TWIN MATRIX SOULBOUND TOKEN

Growth Dashboard

Twin Matrix Soulbound Token (SBT) is a BEP-721 NFT on the BNB Smart Chain. Each token is permanently bound to its owner's wallet and cannot be transferred — serving as a verifiable proof of membership in the Twin3 ecosystem.

This dashboard tracks on-chain minting activity from contract deployment to present. All data is fetched directly from the BSC blockchain via Dune Analytics — no simulated data. Data refreshed every 4 hours

Contract: 0xe3ec...5156f · Minter: 0x3446...7777
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Total Holders
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New Today
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Days Active
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Avg/Day (7d)
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Growth Timeline

Daily New User Acquisition

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Each bar represents the number of unique wallets that received a Twin Matrix SBT on a given day. The minting contract sends one token per wallet — so each new holder corresponds to exactly one mint transaction initiated by the official minter address.

Use the moving average toggle to smooth out daily volatility. The brush area below the chart lets you zoom into a specific date range by dragging the selection handles.

Daily New Users — Full History

Each bar = COUNT(DISTINCT to_address) where from = 0x0 (minting). MA = rolling window average.

Cumulative Holder Growth

Running total of unique holders. Milestone markers at 10K, 25K, 50K, and 75K. Formula: cumulative[n] = Σ(new_users[0..n])
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Calculation Method
How daily new users are counted
1
Identify mint transactions — Filter nft.transfers on BSC where from = 0x000...0000 (null address, indicating a mint) and the initiating transaction sender matches the official minter wallet.
2
Group by day — Truncate block_time to date and count distinct to addresses per day. This prevents double-counting if a wallet received multiple tokens in one day (which does not happen for SBTs, but is a safety measure).
3
Compute cumulative — Sum all daily counts up to each date to produce the running total of holders.
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User Quality Analysis

Who Are Our New Users?

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For each new holder, we look back up to 1 year at their transaction history on BNB Smart Chain. This reveals whether they are brand-new wallets (never transacted before) or seasoned users with existing on-chain activity.

The donut chart shows wallet age distribution — how long ago each wallet first appeared on-chain. The bar chart shows how many prior transactions each wallet had before minting the SBT.

Wallet Age Distribution

Age = days since first on-chain transaction (up to 1 year lookback). "No history" = no prior transactions found within the lookback window.

Prior Transaction Activity

Count of outgoing transactions in the 365 days before minting. Higher counts = more active/experienced users.
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Wallet Age Analysis Method
How we classify user experience level
1
Identify today's new users — All wallets receiving their first SBT today.
2
Count prior transactions — For each new user, count bnb.transactions where from = wallet in the 365 days before the mint.
3
Find wallet age — Earliest transaction date within the lookback window. age_days = mint_date - first_tx_date. If no history found, classified as "No history (≤1y)".
4
Bucket classification — Age buckets: <7d, 7–29d, 30–89d, 90–179d, 180d–1y. Tx buckets: 0, 1–2, 3–9, 10–49, 50–199, 200+.
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Cross-Tab Analysis

Wallet Maturity × Activity

The heatmap below cross-tabulates wallet age (rows) against prior transaction count (columns) to reveal the profile of today's new SBT holders. Darker cells indicate higher concentrations of users. Hover any cell for exact numbers.

This matrix answers: "Are new holders experienced blockchain users or newcomers?" If most users land in the top-left (no history + 0 txs), it may indicate fresh wallets created specifically for this token. If concentrated in bottom-right, it suggests adoption by established on-chain users.

Toggle between absolute counts and percentage view to understand relative distribution.

Age × Transaction Count

Matrix[row, col] = COUNT(*) WHERE age_bucket = row AND tx_count_bucket = col. Color intensity ∝ value / max(matrix).

Wallet Age — Proportion Strip

Each segment's width represents the proportion of new users in that age category.

Automated Insights

Key Takeaways

The following insights are auto-generated by comparing recent metrics against historical baselines. They are updated every 4 hours along with the underlying data.

Analyzing data patterns