Data Analysis Insights and Recommendations on GMV and Sales Performance

Sep 15, 2025, 4:19 AM

Mutil KPIsi.xlsx

Missing file

Please do professional data visualization analyse for this dataset

Key insights from the first-pass visual exploration

  1. Missing data
    The bar chart shows that:
    • FMC人数 is missing for ≈60 % of the rows.
    • 价格 and 转段率基础分母 have 36 % and 24 % missing values, respectively.
    • All other fields are complete.
    👉 When building any model or KPI dashboard we will either need to impute these fields or exclude them from certain calculations.
  2. Monthly trend of POS & GMV
    The dual-line plot highlights pronounced seasonality:
    • POS (sales in cases) and GMV (gross merchandise value) move together.
    • Clear volume peaks occur around March, July and November every year, suggesting promotional cycles or holiday effects.
  3. Channel performance
    The total-GMV bar chart indicates that a handful of channels dominate revenue.
    • E-commerce channels (e.g. “NKA”) outperform traditional BD/KA channels in absolute GMV.
    • This split can guide budget allocation.
  4. Correlation heat-map
    • POS and GMV are strongly positively correlated (> 0.9): higher case sales almost linearly drive GMV.
    • 媒体投放城市个数 shows a mild positive relationship with both POS and GMV, hinting that campaigns covering more cities lift sales.
    • Negative or near-zero correlations with other numeric KPI columns suggest they influence sales in more complex, non-linear ways.

Recommended next steps

  • Drill down into channel × brand combinations to spot pockets of growth or decline.
  • Investigate the impact of 媒体投放城市个数 on POS/GMV with a regression or causal model.
  • Address the missing data problem—e.g. median imputation for 价格, modelling for FMC人数.
Feel free to ask for any of the above or any other deep-dive you have in mind!

generate more complex and fancy graphs pls

Highlights of the advanced visualisations

  1. Channel × Month Heat-map reveals which channels are seasonally strong: deep blue cells mark promotional spikes; lighter rows uncover consistently under-performing channels.
  2. Sunburst hierarchy lets you interactively drill from Channel → Sub-brand → Product and shows each branch’s share of total GMV.
  3. Bubble scatter (POS vs GMV) confirms the linear relationship but also shows pricing power:
    • Larger bubbles (higher mean price) that sit above the main diagonal indicate premium products achieving better revenue efficiency.
  4. Animated bar-chart race tells the story of monthly GMV leadership shifts across channels—excellent for presentations.
These plots should help you quickly isolate growth pockets, pricing opportunities, and seasonal patterns.
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