Total Sales all Regions and Channels

$3,941,901

Total Sales - Fresh Category

$1,319,554

Number of Locations

124

Average Sales Across all Regions and Channels

$31,789.52

Maximum Sales Across all Regions and Channels

$130,877

Minumum Sales Across all Regions and Channels

$4,129

SUMMARY INSIGHTS

Original Set of Research Questions Here

  • RQ1
  • RQ2
Insights
  • Insights 1

  • Insights 2

You can add a link to an outside source: https://www.donorsearch.net/our-data/

Map of Portugal
Sales Distribution by Department and Channel
Channel Fresh Milk Grocery Frozen Soap Deli
Hotel_Cafe 48% 13.0% 16% 15% 3% 4.5%
Retail_Store 14% 21.9% 38% 4% 18% 3.4%
Sales Distribution by Department and Region
Region Fresh Milk Grocery Frozen Soap Deli
Lisbon 35.8% 17.7% 23.9% 9.7% 8.6% 4.37%
Porto 29.9% 15.4% 27.9% 12.2% 11.1% 3.51%
Fresh and Total Sales by City and Channel
Region Channel Total Fresh Grocery Milk Frozen Soap Deli
Lisbon Hotel_Cafe $1,538,342 $761,233 $237,542 $228,342 $184,512 $56,081 $70,632
Lisbon Retail_Store $848,471 $93,600 $332,495 $194,112 $46,514 $148,055 $33,695
Porto Hotel_Cafe $719,150 $326,215 $123,074 $64,519 $160,861 $13,516 $30,965
Porto Retail_Store $835,938 $138,506 $310,200 $174,625 $29,271 $159,795 $23,541
Dataset

SEGMENTATION MODEL INSIGHTS
Write some amazingly interesting insights about the customer segmentation here!

  • Bullet 1

  • Bullet 2

  • Bullet 3

Note this content does not specify a fixed row height. So it will increase or shrink based on the amount of content. Thus, it allows the cluster image box to adjust size accordingly. (Contrast with the fixed row height specification method for Channel insights → )

CUSTOMER SEGMENTATION (CLUSTERING)

CHANNEL CLASSIFICATION MODEL INSIGHTS
Write some amazingly interesting insights about the channel classification
- With a value of 27.36 and a p-value of 0.00, the model is statistically significant, meaning it explains a significant portion of the variability in the dependent variable.
- Fresh, Frozen, Milk, and Delicassen variables are all not statistically significant.
- This indicates that Detergents_Paper and Grocery could be key variables in predicting changes in Channel.b.
Note this card method for coding the narrative content fixes the size, in this case to 40%.

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CHANNEL CLASSIFICATION MODEL
Observations 124
Dependent variable Channel.b
Type OLS linear regression
F(6,117) 27.36
0.58
Adj. R² 0.56
Est. S.E. t val. p
(Intercept) -1.26 0.40 -3.14 0.00
pctFresh -0.22 0.15 -1.50 0.14
log(Detergents_Paper) 0.12 0.03 4.66 0.00
log(Frozen) -0.02 0.03 -0.95 0.35
log(Grocery) 0.11 0.05 2.28 0.02
log(Milk) -0.01 0.04 -0.27 0.79
log(Delicassen) 0.02 0.03 0.55 0.58
Standard errors: OLS

New Features Added to Dashboard

  • Feature 1 - I first changed the theme of the dashboard to moon

  • Feature 2 - I made all maps, plots, and clusters interactive

  • Feature 3 - I added a new plot for Frozen and Total sales by Channel

  • Feature 4 - I added a new plot for Frozen and Total sales by Region

  • Feature 5 - I added a new table for Sales Distribution by Department and Region

  • Feature 6 - I also changed the leaflet map type to CardoDB.Voyager

  • Feature 7 - I added a value box for the average sales across all regions and channels

  • Feature 8 - I added a value box for the maximum sales across all regions and channels

  • Feature 9 - I added a value box for the minimum sales across all regions and channels

  • Feature 10 - I changed the channel classification model to include all store bought items