user scoring for fun and profit
predictive modeling in a social shopping service
Wrapp
social shopping
For our users
- free and paid digital gift cards
- send to friends or claim for yourself
- everything in a convenient mobile app wallet
For our partners
-
viral spread of offers through social media
- advanced targeting
- advanced real-time analytics
wrapp in numbers
1.7 M users
18M gifts sent
160 M friends of users
200+ merchant partners
Campaigns
Last week "Kanelbullens dag" 100k+ gift cards sent in Sweden
why predictive modeling at wrapp?
- Fraud
- Recommendations
- LTV
- Campaign budgeting
Fraud
- Many types of fraud
- Gift card trading
- Gift card stacking
- Gift card hoarding
- Credit card fraud
- Predictors
- social network activity
- earlier wrapp activity,
- demographics
- geolocation
- credit card data
- Output
- Fraud risk class
Recommendations
Recommend campaigns to users and users for campaigns
- predictors
- social activity
- demographics
- output
- your favourite campaign
- best recipient for a campaign
order value
- predictors
- social activity
- demographics
- campaign metadata
- output
- predicted order value for a user
Campaign budgeting
Not all gift cards are redeemed, merchants want to know how many they have to pay for
Fraud
In Wrapp we are facing at least two types of fraud
- Credit card fraud
- Account fraud
building models
Most of our models are quite simple linear models
R = M x p
R =response
M= model
p = predictor vector
Building a good model basically entails setting the weights in M appropriately. This can be done with a few methods:
- Heuristics (aka. pure guesswork)
- Logistic regression
- Machine learning methods
quantified Heuristics
Can actually be very good, especially if you quantify them so they are applied equally. Especially useful for less tangible response variables
Basic methodology
-
list everything you can think of that
could impact your response - Assign weights to each manually
- Test, test, test
We are using "quantified heuristics" for our fraud filters and our recommendation engine, however for the latter we are collecting statistics to help improve our weighting.
linear regression
Basic methodology
1. Collect data
2. Run your favorite regression model (glm)
3. Profit
case study: predicting AOV
who are the misers?
hypothesis
a small percentage of users are doing a majority of our "low value transactions"
1) look at the data
Dataset: 200k transactions
aov histogram
campaign histogram
2) build a model
glm(transactions)
3) Profit?
prediction is just step 1
what if we change the rules?
what if we could collect better data?
future: where do we want to go
Better predictive modelling requires intervention, not just passive data collection
Examples: automatically change recommenders
measure response, apply change them again