Using Big Data to Engage & Convert Valuable Customers March 2014
Sojern by the Numbers in 2013 Global Data Footprint 10B Impressions 1.9MM Car Rentals 1.1MM Heads in Beds 500+ Global Clients 280MM Boarding Passes 160MM+ Traveler Profiles U.S. 50 States EMEA 45 Countries ASIA 12 Countries S. AMERICA 9 Countries 11 Locations 90 Employees 2
Topics +Big Data +Data Types +Using Data in the Marketing Funnel +Using Data in Results Analysis 3
I Don t Care What the Data says 4
Question: Find x 5
Topics +Big Data +Data Types +Using Data in the Marketing Funnel +Using Data in Results Analysis 6
25,000,000,000,000,000,000 bytes of digital data created every day SearchEngineLand February 2013 7
Big Data Defined It doesn t fit on excel Stephane Hamel, CSO Cardinal Path High volume, high velocity, high variety information assets that demand cost-efficient, innovative forms of processing for enhanced insight and decision making Gartner 4 Vs of Big Data: Volume, Variety, Velocity, Veracity. IBM / Forrester data centric applications driving some experience to a customer and causing them to do some things in real time Paul Maritz, CEO Pivotal 8
Real Time Digital Marketing Should we buy an impression against this user? How much should we pay/bid on this impression? What creative should we show them? ALL HAPPENS IN ABOUT 80 milliseconds Offsite2013 9
Audience Data + Programmatic Buying Bidding on an impression in real time to show one specific ad to one consumer in one specific context at the right time 10
A Huge Topic. right message, to the right person, at the right time, in real time 11
Digital Pathway to a Booking Beyond The Last Click: Google White Paper 2011 12
Topics +Big Data +Data Types +Using Data in the Marketing Funnel +Using Data in Results Analysis 13
Wrong? 14
Capturing, Curating and Activating data Data Sources Curate Build/Maintain Traveller Profiles TRAVEL Capture Collect/Aggregate Data Activate Engage Travellers Via Media Channels BOOK RESEARCH 15
Data Types for Real Time Targeting Internal: + Onsite visits / behaviour (who / what) + CRM (who / what) + Loyalty (who / what) + Product / yield (what) External: + Intent (who / what) + Behavioural / history (who / what) + Context (where) Derived / Predicted: + When + Who / what 16
For a Hotel Group. For powerful, cost effective customer acquisition I want to know key data attributes of the customers I m engaging with Billions of Data Points Capture / Analyse Key Data Attributes + Destination intent + Dates of travel + Trip length + Party size + % audience overlap + Past booking behaviour + Loyalty status + Room availability + Room yield 17
Deriving and Targeting the When DAYSBETWEENBOOKINGHOTEL&STAY 32% 23% 19% hot el 6% 4% 4% 10% DAYS 31+ 22-30 15-21 8-14 3-7 1-2 0 18
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Topics +Big Data +Data Types +Using Data in the Marketing Funnel +Using Data in Results Analysis 20
Whole Funnel Marketing 21
Whole Funnel Marketing BRANDING Ave viewer watches 20+ hours of online video / month - Rich branding - Interaction / call to action opportunities Audience Targeting - Destination intention - x weeks before intended trip - E.g. hotel facilities 22
Whole Funnel Marketing PROSPECTING 23
Whole Funnel Marketing PROSPECTING Audience Targeting: new customers - Destination intent - Party size - Date travelling - Act-alikes - Dynamic creative: destination + product offer 24
Whole Funnel Marketing SMART RETARGETING 25
Whole Funnel Marketing Retargeting site visitors using e.g: - Loyalty status - Product yield / availability - Offsite behaviour SMART RETARGETING 26
Topics +Big Data +Data Types +Using Data in the Marketing Funnel +Using Data in Results Analysis 27
I Have the Data 28
Tale of 2 Campaigns Campaign 1 Campaign 2 Cost $100,000 $100,000 Conversions 3,784 3,307 CPA $26 $30 29
Tale of 2 Campaigns Campaign 1 Campaign 2 Cost $100,000 $100,000 Conversions 3,784 3307 CPA $26 $30 Conv / Loyalty Status: Highest Tier 2562 312 High Tier 972 563 Mid Tier 98 752 Low Tier 22 807 Non Member 130 873 30
Tale of 2 Campaigns Campaign 1 Campaign 2 Cost $100,000 $100,000 Conversions 3,784 3307 CPA $26 $30 Conv / Loyalty Status: Highest Tier 2562 312 High Tier 972 563 Mid Tier 98 752 Low Tier 22 807 Non Member 130 873 Non Highest Tier CPA $97 $45 31
Extrapolating from Incomplete Data 32
Search for Incrementality Lift studies can help refine targeting and attribution Prospecting Retargeting Hotel Sub Brand 1 13% 12% Hotel Sub Brand 2 11% -4% Hotel Loyalty Programme 4% -8% Charity ads vs. campaign ads to same audience targeted profiles for a major hotel group 33
Attribution Options Last Click All credit goes to the last click First Click All credit goes to the first click Post View Last channel to show an ad gets the credit 34
Clicks? 95% of clicks don t convert 90% of converters don t click 35
Advanced Attribution Algorithmic Attribution Let the data decide where to assign credit Source: Tagman VIS 36
Marketers will say my job has always been to understand customer segments. The shift is to go from the segment to the individual. It spells the death of the average customer. - Ginni Rommety, CEO of IBM Offsite2013 37
Who cares? It s what you re doing with it Stephane Hamel, CSO Cardinal Path 38
Thank you Stephen Taylor VP & MD International E stephen.taylor@sojern.com P +44 7768 454758 39