| Attributes | Data type | Counts* | Values | Description |
|---|---|---|---|---|
| Age | int64 | 954 | 27 - 38 year olds | Age of user |
| FrequentFlyer | object | 954 | Yes // No // No Record | Whether Customer takes frequent flights |
| AnnualIncomeClass | object | 954 | High // Medium // Low Income | Class of annual income of user |
| ServicesOpted | int64 | 954 | Range = 1 - 6 | Number of times services opted during recent years |
| AccountSyncedToSocialMedia | object | 954 | Yes // No | Whether Company Account Of User Synchronized to Their Social Media |
| BookedHotelOrNot | object | 954 | Yes // No | Whether the customer book lodgings/Hotels using company services |
| Churn | int64 | 954 | 0 = Doesn’t Churn // 1 = Churned | 1- Customer Churns 0- Customer Doesn’t Churn |
Analyzing relationships between churn behavior and frequent flyer status and/or hotel bookings.
<aside>
$$ One-dimension\;Analysis $$
</aside>
30%* of customers who haven’t booked hotels with us churned
13% of customers who have booked hotels with us churned
<aside> 💡
Revealing an opportunity to boost retention through hotel + flight promotions.
</aside>
*Calculation = 18% / (18% + 42%) = 30%
<aside> 💡
Suggesting that we are not the top choice for frequent flyers
</aside>
**Calculation = 15% / (15% + 15%) = 50%
<aside>
$$ Two-dimension\;Analysis $$
</aside>
