Dansu Growth Engine

Overview

Dansu was growing but hitting organic plateaus and over-relying on festival seasonality. I needed a strategy that wasn't just "run ads harder." The answer was a structured growth engine built on three pillars — executed with automation at the core so a one-person operation could run at meaningful scale.

The Problem

Single-channel dependence is structural fragility. When Instagram suppressed reach or festival season ended, revenue suffered. I had strong individual tactics — paid ads, creator partnerships, organic content — but no system connecting them. Growth was manual and inconsistent. The business was also consuming far too much of my time on repeatable operational work rather than on creative and strategic decisions.

What I Did

I diagnosed three overlapping failures: not enough first-touch discovery surfaces, too much reliance on one channel, and too many manual processes consuming time that should go toward growth decisions.

My strategy had three pillars:

1. Multiply discovery surfaces

Rather than relying on the brand account alone, I built infrastructure to reach potential customers across more touchpoints. This included a creator discovery engine to find and activate relevant Instagram creators, multi-account themed satellite networks, festival and B2B partnerships, and remix distribution loops. More first-touch impressions across more contexts meant lower CAC over time.

2. Reduce channel dependence

I built direct IG-to-DM-to-purchase funnels that didn't depend on algorithm reach. I expanded into gym and running verticals to decouple from festival seasonality. Paid ads fed organic loops and vice versa. The goal was a demand base that didn't collapse when one channel moved.

3. Automate the entire pipeline

Manual execution at scale isn't execution — it's a bottleneck. I built a full automation stack using n8n, Supabase, RocketAPI, and the Gmail API:

• CRM pipeline: Scraped festival sites and Instagram accounts, parsed and enriched contact info, scored leads by relevance, triggered personalised outreach automatically. Supabase maintained status across every lead. Deduplication prevented double-sends.
• Creator discovery: Scraped content signals (views, likes, engagement velocity, posting cadence), ranked creators, synced into Supabase with a scoring layer, auto-triggered outreach for top candidates.
• Ops Brain: Fulfilment anomaly flags, stock forecasting, supplier comms tracking, customer pipelines — exceptions surfaced automatically rather than discovered through complaints.
• Reliability layer: Idempotency so re-runs never duplicated work; dedupe keys on every record; job state machine (queued → running → success/failed → retried/escalated); retry-with-backoff for flaky APIs; audit logs so failures were diagnosable, not silent.

Every automation was scoped like a product: requirements, MVP, reliability validation, iteration against failure logs.

Impact

• Reduced operational time from ~40 hours/week to under 10
• Outreach capacity scaled from approximately 20 contacts per week to thousands per week
• Lower CAC through diversified demand and compounding organic reach
• More consistent traffic week-on-week — not spiked by season or a single algorithm
• Scalable B2B pipeline for festival and wholesale conversations
• Business ran reliably without founder-in-the-loop for most decisions — a precondition for clean exit