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

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!

3

Predictive Modeling in Telemarketing Acquisition

“Let’s Crawl”

A Non Profit Case Study

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

5

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

6

Demographic

Who am I?

Transactional

What have I done?

Psychographic

What do I do?

First Step

7

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

8

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%

9

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

10

• 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

11

• 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

12

• 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

13

• 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

14

Lessons Learned

15

• 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

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”

• 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

17

• 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

18

• 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

19

• 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

20

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

21

Control Campaign (Direct Mail Sent to Every Customer)

Multi-Channel Customer Retention Strategy

Multi-Channel

22

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

23

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

24

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

25

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

26

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

27

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

28

Control Campaign (Direct Mail Sent to Every Customer)

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

29

Multi-Channel Campaign

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%

30

Multi-Channel Campaign Summary

Lessons Learned

31

• 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

The Impact of Skills Based Routing

With Real Time Scoring

“Off and Running”

Challenge

33

• 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”

Results

34

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%

Solution

35

• 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

Results

36

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%

Lessons Learned

37

• 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!

Rapid Response and Real-Time Scoring of Internet Leads

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

39

Solution

40

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

41

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

42

Lessons Learned

43

• 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!

Questions and Answers

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