In an industry crowded with legacy players and endless spreadsheets, how can early-stage spirits brands collapse decisions that used to take a week into a single day? At Bar Convent Brooklyn, Philipp Klumpp (Founder & CEO) and David Porter of Beyond The Agency break down four tactical, low-barrier AI workflows that give challenger brands a decisive competitive edge. Philipp and David bypass theoretical AI hype to show the exact mechanics of plugging daily sales, inventory, and D2C data into LLMs like Claude and ChatGPT to drive immediate commercial results.
Park Street Imports is the back-office and importing solution for alcoholic beverage brands launching and scaling in the U.S. market.
Beyond The Agency’s Presentation Transcript
Philipp Klumpp: As an early-stage spirit brand, AI allows you to reach critical commercial decisions faster. What used to take a week of manual analysis now takes a single day.
I’m Philipp Klumpp, founder and CEO of Beyond The Agency. We are a growth agency for spirit brands specializing in digital strategy and direct-to-consumer (D2C) execution. In 2025 alone, our digital and D2C services accounted for roughly 30,000 cases sold online, and we generate about 50% of the annual impressions for the brands we represent.
Today, we’re going to walk through four concrete ways you can feed your sales and depletion data into models like Claude or ChatGPT to get faster answers, reduce media waste, prevent account churn, and make smarter market expansion decisions.
Use Case 1: The Media Waste Map
David Porter: Brands often spend significant marketing capital without verifying if physical product is available in those target markets.
┌─────────────────────────────────────────────────────────────────────────────┐
│ BUILDING A MEDIA WASTE MAP │
├─────────────────────────────────────────────────────────────────────────────┤
│ 1. INPUT DATA ➔ Total media impressions & CPM breakdown by zip code. │
│ 2. LAYER DATA ➔ Wholesale depletion reports & live retail inventory levels. │
│ 3. AI ANALYSIS ➔ Claude/ChatGPT maps media reach against stock availability. │
│ 4. OUTPUT ➔ Identifies high-ad-spend zip codes lacking distribution. │
└─────────────────────────────────────────────────────────────────────────────┘
If your ad campaigns generate high impressions in zip codes where you lack retail distribution, you are wasting capital. A media waste map highlights whether you should prioritize opening new retail accounts in those active ad zones or shift your media budget to align with existing retail stock.
Use Case 2: First-Order & At-Risk Account Rescues
David Porter: Getting a first bottle placement is just the beginning; the real work is driving reorder velocity. Accounts often order one or two cases, become solid customers, and then quietly stop reordering. Because field sales teams have limited bandwidth—often three reps covering an entire multi-state region—detecting at-risk accounts manually in giant Excel sheets takes months.
┌─────────────────────────────────────────────────────────────────────────────┐
│ AT-RISK ACCOUNT IDENTIFICATION LOOP │
├─────────────────────────────────────────────────────────────────────────────┤
│ • Data Inputs ➔ Depletion reports & historical account order frequency. │
│ • AI Prompt ➔ Identify accounts exhibiting a drop-off in order cadence. │
│ • Targeted Output ➔ Filters a list of 100 accounts down to the 12 highest- │
│ value accounts requiring an immediate field visit. │
└─────────────────────────────────────────────────────────────────────────────┘
For example, when preparing for a field visit to Washington, D.C., feeding order histories into an LLM allowed a brand to narrow 40 inactive accounts down to 12 high-potential accounts. The rep focused their visits exclusively on those 12, successfully securing reorders from 4 major accounts during that trip.
Use Case 3: Data-Driven Market Expansion
David Porter: Deciding where to expand next can be challenging. Rather than guessing, brands can combine their internal D2C data (such as high-performing Shopify zip codes) with market insights:
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Upload Existing Success Metrics: Input data from zip codes where your brand currently achieves high velocity.
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Scrape Ideal Account Profiles: Use AI to analyze social channels and account densities in prospective target markets.
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Generate a Prioritized Expansion Grid: Rank prospective cities based on demographic fit and existing brand awareness.
In one client case study, Washington, D.C. emerged at the top of the expansion priority list, outranking other markets that initially seemed more attractive.
Use Case 4: Leveraging Native E-Commerce AI Insights
Philipp Klumpp: You don’t always need to build custom software—you can start by asking simple, open-ended questions within the analytics tools you already use.
For a marketplace managing over 100 SKUs, we used native e-commerce analytics tools and simply asked: “What hidden patterns in our checkout data should we be paying attention to?”
The model revealed an unpromoted product bundle that accounted for a 21% higher average order value (AOV) at checkout. We immediately shifted our paid media and content strategy to promote that bundle, which became a major D2C success and was eventually rolled out as a physical retail SKU.
The Progression to Agentic Dashboards
┌─────────────────────────────────────────────────────────────────────────────┐
│ THE THREE STAGES OF AI ADOPTION │
├─────────────────────────────────────────────────────────────────────────────┤
│ 1. MANUAL CHAT ➔ Uploading spreadsheets into Claude/ChatGPT sessions. │
│ 2. AUTOMATED CRONS ➔ Background tasks processing data weekly on schedule. │
│ 3. MULTI-AGENTIC ➔ Dashboards aggregating Nielsen, VIP, & D2C data │
│ WORKFLOWS to deliver proactive, predictive alerts. │
└─────────────────────────────────────────────────────────────────────────────┘
We created an aggregated, four-slide dashboard view powered by multi-agentic workflows and Postgres databases. By consolidating Nielsen, VIP wholesale, and Shopify e-commerce data into a single unified view, brand founders can instantly analyze volume pull-through, rate-of-sale, velocity, and consumer demographics.
Emerging Channel Strategy: Agentic Engine Optimization (AEO) via Reddit
As consumers increasingly rely on AI search models (LLMs) to answer queries like “What is the best premium tequila for a gift?”, brands must optimize for Agentic Engine Optimization (AEO).
LLMs crawl user-generated platforms like Reddit to validate brand credibility. Maintaining an active brand presence on Reddit—participating in category subreddits and responding to discussions—is essential to ensure your brand gets recommended by AI search engines.
Using AI to streamline data analysis gives emerging spirit brands the speed required to outmaneuver larger competitors.