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Who’s Calling?
Customizing the Caller Experience to Feed the Bottom Line
Ken DawsonKen DawsonChief Marketing OfficerChief Marketing OfficerInfoCision Management Corp.InfoCision Management Corp.www.infocision.comwww.infocision.com
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Agenda
• Best practices in acquisition…Real-Time
• Scoring leads
• Customized offers
• Multi-channel marketing using business intelligence
• Use of skills based routing
• Online lead generation
• It’s all about the ROI! Run towards the Light!
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Predictive Modeling in Telemarketing Acquisition
“Let’s Crawl”
A Non Profit Case Study
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Challenge
• Non Profit clients traditionally use rental or exchange lists for acquisition efforts
• A 20% success rate of these lists is typical creating a tremendous “sunk cost”
• The goal is to develop and use a predictive model to improve results on rental lists
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Solution
• First step:
• Apply the model to rental lists to develop segmentation strategies
• Improve performance and drive down costs by eliminating lower deciles
• Second Step:
• Utilize analytics and variable script technology to customize the marketing message and/or the offer for further penetration of list
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Demographic
Who am I?
Transactional
What have I done?
Psychographic
What do I do?
First Step
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First Step
Define the current donors with profiling
Apply the model to
rental list and segment
prospects
Model the current donors to target for acquisition
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First Step
ControlCompleted Calls
DonorsRevenue Per Call
Response Rate
195,100 15,749 $3.46 8.07%
ModelCompleted Calls
DonorsRevenue Per Call
Response Rate
95,236 9,395 $4.08 9.86%
+17% +21%+17% +21%
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Second Step
• Now that the audience is scored and segmented
• How do we now impact the offer? Auction method?
• How do we customize the appeal? Prevention or treatment?
• Analyze various affluence indicators and their relationship to gift amounts
• Apply this information to develop a dynamic gift ask utilizing variable scripting technology
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• Findings:
• Household income displayed the highest correlation to gift amounts
• Household incomes were then broken into five income bands ranging from low to high
• Each income band was given a specific gift ask
• The key metrics we were looking to influence were:• Response rate
• Average gift
• Dollars per call
• Efficiency
Second Step
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• The five income bands used are:
• The second step was to index these incomes by the Cost of Living Index to normalize data
Estimated Household Income
$1 - $39,999$40,000 - $74,999$75,000 - $124,999$125,000 - $249,999$250,000 +
Second Step
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• The results were conclusive against the control:
Second Step
Control GRC$/CC CC RR% Avg. Gift$3.08 3,073 8.14% $37.47
Dynamic GRC$/CC CC RR% Avg. Gift$3.92 2,549 9.42% $41.66
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• Dynamic GRC results against control:
• Revenue per call increased by 27%
• Response rate increased by 16%
• Average gift increased by 11%
• Also showing an increase were credit card rates at 12%
• Not only were gross conversions impacted but stick rate and ROI dramatically improved
Second Step
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Lessons Learned
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• Predictive modeling can have a dramatic impact on acquisition results…even on response lists
• Crafting a message that resonates with the customer can increase both conversion AND retention (lifetime value)
• Access to analytic tools and clean data paramount to modeling and segmentation
• True value of this data must be unlocked with technology that allows truly customized offers to the consumer
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Utilizing Analytics and a Progressive Multi-Utilizing Analytics and a Progressive Multi-touch Marketing Strategy To Reduce touch Marketing Strategy To Reduce
Customer “Churn” Customer “Churn”
““Now We are Walking”Now We are Walking”
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• Client Profile: • National Communications provider with
“millions” of customers• Regional competition driving variable offers
that are hard to manage• Brick and Mortar stores carry significantly
higher cost structure and are focused on acquisition NOT retention
Multi-Channel
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• Shrinking retention budgets• Increasing mail and postage costs• Diminishing response rate to static Direct
Mail offers• Basic segmentation strategy did not
accurately reflect “churn”
Challenge
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• Utilize analytics and modeling to identify likely to “churn” customers
• Utilize analytics and modeling to unlock variable offers and segmentation
• Propose a multi-channel and cost-progressive strategy to increase ROI and marketing effectiveness
• Employ multiple call center strategies to reduce talk time and expense
Solution
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• Control Retention Program:• Basic segmentation strategy based on contract
expiration
• Direct Mail offers driven by current plan and usage only
• Timing starts at 90 days to expiration and continues through 60 days after contract expiration
• Each customer is mailed multiple times with same or similar offers
• Drive customer to inbound phone call for contract signing
Control
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Control
Units CostCustomers
Saved
Customer Base 3,000,000Identification Filter 0Customer Sent Direct Mail 3,000,000 $21,600,000Inbound Calls from Direct Mail 750,000 $4,125,000 202,500Total Cost of Direct Mail Campaign $25,725,000Direct Marketing Cost per Customer Saved $127.04
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Control Campaign (Direct Mail Sent to Every Customer)
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Multi-Channel Customer Retention Strategy
Multi-Channel
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1. Business Intelligence Group and Analytical Modeling
2. Variable Scripting and Offers
3. One-to-one Direct Mail/Digital Printing
4. Target Routing/Skill Based Routing
5. IVR Verification
6. Best Time To Call/Bucket Calling Efficiency Based Dialing Strategies
7. Front-end (Starter) / Back-end (Closer) Based Dialing Strategies
Strategies Employed
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Multi-Channel
Payment issues
2.225 m customersto be targeted
Certain geographies
Do not contact
2.25M customersto be targeted
Filter out
Propensity to churn
Over-utilization
Contract expiration
Old equipment
Low usage
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Multi-Channel
Keepcustomeraway from
retailoutlet
160kcalls
8 % RR
Do not textNon-
responders
E-VerificationE-Contract
Offer Basedon
BI Model
Offer Basedon
plan type
Phoneexclusive
offer
Filter
225kremoved
Old equip/No text
capability 160K Calls@ 8% RR
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Multi-Channel
Do Not Mail
E-VerificationE-Contract
Filter
ROI Filter:Usage/
Profitability
BadAddresses
Keepcustomeraway from
retailoutlet
461KRemoved
PhoneExclusive
Offer
Personalized
Geography
Offer BasedOn BI Model
Offer BasedOn Plan Type
DemographicPsychographic
Drivers
55KCalls @4% RR
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Multi-Channel
Do Not Call
E-VerificationE-Contract
Filter
Respondentsto Text orDirect Mail
Keepcustomeraway from
retailoutlet
BillingCycle
405KContacts
Made
Offer BasedOn BI Model
Offer BasedOn Plan Type
PhoneExclusive
Offer
74KCalls
MoreStringent
ROI Filters
649KRemoved
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Units CostCustomers
Saved
Customer Base 3,000,000Identification Filter 0Customer Sent Direct Mail 3,000,000 $21,600,000Inbound Calls from Direct Mail 750,000 $4,125,000 202,500Total Cost of Direct Mail Campaign $25,725,000Direct Marketing Cost per Customer Saved $127.04
Multi-Channel
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Control Campaign (Direct Mail Sent to Every Customer)
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Metric CostCustomers
Saved
Customer Base 30,000,000Identification Filter (27,775,000)Text Filter (225,000)Customer Sent Text 2,000,000 $80,000Inbound Calls from Text 160,000 $880,000 83,200Direct Mail Filter (460,920)Customer Sent Direct Mail 1,379,080 $992,938Inbound Calls from Direct Mail 55,163 $303,398 28,685Outbound Telemarketing Filter (648,719)Outbound Telemarketing Universe 675,198Outbound Telemarketing Contacts 405,119 $2,329,432 190,406Inbound Calls from Outbound TM 74,272 $408,495 38,621Total Cost of Direct Marketing Campaign $4,994,261 340,912Direct Marketing Cost per Customer Saved $14.65
Multi-Channel
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Multi-Channel Campaign
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Multi-Channel
ControlMulti-
Channel
Customers Saved 202,500 340,912
Direct Marketing Campaign Cost $25,725,000 $4,994,261
Cost per Customer Saved $127.04 $14.65
Sales Revenue of Saved Customers $96,130,800 $161,837,687
Percent of Saved Revenue Spent on Direct Marketing Efforts
26.76% 3.09%
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Multi-Channel Campaign Summary
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Lessons Learned
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• Analytics and modeling can be used to identify customers likely to “churn”
• Additional modeling can be used to craft the appropriate offer
• There must be communication and cooperation among all channels to identify the best approach in order to reduce marketing costs
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The Impact of Skills Based Routing
With Real Time Scoring
“Off and Running”
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Challenge
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• Leading consumer product company is seeing substantial success in new product launch– (YES, it’s a good thing)
• Calls driven by DRTV offers and have significant spikes
• Incumbent and in-house center have been taking calls for several years
• Over $700 average sale with no advertised price• Current strategy is “answer the calls, stupid”
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Results
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Initial Benchmarking ResultsRank Call Center Calls Handled Conversion Rate
First In-House 87,375 24.13%Second Competitor 25,596 22.49%Third InfoCision 38,192 21.98%
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Solution
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• First, implementing skills based routing to move beyond next available agent methodology
• Use real-time as well as historical data to make sure the best agents are taking the most calls
• Second, the application of real-time scoring to further enhance results
• “Ping” inbound callers against the pre-scored consumer database
• Use the real-time scoring to build the call queue and prioritize best leads
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Results
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Initial Benchmarking ResultsRank Call Center Calls Handled Conversion Rate
First In-House 87,375 24.13%Second Competitor 25,596 22.49%Third InfoCision 38,192 21.98%
Skills Based + Real Time Scoring Routing ResultsRank Call Center Calls Handled Conversion Rate
First InfoCision 32,985 26.41%Second In-House 78,649 23.74%Third Competitor 24,327 20.09%
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Lessons Learned
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• Analytics can be used real-time to determine the best agent to answer calls.
– You must look at both historical as well as real-time results to prioritize
• Real-time scoring can be used to create the priority queue as well as drive IVR solutions when needed to enhance overall results
• Superior customer experience (hold times, one call resolution, abandon rates, etc)
• HIGHER ROI!
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Rapid Response and Real-Time Scoring of Internet Leads
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Challenge
• Following up on internet driven leads quickly
• Combining self-reported data and real-time scoring to customize offer or message
• Matching the online lead with the right agent or counselor
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Solution
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Rapid Response Routing
Here’s how R3 works: Fast Response
A request comes in from yourwebsite
Quick Routing
Data is appended, offer created,Call is routed to Agent forOutbound dial
Intelligent Transfer
Calls are transferred to agentsor counselors if needed
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Market Applications
EducationStudent requests information
about specific campus or educational program
FinancialProspect requests more
information about a specific type of loan or offer
CommercialCustomer expresses interest
in a specific product line or service
Calls are routed to Agents or Counselors who are trained and knowledgeable on
those specific products and markets
Rapid Response Routing
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Lessons Learned
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• In the classic Crawl, Walk, Run scenario call centers have migrated through the steps to integrate intelligence into our marketing on all levels
• It’s imperative to have access to the best data in a real-time environment to maximize ROI
• The best models may not see the light of day without superior technology
• Don’t be afraid of the infrastructure or budgetary constraints of YOUR organization...find partners that have solutions in place to implement for you
• YOU CAN DO IT!
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Questions and Answers
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Who’s Calling?
Customizing the Caller Experience to Feed the Bottom Line
Ken DawsonKen DawsonChief Marketing OfficerChief Marketing OfficerInfoCision Management Corp.InfoCision Management Corp.www.infocision.comwww.infocision.com
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