Daily Sales
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Store Performance
| Store | Status | Conditions | Total Sales | POS | Instacart | DoorDash | UberEats | Grubhub | Trans. | Avg Ticket | Daily Pace | vs. 7d | vs. 30d | Hourly |
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Period Comparison — By Store
Transaction Feed
Hourly Sales — Today
Projected End-of-Day
Current Conditions
Fetching weather…
Channel Split — Today
Month-to-Date
Period Comparison
Store Rankings
Sorted by today's total sales
| # | Store | Total Sales | Share of Total | Transactions | Avg Ticket | Remote % | Hrs Open | vs. 7d ago | vs. 30d ago |
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Channel Breakdown
POS in-store vs. Instacart vs. DoorDash vs. UberEats vs. Grubhub — today
| Store | Total Sales | POS Sales | POS % | Instacart $ | IC % | IC Orders | DoorDash $ | DD % | DD Orders | UberEats $ | UE % | UE Orders | Grubhub $ | GH % | GH Orders | Remote Total | Remote % |
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Channel Split Methodology
Remote order penetration varies significantly by market. Florence (#309), as the largest market, sees the highest remote adoption (~8–12% combined).
Johnsonville (#319) and Pamplico (#427), being small rural communities with limited delivery driver coverage, see very low remote volumes (~2–4%).
Mullins (#325) is a mid-size rural county seat with moderate penetration (~5–7%). Ridgeland (#333) sits between rural and suburban (~4–6%).
Statesboro (#440), as a college-town market (Georgia Southern University), sees solid remote adoption with strong DoorDash/Instacart usage (~4–6%). Sales for Store 440 are entered manually and will display once recorded.
Instacart typically represents ~55–65% of remote volume as the primary grocery delivery platform. DoorDash handles ~20–25%.
UberEats and Grubhub are newer grocery partners with lower but growing share (~10–15% combined).
Remote avg. tickets are higher (~$85–110) due to larger basket sizes and delivery minimums. Stores with 0% on any channel indicates that platform is not active at that location.
Settings
Adjust simulation parameters — changes apply immediately to live sim & catchup
Simulation Variance
Daily Drift Range
Each store gets a random daily multiplier drawn from this range. Simulates above/below average days.
×
Default: 0.87 — defines the floor of a "slow day"
spread
Default: 0.30 — max = min + spread. Higher = more volatile days.
Weather Impact Scaling
Scales the effect of weather multipliers. 1.0 = full effect. Lower values dampen weather's influence on sales.
×
Default: 1.0 — at 0, weather is ignored; at 2.0, impact is doubled
Store Hours
| Store | Open (24h) | Close (24h) | Hours Open |
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Average Daily Sales Target (Base)
| Store | Daily Base Target | Instacart % of Total | DoorDash % of Total | UberEats % of Total | Grubhub % of Total | Effective POS Target |
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ℹ️ How settings work: Changes apply immediately when you click Apply & Recalculate. The live simulator and catchup engine will use the new values going forward. Historical data is not altered. Settings are saved to localStorage and persist across sessions.