the warranty data lake – after, inc

The Warranty Data Lake How Big Data and Data Science Can Drive Program improvements

March 2016

Richard Vermillion, CEO of After, Inc. and Fulcrum Analytics, Inc.

Who is After, Inc.?

Just What Is a “Data Lake”?

Why a Warranty Data Lake?

Warranty Data Science •  Customer Data •  Claims Data •  Spatial Data •  Telematics •  Parts/Supplier/Lot Data •  Shipment, Engineering, Dealer Data

What does it all mean?

Agenda

Founded in 2005 as a division of Fulcrum Analytics, we leverage our dual-expertise in data analytics and consumer marketing to help companies establish world-class warranty businesses. We help manage program risk and optimize sales and marketing programs from start to finish — absorbing almost all of the management and technology burden. As your partner, we are singularly committed to creating the greatest possible value for you and your customers.

Who is After, Inc.?

What is a Data Lake?

TRADITIONAL REPOSITORIES

Large, well-organized data warehouses

That deliver some data to retail data marts

Much larger, more loosely organized lakes

That you can sample at will

DATA LAKES

What is a Data Lake?

A ”Data Lake” is a large storage repository and processing engine

Data Lakes are built on a “Big Data” platform like the Hadoop stack •  Volume •  Velocity •  Variety

Focus on variety of data they hold than just volume or velocity: •  Flat files •  Relational •  Hierarchical •  Unstructured, free-form text •  Graph/network •  Real-time

Also distinguished by how they are built and loaded •  Focus on agility •  Iterative development to meet changing business needs •  Support innovation •  Destroy silos

Not an agile process

Data Warehouse or Mart

INNOVATION KILLER

Generate Business & Technical

Requirements

Design Conformed Data Model

Develop ETL for Each Data

Source

Build Reports & Connect BI

Tools

Too Limited • Drop fields that don’t fit

• Aggregate away detail

• Only answer questions you anticipated

Too Slow • Waste time fully understanding each data source

• Business requirements change by the time it’s built

Too Expensive • Costly commercial hardware/software

• Especially if you aren’t sure each data source is valuable

Supports an agile process

Data Lake

INNOVATION ENABLER

Load Data As-Is

Conform and Model What You Need

Iterate ELT for Each Data

Source

Access All Data Types with One Toolset

Not Limited • Keep all the fields in their original format

• Keep the detail data

• Pose and answer questions you never anticipated

Not Slow • Easy to get started, just copy raw data in

• Incremental changes as business requirements change

Not Expensive • Cheap storage, open source tools

• Defer hard work until you have a use for the data

Build to optimize warranty, extend to impact other areas

Why a Warranty Data Lake?

Warranty Chain is unusual, if not unique, in how many areas of the business it touches: •  Customer – sales, usage, service, satisfaction •  Engineering – quality, reliability, design •  Service – repairs, claims •  Distribution – dealers, retailers •  Finance – insurance, reserves, credit

Spans front-office and back-office

Many relevant, but siloed, data sets

Large opportunities for analytical improvements

Large (usage) data sets on the horizon

By collecting multiple types of data, we can reduce reliance on lagging indicators (e.g., claims paid) and also identify, or develop new leading indicators for program performance.

Why a Warranty Data Lake?

Key analytical questions a warranty data lake can help answer: •  Claim reserve and loss modeling •  Early warning for losses •  Claim & service triage optimization, “no fault found”

minimization •  Fraud and/or service anomaly detection •  Customer satisfaction warning signs •  Sales & marketing effectiveness •  Field usage and relation to service events •  Recall cost estimation

Unstructured Data & Text

Claim notes include more details than codes… but less structure

Codes impose structure and simplify analysis •  But only for problems you anticipated and created codes for

Text mining and natural language processing can unlock the secrets in claim and repair notes

Visualization techniques such as word trees and clouds enable free text exploration

Unstructured Data & Text

Tweets, product reviews and other user-generated content also can be collected and analyzed

Sentiment analysis, topic modeling, keyword & semantic analysis

Bayesian classifiers (like your spam filter) can be trained to spot quality complaints and failure reports

Possible early warning of service problems

Spatial Data

Spatial data (precipitation, temperature, crop production, land use, etc.) can be a proxy for usage

For example, snow accumulation Data spatially correlated with claims

Can be predictive of frequency

Usage Data & Telematics

The real ‘big data’ of warranty (high volume, high velocity)

Usage data, telematics, “Internet of Things”, sensor data

Real-time streaming or collected at service event

Tracking Device (GPS + Altimeter + Accelerometer) with post-processing can identify:

•  Mileage and usage time

•  Hard driving and hard braking incidents

•  Inefficient shifting

•  Idle time

•  Geography and location of usage

•  Altitude changes

More accurately map usage cycles to calendar time (relevant for warranty)

Segment light vs. heavy usage and model differing expected losses

• 

Graph Data Graph data models the world as nodes connected by edges

Developed for web search (pages connected by hyperlinks) and social network analysis (users connected by friendship)

Can capture the complex relationships between parts, assemblies and sub-assemblies

Store part explosions and proximity natively as directed graphs

Capture failure sets of parts serviced together (causal or not)

Customer and Marketing Data

Product registration data

Demographic & third-party data

Marketing event data

Promotion participation

Direct mail participation and response

Email sends, opens, click-through

Customer service calls and dispositions

Clickstream data

Mobile app engagement

Consumer research and survey responses

Price and messaging test results

Other Useful Data Claim Workflow Details

Unit Shipment Data

Inventory Data

Dealer Information (locations, ownership, firmographics, etc.)

Retail Unit Sales, Pricing and Discounting

Damaged Product Images (especially for ADH claims)

ESP Terms & Conditions (often unstructured).

What does it all mean? A Data Lake provides a repository and processing engine to gather all relevant data for analysis

Handling variety is the key to breaking down data silos

A Warranty Data Lake can bring together front-office and back-office data to optimize program performance and improve loss projections

No one is going to build a warranty data lake with every data source discussed, but at After, we see companies beginning to build – and incrementally add to – starter data lakes

Create the corporate data asset to drive 21st century program improvement.

Richard Vermillion, CEO After, Inc. + Fulcrum Analytics, Inc.

https://www.linkedin.com/in/rvermillion

rvermillion@afterinc.com

www.afterinc.com

800.374.4728 212.651.7000

70 West 40th Street, 10th Floor

New York, NY 10018

afterinc.com

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