Analytical Data Sourcing and Optimization

Size: px

Start display at page:

Download "Analytical Data Sourcing and Optimization"

Marilyn Lindsay Ford
8 years ago
Views:

1 Analytical Data Sourcing and Optimization Willy Sennott Sr. Director, Business Analytics & Research, People to People Ambassador Programs Ozgur Dogan SVP, Data Solutions Leader, Merkle

2 Presenter Backgrounds Willy Sennott Sr. Director, Business Analytic and Research at People to People. 14 years in the Finance, Marketing and Analytics function at PTP Oversees all Marketing analytics, business ROI, pricing, list acquisition, customer research and modeling processes. CPA and graduate of University of Washington Business School Ozgur Dogan Data Solutions Leader at Merkle Oversees the delivery of digital and offline data sourcing and optimization solutions for Merkle s clients across all industry verticals Spent 9 years at Merkle and has 15 years of industry experience in building, implementing and integrating CRM solutions Technical MBA Degree from the University of Georgia 2

3 Agenda Understanding the CRM data landscape Quantitative framework to assess value of data Industry Perspective & Case Study: People to People What s Next: Digital Data Innovation 3

4 Agenda Understanding the CRM data landscape Quantitative framework to assess value of data Industry Perspective & Case Study: People to People What s Next: Digital Data Innovation 4

5 Trends in the Data Industry: 2013 Data Explosion Big Data is complex Need for multiple sources Change in what s valuable Privacy Increase of data sources, digital, fragmentation, and overall volume of data in unlike any other time in the industry Big Data is driving big confusion; not always translating into insights No single provider of data can do it all - there are plenty of data companies out there, but no company can have all the answers Smart Data is not a commodity Increased focus on consumer preference and data privacy 5

driving big confusion; not always translating into insights No single provider of data can do it all - there are plenty of data

6 In the age of Big Data, the amount of available CRM data is becoming more overwhelming SYNDICATED RESEARCH LIST MANAGEMENT SEGMENTATION TOOL PROVIDERS CREDIT DATA BUSINESS TO BUSINESS TRANSACTIONAL DATA COMPILERS 1 st Party CLIENT DATA 3 rd Party INTERNATIONAL DIGITAL DATA SPECIALTY COMPILERS 6

PROVIDERS CREDIT DATA BUSINESS TO BUSINESS TRANSACTIONAL DATA COMPILERS 1

7 Incredible amount of digital investment is accelerating innovation in the enabling data, technology, and analytics Innovation in digital data is creating opportunities for both online and offline audience targeting Display Media LUMAscape

Innovation in digital data is creating opportunities for both

8 Traditional Data Sourcing Model is Broken Data providers first recommend the data they own because of their business model and everyone says their data is the best $ Incentive system is broken. The more money marketers spend on data the more money the data brokers make There is limited or no accountability for business performance Data source recommendations are made based on aggregate list level performance data 8

The more money marketers spend on data the more money the data brokers make There is limited or no

9 How is the Analytic Approach Different Than Traditional Model? Traditional Approach to Data Sourcing Analytic Approach to Data Sourcing Recommendations Sell what we own; it s easiest to build. Driven by unbiased optimization approach Incentives Sell what we own; it s highest margin for us. Fully aligned with cost efficiency and performance goals Analytics Sell what we own; assume it s good. Use of Advanced Analytics and Granular Data Approach Product centric Consultative and solution oriented 9

build. Driven by unbiased optimization approach Incentives Sell what we own; it s highest margin for us.

10 Agenda Understanding the CRM data landscape Quantitative framework to assess value of data Industry Perspective & Case Study: People to People What s Next: Digital Data Innovation 10

11 Quantitative Framework for Assessing the Value of Data Key Dimensions for Evaluation: Data vendors provide sample files for evaluation Vendor 4 Composite Optimization Score Universe Expansion: does source increase breadth and coverage Descriptive Power: does source bring texture to an audience Source Quality: is the data reliable and accurate Predictive Power: does this data increase the predictive value of existing models 11

breadth and coverage Descriptive Power: does source bring texture to an audience Source Quality: is the

12 Digital Data Evaluation Example Data Optimization Lab Results: All Data Providers Source 2 Top Scoring Providers Descriptive Power Index Source Predictive Power Index Source 1 very high reach, moderate information richness, and high predictive strength. Source 2 medium reach, highest descriptive power, and the good predictive power. Other data providers delivered lower value. Universe Expansion Index represented by Bubble size. For illustration purposes 12

information richness, and high predictive strength.

13 Agenda Understanding the CRM data landscape Quantitative framework to assess value of data Industry Perspective & Case Study: People to People What s Next: Digital Data Innovation

14 People to People why we are passionate about what we do Remember not to blink!! 14

15 Premier Ambassadors Offerings Under the People to People Name Student Student Ambassadors Leadership Citizens Program Type Overseas Domestic Overseas Program Focus Student education and Student education and Professional exchange cultural exchange; leadership development; and cultural exchange, Primary profit driver Lower margin but feeder natural extension of to overseas program existing business model Target Market Age Age Age Primary Pricing Range $5,000 - $7,000 $1,700 - $3,000 $5,000 - $6,500 Length Days 5 10 Days 8 12 Days Three programs generated 96% of gross revenue and receipts in 2011 and will again in 2012 Annual Historical % of Individuals Traveled 60% - 75% 15% - 30% 5% - 10% Primary Destinations South Pacific Washington D.C. China Europe New York South Africa Asia California Russia Boston India 15

business model Target Market Age 10 18 Age 10 18 Age 45 80 Primary Pricing Range $5,000 - $7,000 $1,700 - $3,000 $5,000 - $6,500 Length 14 21 Days 5 10 Days 8 12 Days Three programs generated 96% of

16 Our Current Business Challenges and How We are Using New Data to Overcome Them Attitudinal Will let child travel Does child have interest Will attend meeting Demographics (age and income) Challenge is to get to here Psychographics Buying behavior, etc. Traditional data elements do not allow us to triangulate to our unique audience Of the audience population of ~ 29M there are currently less than 500K customers across the industry with no particular competitor owning more than 100K. PTP owns 20K of this audience. 16

Traditional data elements do not allow us to triangulate to our unique audience Of the audience population of ~ 29M there are

17 PTP: Refining our customer profile through Primary research Utilizing a MaxDiff Survey technique with both our current audience and the general population we are able to identify 8 segments within our general target market 3 Segment most likely to travel with PTP 2 Segments somewhat likely to travel with PTP 3 Segments not likely to travel with PTP ROI IMPACT: Identifying top 3 and bottom 3 segments PRIOR to investing hard DM $ in the prospects eliminates 54% of our initial marketing spend while retaining 88% of our customers 17

Segments somewhat likely to travel with PTP 3 Segments not likely to travel with PTP ROI IMPACT: Identifying top 3 and bottom 3

18 How our Traditional Data sourcing and Modeling process works Prospect / Lead Sourcing Traditional focus on compiled and DMA vendors A performance hierarchy developed from historical performance Sources based on ability to provide new prospects versus new data elements Modeling Regression using traditional data (largely demo and purchase behavior) Client specific indexes used to overlay against the model 18

Sources based on ability to provide new prospects versus new data elements Modeling Regression using

19 Risks with expanding Big Data Initial Lure exciting sexy Intuitive value Wrong Audience Outside of key data group Decision makers usually Missing ROI How does it change message? How does it change offer? 19

data group Decision makers usually Missing ROI How

20 How We See Online Data Aligning Us With Our True Customer Prospect / Lead Sourcing Traditional focus on compiled and DMA vendors A performance hierarchy developed from historical performance Sources based on ability to provide new prospects versus new data elements Online targeting and behaviors Resourcing data elements versus prospects or leads directly ROI challenge Modeling Regression using traditional data (largely demo and purchase behavior) Client specific indexes used to overlay against the model Using online behavior in first pass model Leveraging continued online and CRM data elements to remodel throughout the process 20

elements versus prospects or leads directly ROI challenge Modeling Regression using traditional data (largely demo and purchase behavior) Client specific

21 Agenda Understanding the CRM data landscape Quantitative framework to assess value of data Industry Perspective & Case Study: People to People What s Next: Digital Data Innovation

22 Connecting the Precision of Offline with the Reach of Digital Digital Big Data Platform Offline $50 Billion in DM spend 5 Year CAGR (-2%) Focus on PII Information High Cost Media Bridging the Gap Between Offline & Digital Marketing Connecting Anonymous and PII Data Integrated Offline with Digital Targeting Digital $15 Billion in Spend 5 Year CAGR (15%) Focus on Anonymous Data Limitless, Low-Cost Media 22

23 Next Generation Digital Data Platform Agnostic Data Collection & Onboarding Digital Data Platform Offline & Online 1 st Party 2 nd Party 3 rd Party Reach, Accuracy, Privacy Pixel Attributes Cookie Match Match to IP Address Data Market Place Target Audience Digital Media Partners Exchanges and DSPs Data Evaluation Module DMPs Agencies Marketers Targeting Optimization Module Predictive analytics Experimental design Segmentation 23

24 Evolving Data Landscape Future = (Anonymous + Personal) + (Offline + Digital) Public Domain CRM will be powered by a combination of offline, online, anonymous and personal data. Access to Smart data is not a commodity, and it won't be in 10 or 20 years. 24

25 Summary CRM data landscape is changing rapidly due to innovation and Big data explosion Analytically-led, unbiased approach is needed to determine the best mix of valuable digital and offline data sources that will drive high performance We are in the early but accelerating stages of an exciting journey and new approaches will be necessary.first movers will have a significant advantage 25

26 Thank you! Willy Sennott: Ozgur Dogan: 26

Matt Mobley Chief Marketing Technology Officer

Technology Infrastructure Matt Mobley Chief Marketing Technology Officer Here is what we have talked about so far. Segment Strategy Customer Strategy Portfolio Strategy Program Strategy These strategies