Using Advanced Analyics to bring Business Value

Copyright © 2013. Tiger Analy7cs

Using Advanced Analy7cs to bring Business Value

Two case studies in Digital Adver7sing

_________________________

Mahesh Kumar CEO, Tiger Analy6cs


Tiger Analy7cs

•  Bou6que consul6ng firm solving business problems using advanced data analy6cs

•  Focus areas –  Digital adver6sing and Social Media –  Marke6ng and Customer Analy6cs –  Retail and CPG –  Transporta6on

•  Offices in Bay area, North Carolina, and India


•  Display Adver6sing through Real-‐6me bidding (RTB) –  Background on RTB –  Business problem: improve CTR –  Solu6on Approach and Results

•  Credit Card Customer Acquisi6on via Facebook Ads –  Facebook ads plaOorm –  Business problem: op6mal targe6ng and bidding –  Applica6on to credit card marke6ng

3

Overview – Two case studies


Display Adver7sing through Real-‐7me bidding (RTB)

Case Study # 1


5

Display Adver7sing – Real Time Bidding

Milliseconds to bid and load ad …

Waiting for ad from ad exchange

…

Male, 20-30 yrs, NYC Tech user


6

Display Adver7sing – Real Time Bidding

Targeted Ads


Real Time Bidding (RTB) for Display Ads

7

Ad-‐Exchange Ad-network 2 Publisher (NYT.com) Tech section

Ad-network 1

Ad-network 3

Advertiser

Advertiser

Advertiser

Male, 20-30 yrs, New York, Tech user

•  The en6re process takes less than 500 milliseconds •  RTB share of online ads is es6mated to be $2B per year


Business Problem

•  Click-‐through rate (CTR) predic6on: Given a campaign line, what is the predicted CTR for an impression based on –  User characteris6cs –  Webpage characteris6cs

•  Iden6fy impressions with highest CTR?

8


Maximizing the CTR is Cri7cal For Cost Op7miza7on

9

High CTR is good for everyone: users, advertiser, and publisher

High CTR

Relevant content for

Users

Revenue maximiza6on for

Publisher Relevant

audience for Adver6ser


Sample data

10


Data challenges

•  Challenges –  More than 5000 variables –  Hundreds of millions of data points –  Sparse and missing data –  Clicks are very rare (typically 1 click in every 3,000 impressions)

11


•  Case sampling –  Keep all impressions with clicks –  Keep only a random sample of 1% non-‐clicks

•  This reduced the data size by 100-‐fold, but predic6on accuracy was as good as when using all data

12

Reducing the number of data sets


Logis7c Regression results

13

- "

50 "

100 "

150 "

200 "

250 "

300 "

350 "

400 "

450 "

1" 1001" 2001" 3001" 4001" 5001"

predicted!baseline!

K K K K K K 0% 20% 40% 60% 80% All data

•  Top 20% of data got 232 out of 415 (56%) of clicks •  A liY of 180%

Copyright © 2013. Tiger Analy7cs 14

Insights – Final set of variables


Twi\er – CTR Predic7on

15

NLP


Credit Card Customer Acquisi7on Through Social Media Marke7ng

Case Study # 2


Social Media provides rich data to marketers


Ads on Facebook Newsfeed on Desktop Newsfeed on Mobile

Right Hand Side on Desktop

Sponsored Story

Image source: Facebook


Facebook Ad Pla`orm -‐-‐ targe7ng

19


Facebook Ad Pla`orm -‐-‐ pricing

20


Case study: credit card marke7ng

The image cannot be displayed. Your computer may not have enough memory to open the image, or the image may have been corrupted. Restart your computer, and then open the file again. If the red x still appears, you may have to delete the image and then insert it again.


Cash Back


1,000,000 Impressions

300 Clicks

3 Applica7ons

1 Approval

Conversions are rare events when compared to clicks. The challenge is to be able to make meaningful inferences based on very little data, especially early on in the campaign.

Click-‐through rate 0.03%

Conversion rate 1%

Approval rate 33%


Micro Segments

1 Segment 50 Segments

50 x 2 = 100 Segments

2 Genders 4 Age Groups

100 x 4 = 400 Segments

25 Interest Clusters

400 x 25 = 10,000 Segments


Methodology

•  Iden6fy high performance segments –  Sta6s6cally significant difference in ctr, cpc, cost per conversion, etc. –  Use ctr as a proxy for conversion rate

•  Ac6ons on high performance segments –  Allocate higher budget –  Increase bid price

23


Data aggrega7on

Segment Level Data (Sparse and Noisy)

Iden7fy Important Dimensions (Using Sta7s7cal Models)


Segment performance es7ma7on

Model Es7mates

Observed Performance

Prior Knowledge

Inferred Performance


Bidding

Brand A

Brand B

Other Compe77on for Ad Space

Bid: $1.60

Bids

WIN

Bids will differ by Ad and Micro segment, and will change over

7me


Budget Alloca7on •  Increase budget for high

performance segments and reduce for low performance ones

–  Business rules around minimum and maximum limits


Methodology Segment Level Observed Data

Inferred Performance Indicators Based on priors, observed, model es6mates

Cost per Applica6on

Success Rate

Dynamic Budget Alloca7on Based on inferred performance indicators

and business constraints

Historical Campaign Data

Priors of Performance Indicators

Weighted Data Click vs. view through, card value, applica6on result, recency, delay in view-‐through appls

Cost per Acquisi6on

Model Performance as a func6on of targe6ng

dimensions

Model Es7mates of Performance Indicators

Dynamic Bid Alloca7on Based on observed/historical

Bid-‐Spend rela6onships

Con7nual monitoring and analysis

Business Constraints


Results: Increased CTR

29

•  Overall increase in CTR by 50% across more than 100 brands


Results: Lower costs

30

•  Overall decrease in CPA of 25% across more than 100 brands


Concluding remarks

•  Online and social adver6sing are fast growing areas with –  Plenty of data –  A large number of interes6ng problems

•  Predic6ve analy6cs can add a lot value in this business –  Significant improvement in CTR means beber targeted ads –  As much as 25% reduc6on in cost of media

•  Our solu6ons are being used by several leading startups to serve billions of ads for Fortune 500 companies

31


Ques7ons / Comments ? [email protected] www.7geranaly7cs.com

32

Technology

Using Advanced Analyics to bring Business Value