# 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

## wrapp in numbers

1.7 M users
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
• 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
• best recipient for a campaign

## order value

• predictors
• social activity
• demographics
• 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
• Seasonal effects
• ## 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
1. list everything you can think of that
2. Assign weights to each manually
3. 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

By cortex

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