Module Two: Connected CRM Value Creation. How to Integrate the Connected Consumer View Across Media and Channels

Transcription

1 Module Two: Connected CRM Value Creation How to Integrate the Connected Consumer View Across Media and Channels 1

2 Creating Value Through Connected CRM Improve Customer Experience Know the customer across channels Know about service issues and needs Communicate consistently across channels Drive Revenue Understand customer value potential Understand customer needs and affinity Optimize channel sales potential Increase Efficiency Differentiate cost of service Promote use of appropriate & efficient channels Get it right, the first time 2

3 Capitalizing on Moments of Truth Seizing opportunities for greater sales and better customer experience will produce growth through retention and expansion. Each day, millions of customer interactions Inbound Transactions Customer Service Problem/Issue Relatively small number generate incremental value Barriers Organizational alignment Disparate systems that don t communicate Overwhelming amount of data, typically captured for operational implementation Lack of informed offers with individual relevancy Lack of measurement and understanding of customer potential, migration, and retention impact 3

4 Channel Media Connected Customer Interaction Inbound Outbound Phone/IVR Mobile ATM Branch TV Print Direct mail Search Social Site Direct response Radio Direct Response Web Display Audience Identification Service/Offer Prioritization Channel Optimization Audience Selection Offer Selection Contact & Media Optimization Information Insights Measurement Targeting Optimization Agility 4 Strategy Value Segmentation Measurement Execution Analytics Integration Organization Technology Database Access Automation

5 Attribution and Measurement Cross Media and Channel Optimization 5

6 Integration Attribution and optimization enables value creation once the data has been integrated The Digital Marketing Value-Chain Data Management Integration Segmentation LTV Media history Attribution & Optimization Attribution Mix optimization and planning Optimized plan by segment Individual level targeting Execution Direct integration to media marketplaces (exchanges, bidding platforms, etc.) Real time bid management and optimization First Party Data Second Party Data Third Party Data 6

7 Once we have the connected consumer view, integrated analytics allows us to optimize at the segment & individual level Connected Customer View Measurement & Attribution Segment and Individual Level Optimization Month 1 Month 2 Month 3 Product LTV Segment Demographics Display Life Events Print Search Mobile DM &EM TV/Video Site Social Optimization starts with a connected customer view across media and channels. This is the only way to truly create customer and segment level optimization. Attribution allows us to accurately determine the performance of historical marketing activity. It also must give insight into drivers behind marketing performance. Optimization engine prescribes optimal mix, cadence, and targeting that create maximum ROI given business constraints. Output is a segment and consumer-specific mix, contact cadence, and targeting plan 7

8 Attribution Goal: Accurately allocate credit to every marketing touch leading to conversion Actual experience Day 8-30 Day 1-7 Day 0-1 $ New Customer: Inspiron purchase Direct Rules Based 100% 30% 40% 30% Customer calls the 800, so TV gets credit TV over-attributed Consumer clicked on site link. Rules: TV and DM exposure possible in last 7 days, so assign credit Better but still not accurate Modeled 3% 14% 3% 5% 5% 5% 15% 5% 5% 40% Model-adjusted interaction Most accurate and actionable Mass and Offline Digital Direct mail sent TV view Newspaper view Display view Website visit Social visit Paid search click 8

9 Integration of top-down and bottoms up Top-down media mix model (Traditional media mix model: DMA by week level, 12+ months of data) Cost per inquiry by tactic National media (TV & radio) $140 Local media (TV & radio) $200 Direct mail $180 Digital $83 Bottom-up digital attribution model Calibration layer (New media mix model: zip by day level, can be built with only 1 month of data, 70+ programs estimated) Cost per inquiry by tactic Display $60 Video $80 Search $91 Lead Gen $75 Social $113 Remarketing - $12 Guaranteed - $80 Non-guaranteed - $30 Auto / insurance - $18 Video 1 - $121 Video 2 - $35 Video 3 - $213 Video 4 - $23 Video 5 - $8 Video 6 - $4 Search 1 - $115 Search 2 - $87 Agg 1 - $105 Agg 2 - $103 LG 1 - $190 LG 2 - $163 Search 3 - $39 Agg 3 - $58 LG 3 - $87 Agg 4 - $53 LG 4 - $74 Agg 5 - $14 Agg 6 - $126 LG 5 - $32 Agg 7 - $25 Agg 8 - $5 LG 6 - $29 LG 7- $11 Agg 9 - $5 Agg 10 - $4 9

10 Display Online Video Social Search Online Lead Gen Mobile SMS Direct Mail Outbound Telemarketing DRTV Print Radio National TV Outdoor Practical Attribution Framework Top-Down Modeling (Marketing Mix) Calibration Layer Digital Media Direct Media Mass Media Modeling that estimates across all media at broad level Calibration layer integrates the absolute accuracy from topdown with the relative accuracy from bottom-up. This includes in market testing Future Extend mass media measurement down to user level Digital Attribution Anonymous (Cookie/IP) Direct Attribution PII (Address, , Phone) Bottom-up modeling Modeling specific to media categories that is derived from most granular level available 10

11 Media and channel optimization analytics take a forward looking view to generate segment and individual-level plans Customer-level optimization Agile, segment level cross media plan Who to target Segment and Individual Level Optimization What product and offer Month 1 Month 2 Month 3 When and how often to target Where to reach customer (channel/media) How should we engage the consumer? What is the right message? How much should we spend to target them? 11

12 Case Study: Insurer Measurement Evolution Background Client had significant acquisition budget split across online/offline channels with historical bias towards mass media brand spend Had used last-view attribution approach for all decisioning which they knew was inaccurate Solution Understand true historical performance of all media towards generating high-value segment conversion and define go-forward optimal budget allocation (Top Down) Optimize actual execution and performance of new mix within digital and direct mail (Bottom up) 12

13 Attribution Approach Comparison Topdown Probabilistic (MMO) attribution approach yields dramatically different results than direct attribution. Conclusion was made to shift dollars from offline to online. Acqusition Costs by Media $1,200 $1,000 $800 $600 $400 $200 $ Display Paid Search Direct (Before) Online Video Direct Mail DRTV Mix Model (After) Brand TV Display has the lowest cost per and greatest growth potential Test into significantly more Display Many media are contributing to search lead volumes Increase Paid Search Online Video is also much more effective than previously understood. Expand video Brand TV Program is performing better than thought. Even so: Shift from TV to Digital 13

14 Accurately attributing performance within media Bottomup Now that, decision to shift budget to digital was made, challenge shifted to how to effectively spend that money. Conversion for the sequence Probability D1 D2 D3 Conversion for the sequence without D1 D2 Weight for D1 = [ Probability(conversion for the sequence) - Probability (conversion for the sequence without D1) D3 $ $ New Life Policy, Segment A New Life Policy Segment A The cookie level data contains a wide range of ad interaction sequences Given any sequence of interactions, we calculated the probability of conversion for that sequence By comparing these conversion probabilities for interaction sequences we isolated the individual impact of each of the interaction and assign a weight to it 14

15 Accurately attributing performance within media Bottomup Bottom-up attribution approach yielded very granular, actionable outputs Incremental performance report based on attribution methodology Compare to last click performance report Incremental Model Rules Video 5 Video 2 Display 5 Video 1 Display 4 Video 3 Video 4 Display 6 Display 2 Display 1 Display 3 Reporting down to granular level. Specific sites, creative, programs, etc. 15

16 Informed Digital Media Applying existing assets and knowhow to new media 16

17 Digital Media Execution 17

18 Digital Media Opportunity Technology Technology Current Market Approach Normal Evolution Lead Competitive Advantage Opportunity Ad Tech& Data Driven Skillsets Lead Skill sets Skill sets 18

19 DM List Plan Analogy Typical DM List Plan Client Goal: 200,000 Grand Totals 450, % 237, % $87.47 $23, $42.21 Keycode Use Type List Name Selection Order Merge Net Mail Quantity Retention Quantity Responses Response Cost Per CPM Total Cost % Acq MMOV R 2 List 1 1 Month New Mover Age , % 27, % $60.18 $1, $ MMOV Continuation List 2 1 Month New Mover Age , % 27, % $74.91 $2, $ MMOV R 3 List 3 1 Month New Mover Age , % 27, % $60.18 $1, $ MNATG Continuation List 4 1 Month Subscribers Age , % 29, % $89.83 $2, $ MVET Continuation List 5 1 Month Members Age 60-79, HHI $50K+ 50, % 27, % $93.89 $2, $ MANIM R 2 List 6 1 Month Members Age 60-79, HHI $50K+ 50, % 23, % $86.74 $2, $ MANIM Continuation List 7 1 Month Members Age 60-79, HHI $50K+ 50, % 23, % $ $2, $ MGRIP Continuation List 8 1 Month Buyer Age , % 26, % $ $3, $ MGRIP R 2 List 9 1 Month Buyer Age , % 26, % $89.23 $2, $ What if your DM agency told you that from now on you couldn t use your model to select names from lists, that the lists sources themselves might not be visible to you, and that you could not de-dup across lists so if you buy the same person on 3 lists well, too bad 19

20 DM List Plan Analogy Today s Typical Digital Media Plan Media Partner Flight Placements CPM Monthly Spend Total Spend Total Impressions Publisher 1 Jan 1 - March 31 ROS $ $ 35, $ 105, ,500,000 Publisher 2 Jan 1 - March 31 ROS $ 8.00 $ 25, $ 75, ,375,000 Publisher 3 Jan 1 - March 31 ROS $ $ 45, $ 135, ,250,000 Ad Network 1 Jan 1 - March 31 RON $ 0.75 $ 50, $ 150, ,000,000 Ad Network 2 Jan 1 - March 31 RON $ 1.00 $ 50, $ 150, ,000,000 Ad Network 3 Jan 1 - March 31 RON $ 1.15 $ 50, $ 150, ,434,783 Ad Network 4 Jan 1 - March 31 RON $ 0.85 $ 50, $ 150, ,470,588 Ad Network 5 Jan 1 - March 31 RON $ 1.00 $ 50, $ 150, ,000,000 Ad Network 6 Jan 1 - March 31 RON $ 1.25 $ 50, $ 150, ,000,000 Ad Network 7 Jan 1 - March 31 RON $ 3.00 $ 50, $ 150, ,000,000 Ad Network 8 Jan 1 - March 31 RON $ 2.25 $ 50, $ 150, ,666,667 Ad Network 9 Jan 1 - March 31 RON $ 1.25 $ 50, $ 150, ,000,000 Ad Network 10 Jan 1 - March 31 RON $ 1.00 $ 50, $ 150, ,000,000 DSP Jan 1 - March 31 RON $ 0.75 $ 50, $ 150, ,000,000 Total $ 1,965, ,544,697,038 Well, guess what? That is pretty much exactly what is happening in most digital media buys today 20

21 Current and historical model Agency Buying is relationship based with targeting and optimization done at a very coarse grain level like Mad Men Black box ad networks Black box ad networks Black box ad networks Direct sales force Transparency line ends at the network and publisher level what falls below the line is black box Publisher Remnant inventory Publisher Publisher Remnant inventory Publisher Publisher Remnant inventory Publisher Publisher Premium inventory Publisher Buying is done across numerous platforms without the ability to manage frequency and cost resulting in significant waste 21 Just as bad (or worse), targeting capability does not allow for individual targeting of the greens

22 Future model Data & enabling technology Trading desk / New agency Trading desk/ New agency Data & analytic expertise Consolidated Buying Platform (DSP) Buying is done using a data-driven targeting skill-set and mind-set no more Mad Men Consolidated buying platforms allow for complete transparency and granular targeting no more black box Real-time bidding auction Real-time-bid environment allows for access to premium and remnant inventory that gets bid on auction-style based on the value to the advertiser Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher 22 Control of targeting at individual level allows for getting more of the greens and less of the reds while managing frequency and cost

23 Trading Desk Consolidated Buying Platform (DSP) Targeting Framework Lookalike Modeling Online Audience Segments Re-Targeting Online-Offline Direct Match Match converted consumers to anonymous ID and create lookalike predictive model to identify like cookies/ placement opportunities through RTB Identify high performing online audience segments ( auto intenders ) and target these anonymous users through the DSP Identify users visiting site (anonymous or authenticated) and target customized impressions after they leave the site Match offline top deciles to cookies through third party match providers and target known consumers on a 1-1 level 23

24 Impact of the Future Model An Advertiser working with multiple ad networks and DSPs in 2011, migrated ad spend Merkle s platform in Conversions tripled while dramatically reducing spend Before=100 Month 2011 After Month 2012 Difference Spend Conversions CPM Response rate CPA Reduced waste Cost 100 efficiency 56 Reach Targeted audience % +161% -44% +106% -73% 24

25 Client Roadmap for Future Model Adoption Focus on Audiences Display media plan Many Ad Networks Ad.com Collective Media Tribal Fusion Display media plan One platform Cross sell & upsell Prospecting Winbacks Data-driven skillsets Buying clout Negotiation skills Relationships Manual management Data-driven decision-making De-averaged pricing Impression-level buying Automated management Data and enabling technology infrastructure Ad server Connected CRM Platform Offline Media database cr CRM database Online Media database 25

26 The Future Model for Buying Client Digital Media Optimization Platform Analytics & Media buying services Centralized Demand-Side Platform (DSP) Display Video Real-time biddable supply sources Mobile Social Connected CRM Platform Offline Media database Online Media database Publisher direct cr CRM database 26

27 Program Examples Informed Digital Media Top Three US Bank Super Regional Bank 27

28 Case Study: Integrated online media acquisition Current State Targeting conversion not clicks Opportunity Most banks and insurance companies are not applying their datadriven marketing assets and knowhow to online media that are targetable and measurable at the individual consumer level. Media partners are targeting on clicks and not leads and conversions. Finding the wrong customers online Consumer s choice of conversion path Financial services and insurance companies have to address adverse selection. Targetable online media allow us to quickly learn what targeting and media spend drives high-value leads and conversions vs. low-value leads and conversions. Most online targeting never moves beyond anonymous targeting to value. Consumers move across media and channels in their research and buying practices. We must support online engagement with offline (branch, call center) quotes and account openings. 28

29 Merkle s Digital Optimization Platform combines online with offline data Anonymous data Hourly Anonymous Digital Data Real-time Identifiable (CRM) data Lead Data CDI Key Management & Data Hygiene Demographic Data Customers/ Accts 29

30 Integrated Digital /Offline Acquisition Program Demand Generation Engage & Enable Convert & Analyze Leads 4 30

31 Aligning to Consumer Behaviour Online audiences don t always click on ads they often see an ad and go Aligning to Consumer Behavior directly to the client s site or return through organic search Write to the users cookie when an ad is viewed Show landing page when user returns to the site 31

32 Integration of Online Targeted Media Integrated Search + Display Program enables incremental value Connected CRM Platform Attribution Customer Recognition Marketing Rules Algorithm customization Budget Allocation Remarketing Rules Merkle Search Program Upper funnel keyword bidding based on display audience and contextual insights Keyword optimization based on offline scoring Continuous interaction and adaptation Merkle Display Program Leverage keywords to inform retargeting creative and bid strategies Contextual targeting based on converting keywords 32

33 Campaign Tactics and Outcomes Scale Tactics informed by CONNECTED data Direct Match High Value Accuracy Remarketing Medium Value Accuracy Lookalike Majority Value Accurate Direct Match List Users from offline prospect or customer lists. Market with the right offer and product Remarketing Users who have identified themselves on your site or . Remarket with the right offer and product Lookalike Leverage user online profile data t find more users that have been identified as high value 33

34 Volume Index Month 1=100) Optimization Results on Customer Outcomes CPA Index (Month 1=100) 35% HV Accounts 60% HV Accounts 34

35 Targeting the Customers You Want Targeted prospects generated higher balances compared to other programs Targeted Online Pilot Checking Balances 54 Other Onlne Prospect 81 DM Prospects Household Deposit Balances 100 Targeted Online Pilot 36 Other Onlne Prospect 70 DM Prospects Population generated higher multi-product penetration Targeted Online Pilot Checking + Money Market 14 Other Onlne Prospect 49 DM Prospects Targeted Online Pilot Checking + Loan 27 Other Onlne Prospect 124 DM Prospects 35

36 36 Table Discussion