Sales volume measures how many units of a product were sold during a defined period. In a Christmas-sale analysis, it helps identify products that performed strongly and may deserve future advertising attention. section 4.2.1
Use sales volume alongside revenue, unique customers, conversion, and month-on-month performance rather than treating it as an unquestioned definition of “best.” section 4.2.1 section 4.4.3
A practical sales-volume comparison proceeds by:
- Comparing November and December volumes to identify absolute changes. section 4.4.3
- Inspecting the change distribution and checking for outliers before selecting products. section 4.4.3
- Calculating percentage change because absolute differences can underrepresent products with small baseline sales. section 4.4.3
- Applying minimum-volume and percentage-change cutoffs, while handling products with no November sales separately. section 4.4.3
Finding December high performers
A sales analysis can select products with more than ten November units and over 200% growth, while separately retaining products selling over 100 units only in December. Results can then be grouped by category, subcategory, and brand. section 4.4.3
league_table["volume_diff_pct"] = (
100 * (league_table["volume_diff"] / league_table["november_volume"])
)
fig, axis = plt.subplots()
(
league_table["volume_diff_pct"]
.replace([np.inf, -np.inf], np.nan)
.dropna()
.loc[lambda x: x.between(-101,501)]
.hist(bins=50, ax=axis)
)
axis.set(
title="Distribution of month-on-month percentage change in volume",
xlabel="% difference between sales in November and December",
ylabel="Frequency"
)
Calculate percentage month-on-month sales-volume change, replace infinite values caused by zero baselines, and remove missing or extreme values before examining the distribution. section 4.4.3
