Propensity Scores
While the customer is always right, it’s paramount to always find the right customer!
To that end, Zeta leverages the collected customer data in multiple use cases such as campaigns and experiences for effective targeting. ZMP provides you with propensity scores to help improve your customer-targeting mechanisms, thereby enhancing your business outcomes. For example, with price-sensitive score-based segmentation, you can target concerned users with discount messaging while price-insensitive customers can be pitched in with loyalty-type messaging. Similarly, with purchase channel preference scoring, you can target users with their preferred vertical-themed messages.
You can use Zeta propensity scores for both prospect and customer audiences.
From the menu on the left, navigate to Audiences > Segments & Lists. Click on Explore Audience.
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Within the Audience Explorer, click on Zeta Data > Propensity Scores. Choose the types of scores you want to segment the users based on.
Once done, set your desired scores and click on Save.
Zeta supports the following retail-specific scored attributes:
Price Sensitivity Score
This score measures the price sensitivity of the customer. If a customer is more price sensitive (i.e., more inclined to make a purchase if discounts are available), the score will be closer to 0. Valid values are from 0 to 1.
Model Efficiency Metrics
Top Features
Thousands of Zeta Data signals are used to come up with this score and following are the top features contributing to the model score.
Discretionary spending
Household Income
Net-worth
Education
Credit card usage
House price
Number of Adults in Household
Example Use cases
Marketers can create 2 different segments - one targeting price-sensitive customers with a discount-focused theme and discount code, and 2nd segment targeting low price-sensitive customers with a loyalty/VIP treatment-focused message.
Retail Purchase Frequency
This score predicts if a person is a high-volume buyer and if there may be a near-term purchase opportunity. If a customer is more likely to make a near-term purchase, the score will be closer to 1.
Model Efficiency Metrics
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
Total Payments
Vehicle
House size
Prepayment frequency
Heavy transactors
Affordability
Age
Marital status
Example Use cases
Marketers can create a segment targeting price-high purchase frequency customers as high-value customers and reach out to them via high-cost marketing channels
Discount Stores Affinity
This score measures a customer’s affinity towards discount stores. If a customer is more likely to shop at a discount store, the score will be closer to 1.
Model Efficiency Metrics
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
Household Income
Age
Net-worth
State
Gender
Discretionary Spend
Number of Accounts
No. of children
Example Use cases
Marketers can create a segment targeting prospect customers who have a higher affinity towards discount stores.
Discount Apparels Affinity Score
This score measures a customer’s affinity toward discount apparel stores. If a customer is more likely to shop at a discount apparel store, the score will be closer to 1.
Model Efficiency Metrics
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
State
Age
Household Income
Net-worth
Property size
Gender
People in Household
Life Insurance loyalty
No. of children
Example Use cases
Marketers can create a segment targeting prospect customers who have a higher affinity towards discount apparel stores.
Mass Club Store Affinity Score
This score measures a customer’s affinity towards wholesale or mass club stores. If a customer is more likely to shop with wholesalers, the score will be closer to 1.
Model Efficiency Metrics
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
Household Income
Net-worth
Discretionary spend
Credit card spend
State
Age
Gender
People in Household
Example Use cases
Marketers can create a segment targeting prospect customers who has a higher affinity towards mass club stores.
Retail (In-person)Channel Preference
This score measures a customer’s affinity towards shopping at retail stores (in-person). If a customer is more likely to shop in person, the score will be closer to 1.
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
Household Income
Net-worth
Recent mortgage application
Education
Discretionary Spend
State
Age
Gender
Example Use cases
Marketer wants to create 2 segments with different messaging for customers who will most likely visit the store vs customers who will most likely purchase online
Online Channel Preference
This score measures a customer’s affinity toward shopping online. If a customer is more likely to shop online, the score will be closer to 1.
Top Features
Thousands of Zeta Data signals are used to come up with this score and the following are the top features contributing to the model score.
Household Income
Net-worth
Year over year spend
Open auto loans
State
Age
Gender
People in Household
Example Use cases
Marketer wants to create 2 segments with different messaging for customers who will most likely visit the store vs customers who will most likely purchase online