Q3 2026 Business Review — Juniper & Pine report card
ClearFigure
Report card

Q3 2026 Business Review — Juniper & Pine

Checked 2026-10-30
chk_ce191ea66596

18 numbers checked: 5 correct, 6 wrong, 6 misleading, 1 can't verify.

✓5 correct ✗6 wrong !6 misleading ?1 can't verify

About your data

orders.csv has 12 rows that are exact copies of another row. Each row should be one order, so counts and sums were computed once per order.

orders.csv mixes currencies (CAD: 231 rows, USD: 26,751 rows). Sums across them add different money as if it were the same.

Region is blank in 15 of 26,982 rows in orders.csv.

store_summary.xlsx has 1 total row mixed in with the data. ClearFigure set it aside; any sum that includes it counts everything twice.

One question

When this analysis says "sales", which does it mean? Until you answer, ClearFigure read it as Net sales. Your answer is saved to your company glossary so you are never asked again.

Every number, checked

✗
Store net sales for Q3 2026 totaled $784K.
Right number
$392,239
stated $784K

This number includes the file's total row, so it counts everything twice. Without it: $392,239.

Total row counted as data
Show work
  1. a: All rows, add up Q3 2026 Net Sales: $392,239
  2. Compute (a) = $392,239
  3. The stated number matches this instead:
  4. a: All rows, add up Q3 2026 Net Sales (including the file's total rows): $784,478
  5. Compute (a) = $784,478
  • Assumed: store_summary.xlsx has no date column; its title says it covers Q3 2026
✗
The refund rate dropped by 3.2% between Q1 and Q2.
Right number
-51.6%
stated 3.2%

Refund rate moved from 6.3% to 3.0%. That is 3.2 percentage points, or -51.6% in percent terms; the sentence mixes the two.

Percent vs. percentage points
Show work
  1. a_n: Rows where Order Date in Jan 1 – Mar 31, 2026, add up Refunds: 13,675.85
  2. a_d: Rows where Order Date in Jan 1 – Mar 31, 2026, add up Gross Sales: 217,390.33
  3. b_n: Rows where Order Date in Apr 1 – Jun 30, 2026, add up Refunds: 6,721.04
  4. b_d: Rows where Order Date in Apr 1 – Jun 30, 2026, add up Gross Sales: 220,862.46
  5. Compute (((b_n ÷ b_d)) - ((a_n ÷ a_d))) ÷ ((a_n ÷ a_d)) × 100 = -51.6%
  6. The stated number matches this instead:
  7. a_n: Rows where Order Date in Jan 1 – Mar 31, 2026, add up Refunds: 13,675.85
  8. a_d: Rows where Order Date in Jan 1 – Mar 31, 2026, add up Gross Sales: 217,390.33
  9. b_n: Rows where Order Date in Apr 1 – Jun 30, 2026, add up Refunds: 6,721.04
  10. b_d: Rows where Order Date in Apr 1 – Jun 30, 2026, add up Gross Sales: 220,862.46
  11. Compute (((b_n ÷ b_d)) - ((a_n ÷ a_d))) × 100 = -3.2 points
  • Assumed: "Q1" read as Q1 2026
  • Assumed: "Q2" read as Q2 2026
✗
Across our six stores, the average order value was $83.05.
Right number
$75.43
stated $83.05

This is a plain average of 6 group averages, so a small group counts as much as a large one. Weighted by Q3 2026 Orders: $75.43.

Averaging averages
Show work
  1. a: All rows, weighted average of Q3 2026 Avg Order Value weighted by Q3 2026 Orders: $75.43
  2. Compute (a) = $75.43
  3. The stated number matches this instead:
  4. a: All rows, plain average of Q3 2026 Avg Order Value: $83.05
  5. Compute (a) = $83.05
  • Assumed: Q3 2026 Avg Order Value is an average, so it is weighted by Q3 2026 Orders
✗
We processed 2,664 orders in Q3.
Right number
2,652
stated 2,664

12 rows are exact copies of another order row. Counting each order once: 2,652.

Duplicates
Show work
  1. a_n: Rows where Order Date in Jul 1 – Sep 30, 2026, count distinct Order ID: 2,652
  2. Compute (a_n) = 2,652
  3. The stated number matches this instead:
  4. a_n: Rows where Order Date in Jul 1 – Sep 30, 2026, count them (without removing duplicate rows): 2,664
  5. Compute (a_n) = 2,664
  • Assumed: "Q3" read as Q3 2026
✗
Online accounted for 62% of customers in Q3.
Right number
72.5%
stated 62%

This share was calculated over orders but the sentence says customers. Over customers: 72.5%.

Wrong base
Show work
  1. a_n: Rows where Channel = Online and Order Date in Jul 1 – Sep 30, 2026, count distinct Customer ID: 1,082
  2. t_n: Rows where Order Date in Jul 1 – Sep 30, 2026, count distinct Customer ID: 1,493
  3. Compute (a_n) ÷ (t_n) × 100 = 72.5%
  4. The stated number matches this instead:
  5. a_n: Rows where Channel = Online and Order Date in Jul 1 – Sep 30, 2026, count distinct Order ID: 1,648
  6. t_n: Rows where Order Date in Jul 1 – Sep 30, 2026, count distinct Order ID: 2,652
  7. Compute (a_n) ÷ (t_n) × 100 = 62.1%
  • Assumed: "Q3" read as Q3 2026
✗
East net sales were $116K in Q3.
Right number
$105,877
stated $116K

The data says $105,877, not $116K.

Blanks silently dropped
Show work
  1. a: Rows where Region = East and Order Date in Jul 1 – Sep 30, 2026, add up Net Sales: $105,877
  2. Compute (a) = $105,877
  • 6 rows with a blank Region were left out of this calculation.
  • Assumed: "Q3" read as Q3 2026
!
Gross sales in Q3 totaled $272K.
Data says
$272,391
stated $272K

The number checks out ($272,391), but these rows mix currencies (CAD, USD); 201 rows in CAD were added as if they were USD.

Mixed units or currencies
Show work
  1. a: Rows where Order Date in Jul 1 – Sep 30, 2026, add up Gross Sales: $272,391
  2. Compute (a) = $272,391
  • Assumed: "Q3" read as Q3 2026
!
October net sales are down 70% compared with September, a sharp slowdown.
Data says
-69.7%
stated 70%

The number checks out (-69.7%), but the data for Oct 2026 only runs through Oct 15, 2026 (48% of the period), so it is being compared with a full period.

Partial periodOne outlier drives the averageMixed units or currencies
Show work
  1. a: Rows where Order Date in Sep 1 – Sep 30, 2026, add up Net Sales: $118,979
  2. b: Rows where Order Date in Oct 1 – Oct 31, 2026, add up Net Sales: $36,006
  3. Compute ((b) - (a)) ÷ (a) × 100 = -69.7%
  • Assumed: "September" read as Sep 2026
  • Assumed: "October" read as Oct 2026
!
Thanks to the new loyalty program, South net sales fell 7% from Q2 to Q3.
Data says
-7.4%
stated 7%

The number checks out (-7.4%), but this data can show the change, not that the new loyalty program caused it. Other things changed at the same time.

Cause claimed from a pattern Blanks silently dropped
Show work
  1. a: Rows where Region = South and Order Date in Apr 1 – Jun 30, 2026, add up Net Sales: $44,255
  2. b: Rows where Region = South and Order Date in Jul 1 – Sep 30, 2026, add up Net Sales: $40,972
  3. Compute ((b) - (a)) ÷ (a) × 100 = -7.4%
  • 3 rows with a blank Region were left out of this calculation.
  • Assumed: "Q2" read as Q2 2026
  • Assumed: "Q3" read as Q3 2026
!
Central net sales grew 52% from August to September.
Data says
52.1%
stated 52%

The number checks out (52.1%), but this increase starts from an unusually low month (Aug 2026 was 32% below the months before it). Shifting by one month: Jul 2026 to Aug 2026: -31.5%.

Cherry-picked window Blanks silently dropped
Show work
  1. a: Rows where Region = Central and Order Date in Aug 1 – Aug 31, 2026, add up Net Sales: $8,309.54
  2. b: Rows where Region = Central and Order Date in Sep 1 – Sep 30, 2026, add up Net Sales: $12,642
  3. Compute ((b) - (a)) ÷ (a) × 100 = 52.1%
  • 1 row with a blank Region were left out of this calculation.
  • Assumed: "August" read as Aug 2026
  • Assumed: "September" read as Sep 2026
!
Average order value in September was $135.
Data says
$134.90
stated $135

The number checks out ($134.90), but one row ($50,000) drives this number. Without it the average order value is $78.30; the typical (median) value is $67.42.

One outlier drives the averageMixed units or currencies
Show work
  1. a_n: Rows where Order Date in Sep 1 – Sep 30, 2026, add up Net Sales: $118,979
  2. a_d: Rows where Order Date in Sep 1 – Sep 30, 2026, count distinct Order ID: 882
  3. Compute ((a_n ÷ a_d)) = $134.90
  • Assumed: "September" read as Sep 2026
!
December 2025 net sales jumped 45% over November, showing strong momentum.
Data says
45.4%
stated 45%

The number checks out (45.4%), but the same jump happened a year earlier (Nov 2024 to Dec 2024: 54.3%), so this looks seasonal rather than a new trend.

Seasonality called a trend
Show work
  1. a: Rows where Order Date in Nov 1 – Nov 30, 2025, add up Net Sales: $72,355
  2. b: Rows where Order Date in Dec 1 – Dec 31, 2025, add up Net Sales: $105,194
  3. Compute ((b) - (a)) ÷ (a) × 100 = 45.4%
  • Assumed: "November" read as Nov 2025
?
Customer acquisition cost fell to $32 in Q3.
Status
—
stated $32

None of the files has ad spend or new customers data, which this claim needs (customer acquisition cost).

✓
Net sales for Q2 2026 came to $214K.
Data says
$214,141
stated $214K

Matches the data: $214,141.

Show work
  1. a: Rows where Order Date in Apr 1 – Jun 30, 2026, add up Net Sales: $214,141
  2. Compute (a) = $214,141
✓
Sales in Q1 2026 were $217K.
Data says
$217,390
stated $217K

Matches if "sales" means gross sales: $217,390. If it means net sales: $203,714.

Show work
  1. a: Rows where Order Date in Jan 1 – Mar 31, 2026, add up Gross Sales: $217,390
  2. Compute (a) = $217,390
✓
West net sales grew 12% from Q1 to Q2.
Data says
12.2%
stated 12%

Matches the data: 12.2%.

Blanks silently dropped
Show work
  1. a: Rows where Region = West and Order Date in Jan 1 – Mar 31, 2026, add up Net Sales: $64,867
  2. b: Rows where Region = West and Order Date in Apr 1 – Jun 30, 2026, add up Net Sales: $72,757
  3. Compute ((b) - (a)) ÷ (a) × 100 = 12.2%
  • 6 rows with a blank Region were left out of this calculation.
  • Assumed: "Q1" read as Q1 2026
  • Assumed: "Q2" read as Q2 2026
✓
Wholesale average order value was $413 in Q3.
Data says
$412.83
stated $413

Matches the data: $412.83.

Tiny sample
Show work
  1. a_n: Rows where Channel = Wholesale and Order Date in Jul 1 – Sep 30, 2026, add up Net Sales: $2,477.00
  2. a_d: Rows where Channel = Wholesale and Order Date in Jul 1 – Sep 30, 2026, count distinct Order ID: 6
  3. Compute ((a_n ÷ a_d)) = $412.83
  • This is based on only 6 orders, too few to generalize from.
  • Assumed: "Q3" read as Q3 2026
✓
Riverside was our top store by net sales in Q3.
Data says
#1
stated top

Matches the data: #1.

Show work
  1. a: All rows, add up Q3 2026 Net Sales, one result per Store
  2. Order the groups from highest and find Riverside: #1 of 6
  • Assumed: store_summary.xlsx has no date column; its title says it covers Q3 2026

How ClearFigure read your files

orders.csv — one row per order (with 12 repeated order IDs)

26,982 rows
  • Header found on row 4, not row 1.
  • Converted text to values in: Order Date, Gross Sales, Refunds, Net Sales.
  • Found 12 row(s) that are exact copies of another row (kept for now; see duplicates check).
ColumnRead asUnitWhyConfidence
Order IDOrder IDexact — exact match on 'order id'; values fit (id, 1.00)97%
Order DateOrder dateexact — exact match on 'order date'; values fit (date, 1.00)97%
RegionRegionexact — exact match on 'region'; values fit (category, 1.00)97%
ChannelSales channelexact — exact match on 'channel'; values fit (category, 1.00)95%
Customer IDCustomer IDexact — exact match on 'customer id'; values fit (id, 1.00)97%
Gross SalesGross salesUSDexact — exact match on 'gross sales'; values fit (money, 1.00)97%
RefundsRefundsUSDexact — exact match on 'refunds'; values fit (money, 1.00)97%
CurrencyCurrencyexact — exact match on 'currency'; values fit (category, 1.00)97%
Net SalesNet salesUSDexact — exact match on 'net sales'; values fit (money, 1.00)97%

store_summary.xlsx · Stores — one row per store

6 rows
  • Merged a two-row header (group labels + column names) at row 5.
  • Header found on row 5, not row 1.
  • Set aside 1 total row(s): Total.
ColumnRead asUnitWhyConfidence
StoreStoreexact — exact match on 'store'; values fit (category, 1.00)97%
RegionRegionexact — exact match on 'region'; values fit (category, 1.00)97%
Q3 2026 OrdersOrderscountexact — exact match on 'orders'; values fit (integer, 1.00)97%
Q3 2026 Avg Order ValueAverage order valueexact — exact match on 'avg order value'; values fit (number, 0.85)82%
Q3 2026 Net SalesNet salesexact — exact match on 'net sales'; values fit (integer, 0.75)73%