Transcript Segment 1 - Utility Payment Conference

Using skip tracing to your
advantage
Bill Butler, Collections Product Manager
September, 2012
Utility Payment Conference
©2012 Experian Information Solutions, Inc. All rights reserved. Experian and the marks used herein are service marks or registered trademarks of
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No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian.
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Industry challenges
Active Accounts past due

Providing shut-off notices and settlement offers

Identifying landlords or co-habitants
Outsourcing Accounts
 Deciding which accounts to outsource

Deciding when to recall account from agency
Inactive and Late Stage Accounts
 Merging accounts

Tracking consumers
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Dynamic nature of consumer information
Consumer ID information is constantly changing
Tracking consumers
• Individuals move on average 11.7 times/lifespan
• About 42M Americans (14%) move each year
• Experian’s credit database shows that consumers are trending toward using
wireless phones as primary phone versus landline
• There are over 4M births, 2M deaths, 2M marriages, and 1M divorces
• Socials inexact due to shared SSN, fraud & key entry errors not caught by
algorithms
• Over 5% use “Jr.” and “Sr.”
Key for utilities is to identify providers that can pull
together information from multiple data sources to
identify consumers.
Some sources include: directory assistance, credit and marketing
databases, and alternative data such as rental and payday lending.
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The collections lifecycle
EVALUATE
PRIORITIZE
LOCATE
REVIEW
MONITOR
Scrub your
accounts for
consumers
in protected
status.
Score your
inactive
accounts
can help
maximize
resource
use
Search for
new skip
tracing
phone or
address
data to find
consumers /
businesses
Identify
updated
credit
information
and
attributes
Monitor your
accounts for
financial
changes
and new
phones and
addresses
(Bankruptcy,
deceased,
military, etc.)
Collections lifecycle
Focus for skip tracing on these key areas
PLUS mining your own data for dollars!
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Evaluating accounts for regulatory risk
Key Goals
EVALUATE
 Find consumers in protected status
 Identify consumers that meet requirements for special programs
Options
 Third party providers offer account filtering and data scrub services
 Examples:
Bankruptcy
Fraud
Cell Phone Type
Litigious Debtor
Deceased
Military
Experian’s clients have suggested a streamlined approach is best
which includes multiple data filters
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Prioritizing accounts to maximize resource
efficiency and improve collection activity
Scores can be used for:
 Segmenting and prioritizing portfolios
PRIORITIZE
 Determining appropriate treatment strategy for each account
 Increasing collections and recoveries
 Reducing costs associated with collections
Helps in four key areas:
 Service shut-off decision making
 Final-bill collections
 Assigning accounts to collection agencies
 Internal Recovery collections
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Case study #1 using Experian’s Priority Score
Charged-off accounts for large electric / gas utility
Cumulative dollar capture by 10% increments
Dollars Collected
100%
80%
60%
40%
20%
BaseLine
PriorityScore
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Accounts Worked
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Case study #1 using Experian’s Priority Score
Accounts 3-4 cycles past due for mid-size electric utility
Cumulative dollar capture by 10% increments
100%
Dollars Collected
80%
60%
40%
20%
BaseLine
PriorityScore
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Accounts worked
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Integrating scoring into your account flow
Determine where scoring fits best:
Prioritization
Treatment
Outsourcing
Prioritization strategies can and should be used in
tandem with skip tracing
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Applying scoring to your business
801 - 900
A
Highest
probability of
payment
Highest return with little effort
601 - 800
B
High probability of
payment
301 - 600
C
Optimal area to place resources
Medium
probability of
payment
100 - 300
D
Low probability
of payment
Less profitable
NOTE: The score ranges are based on Experian’s Priority Score used for case studies
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Find consumers, businesses, and co-habitants
 Locate individuals and businesses
 Find listed and unlisted phone numbers
LOCATE
 Find wireless phone numbers
 Identify co-inhabitants by premise
 Update your consumer address and phone information
 Determine change of address verification
 Address standardization
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Skip tracing data sources
Traditional Data Sources
 Directory Assistance / 411 phones
 Wireless and alternative landline phones
 Marketing sources
New and Unique Data Sources
 Credit data
►
High percentage of wireless numbers
 Rental data
 Payday lending data
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►
Underbanked consumers
►
Consumers with no credit
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Considerations for skip tracing
1) Data should be updated from legitimate sources
2) Quality is more important than quantity
3) Prioritization scores help with resource and placement optimization
4) Cell phone indicators are important with auto-dialers
5) Right party contact rates are influenced by the quality of the supplier
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Mining your data for dollars!
Case studies using Experian’s PINpoint
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Importance of mining your own data
Business need
Solutions
Impact
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

Link disparate customer databases
Cross-sell existing customers
 Mitigate risk
 Identify linkage service or tools that enable you to view
all relationships belonging to a single consumer
 Drive profitability thru merging accounts tied to
collections
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Realities of disparate databases
Consumer ID information can vary across databases
Database 1
Peggy, Lee
Address #1
SSN #2
Variations in data exist
• Previous address
Database 2
Smith, Margaret B.
Address #3
SSN #1
Database 3
Lee, Margaret
Address #1
SSN #1
• Current address
• Work address
• Parent’s address
• Correct SSN
• Mistyped SSN
How does an
organization gather all
data related to Peggy
Smith?
• Nicknames
• Maiden names
Database 4
Smith, Peg
Address #2
SSN #1
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A single customer view defined
Single Customer View
Checking
and
savings
accounts
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Credit
cards
Utilities
Mortgages
Personal
loans
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What are the benefits of mining your own data?
 Reduce outstanding collectable amounts
►
Link outstanding closed balances to
current, active accounts to enable
“smart” balance transfer initiative
 Enrich and update customer information
(e.g., best name, best address, best Social
Security number, deceased indicator)
►
Critical to improving customer experience
 Provide a critical single view of the customer
base (an enhanced understanding of previous
account history) and improved customer service
►
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Optimizes resources for maximum
efficiency and for revenue growth
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Client case study
Using PINpoint ServicesSM to mine your data
Challenge
and objective
Solution:
Experian’s
PINpoint
ServicesSM
Accurately linking
active and
inactive accounts
2.7 million+
customer records
sent to Experian
Client
One of America’s
leading energy
companies with
close to five
million customers
Identifying preexisting
relationships at
the point of
application
$6.2 million in
bad debt was
missed in the
matching process
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Results:
$1.02 million in collected
unpaid balances
PINpoint ServicesSM identified
$1.7 million+ in uncollected, bad
debt that could be linked to current,
active customers
$1.0 million (or 57% of bad-debt
posted) was collected immediately
Utilizing PINpoint ServicesSM
to update its database of energy
customers in additional states and
will begin to link bad debt to active
customers
Going forward, plans to leverage
PINpoint ServicesSM in improved
identity validation, bad debt match
at account initiation, and enrich its
customer records with information
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Sample linkage analysis
Patterns identify primary opportunity
Customer data integration report
Active vs. write-off with balance amount
Pattern
Active
1
X
2
X
3
Balance
Amount
Active
Balance amount
(write-off)
Number
of total
records
Number
of records
(active)
Number of
records
(write-off)
Number of
consumers
PIN level
$0.00
$0.00
5,161,555
5,161,555
0
4,520,576
X
$0.00
$23,067,927.25
195,656
71,215
124,441
44,067
X
$0.00
$242,631,340.71
712,045
0
712,045
377,931
6,069,256
5,232,770
836,486
4,942,574
Write
off
$23,067,927 in
write off balances
identified as linked
to active customers
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44,067 customers
identified with
both write-off AND
an active account
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Balance transfer segmentation strategy
Total outstanding dollar amounts by segment
Segment 1:
$14,418,591
Segment 2:
$1,587,776
Segment 3:
$5,332,139
Segment 4:
$1,782,410
 X-tab provides insight on total balances by cell and quadrant
 Cells map to total number of unique consumers
 Ability to better target highest value segments
 Helps forecast collection recovery results
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Balance transfer segmentation strategy
PIN counts by dollar amount and time
 X-tab provides insight on
number of consumers by
dollar amount and time
frame in months or years
 Allows you to prioritize
highest opportunity
transfers in addition to
using scores
 Ability to create separate
transfer strategies based
on all available data
points
Segment 1: 5,380
Segment 2: 8,025
Segment 3: 1,048
Segment 4: 1,386
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Experian Contacts
Bill Butler
Diana Csutak
Product Marketing Manager
T: 714 830 5497
[email protected]
Utilities Consultant
T: 562 621 9760
[email protected]
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