"Being creative with data" 25th November - KXEN presentation

THE DATA MINING AUTOMATION COMPANY TM

Using Predictive Analytics

Presented by: Alan Furness November 25th 2009


KXEN, Inc Company Confidential2

Agenda

• KXEN – Company Background• The Impact of a Changing Environment• Getting the First Mover Advantage • Examples



Agenda




Requires More Focused Models

Requires More Current Data

Requires Frequent Updates

Your Environment and Goal Change over Time

Markets Are Different

Customers AreDifferent

Products/Channelsare Different

Your Strategies Change

Customer Situations Change

The Environment Changes



Combining data in models

• CRM• EPOS and shopping data• eCommerce• Web analytics

- Behavioural

- sequencing

• Unstructured (text)- Attitudinal

• Social Networks Analysis- Churn and fraud



Too much data…..?

Have you got lots of data?

Are you getting Value from it?

Analytics is about Insight



Do More…Modeling Automation is Key!

Legacy Data Mining

3 Weeks

Select VariablesSelect

VariablesUnder-stand

Under-stand ApplyApply Prepare Data Prepare Data Build

ModelBuild Model

Test ModelTest

Model

Next Generation

3 Hours

Bu

ild M

od

elB

uild

Mo

del

Un

der

stan

dU

nd

erst

and

Ap

ply

Ap

ply

Variable selectionData preparation

Variable encoding Missing value handling Outlier handling Binning and banding

Model FittingModel TestingModel ReportingModel Deployment

KXEN Automates



Agenda




Goals:

Grow Customer Grow Customer RelationshipsRelationships

Improve Improve Marketing Marketing EfficiencyEfficiency

• Understand each customer’s unique needs- Customise offers

- Personalise sites

- Serve up the right offer

• While tracking the impact of the offers



Behavioral-Based Insight

Thousands of Thousands of AudienceAudienceSegmentsSegments

Thousands ofThousands ofPotential AdvertisingPotential Advertising

PlacementsPlacements

IngestIngest

LargeLargeVolumes ofVolumes of

Self-expressedSelf-expressedUser DataUser Data

DeliverDeliver

Real-timeReal-timeRecommendations and Recommendations and

PredictionsPredictions

ModelModel

DynamicDynamicContinuousContinuousFast cycleFast cycle

Next Best Offer

Next Best Activity

Marketing Mix

Retain

Cross-sell

Attribution

Acquire

Next Best Product



For Each Customer… Determine

• The Right - Product

- Channel

- Creative

- Offer

- Timing

Product

Timing Channel

Offer

Creative

Customer

Next Best Offer



How Analytics Can Help you be a First Mover

• Provide the right content

• On the right media• At the right time• To sell the right product • To the right prospects

• While being able to- attribute the cost and value

- understand the net value

Grow Grow Customer Customer

RelationshipsRelationships

Improve Improve Marketing Marketing EfficiencyEfficiency



Example : Ad Attribution for Financial Services Provider

Solution Solution

Solution Situation

• Spends $48MM /year with DoubleClick• Data volumes are large

- 500m users (cookies) per month- 12b impressions per month- 450 distinct page placements- 300 distinct creatives

• Users see the ad many times before they convert, what are the key drivers of conversion?

• Data Preparation on the Amazon Cloud – 6 days• Model Building with KXEN took 90 seconds

- 130k rows

- 100 columns

- High accuracy and robustness• Key drivers and business insight identified



Solution

Example : Ad Attribution for Financial Services Provider (cont.)

Variable %

% of impressions from the site they visit most often 21%

number of creatives seen 19%

internet domain 13%

site page they see most often 11%

city 8%

number of days with impressions 7%

DMA 6%

site they see most often 5%

number of operating systems 5%

number of domains 4%

Key DriversTop Referring Site Freq

1 - Crossed Frequency

Fool.com, KxOther, Seeking Alpha 1.32% 4.51%

CNNMoney, TheStreet.com 3.44% 2.29%

Bloomberg.com, Silicon Alley Media , Vibrant Media Inc, Wall Street Journal

Online, Yahoo 39.76% 1.24%

InvestingChannel, MSN 36.57% 0.75%

AOL.com, Turn, Inc., United 18.92% 0.53%



Example : Conversion for an Ad Network

Solution Solution

Solution Situation

• Ad Network collects customer shopping behavior from a number of retailers

• which customers would be good prospects for which offers

• Revenue is based on the number of click-throughs obtained – accurate targeting means more $$

• Need to develop and refresh acquisition and cross-sell models for each retailer, monthly

• Built infrastructure for the data collection and preparation

• Automate the model refresh and model validation• Analyst only reviews model results, rather than

manually train each model



Example: Online Conversion

Situation• Due to limited service representatives, an interactive chat

company needed to predict which on-line visitors are more likely to have a “positive outcome” if engaged in a live chat session.

Situation

Process • Using click-stream behavior to build a random test model• Models are built and embedded on the website that

predict the probability of a positive reaction.• Visitors are scored every 10 seconds• The highest scoring visitors are approached

Process

Solution • Increase conversion rates• Enhanced customer experience• Optimised service center resources

Solution

Train, and refresh KXEN Model

Predict Customer Value

Optimize Call Centre Resources

Collect Real-Time Click-Stream Data



Josh Kirby c1993

Example : Royal Shakespeare Company

Solution Situation

• Reach broader diverse audiences• Encourage core of loyal customers to visit more

Results

• Stratford ticket sales up 61%• Regulars up 80%• New London market identified• Donations up 67%• Membership increased by 40%• London DM strategy – 45 minutes

Solution

•Segmentation models



At the end of the day…..

• Modelling and Analytics only provide insight….

• The insight has to be actionable

•And usually the action is a marketing one

The Far Side


Thank You

Technology

"Being creative with data" 25th November - KXEN presentation