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KNOWLEDGE FOR ACTION
Identifying & Maintaining Great Financial Advisors
Data at a Glance:Observation Window: Jan. 2008 - Feb. 2016
Data are collected from a leader in the financial services industry and track ~174,000 financial advisors selling financial products:
• Transactional Data: Over 3 million individual sales transactions of financial products
• Activity Data: Over 17 million individual interactions between LFG and its advisors
• Opportunities Data: Over 2 million records of individual contract opportunities submitted by advisors
• 3rd Party Advisor Data: Marketing and financial services industry data profiling advisors and prospects
• Survey Data: Self-reported survey data on advisors
Submit your proposal by Monday, June 6th, 2016. Learn more at http://wcai.wharton.upenn.edu/research
Sponsored by Lincoln Financial Group
Presentation recording and slide deck will be made available via wcai.wharton.upenn.edu by Monday, May 23, 2016
WEBINAR WILL BEGIN SHORTLY
KNOWLEDGE FOR ACTION
A WCAI Research OpportunitySponsored by Lincoln Financial Group
May 20th, 2016
Identifying & Maintaining Great Financial Advisors
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Introductions
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Brandon KrakowskyResearch Director WCAI
Debbie ShapiroAssociate Research Director WCAI
Colleen O’NeillExecutive DirectorWCAI
Elea FeitSenior Fellow, WCAIAssistant Professor of Marketing, Drexel University
Anjna KumarChannel & Data Insights –Business IntelligenceLincoln Financial Group
Sandeep AjithManager, Sales Reporting ProjectsLincoln Financial Group
Michael BaldinoMarketing AnalystLincoln Financial Group
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Lincoln Financial Group provides advice and solutions that help empower Americans to take charge of their financial futures with confidence and optimism.
NYSE:LNC
110 yearsFounded in 1905, Abraham Lincoln’s son,Robert Todd Lincoln, grants permission
to use his father’s likeness.
9,308current
employees
life, income, retirement,employee benefits, advice
Our core business areas focus on supporting, preserving and enhancing people’s lifestyles and
retirement outcomes.
More than
17 million peopletrust Lincoln for their retirement,insurance and wealth protection
needs
As of December 31, 2015
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Lincoln Financial Products
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Product offerings through Lincoln Wholesale Distribution
Life Insurance and Long Term Care (MoneyGuard)
Fixed and Variable Annuities
Defined Contribution (401k and 403b)
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We’re looking forward to developing a predicted lifetime value model of our advisors, enabling us to view our advisors as assets and our sales / marketing initiatives as quantifiable investments in those advisors.
• Selection: developing a model that predicts an advisor’s future value and enables Lincoln to act on retaining the best advisors
• Segmentation: identifying the demographic, exchange, and psychographic variables that drive value in each of our advisor groups
• Prospect Targeting: developing a profile of our best advisors and leveraging it to target the most valuable advisors in our prospect pool
• Resource Allocation: maximizing the return of our advisor marketing / sales touchpoints
Areas of Interest:
KNOWLEDGE FOR ACTION
A Few Operational Notes
This webinar is intended for researchers & scholars. It should not be published or presented without permission from WCAI.
Questions about our research program or for press inquiries:
To get access to the data, research teams must submit a proposal to WCAI and the sponsor for approval. More details at the end of this presentation.
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Data Background
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Data Background
10
• Data are collected from Lincoln Financial Group, a company that is largely known for selling life insurance, annuities, and other financial products
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Data Background
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• Data are collected from Lincoln Financial Group, a company that is largely known for selling life insurance, annuities, and other financial products
• Financial advisors are observed selling these products for LFG over an eight-year period
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Data Background
12
• Data are collected from Lincoln Financial Group, a company that is largely known for selling life insurance, annuities, and other financial products
• Financial advisors are observed selling these products for LFG over an eight-year period
• Each transaction is an individual payment on a single contract for a single financial product
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Data Background
13
• Data are collected from Lincoln Financial Group, a company that is largely known for selling life insurance, annuities, and other financial products
• Financial advisors are observed selling these products for LFG over an eight-year period
• Each transaction is an individual payment on a single contract for a single financial product
• Transactions include lump sum payments and periodic payments
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Data Background
14
• Each contract is tied to a single financial product
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ContractProduct
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Data Background
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• Each contract is tied to a single financial product
• There are one or more transactions tied to a single contract
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Transaction
Transaction
Transaction
ContractProduct
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Data Background
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• Each contract is tied to a single financial product
• There are one or more transactions tied to a single contract
• Each transaction is tied to a single financial advisor
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Transaction
Transaction
Transaction
ContractProduct
Advisor A
Advisor A
Advisor B
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A Single Financial Advisor in the Data
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Ben is a financial advisor who works in Ventura County in California, and sells financial products for Lincoln Financial Group
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A Single Financial Advisor in the Data
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Ben sells one of his clients a variable annuity and creates a contract specifically for that product
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Variable Annuity Contract
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A Single Financial Advisor in the Data
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Ben’s client makes an initial lump sum payment to LFG for $47,000
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Variable Annuity Contract
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A Single Financial Advisor in the Data
20
One month later, he makes a second lump sum payment of $3,900
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Variable Annuity Contract
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A Single Financial Advisor in the Data
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For every month after that, Ben’s client adds to his variable annuity by making a periodic payment of $100 …
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Variable Annuity Contract
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A Single Financial Advisor in the Data
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For every month after that, Ben’s client adds to his variable annuity by making a periodic payment of $100 …
… We can see that every payment made to LFG on the client’s variable annuity is linked to the contract
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Variable Annuity Contract
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We can also see that each individual payment is linked to the financial advisor Ben
A Single Financial Advisor in the Data
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Variable Annuity Contract
Ben Ben Ben Ben Ben Ben Ben Ben
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Additionally, LFG might send Ben an anonymous financial advisor survey to learn more about him and his portfolio …
A Single Financial Advisor in the Data
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Variable Annuity Contract
Ben Ben Ben Ben Ben Ben Ben Ben
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Additionally, LFG might send Ben an anonymous financial advisor survey to learn more about him and his portfolio …
… This can also be linked directly to Ben
A Single Financial Advisor in the Data
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Variable Annuity Contract
Ben Ben Ben Ben Ben Ben Ben Ben
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A Single Financial Advisor “Team” in the Data
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Sometimes, Ben works with another advisor, Rocco, in the same office …
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Note: Advisor “teams” are inferred from the data
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A Single Financial Advisor “Team” in the Data
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Sometimes, Ben works with another advisor, Rocco, in the same office …
... This means they work as part of a team on a single contract and product
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Note: Advisor “teams” are inferred from the data
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A Single Financial Advisor “Team” in the Data
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Taking our previous example of the variable annuity, we can see that each individual payment is linked to either Ben or Rocco across the contract …
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Variable Annuity Contract
Ben Rocco Rocco Rocco Rocco Rocco Rocco Rocco
Note: Advisor “teams” are inferred from the data
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A Single Financial Advisor “Team” in the Data
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Taking our previous example of the variable annuity, we can see that each individual payment is linked to either Ben or Rocco across the contract …
... And we can assume that individual financial advisors are compensated a percentage of the payments they process
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Variable Annuity Contract
Ben Rocco Rocco Rocco Rocco Rocco Rocco Rocco
Note: Advisor “teams” are inferred from the data
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A Single Financial Advisor “Team” in the Data
30
So we can see all contracts that are linked to Ben within the observation window …
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Contract BContract A
Ben
Note: Advisor “teams” are inferred from the data
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A Single Financial Advisor “Team” in the Data
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So we can see all contracts that are linked to Ben within the observation window …
... And the contracts shared with other advisors on his team
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Contract C
RoccoBen
Contract BContract A
Note: Advisor “teams” are inferred from the data
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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Products
34
We observe 6 different groups of LFG products being sold as part of the individual transactions within our observation window
• These include:– (VA) Variable Annuities– (LIFE) Life Insurance– (FA) Fixed Annuities– (MG) MoneyGuard (Long-term care insurance)– (EB) Executive Benefits– RPS Small Market (Retirement Plan Services)
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Products
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• Product groups are divided into 24 different financial products
Product Group Product
More info on LFG products: https://www.lfg.com
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Number of Contracts by Product Group
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Number of Contracts by Product Group
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Over 550K contracts are for variable annuities
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Number of Products Sold by Each Advisor
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# of Products Sold
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# of Products Sold
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Number of Products Sold by Each Advisor
Over 117K advisors are selling products from a single product group
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# of Products Sold
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2 advisors are selling products from all 6 product groups
Number of Products Sold by Each Advisor
Over 117K advisors are selling products from a single product group
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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Transactions
42
The dataset includes over 3 million individual transactions of LFG financial products from Jan. 2008 to Feb. 2016
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Transactions
43
The dataset includes over 3 million individual transactions of LFG financial products from Jan. 2008 to Feb. 2016
• Transactions are payments on financial contracts for individual financial products, like variable annuities and life insurance
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Transactions
44
The dataset includes over 3 million individual transactions of LFG financial products from Jan. 2008 to Feb. 2016
• Transactions are payments on financial contracts for individual financial products, like variable annuities and life insurance
• Transactions include lump sum payments and periodic payments
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Transactions
45
The dataset includes over 3 million individual transactions of LFG financial products from Jan. 2008 to Feb. 2016
• Transactions are payments on financial contracts for individual financial products, like variable annuities and life insurance
• Transactions include lump sum payments and periodic payments
• We observe ~174,000 financial advisors processing transactions on more than 1.2 million different contracts
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Transactions
46
The dataset includes over 3 million individual transactions of LFG financial products from Jan. 2008 to Feb. 2016
• Transactions are payments on financial contracts for individual financial products, like variable annuities and life insurance
• Transactions include lump sum payments and periodic payments
• We observe ~174,000 financial advisors processing transactions on more than 1.2 million different contracts
• It can be assumed that individual financial advisors are compensated a percentage of the payments they process
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Transactions
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Some of what’s included in a single transaction
• Financial contract:– Unique identifier– Product name and description– Start date of coverage
Note: Some contracts started before the observation window
• Financial advisor:– Unique identifier– Advisor county and state
• Customer:– Name
Note: This is hashed to de-identify the customer
• Details of the sale:– Transaction date– Payment amount
Note: This is the amount paid to LFG, not the advisor
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Number of Contracts Per Single Advisor
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# of Contracts
Note: These include ALL contracts, including those that were “reversed” or returned
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# of Contracts
Note: These include ALL contracts, including those that were “reversed” or returned
Number of Contracts Per Single Advisor
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Over 56K advisors are working on a single contract throughout the entire observation window
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# of Contracts
Note: These include ALL contracts, including those that were “reversed” or returned
Number of Contracts Per Single Advisor
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14 advisors are connected to 1,000-10,000 contracts
Over 56K advisors are working on a single contract throughout the entire observation window
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Total Number of Contracts Per Team Size
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# of Advisors per Team
Note: Advisor “teams” are inferred from the data
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Total Number of Contracts Per Team Size
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# of Advisors per Team
Note: Advisor “teams” are inferred from the data
Over 1.1 million contracts are associated with advisors who work alone
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Total Number of Contracts Per Team Size
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# of Advisors per Team
Note: Advisor “teams” are inferred from the data
Over 127k contracts are associated with teams of 2 advisors
Over 1.1 million contracts are associated with advisors who work alone
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Total Number of Contracts Per Team Size
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# of Advisors per Team
Note: Advisor “teams” are inferred from the data
Over 1.1 million contracts are associated with advisors who work alone
There is a single contract associated with 10 different advisors
Over 127k contracts are associated with teams of 2 advisors
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Average Number of Contracts Per Team Size
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Note: Advisor “teams” are inferred from the data# of Advisors per Team
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Average Number of Contracts Per Team Size
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Note: Advisor “teams” are inferred from the data# of Advisors per Team
An average of 5 contracts for a team of 6 advisors
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Transactions: Data Snapshot
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Makes initial paymentMakes 2nd payment
Makes recurring payments
Advisor linked to transaction
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QUESTIONS
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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Activities
60
The dataset includes over 17 million individual interactions between LFG and its advisors from Jan. 2008 to Feb. 2016
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Activities
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The dataset includes over 17 million individual interactions between LFG and its advisors from Jan. 2008 to Feb. 2016
• Internal activities– E-mail communications– Sales calls– Snail mail activities– Literature orders
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Activities
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The dataset includes over 17 million individual interactions between LFG and its advisors from Jan. 2008 to Feb. 2016
• Internal activities– E-mail communications– Sales calls– Snail mail activities– Literature orders
• External activities– Face to face sales meetings
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Activities
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The dataset includes over 17 million individual interactions between LFG and its advisors from Jan. 2008 to Feb. 2016
• Internal activities– E-mail communications– Sales calls– Snail mail activities– Literature orders
• External activities– Face to face sales meetings
• “Hypothetical” activities– Communications for client prospects and potential contracts
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Activity Types
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Activity Types
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Most activities are internal sales calls and e-mail communications
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Activities
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Some of what’s included in a single activity
• Financial advisor:– Unique identifier
• Financial product:– Relevant product line and name
• Details of the interaction:– Interaction date– Type and Description
e.g. Internal Sales Call (Internal Activity), One on One (External Activity)
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Number of Activities vs. Number of Contracts for Advisors
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Number of Activities vs. Number of Contracts for Advisors
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Advisors who have more interactions with LFG have more contracts
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Activities: Data Snapshot
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Receives sales call from LFG rep
Has one on one meeting with LFG rep
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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Opportunities
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The dataset includes over 2 million records of new contract requests submitted by advisors to LFG
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Opportunities
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The dataset includes over 2 million records of new contract requests submitted by advisors to LFG
• Some opportunities become contracts – This means we’ll eventually see transactions
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Opportunities
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The dataset includes over 2 million records of new contract requests submitted by advisors to LFG
• Some opportunities become contracts – This means we’ll eventually see transactions
• Other opportunities never become contracts– They may have been withdrawn or the policy applicant may have been
declined by LFG
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Opportunities
74
Some of what’s included in a single opportunity
• Financial advisor:– Unique identifier
• Financial product:– Relevant product line and name
• Details of the opportunity:– Date of submission– Pending Status
e.g. Open, Closed, Declined
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Number of Opportunities vs. Number of Contracts for Advisors
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Number of Opportunities vs. Number of Contracts for Advisors
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Small number of advisors who submit a lot of opportunities
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Opportunities: Data Snapshot
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Submits the contract request to LFG
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QUESTIONS
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
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3rd Party Financial Advisor Data
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The dataset includes 3rd party marketing and financial services industry data, profiling over 300,000 individual financial advisors
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3rd Party Financial Advisor Data
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The dataset includes 3rd party marketing and financial services industry data, profiling over 300,000 individual financial advisors
• The data is compiled from general direct-marketing industry data and state regulatory data filings
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3rd Party Financial Advisor Data
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The dataset includes 3rd party marketing and financial services industry data, profiling over 300,000 individual financial advisors
• The data is compiled from general direct-marketing industry data and state regulatory data filings
• It provides additional background and demographic information about financial advisors selling products for LFG
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3rd Party Financial Advisor Data
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The dataset includes 3rd party marketing and financial services industry data, profiling over 300,000 individual financial advisors
• The data is compiled from general direct-marketing industry data and state regulatory data filings
• It provides additional background and demographic information about financial advisors selling products for LFG
• It also profiles financial advisors who are not selling products for LFG– These are considered “prospects”
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3rd Party Financial Advisor Data
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The 3rd party data profiles financial advisors selling products for LFG …
Advisors SellingLFG Products
3rd PartyFinancial Advisor Data
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3rd Party Financial Advisor Data
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The 3rd party data profiles financial advisors selling products for LFG …
… It also profiles prospects
Advisors SellingLFG Products
3rd PartyFinancial Advisor Data
AdvisorProspects
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3rd Party Financial Advisor Data
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Some of what’s included for a single financial advisor
• Advisor– Primary county and metropolitan area– Age, gender, & marital status– Income and net worth– Preferred sales channel– Date became registered as an advisor– Number of states registered in
• Broker-Dealer Firm– Type of firm
e.g. Bank, Wire, Regional, Independent– Products offered
e.g. Municipal Securities, Mutual Funds, Private Equity Funds
• And many other variables …
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Note: 3rd party marketing data points include general direct-marketing preferences, not LFG specific
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Marketing Data: Number of Advisors by Income
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Note: Some 3rd party marketing data is self-reported, so data point values may be unknown for some advisors
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Marketing Data: Number of Advisors by Income
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Note: The income is unknown for some of the advisors
Note: Some 3rd party marketing data is self-reported, so data point values may be unknown for some advisors
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Financial Services Industry Data: Number of Advisors Registered by Number of States/Territories
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# of Sates
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Financial Services Industry Data: Number of Advisors Registered by Number of States/Territories
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# of Sates
Many advisors registered in a single state
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3rd Party Financial Advisor Data: Data Snapshot
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Self-reported Income Self-reported preferred DM channel
Data from direct-marketing industry …
Note: 3rd party marketing data points include general direct-marketing preferences, not LFG specific
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3rd Party Financial Advisor Data: Data Snapshot
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Data from direct-marketing industry …
… and data from the financial services industry
Other financial products offered
Self-reported Income Self-reported preferred DM channel
Number of states registered in
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Activities
Transactions
3rd Party Advisor Data
Self-Reported Survey Data
Financial Advisors
Products
Opportunities
Data Structure
KNOWLEDGE FOR ACTION
Self-Reported Survey Data
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The dataset includes survey responses from 8 different surveys sent by LFG to advisors who sell its products and prospects who currently do not
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Self-Reported Survey Data
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The dataset includes survey responses from 8 different surveys sent by LFG to advisors who sell its products and prospects who currently do not
• Surveys capture information about advisors and LFG itself:– Identify trends in the financial product market– Measure preference for new LFG product concepts– Gauge familiarity and perceptions of the LFG brand– Evaluate effectiveness of LFG marketing initiatives
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Self-Reported Survey Data
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Some of what’s included in a single survey submission
• Financial advisor:– Unique identifier
• Survey responses:– Answers to over 30 single choice and multiple choice questions– Free-text responses– Additional demographic information
• Details of the submission:– Date of submission
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What is the average size of an individual client portfolio that you manage?
Example of Survey Question with Responses
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Self-Reported Survey Data: Data Snapshot
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Single Choice Question Example:Which of the following best describes your current job?
KNOWLEDGE FOR ACTION
Self-Reported Survey Data: Data Snapshot
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Single Choice Question Example:Which of the following best describes your current job?
Single Choice & Free-Text Question Example:Do you work as a sole practitioner, as part of a team, or other?
KNOWLEDGE FOR ACTION
QUESTIONS
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KNOWLEDGE FOR ACTION
Possible Research Areas
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• Selection: developing a model that predicts an advisor’s future value and enables Lincoln to act on retaining the best advisors– What components are needed to predict lifetime value (PLTV) of an advisor?– How can we develop sound customer attrition rate methodologies amongst our product
offerings– Are there product focused conclusions we can make to inform our retention strategies?
• Segmentation: identifying the demographic, exchange, and psychographic variables that drive value in each of our advisor groups– What would these segments look like? – What variables would be coupled with PLTV to develop segments?
• Resource Allocation: maximizing the return of our advisor marketing / sales touchpoints– How does geographic and temporal factors influence an individual advisor’s performance?– What marketing and sales activities generate the most value?– Are there product focused conclusions to inform our cross sell strategies?
• Prospect Targeting: developing a profile of our best advisors and leveraging it to target the most valuable advisors in our prospect pool– Are there product focused conclusions we can make to inform our prospecting strategies?
KNOWLEDGE FOR ACTION
Possible Research Areas
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• Selection: developing a model that predicts an advisor’s future value and enables Lincoln to act on retaining the best advisors– What components are needed to predict lifetime value (PLTV) of an advisor?– How can we develop sound customer attrition rate methodologies amongst our product
offerings– Are there product focused conclusions we can make to inform our retention strategies?
• Segmentation: identifying the demographic, exchange, and psychographic variables that drive value in each of our advisor groups– What would these segments look like? – What variables would be coupled with PLTV to develop segments?
• Resource Allocation: maximizing the return of our advisor marketing / sales touchpoints– How does geographic and temporal factors influence an individual advisor’s performance?– What marketing and sales activities generate the most value?– Are there product focused conclusions to inform our cross sell strategies?
• Prospect Targeting: developing a profile of our best advisors and leveraging it to target the most valuable advisors in our prospect pool– Are there product focused conclusions we can make to inform our prospecting strategies?
Something else not on this list!
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Proposal Format
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Proposals should be in PDF format, no more than 2,000 words, and include the following information:• Title• Author(s) name, title, affiliation and e-mail address: Please designate a corresponding
author.• Summary information: a single slide that visually summarizes team & project.• Abstract• Introduction: Describe expected contribution(s), covering both the academic and practical
aspects. Please keep it concise, and cite relevant work as necessary to explain your academic contribution. There is no need to include a lengthy literature review.
• Detailed project proposal: Please include enough detail that we can assess the feasibility & merit of the proposed approach. For example, modeling projects should include at least a sketch of the model. In addition, include a rough estimate of how long the project will take. Also include the business relevance of your research and the impact for the sponsor as well.
• Data Needs: Bulleted list of data required or requested for analysis not explicitly mentioned during this webinar. While we can’t guarantee the inclusion of these items, we are happy to investigate the availability.
• Biographies: Include up to a paragraph-long biography highlighting what each team member will contribute to the project.
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Proposal Submission Process
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• Read more about the proposal format and Submit Proposals here:http://wcai.wharton.upenn.edu/for-researchers/get-the-data/
No later than Monday, June 6th, 2016, at Midnight US Eastern
– Proposals will only be accepted online
– A single PDF combining the written portion and the single slide
• Proposals will be evaluated both on academic contribution and potential to significantly improve the data sponsor’s practice by the proposal review committee:
Eric Bradlow (WCAI), Pete Fader (WCAI), Elea Feit (Drexel University),Andrew Petersen (Penn State)
and representatives from Lincoln Financial Group
• Contact [email protected], if you have questions prior to submitting your proposal.
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Other WCAI Opportunities for Researchers
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If you registered for this webinar, you will receive regular announcements regarding:
• Upcoming Research Opportunities:
- Marketing Optimization & Personalized Treatment Solutions for Customers of a Multinational Bank
- Predicting & Preventing Cyber-Attacks in the Financial Services Industry
Also find us at:
• SSRN Research Paper series: http://www.ssrn.com/link/Wharton-Cust-Analytics-Initiative-RES.html
• Sign up for updates: http://wcai.wharton.upenn.edu/newsletter/
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