Banyan - Sutra Project Workshop 3 Session
Objectives
Cross Sell and Upsell and Increase Lifetime value of customer
Question that needs to be answered: What campaign should i run to achieve above
Business lines in scope:
- CM (Hotel)
- MYK (Hotel)
- PB (Hotel)
- Banyan Tree Essentials (SPA Retail)
Probability and ROI Potential
Determine which strategy to execute based on historical data
Themes:
- Visit New Property
- Visit Another Property
- Come Back One More Time
- Visit Shopify
- Upsell Purchases
Market Basket Analysis Approach
- Using historical data to identify most frequent combination and confidence level of it happening
- Use historical data to identify potential additional spend
- Calculate expected return on investment taking into consideration confidence and potential additional spend
- Calculate return on investment based on budget allocated to strategy
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Customer Cluster Segmentation
Determine what behaviour cluster is there in existing customer base
Available features of Customers
Demographic Features
Age | Age Group (Generation Category) |
Gender | Gender Rationalise |
Nationality (Country, Region, Sub Region) | # of children (derived from stay) |
Address (Country, Region, State) | |
Children or no children (derived from stay) |
Spent Features
LTV Spent | Avg LTV Spent | LTV Spent Bin | Avg LTV Spent Bin |
LTV Room Spent | Avg LTV Room Spent | LTV Room Spent Bin | Avg LTV Room Spent Bin |
LTV Food Spent | Avg LTV Food Spent | LTV Food Spent Bin | Avg LTV Food Spent Bin |
LTV Spa Spent | Avg LTV Spa Spent | LTV Spa Spent Bin | Avg LTV Spa Spent Bin |
LTV Gallery Spent | Avg LTV Gallery Spent | LTV Gallery Spent Bin | Avg LTV Gallery Spent Bin |
LTV Others Spent | Avg LTV Others Spent | LTV Others Spent Bin | Avg LTV Others Spent Bin |
LTV Shopify Spent | Avg LTV Shopify Spent | LTV Shopify Spent Bin | Avg LTV Shopify Spent Bin |
Jan Spent | Apr Spent | Jul Spent | Oct Spent |
Feb Spent | May Spent | Aug Spent | Nov Spent |
Mar Spent | Jun Spent | Sep Spent | Dec Spent |
Stay Features
First Hotel Visit Date | Last Hotel Visit Date | LT Visits | First Hotel Visit Date Bin |
First CM Visit Date | Last CM Visit Date | LT CM Visits | Last Hotel Visit Date Bin |
First MYK Visit Date | Last MYK Visit Date | LT MYK Visits | LT Visits Bin |
First PB Visit Date | Last PB Visit Date | LT PB Visits | LT Nights Bin |
Day count from first Hotel Visit Date | Day count from last Hotel Visit Date | LT Nights | LT CM Visits Bin |
LT Average # Adults Per Stay | Day count from last CM Visit Date | LT Average Nights Per Stay | LT MYK Visits Bin |
LT Average # Children Per Stay | Day count from last MYK Visit Date | LT PB Visits Bin | |
Child Ratio | Day count from last PB Visit Date | ||
Adult Ratio | |||
Jan Frequency | Apr Frequency | Jul Frequency | Oct Frequency |
Feb Frequency | May Frequency | Aug Frequency | Nov Frequency |
Mar Frequency | Jun Frequency | Sep Frequency | Dec Frequency |
Shopify Features
First Purchase Date | |
LT # Purchases | LT # Purchases Bin |
Last Purchase Date | Last Purchase Date Bin |
Day count from last Purchase Date | |
Activity Features
Biz Line Visited | |
Biz Line Sequence | |
First Engagement Date across Biz Line | |
Day count from first engagement date across Biz Line | |
Analytics Features
LT Recency | Cluster Number |
LT Frequency | |
LT Monetary | |
LT AOV | |
Recency Bucket | |
Frequency Bucket | |
Monetary Bucket | |
RFM Segment | |
RFM Order | |
RFM Segment Label | |
RFM Segment Label 2 |
Selected features for Clustering
Analytics Features
LT Recency | |
LT Frequency | |
LT Monetary | |
LT AOV |
Stay Feature
LT Average Nights Per Stay | Jan Frequency |
Feb Frequency | |
Mar Frequency | |
Apr Frequency | |
May Frequency | |
Jun Frequency | |
Jul Frequency | |
Aug Frequency | |
Sep Frequency | |
Oct Frequency | |
Nov Frequency | |
Dec Frequency |
Demographic Features
Child Ratio | |
Adult Ratio | |
Spent Features
Avg LTV Room Spent | Jan Spent |
Avg LTV Food Spent | Feb Spent |
Avg LTV Spa Spent | Mar Spent |
Avg LTV Gallery Spent | Apr Spent |
Avg LTV Others Spent | May Spent |
Avg LTV Tips Spent | Jun Spent |
Avg LTV Transport Spent | Jul Spent |
Avg LTV Shopify Spent | Aug Spent |
Sep Spent | |
Oct Spent | |
Nov Spent | |
Dec Spent |
Clusters to focus on based on selected strategy
Strategy: MYK or PB → CM
Time Frame: → May, Jun, Jul
Cluster: 1, 3, 4 ,14
Cluster 1:
- Highest Jul Frequency
- 2nd highest tendency to spend
- 64% room, 27% food, 6% spa, no shopify
- Medium Frequency
- Slightly higher room and lower F&B % compared to cluster 3 and 4
Cluster 3:
- Highest May Frequency
- 4th highest tendency to spend
- 61% room, 28% food, 7% spa, no shopify
- Medium Frequency
Cluster 4:
- Highest Jun Frequency
- 3rd highest tendency to spend
- 61% room, 30% food, 6% spa, no shopify
- Medium Frequency
Cluster 14:
- Highest Shopify Spent
- Highest Room Spent
- 61% room, 24% food, 9% spa
- Shopify spend exist
- Highest Frequency
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Customer Eligibility Analysis
Eligibility elimination by
- Last visit
- Frequency Level (1, Mid, High)
Customer Marketing Message
Objective: Visit Another Property
Target Hotel: CM
Time Frame: May, Jun, Jul
Customer Marketing Mode Effectiveness
Email Effectiveness