All Regions and Channels - Total Sales
$3,941,901
Fresh Category - Total Sales
$1,319,554
Number of Locations
124
Grocery Category - Total Sales
$1,003,311
- Top Regions by Total Sales: Lisbon, Porto
- Top Channels by Total Sales: Hotel_Cafe, Retail_Store
- Total Number of Customers: 124
- Average Spending per Customer: $31,789.52
- Most Popular Product Category: Fresh
| 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% |
| 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 |
SEGMENTATION MODEL INSIGHTS
1. Fresh Enthusiasts in Lisbon:
Higher propensity for purchasing Fresh products in Lisbon than in Porto.
Action: Launch a “Fresh Picks of the Week” campaign, featuring seasonal items and local favorites at a discount. Also, host cooking classes and live demos at stores to engage the community.
2. High-Value Customer Identification:
Concentrated pockets of high expenditures in specific regions.
Action: Use data analytics to further drill down into spending behavior, creating profiles for these high-value customers. Offer premium services such as early access to new products and exclusive shopping hours.
3. Strategic Upselling to Grocery Buyers:
Predominantly Grocery item purchases with low crossover to Fresh categories.
Action: Introduce a “Healthy Kitchen” program that offers discounts on fresh ingredients when bought together with certain Grocery staples. Provide recipes and meal prep tips to inspire more diverse food shopping.
CHANNEL CLASSIFICATION MODEL INSIGHTS
1.Effective Model Fit: The regression model demonstrates strong explanatory power, with a significant F-statistic and R-squared value of 0.56.
2.Variable Impact: Spending on fresh products exhibits a negative correlation with certain channels, while purchases of detergents and paper products positively influence channel classification.
3.Statistical Significance: All variables, including intercept, are statistically significant, underscoring their relevance in predicting channel behavior and informing tailored marketing strategies.
| Observations | 124 |
| Dependent variable | Channel.b |
| Type | OLS linear regression |
| F(2,121) | 75.72 |
| R² | 0.56 |
| Adj. R² | 0.55 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | -0.70 | 0.17 | -4.18 | 0.00 |
| pctFresh | -0.35 | 0.13 | -2.58 | 0.01 |
| log(Detergents_Paper) | 0.17 | 0.02 | 8.43 | 0.00 |
| Standard errors: OLS |
- I changed the theme of the dashboard from Cosmo to Superhero dark theme.
- I added a new Value box: Total Sales - Grocery.
- I changed the colors of all the valueBoxes.
- I changed icons in some valueboxes.
- I added insights in the Visualizations and Models tab.
- I added a new table called “New Features” in the tables tab.
- I added a new row and column where it displays a clustering chart of the customer segmentation in the Models tab.
- I changed my layout in the models tab.
- I changed the icon of the main tab. 10.I made changes in the sizes of my boxes to have a better visualization of my information.