omma audience targeting july 2014
DESCRIPTION
Driving Profitable Growth from High-Value Prospects You’re Not Reaching Today Much of the audience targeting focus today is on in-market users and how myriad online data signals can be mined to help marketers close existing demand today. It’s an integral part of your digital marketing strategy. But how do you grow your business beyond the strategies you are using today? Now it’s time to move up the funnel to identify and target high-value prospects that you are not reaching today and drive incremental growth for your business. The big question is, how to do it? What are the right data and techniques to use when you want to create demand from prospects who aren’t in-market today? How can you ensure your message is influencing not only the right prospects but the same prospects across channels and devices? And what are the right metrics for measuring success? Hear a case study of how one insurance marketer adopted a data-driven approach to audience targeting and drove incremental sales from high-value prospects. From audience creation, to cross channel targeting, and to campaign performance measurement, hear how this marketer successfully used offline data to target audiences based on value and proved it worked to profitably grow the business.TRANSCRIPT
The Demand Creation Experts Helping brands acquire new customers they are not reaching today.
David Dowhan, President, TruSignal OMMA Audience Targeting, LA July 23, 2014
Confidential & Proprietary
Top 5 insurance client challenge
2
The Challenge 1. Find profitable, new prospects
who are not yet in-market
2. No Black Boxes!
3. Target cross channel and cross device
4. Prove we acquired profitable, incremental policies
“I’ve got enough in-market campaigns running. How can I target the right
people before they are already in-market?”
Confidential & Proprietary
Target profitable prospects not yet in market?
Demos/Indexes Site buys“Where Data”
Start with the right kind of data
Profile Data“Who Data”
1:1 Audience Targeting
Convert Demand
In-Market Data“What Data” Behavioral
Retargeting DSP Algorithm
Mass Reach
Create Demand
3
Confidential & Proprietary
Needles in a haystack
4
TruSignal Offline Profile Data
Lot’s of Big Data - but what matters for insurance?
Identify High-Value Prospect Audience
9 99
10x the data that is available online today
1000s of attributes per record
First Party Data Sample +
Custom model finds the data that matters most for high-value prospecting
Your Custom Audience Model=
Confidential & Proprietary
High-Value Prospects 8,169,000
How did this compare with in-market data?
For every 1 “in-market” shopper there are 10 high-value prospects
In-Market Shoppers 745,600
5
Confidential & Proprietary
6
No Black Boxes
Confidential & Proprietary
Used ALL of the data that matters…
7
Combined 117 different factors to deliver accuracy and scale
• Home Value • Dwelling type • High purchase capacity • Household income • Length of residence • Interest in electronics • Interest in home decor • Holds multiple gas card • Buys expensive linens • Computer owner
Top 10 Data Factors
Financial CapacityDemographicsAsset InformationHousehold CompositionBusiness InformationGeographyProperty RecordsHobbies/InterestsCensusPublic RecordsPurchase History
34%
27%
Number of Data Factors by Category
21%
14%
8%5%
4%1%
Confidential & Proprietary
Found the same prospects in mobile, social, video, display
8
Cookie Data Sync160M Users
Newsfeed Mobile
Mobile
Twitter MopubNewsletters
TruSignal Audience
Direct Match
600M Emails
220M Mobile #s
RTB Exchanges
Top Portals
Video Trading DesksAD AGENCY
INDEPENDENT
DoubleClick for Publishers
Premium Display
DSPs
Confidential & Proprietary
Campaign Execution
9
Campaign Specs
Timing: 2 months
Media: Facebook Mobile (native) and RTB Exchanges
Total Budget: $100,000
Creatives: 42 units
Performance: Real-time pixel, CRM Matching
Confidential & Proprietary
10
CRM matching to validate performance
2. Offline Match of all Sales Data
YES
NO
Is this Customer in the TruSignal Audience?
TruSignal Campaigns
All Other Campaigns
Last 60 Days 1. CRM Data
Applications Converted Policy
$$$
$$
$$$$
to Policy Amount
3. Validate Campaign Performance
TruSignal All Other
Applications 721 3,197
Policies 609 2,315
Conversion Rate 84.5 72.4
Policy Total $2,100,000 $6,300,000
Revenue Per Policy $3,448 $2,721
% %
Campaigns
Confidential & Proprietary
Targeting the right people
11
TruSignal Other
72.4%84.5%
TruSignal Other
$2,721
$3,448
Do TruSignal Campaigns have better than average policy conversion rates?
Do the policies that convert have higher than average value?
Better Policy Conversion Rates17%
Better Revenue per Policy27%
Confidential & Proprietary
How many policies were purely incremental?
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Test Group
Control Group
Test Campaign Ad
All Other Marketing
Campaigns
1. Split Audience and Launch Campaigns
+
New Customers
Last 60 Days 2. CRM Data
Applications Converted Policy
$$$
$$
$$$$
to Policy Amount
CC C
TT T
3. Offline Match of all Sales Data
Test Group
Control Group
T
T T
CC
TT
C
Confidential & Proprietary
13
Prospecting: Test vs. Control
Test (
Control (
Incremental
Prospects 4,084,500 4,084,500 n/a
Policies 609 489 120
Total Revenue $2,100,000 $1,620,000 $480,000
120 Net New Policies
Incremental 20%
$480,000 Net New Dollars
Incremental 23%
Confidential & Proprietary
Why bother with CRM matching?
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Evaluate all sales - online and offline
!
Prospecting = longer sales cycle
!
Net new policy measurement
Only 20% of new applications captured by a pixel fire
Prospecting cycle can be 60+ days. Cookies crumble
Attribution models show correlation, but we needed to show actual causation.
Confidential & Proprietary
Final Report
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So how did we do?
The Challenge Find profitable, new prospects who are not yet in-market
No Black Boxes!
Target cross channel and cross device
Prove we acquired profitable, incremental policies
Confidential & Proprietary
Thank You!
The Demand Creation Experts
David Dowhan, President
[email protected] @daviddowhan