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Data Sources (AMWAY)

Published 2026-05-14Updated 2026-06-302 min read

1. Data Volume

ItemVolume
In-house ABOs (PII masked)N = 1,000
In-house customersN = 5,000
In-house SKUs (Nutrilite + Artistry + Home)~3,000
In-house ABO direct sales~80K
In-house subscriptions~10K (active + cancelled)
Synthetic ABO tree49.5K (average depth of 5 levels)
External (social, weather, FX, regulatory)365 × N

Total ≈ ~700K Neptune edges


2. cohort_tag

ValueMeaningUI Badge
realPII-masked in-house data🟢
synthSynthetic ABO tree and subscriptions🟡
externalSocial, weather, FX, regulatory🔵

3. Four External Data Sources

  • Global: Instagram hashtags · Reddit r/AmwayWentTo · X
  • Country-specific: Naver and Google Trends (Korea), Weibo and Xiaohongshu (Greater China)

3.2 Weather & Environment

  • KMA / global weather APIs (open-meteo)

3.3 Economic (Multi-country FX)

  • Bank of Korea · Statistics Korea + Open Exchange Rates (global FX)

3.4 Regulatory Signals (AMWAY-specific)

  • FTC (US direct-selling regulation)
  • Korea Door-to-Door Sales Act · E-Commerce Act
  • MFDS · FDA health functional food advertising guidance
  • EU Direct Selling Code

4. ABO Tree Synthesis Strategy

# Generate a 5-level ABO tree
import networkx as nx

def gen_abo_tree(root_id, depth_max=5, branching=3):
G = nx.DiGraph()
queue = [(root_id, 0)]
while queue:
node, depth = queue.pop(0)
if depth >= depth_max: continue
for i in range(branching):
child = f"{node}_{i}"
G.add_edge(node, child) # SPONSORED_BY edge
queue.append((child, depth+1))
return G
  • Depth 5, branching 3 → approximately 364 ABOs per tree
  • During synthesis, add noise to PV/BV using level-based distributions (Founders Platinum > Diamond > EDC > EC)

5. Subscription Seasonality and Churn Patterns

Season / EventWeighting
End of quarterSubscription renewals +20%
New Year (January)Nutritional supplements / diet categories +30%
Payday (25th of each month)Auto-payments concentrate
Price-increase announcementMass cancellations within 24 hours

Churn signatures:

  • Three consecutive payment failures
  • Zero activity over 30 days
  • One Downline member dropping out

6. Data Ingestion Pipeline