Case Study: Why Did Q4 2025 Revenue Jump?

The head of growth’s message has been waiting since Lesson 1.1: “Q4 2025 revenue was far higher than any quarter before. Was it Black Friday, more customers, bigger baskets, or a data mistake? I need an answer I can repeat to the board.” This lesson works through the answer the way you would at work: define, check, decompose, test explanations, conclude. Every step uses a tool you already know.

Step 1: define the metric and check the data

The metric is the course’s revenue definition from Lesson 4.2: line revenue after discounts, excluding shipping and cancelled orders. Build one row per order, and assert the facts the analysis relies on before trusting a single number:

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
per_order = items.groupby("order_id", as_index=False)["revenue"].sum()
df = orders.merge(per_order, on="order_id", how="left", validate="one_to_one")

assert df["order_id"].is_unique
assert df["revenue"].notna().all(), "an order has no items"
df = df[df["status"] != "cancelled"].copy()
df["quarter"] = df["order_date"].dt.to_period("Q")

print(df.groupby("quarter")["revenue"].sum().round(0))
print(df.loc[df["quarter"] == "2025Q4", "status"].value_counts())

Revenue went from 80,523 dollars in Q3 2025 to 146,825 in Q4, up 82%. No duplicates, no orders without items. One caveat surfaces immediately: 63 Q4 orders worth 11,321 dollars, about 8% of the quarter, are still processing or shipped. They count as revenue under the definition, but some could still be cancelled. Note it now; it goes in the final answer.

Step 2: decompose the change

Revenue is orders times average order value, and orders are active customers times orders per customer. Computing each factor shows which one moved:

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
df = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
df = df[df["status"] != "cancelled"].copy()
df["quarter"] = df["order_date"].dt.to_period("Q")

q = df.groupby("quarter").agg(
    revenue=("revenue", "sum"),
    orders=("order_id", "count"),
    customers=("customer_id", "nunique"),
)
q["aov"] = q["revenue"] / q["orders"]
q["orders_per_customer"] = q["orders"] / q["customers"]
print(q.round(2).tail(5))
print((q.loc["2025Q4"] / q.loc["2025Q3"] - 1).round(3))

The second print is the decomposition in one line. Orders rose 98% while average order value fell 8%: Q4 was about volume, not bigger baskets, which matches the medians you saw in Lesson 5.1. The extra orders came from both directions: 54% more active customers, and each ordered more often (2.28 orders per customer against 1.77).

Step 3: put it next to last year

A jump is only surprising compared with what normally happens. Lesson 4.4 already showed the comparison that matters:

Q4 was 73% above Q3 in 2024 too, and November was the biggest month in both years. 2025’s Q3-to-Q4 rise is only somewhat stronger than the usual seasonal lift, and Q4’s 180% growth on the year before sits inside the range of the other 2025 quarters (145% to 200%). The board-level message starts to form: the business roughly tripled year over year (2025 revenue was 2.7 times 2024’s), and Q4 is that bigger business hitting its normal holiday peak.

Step 4: test the popular explanations

Each explanation gets one specific number:

import pandas as pd

items = pd.read_csv("order_items.csv")
orders = pd.read_csv("orders.csv", parse_dates=["order_date"])
items["revenue"] = items["quantity"] * items["unit_price"] * (1 - items["discount"])
df = orders.merge(items.groupby("order_id", as_index=False)["revenue"].sum(), on="order_id", validate="one_to_one")
df = df[df["status"] != "cancelled"].copy()
df["quarter"] = df["order_date"].dt.to_period("Q")

first_q = df.groupby("customer_id")["order_date"].min().dt.to_period("Q").rename("first_quarter")
df = df.merge(first_q, on="customer_id", validate="many_to_one")
df["customer_type"] = (df["quarter"] == df["first_quarter"]).map({True: "new", False: "returning"})

q4 = df[df["quarter"] == "2025Q4"]
total = q4["revenue"].sum()
print(q4.groupby("customer_type")["revenue"].sum().div(total).round(3))
bf = q4["coupon_code"] == "BLACKFRIDAY25"
print("Black Friday coupon orders:", bf.sum(), "| share of Q4 revenue:", round(q4.loc[bf, "revenue"].sum() / total, 3))
print(q4.groupby(q4["order_date"].dt.month)["revenue"].sum().round(0))

Black Friday? The BLACKFRIDAY25 coupon was used on 69 Q4 orders, bringing 8.4% of the quarter’s revenue. November was huge, 75,334 dollars, but most of it came from orders without that coupon. Black Friday helped; it does not explain the jump. New customers? Customers placing their first order brought 36% of Q4 revenue, while 64% came from returning customers, who also account for two thirds of the 66,302-dollar rise over Q3. The jump rests mostly on an existing customer base that kept growing and kept coming back. Bigger baskets? Ruled out in Step 2. Price rise? The 5% increase took effect in March 2025, so it lifts Q3 and Q4 equally and explains none of the change between them. Data mistake? The checks in Step 1 found no duplicates or orphaned orders; the only open risk is the 8% not yet delivered.

Step 5: write the answer

The analysis is only finished when someone else can repeat it in a meeting. Lead with the conclusion, give the evidence in numbers, end with the caveats:

Q4 2025 revenue (146,825 dollars, +82% on Q3) was mainly Cartwheel’s normal holiday peak on a business that grew 2.7x year over year. The rise came from order volume (+98%), not order size (average order value fell 8%), with 54% more active customers ordering more often. Q4 was 73% above Q3 in 2024 as well. Returning customers produced 64% of Q4 revenue; Black Friday coupon orders produced 8.4%. Caveats: 8% of Q4 revenue is from orders not yet delivered, and the decline in order value is partly the cost of Q4 discounts, which more than tripled on Q3 (5,581 dollars versus 1,615).

In the exercise you rebuild the decomposition in code. The final lesson turns this analysis into something you can rerun next quarter without starting over.