L H

Liam Horrigan

September 9, 3:18 PM

I'd like to better understand the performance of our various products. For each product, can you please show me the monthly sales in 2022, with a separate column for each month?

Lumi AI

September 9, 3:18 PM

Starting analysis

Isolated context

Class	Label	Content
Table	item	Item master table containing reference-level data for each item. Includes brand, category, pricing, and operational thresholds like safety stock and minimum order quantity.
Table Field	item.description	Description of the item.
Table Field	item.item_id	Unique identifier for each item.
Table	sales_header	Header-level sales transaction data that captures key dates and status information for each sales order. Each row represents a unique sales transaction tracked at the order level.
Table Field	sales_header.order_date	Date when the sales order was placed. Use this value when determining the date on which the sale was made.
Table	sales_line	Line-level sales data that breaks down each item sold within a sales order. Captures quantity, pricing, discounting, cost, and optional promotional attribution. One row per item per order.
Table Field	sales_line.item_id	Unique identifier for each item.
Table Field	sales_line.quantity	quantity of units sold for the item in this order.
Table Field	sales_line.total_line_value	Total value of the item post applying any available discounts

Generated query

Explanation

Join sales_line and sales_header on sales_header_id to get sales data with order dates.
Filter records to only include those where the order_date is in 2022.
Group by item_id to get one row per product.
Use conditional aggregation (CASE WHEN) to sum total_line_value for each month, creating a separate column for each month (Jan to Dec).
Limit the output to 25 rows and sort by item_id.

Assumptions

Assumes order_date in sales_header is the correct field for determining the sale month.
Assumes total_line_value in sales_line represents the sales amount for each line item.
Assumes item_id is sufficient to identify each product.

WITH
  sales_line AS (
    SELECT
      sales_header_id AS sales_header_id,
      item_id AS item_id,
      total_line_value AS total_line_value
    FROM
      base.prem_sales_line_v2
  ),
  sales_header AS (
    SELECT
      order_date AS order_date,
      sales_header_id AS sales_header_id
    FROM
      base.prem_sales_header_v2
  )
  -- DNM_CTE: Do not modify the preceding local abstraction CTE'sSELECT
  sales_line.item_id,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =1THEN sales_line.total_line_value
      ELSE0END
  ) AS jan_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =2THEN sales_line.total_line_value
      ELSE0END
  ) AS feb_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =3THEN sales_line.total_line_value
      ELSE0END
  ) AS mar_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =4THEN sales_line.total_line_value
      ELSE0END
  ) AS apr_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =5THEN sales_line.total_line_value
      ELSE0END
  ) AS may_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =6THEN sales_line.total_line_value
      ELSE0END
  ) AS jun_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =7THEN sales_line.total_line_value
      ELSE0END
  ) AS jul_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =8THEN sales_line.total_line_value
      ELSE0END
  ) AS aug_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =9THEN sales_line.total_line_value
      ELSE0END
  ) AS sep_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =10THEN sales_line.total_line_value
      ELSE0END
  ) AS oct_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =11THEN sales_line.total_line_value
      ELSE0END
  ) AS nov_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =12THEN sales_line.total_line_value
      ELSE0END
  ) AS dec_sales
FROM
  sales_line
  JOIN sales_header ON sales_line.sales_header_id = sales_header.sales_header_id
WHEREYEAR (sales_header.order_date) =2022GROUPBY
  sales_line.item_id
ORDERBY
  sales_line.item_id
LIMIT
  25;

No query available

SELECT
  sales_line.item_id,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =1THEN sales_line.total_line_value
      ELSE0END
  ) AS jan_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =2THEN sales_line.total_line_value
      ELSE0END
  ) AS feb_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =3THEN sales_line.total_line_value
      ELSE0END
  ) AS mar_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =4THEN sales_line.total_line_value
      ELSE0END
  ) AS apr_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =5THEN sales_line.total_line_value
      ELSE0END
  ) AS may_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =6THEN sales_line.total_line_value
      ELSE0END
  ) AS jun_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =7THEN sales_line.total_line_value
      ELSE0END
  ) AS jul_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =8THEN sales_line.total_line_value
      ELSE0END
  ) AS aug_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =9THEN sales_line.total_line_value
      ELSE0END
  ) AS sep_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =10THEN sales_line.total_line_value
      ELSE0END
  ) AS oct_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =11THEN sales_line.total_line_value
      ELSE0END
  ) AS nov_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =12THEN sales_line.total_line_value
      ELSE0END
  ) AS dec_sales
FROM
  sales_line
  JOIN sales_header ON sales_line.sales_header_id = sales_header.sales_header_id
WHEREYEAR (sales_header.order_date) =2022GROUPBY
  sales_line.item_id
ORDERBY
  sales_line.item_id
LIMIT
  25;

Data retrieved

19 rows

Product ID	jan_sales	feb_sales	mar_sales	apr_sales	may_sales	jun_sales	jul_sales	aug_sales	sep_sales	oct_sales	nov_sales	dec_sales
1001	80,500	80,500	80,500	80,500	92,000	92,000	103,500	103,500	92,000	103,500	138,000	184,000
1002	37,500	37,500	45,000	30,000	37,500	45,000	45,000	45,000	30,000	37,500	67,500	67,500
1003	350	350	700	350	700	350	700	350	700	2,800	2,100	2,800
1004	100	100	100	100	100	100	200	100	100	100	1,100	1,000
1005	400	400	400	400	400	400	400	400	400	400	1,600	1,400
1006	75	0	150	75	75	75	75	75	75	75	675	750
1007	4,500	4,500	4,500	4,500	4,500	31,500	18,000	4,500	4,500	4,500	4,500	4,500
1008	6,000	6,000	6,000	6,000	6,000	42,000	42,000	12,000	6,000	6,000	6,000	12,000
1009	600	600	600	600	600	600	600	600	600	600	600	600
1010	450	0	450	450	0	450	450	0	450	0	450	450
1011	200	200	200	400	200	200	200	400	200	200	600	800
1012	600	900	1,200	1,200	600	600	600	900	300	300	600	600
1014	160	80	160	80	160	160	160	80	160	80	240	160
1016	120	240	240	240	240	240	240	240	240	240	360	360
1017	250	500	250	500	250	1,250	1,250	500	250	500	250	500
1018	150	150	150	150	150	150	300	900	300	150	150	150
1019	700	700	350	350	350	350	350	350	350	350	350	350
1020	250	250	250	500	250	250	250	500	250	500	750	1,250
1021	100	200	100	200	100	200	100	200	100	200	100	300
19	19	19	19	19	19	19	19	19	19	19	19	19

Product ID

jan_sales

feb_sales

mar_sales

apr_sales

may_sales

jun_sales

jul_sales

aug_sales

sep_sales

oct_sales

nov_sales

dec_sales

1001

80,500

92,000

103,500

92,000

103,500

138,000

184,000

1002

37,500

45,000

30,000

37,500

45,000

30,000

37,500

67,500

1003

350

700

350

700

350

700

350

700

2,800

2,100

2,800

1004

100

200

100

1,100

1,000

1005

400

1,600

1,400

1006

150

675

750

1007

4,500

31,500

18,000

4,500

1008

6,000

42,000

12,000

6,000

12,000

1009

600

1010

450

1011

200

400

200

400

200

600

800

1012

600

900

1,200

600

900

300

600

1014

160

240

160

1016

120

240

360

1017

250

500

250

500

250

1,250

500

250

500

250

500

1018

150

300

900

300

150

1019

700

350

1020

250

500

250

500

250

500

750

1,250

1021

100

200

100

200

100

200

100

200

100

200

100

300

Summarizing findings

- Monthly sales data for 2022 is provided for each product, with clear seasonality and spikes for certain items. - Some products (e.g., item_id 1001) show significant sales increases in the last quarter, suggesting strong year-end demand or seasonality. - A few products (e.g., item_id 1007, 1008) have large mid-year sales spikes, possibly due to promotions or bulk orders. - Several products have consistently low or flat sales, indicating either niche products or limited market traction.

Analyze the reasons behind the sales spikes for specific products and months (e.g., promotions, seasonality, new launches).

Compare year-over-year sales trends to identify growth or decline for each product.

Segment products by performance (high, medium, low sales) to inform inventory and marketing strategies.

L H

Liam Horrigan

September 9, 3:23 PM

Using this data, can you use Python to classify each product based on overall sales performance, demand seasonality, and overall trend?

Lumi AI

September 9, 3:23 PM

Starting analysis

Isolated context

Class	Label	Content
Table	item	Item master table containing reference-level data for each item. Includes brand, category, pricing, and operational thresholds like safety stock and minimum order quantity.
Table Field	sales_header.order_date	Date when the sales order was placed. Use this value when determining the date on which the sale was made.
Table	sales_line	Line-level sales data that breaks down each item sold within a sales order. Captures quantity, pricing, discounting, cost, and optional promotional attribution. One row per item per order.
Table Field	sales_line.item_id	Unique identifier for each item.
Detail	Seasonality Factor	To find the seasonality factor for each item and month, calculate the average sales for each month over multiple years and compare it to the 12-month moving average of sales. This will show how sales in a specific month differ from the typical monthly sales pattern. The final output will provide the seasonality factor for each item and month.

Generated query

Explanation

Step 1: Aggregate monthly sales for each item for each month in 2022, as previously done, to get columns for each month (jan_sales, feb_sales, ..., dec_sales).
Step 2: Also calculate the total sales for the year for each item (overall sales volume).
Step 3: Output the monthly sales columns and total sales for each item, sorted by item_id, limited to 25 records. This data will be used in Python to classify products based on sales volume, seasonality (by analyzing monthly patterns), and trend (by checking the direction of sales over the months).

Assumptions

The classification logic (seasonality detection, trend analysis, and sales volume categorization) is best handled in Python, as it requires pattern recognition and possibly statistical analysis, which is not straightforward in SQL.
The monthly sales columns are already available from the previous query.

WITH
  sales_line AS (
    SELECT
      sales_header_id AS sales_header_id,
      item_id AS item_id,
      total_line_value AS total_line_value
    FROM
      base.prem_sales_line_v2
  ),
  sales_header AS (
    SELECT
      order_date AS order_date,
      sales_header_id AS sales_header_id
    FROM
      base.prem_sales_header_v2
  )
  -- DNM_CTE: Do not modify the preceding local abstraction CTE'sSELECT
  sales_line.item_id,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =1THEN sales_line.total_line_value
      ELSE0END
  ) AS jan_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =2THEN sales_line.total_line_value
      ELSE0END
  ) AS feb_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =3THEN sales_line.total_line_value
      ELSE0END
  ) AS mar_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =4THEN sales_line.total_line_value
      ELSE0END
  ) AS apr_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =5THEN sales_line.total_line_value
      ELSE0END
  ) AS may_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =6THEN sales_line.total_line_value
      ELSE0END
  ) AS jun_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =7THEN sales_line.total_line_value
      ELSE0END
  ) AS jul_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =8THEN sales_line.total_line_value
      ELSE0END
  ) AS aug_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =9THEN sales_line.total_line_value
      ELSE0END
  ) AS sep_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =10THEN sales_line.total_line_value
      ELSE0END
  ) AS oct_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =11THEN sales_line.total_line_value
      ELSE0END
  ) AS nov_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =12THEN sales_line.total_line_value
      ELSE0END
  ) AS dec_sales,
  SUM(sales_line.total_line_value) AS total_sales_2022
FROM
  sales_line
  JOIN sales_header ON sales_line.sales_header_id = sales_header.sales_header_id
WHEREYEAR (sales_header.order_date) =2022GROUPBY
  sales_line.item_id
ORDERBY
  sales_line.item_id
LIMIT
  25;

No query available

SELECT
  sales_line.item_id,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =1THEN sales_line.total_line_value
      ELSE0END
  ) AS jan_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =2THEN sales_line.total_line_value
      ELSE0END
  ) AS feb_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =3THEN sales_line.total_line_value
      ELSE0END
  ) AS mar_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =4THEN sales_line.total_line_value
      ELSE0END
  ) AS apr_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =5THEN sales_line.total_line_value
      ELSE0END
  ) AS may_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =6THEN sales_line.total_line_value
      ELSE0END
  ) AS jun_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =7THEN sales_line.total_line_value
      ELSE0END
  ) AS jul_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =8THEN sales_line.total_line_value
      ELSE0END
  ) AS aug_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =9THEN sales_line.total_line_value
      ELSE0END
  ) AS sep_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =10THEN sales_line.total_line_value
      ELSE0END
  ) AS oct_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =11THEN sales_line.total_line_value
      ELSE0END
  ) AS nov_sales,
  SUM(
    CASEWHENMONTH (sales_header.order_date) =12THEN sales_line.total_line_value
      ELSE0END
  ) AS dec_sales,
  SUM(sales_line.total_line_value) AS total_sales_2022
FROM
  sales_line
  JOIN sales_header ON sales_line.sales_header_id = sales_header.sales_header_id
WHEREYEAR (sales_header.order_date) =2022GROUPBY
  sales_line.item_id
ORDERBY
  sales_line.item_id
LIMIT
  25;

Data retrieved

19 rows

Item ID	jan_sales	feb_sales	mar_sales	apr_sales	may_sales	jun_sales	jul_sales	aug_sales	sep_sales	oct_sales	nov_sales	dec_sales	total_sales_2022
1001	80,500	80,500	80,500	80,500	92,000	92,000	103,500	103,500	92,000	103,500	138,000	184,000	1,230,500
1002	37,500	37,500	45,000	30,000	37,500	45,000	45,000	45,000	30,000	37,500	67,500	67,500	525,000
1008	6,000	6,000	6,000	6,000	6,000	42,000	42,000	12,000	6,000	6,000	6,000	12,000	156,000
1007	4,500	4,500	4,500	4,500	4,500	31,500	18,000	4,500	4,500	4,500	4,500	4,500	94,500
1003	350	350	700	350	700	350	700	350	700	2,800	2,100	2,800	12,250
1012	600	900	1,200	1,200	600	600	600	900	300	300	600	600	8,400
1009	600	600	600	600	600	600	600	600	600	600	600	600	7,200
1005	400	400	400	400	400	400	400	400	400	400	1,600	1,400	7,000
1017	250	500	250	500	250	1,250	1,250	500	250	500	250	500	6,250
1020	250	250	250	500	250	250	250	500	250	500	750	1,250	5,250
1019	700	700	350	350	350	350	350	350	350	350	350	350	4,900
1011	200	200	200	400	200	200	200	400	200	200	600	800	3,800
1010	450	0	450	450	0	450	450	0	450	0	450	450	3,600
1004	100	100	100	100	100	100	200	100	100	100	1,100	1,000	3,200
1016	120	240	240	240	240	240	240	240	240	240	360	360	3,000
1018	150	150	150	150	150	150	300	900	300	150	150	150	2,850
1006	75	0	150	75	75	75	75	75	75	75	675	750	2,175
1021	100	200	100	200	100	200	100	200	100	200	100	300	1,900
1014	160	80	160	80	160	160	160	80	160	80	240	160	1,680
19	19	19	19	19	19	19	19	19	19	19	19	19	19

Item ID

jan_sales

feb_sales

mar_sales

apr_sales

may_sales

jun_sales

jul_sales

aug_sales

sep_sales

oct_sales

nov_sales

dec_sales

total_sales_2022

1001

80,500

92,000

103,500

92,000

103,500

138,000

184,000

1,230,500

1002

37,500

45,000

30,000

37,500

45,000

30,000

37,500

67,500

525,000

1008

6,000

42,000

12,000

6,000

12,000

156,000

1007

4,500

31,500

18,000

4,500

94,500

1003

350

700

350

700

350

700

350

700

2,800

2,100

2,800

12,250

1012

600

900

1,200

600

900

300

600

8,400

1009

600

7,200

1005

400

1,600

1,400

7,000

1017

250

500

250

500

250

1,250

500

250

500

250

500

6,250

1020

250

500

250

500

250

500

750

1,250

5,250

1019

700

350

4,900

1011

200

400

200

400

200

600

800

3,800

1010

450

3,600

1004

100

200

100

1,100

1,000

3,200

1016

120

240

360

3,000

1018

150

300

900

300

150

2,850

1006

150

675

750

2,175

1021

100

200

100

200

100

200

100

200

100

200

100

300

1,900

1014

160

240

160

1,680

Generated script

Python analysis script has been generated.

import pandas as pd
import numpy as np
from scipy.stats import linregress

defexecution_code(data:pd.DataFrame) -> pd.DataFrame:
    # Prepare month columns and their order
    month_cols = ['jan_sales','feb_sales','mar_sales','apr_sales','may_sales','jun_sales',
                  'jul_sales','aug_sales','sep_sales','oct_sales','nov_sales','dec_sales']
    month_names = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
    month_num = np.arange(1, 13)
    
    # Helper to classify sales volumedefclassify_volume(total):
        if total >= 100000:
            return'High'elif total >= 10000:
            return'Medium'else:
            return'Low'# Helper to classify trenddefclassify_trend(slope):
        if slope > 0.05:
            return'Increasing'elif slope < -0.05:
            return'Decreasing'else:
            return'Stable'# Helper to classify seasonalitydefclassify_seasonality(monthly_sales):
        avg = np.mean(monthly_sales)
        std = np.std(monthly_sales)
        # Seasonality if std deviation is more than 30% of meanif avg == 0:
            return'No Data'if std/avg > 0.3:
            # Check for peak months
            peak_months = np.where(monthly_sales == np.max(monthly_sales))[0]
            iflen(peak_months) == 1:
                returnf'Seasonal (Peak: {month_names[peak_months[0]]})'else:
                peaks = ','.join([month_names[i] for i in peak_months])
                returnf'Seasonal (Peaks: {peaks})'else:
            return'Non-seasonal'# Prepare result list
    results = []
    for idx, row in data.iterrows():
        monthly_sales = row[month_cols].values.astype(float)
        total_sales = row['total_sales_2022']
        # Sales volume classification
        volume_class = classify_volume(total_sales)
        # Trend analysis (linear regression)
        slope, intercept, r_value, p_value, std_err = linregress(month_num, monthly_sales)
        # Normalize slope by mean sales to make it comparable
        norm_slope = slope / (np.mean(monthly_sales)+1e-6)
        trend_class = classify_trend(norm_slope)
        # Seasonality
        seasonality_class = classify_seasonality(monthly_sales)
        results.append({
            'item_id': row['item_id'],
            'sales_volume_class': volume_class,
            'sales_trend': trend_class,
            'seasonality': seasonality_class
        })
    result = pd.DataFrame(results)
    return result

Data retrieved

19 rows

Item ID	sales_volume_class	sales_trend	seasonality
1007	Medium	Stable	Seasonal (Peak: Jun)
1003	Medium	Increasing	Seasonal (Peaks: Oct,Dec)
1009	Low	Stable	Non-seasonal
1010	Low	Stable	Seasonal (Peaks: Jan,Mar,Apr,Jun,Jul,Sep,Nov,Dec)
1014	Low	Stable	Seasonal (Peak: Nov)
1017	Low	Stable	Seasonal (Peaks: Jun,Jul)
1018	Low	Stable	Seasonal (Peak: Aug)
1021	Low	Stable	Seasonal (Peak: Dec)
1004	Low	Increasing	Seasonal (Peak: Nov)
1005	Low	Increasing	Seasonal (Peak: Nov)
1006	Low	Increasing	Seasonal (Peak: Dec)
1011	Low	Increasing	Seasonal (Peak: Dec)
1016	Low	Increasing	Non-seasonal
1020	Low	Increasing	Seasonal (Peak: Dec)
1012	Low	Decreasing	Seasonal (Peaks: Mar,Apr)
1019	Low	Decreasing	Seasonal (Peaks: Jan,Feb)
1002	High	Stable	Non-seasonal
1008	High	Stable	Seasonal (Peaks: Jun,Jul)
1001	High	Increasing	Non-seasonal
19	19	19	19

Item ID

sales_volume_class

sales_trend

seasonality

1007

Medium

Stable

Seasonal (Peak: Jun)

1003

Medium

Increasing

Seasonal (Peaks: Oct,Dec)

1009

Low

Stable

Non-seasonal

1010

Low

Stable

Seasonal (Peaks: Jan,Mar,Apr,Jun,Jul,Sep,Nov,Dec)

1014

Low

Stable

Seasonal (Peak: Nov)

1017

Low

Stable

Seasonal (Peaks: Jun,Jul)

1018

Low

Stable

Seasonal (Peak: Aug)

1021

Low

Stable

Seasonal (Peak: Dec)

1004

Low

Increasing

Seasonal (Peak: Nov)

1005

Low

Increasing

Seasonal (Peak: Nov)

1006

Low

Increasing

Seasonal (Peak: Dec)

1011

Low

Increasing

Seasonal (Peak: Dec)

1016

Low

Increasing

Non-seasonal

1020

Low

Increasing

Seasonal (Peak: Dec)

1012

Low

Decreasing

Seasonal (Peaks: Mar,Apr)

1019

Low

Decreasing

Seasonal (Peaks: Jan,Feb)

1002

High

Stable

Non-seasonal

1008

High

Stable

Seasonal (Peaks: Jun,Jul)

1001

High

Increasing

Non-seasonal

Summarizing findings

- Most high sales volume items (e.g., 1001, 1002, 1008) exhibit either increasing or stable trends and are generally non-seasonal or have multiple seasonal peaks, indicating strong and consistent demand. - Several low sales volume items (e.g., 1004, 1005, 1006, 1011, 1020) show increasing trends with pronounced seasonality, especially peaking in November and December, suggesting opportunities for targeted promotions during these months. - A few items (e.g., 1012, 1019) are experiencing decreasing sales trends and are also seasonal, which may require further investigation to address declining demand. - Medium sales volume items display a mix of stable and increasing trends, with some showing clear seasonal peaks (e.g., 1003 in Oct/Dec, 1007 in Jun), highlighting the importance of aligning inventory and marketing with these periods.

Analyze the specific monthly sales figures for top-performing and declining items to identify drivers of performance.

Investigate the causes behind decreasing trends in certain seasonal products and assess potential corrective actions.

Drill down into promotional activities and inventory management strategies for items with strong seasonality to maximize peak period sales.