Calculation Parameters
Methodology: Calculated exclusively on Online Store RU data. Arc price elasticity of demand based on retail revenue: average daily revenue on non-discount days (baseline) vs discount days. Out of Stock and NA days are excluded.   PED = (ΔRevenue / Revenue_base) ÷ (Discount% / 100). |PED| > 1 — elastic demand (discount effectively boosts sales). |PED| < 1 — inelastic demand (discount has limited impact).
Elasticity by Group
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Calculation Parameters
Methodology: Online Store RU only. For each SKU/brand, the discount is split into two effects: (1) Subsidy — revenue given away on units that would have sold at full price anyway (baseline avg units × discount%); (2) Incremental gain — revenue from genuinely new demand driven by the discount (extra units × discounted price). Efficiency = Incremental gain ÷ Subsidy. Break-even uplift = minimum extra units/day needed for the discount to pay for itself = baseline_qty × discount% ÷ (1 − discount%).
Discount Efficiency by Group
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Calculation Parameters
Methodology: Calculated exclusively on Online Store RU data. For each product, average daily retail revenue is derived from in-stock days (Out of Stock and NA days are excluded from the base). NA days are excluded from both the base and lost-sales count. Lost Revenue = avg daily revenue × number of Out of Stock days.
Lost Sales by Product
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Analysis Parameters
Lift Analysis: For each placement, average daily sales N days before and N days after are compared. Lift = (sales_after − sales_before) / sales_before × 100%. CTR = clicks / views. ROI = (revenue_after − revenue_before − cost) / cost × 100%.
Sales + Placements Timeline
Placement Detail
Discount Data Upload
Example: SKU | 01.12 | 02.12 | 03.12
ART-001 | 20 | 20 | 0
Example: Date | SKU | Discount%
01.12 | ART-001 | 20

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.xlsx, .xls, .csv

Influencer Data Upload

Excel/CSV file with placement reports: date, views, clicks.

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.xlsx, .xls, .csv