel Marketing Growth
Generic AI tools require extensive training and manual data feeding. Business intelligence platforms demand technical expertise and involve your engineering team for every custom query. Traditional dashboards force you to look at predetermined views that may not answer your specific questions.
The Brij AI Analytics Agent is different because it's purpose-built to understand omnichannel marketing data. It knows your brand's data structure, understands the nuances of retail versus DTC purchase behavior, and connects directly to your complete customer data—from product scans and registrations to rebate submissions and sweepstakes entries.
This isn't artificial intelligence as a feature add-on. This is AI designed specifically to solve the analytics challenges facing brands that sell through multiple channels, manage many SKUs, and need to prove ROI on both digital advertising and physical retail activations.
The result is intelligence that's genuinely useful—answers that inform immediate decisions, insights that reveal hidden opportunities, and data access that empowers every member of your team to understand and optimize omnichannel performance.
Join the Beta Program: Get Early Access to the Future of Data Analytics
We're currently enrolling brands in our AI Analytics Agent Beta Program, offering exclusive early access to this transformative capability.
Beta participants will:
- Get hands-on experience with natural language querying before general release
- Influence product development by sharing feedback directly with our team
- Receive dedicated implementation support to ensure you're maximizing value
- Join a community of forward-thinking brands shaping the future of omnichannel marketing
Interested in joining the Beta Program? Click here to let us know.
Ready to replace dashboard fatigue with active intelligence?
Book a demo with our team to see the AI Analytics Agent in action and explore how natural language analytics can accelerate your omnichannel strategy.
