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> Suncorp Analy.cs < Smart data driven marke-ng
> Short but sharp history
§ Datalicious was founded late 2007 § Strong Omniture web analy-cs history § Now 360 data agency with specialist team § Combina-on of analysts and developers § Carefully selected best of breed partners § Evangelizing smart data driven marke-ng § Making data accessible and ac-onable § Driving industry best prac-ce (ADMA)
November 2010 © Datalicious Pty Ltd 2
> Clients across all industries
November 2010 © Datalicious Pty Ltd 3
> Wide range of data services
November 2010 © Datalicious Pty Ltd 4
Data Pla>orms Data collec.on and processing Web analy.cs solu.ons Omniture, Google Analy.cs, etc Tag-‐less online data capture End-‐to-‐end data pla>orms IVR and call center repor.ng Single customer view
Insights Repor.ng Data mining and modelling Customised dashboards Media aKribu.on models Market and compe.tor trends Social media monitoring Online surveys and polls Customer profiling
Ac.on Applica.ons Data usage and applica.on Marke.ng automa.on Aprimo, Trac.on, Inxmail, etc Targe.ng and merchandising Internal search op.misa.on CRM strategy and execu.on Tes.ng programs
> Smart data driven marke.ng
November 2010 © Datalicious Pty Ltd 5
Media AKribu.on
Op.mise channel mix
Tes.ng Improve usability
$$$
Targe.ng Increase relevance
15 tools are proposed largely centred on Adobe
6
Optimised marketing mix RIGHT OFFER
Optimised channel mix
RIGHT CHANNEL
Optimised customer
RIGHT CUSTOMER
Optimised Customer Journey
Once implemented these tools will deliver the capability for each LOB to leverage online data to optimise the customer's journey across any channel or brand within the Suncorp group
TOOL AQUIRE CONVERT GROW
1. Digital campaign tracking & attribution ü ü ü
2. Full digital pathway tracking & attribution ü ü ü
3. Onsite promotion tracking ü
4. Fallout & conversion analysis ü
5. Adv site navigation & form analysis ü
6. Internal search analysis ü
7. Brand portfolio level measurement ü ü ü
8. Site surveys ü
9. Advanced visitor segmentation ü ü ü
10. A/B & content testing ü ü ü
11. Campaign retargeting ü ü
12. Behavioural targeting ü ü
13. Syndicate personalised content ü
14. Digital identification & CRM integration ü ü ü
15. Online to offline conversion ü ü
Anonymous
Semi-Identified
Identified
Increased Personalisation
STRATEGIC GOAL DELIVERED
CAPABILITY EVOLUTION
CUSTOMER JOURNEY
Multi-Media Measurement & Attribution
Site Optimisation
Advanced Segmentation
& Targeting
Multi-Channel Optimisation
Personalised Targeting
CAPABILITY
> Media aKribu.on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
November 2010 © Datalicious Pty Ltd 7
Direct mail, email, etc
Facebook TwiKer, etc
> Campaign flow and calls to ac.on
November 2010 © Datalicious Pty Ltd 8
POS kiosks, loyalty cards, etc
CRM program
Home pages, portals, etc
YouTube, blog, etc
Paid search
Organic search
Landing pages, offers, etc
PR, WOM, events, etc
TV, print, radio, etc
C2
C3
= Paid media
= Viral elements
Call center, retail stores, etc
= Coupons, surveys
Display ads, affiliates, etc
C1
Exercise: Campaign flow
November 2010 © Datalicious Pty Ltd 9
> Duplica.on across channels
November 2010 © Datalicious Pty Ltd 10
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email Pla>orm
Google Analy.cs
$
$
$
> Cookie expira.on impact
November 2010 © Datalicious Pty Ltd 11
Banner Ad Click
Email Blast
Paid Search
Organic Search
Bid Mgmt
Ad Server
Email Pla>orm
Google Analy.cs
$
$
$
$
Expira.on
Banner Ad View
Central Analy.cs Pla>orm
$
$
$
> De-‐duplica.on across channels
November 2010 © Datalicious Pty Ltd 12
Banner Ads
Email Blast
Paid Search
Organic Search
$
Exercise: Duplica.on impact
November 2010 © Datalicious Pty Ltd 13
> Exercise: Duplica.on impact § Double-‐coun-ng of conversions across channels can
have a significant impact on key metrics, especially CPA § Example: Display ads and paid search
– Total media budget of $10,000 of which 50% is spend on paid search and 50% on display ads
– Total of 100 conversions across both channels with a channel overlap of 50%, i.e. both channels claim 100% of conversions based on their own repor-ng but once de-‐duplicated they each only contributed 50% of conversions
– What are the ini-al CPA values and what is the true CPA? § Solu-on: $50 ini-al CPA and $100 true CPA
– $5,000 / 100 = $50 ini-al CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase)
November 2010 © Datalicious Pty Ltd 14
November 2010 © Datalicious Pty Ltd 15
Quick win: Central pla>orm
TV/Print audience
Search audience
Banner audience
> Reach and channel overlap
November 2010 © Datalicious Pty Ltd 16
> Indirect display impact
November 2010 © Datalicious Pty Ltd 17
> Indirect display impact
November 2010 © Datalicious Pty Ltd 18
> Indirect display impact
November 2010 © Datalicious Pty Ltd 19
> Success aKribu.on models
November 2010 © Datalicious Pty Ltd 20
Banner Ad $100
Email Blast
Paid Search $100
Banner Ad $100
Affiliate Referral $100
Success $100
Success $100
Banner Ad
Paid Search
Organic Search $100
Success $100
Last channel gets all credit
First channel gets all credit
All channels get equal credit
Print Ad $33
Social Media $33
Paid Search $33
Success $100
All channels get par.al credit
Paid Search
> First and last click aKribu.on
November 2010 © Datalicious Pty Ltd 21
Chart shows percentage of channel touch points that lead to a conversion.
Neither first nor last-‐click measurement would provide true picture
Paid/Organic Search
Emails/Shopping Engines
> Adobe stacking/par.cipa.on
November 2010 © Datalicious Pty Ltd 22
Adobe can only stack direct paid and organic responses that end up on your website proper.es, mere banner impressions are missing from the stack and cannot be included via Genesis a`er the fact.
Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
November 2010 © Datalicious Pty Ltd 23
Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
> Where to collect the data
November 2010 © Datalicious Pty Ltd 24
Referral visits Social media visits Organic search visits Paid search visits Email visits, etc
Web Analy.cs Banner impressions
Banner clicks +
Paid search clicks
Ad Server
Lacking banner impressions Less granular & complex
Lacking organic visits More granular & complex
November 2010 © Datalicious Pty Ltd 25
Quick win: Data into ad server
> Search call to ac.on for offline
November 2010 © Datalicious Pty Ltd 26
November 2010 © Datalicious Pty Ltd 27 Offline response tracking and improved experience
November 2010 © Datalicious Pty Ltd 28
Quick win: Search call to ac.on
November 2010 © Datalicious Pty Ltd 29
November 2010 © Datalicious Pty Ltd 30 hKp://www.suncorp.com.au?campaign=workshop
> Poten.al calls to ac.on § Unique click-‐through URLs § Unique vanity domains or URLs § Unique phone numbers § Unique search terms § Unique email addresses § Unique personal URLs (PURLs) § Unique SMS numbers, QR codes § Unique promo-onal codes, vouchers § Geographic loca-on (Facebook, FourSquare) § Plus regression analysis of cause and effect
November 2010 © Datalicious Pty Ltd 31
> Unique phone numbers
§ 1 unique phone number – Phone number is considered part of the brand – Media origin of calls cannot be established – Added value of website interac-on unknown
§ 2-‐10 unique phone numbers – Different numbers for different media channels – Exclusive number(s) reserved for website use – Call origin data more granular but not perfect – Difficult to rotate and pause numbers
November 2010 © Datalicious Pty Ltd 32
> Unique phone numbers § 10+ unique phone numbers
– Different numbers for different media channels – Different numbers for different product categories – Different numbers for different conversion steps – Call origin becoming useful to shape call script – Feasible to pause numbers to improve integrity
§ 100+ unique phone numbers – Different numbers for different website visitors – Call origin and -me stamp enable individual match – Call conversions matched back to search terms
November 2010 © Datalicious Pty Ltd 33
> Jet Interac.ve phone call data
November 2010 © Datalicious Pty Ltd 34
November 2010 © Datalicious Pty Ltd 35
Quick win: Unique numbers
> PURLs boos.ng DM response rates
November 2010 © Datalicious Pty Ltd 36
Text
> Media aKribu.on phases § Phase 1: De-‐duplica-on
– Conversion de-‐duplica-on across all channels – Requires one central repor-ng plaiorm – Limited to first/last click ajribu-on
§ Phase 2: Direct response pathing – Response pathing across paid and organic channels – Only covers clicks and not mere banner views – Can be enabled in Google Analy-cs and Omniture
§ Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google Analy-cs or Omniture – Easier to import addi-onal channels into ad server
November 2010 © Datalicious Pty Ltd 37
> Combining data sources
November 2010 © Datalicious Pty Ltd 38
> Single source of truth repor.ng
November 2010 © Datalicious Pty Ltd 39
Insights Repor.ng
> Understanding channel mix
November 2010 © Datalicious Pty Ltd 40
> Website entry survey
November 2010 © Datalicious Pty Ltd 41
Channel % of Conversions
Straight to Site 27%
SEO Branded 15%
SEM Branded 9%
SEO Generic 7%
SEM Generic 14%
Display Adver-sing 7%
Affiliate Marke-ng 9%
Referrals 5%
Email Marke-ng 7%
De-‐duped Campaign Report
} Channel % of Influence
Word of Mouth 32%
Blogging & Social Media 24%
Newspaper Adver-sing 9%
Display Adver-sing 14%
Email Marke-ng 7%
Retail Promo-ons 14%
Greatest Influencer on Branded Search / STS
Conversions ajributed to search terms that contain brand keywords and direct website visits are most likely not the origina-ng channel that generated the awareness and as such conversion credits should be re-‐allocated.
> Adjus.ng for offline impact
November 2010 © Datalicious Pty Ltd 42
+15 +5 +10 -‐15 -‐5 -‐10
Users are segmented before 1st ad is even served
> Ad server exposure test
November 2010 © Datalicious Pty Ltd 43
Banner Impression $ TV/Print
Response Search
Response
Banner Impression $ Search
Response Direct
Response
Exposed group: 90% of users get branded message
Banner Impression $ Search
Response Direct
Response
Control group: 10% of users get non-‐branded message
Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
November 2010 © Datalicious Pty Ltd 44
Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
> Cross-‐channel impact
November 2010 © Datalicious Pty Ltd 45
> Offline sales driven by online
November 2010 © Datalicious Pty Ltd 46
Website research
Phone order
Retail order
Online order
Cookie
Adver.sing campaign
Credit check, fulfilment
Online order confirma.on
Virtual order confirma.on
Confirma.on email
> Tracking offline conversions
§ Email click-‐through aoer purchase § First online login aoer purchase § Unique website or visitor phone number § Call back request or online chat § Unique website promo-on code § Unique printable vouchers § Store locator searches § Make an appointment online
November 2010 © Datalicious Pty Ltd 47
Closer
25%
> Success aKribu.on models
November 2010 © Datalicious Pty Ltd 48
Influencer Influencer $
25% Even AKrib.
Exclusion AKrib.
PaKern AKrib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%
Closer
Channel 1
Channel 1
Channel 1
> Path across different segments
November 2010 © Datalicious Pty Ltd 49
Influencer Influencer $
Channel 2
Channel 2 Channel 3
Channel 2 Channel 3 Product 4
Channel 3
Channel 4
Channel 4
Introducer
Product A vs. B
New prospects
Exis.ng customers
Closer
Brand 1
Brand 1
Brand 1
> Paths across business units
November 2010 © Datalicious Pty Ltd 50
Influencer Influencer $
Brand 2
Brand 2 Brand 3
Brand 2 Brand 3 Brand 4
Brand 3
Brand 4
Brand 4
Introducer
$
$
$
Exercise: AKribu.on model
November 2010 © Datalicious Pty Ltd 51
Closer
25%
> Exercise: AKribu.on models
November 2010 © Datalicious Pty Ltd 52
Influencer Influencer $
25% Even AKrib.
Exclusion AKrib.
Custom AKrib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?
> Common aKribu.on models
§ Allocate more conversion credits to more recent touch points for brands with a strong baseline to s-mulate repeat purchases
§ Allocate more conversion credits to more recent touch points for brands with a direct response focus
§ Allocate more conversion credits to ini-a-ng touch points for new and expensive brands and products to insert them into the mindset
November 2010 © Datalicious Pty Ltd 53
Exercise: Sta.s.cal significance
November 2010 © Datalicious Pty Ltd 54
How many survey responses do you need if you have 10,000 customers?
How many email opens do you need to test 2 subject lines if your subscriber base is 50,000?
How many orders do you need to test 6 banner execu.ons if you serve 1,000,000 banners
Google “nss sample size calculator” November 2010 © Datalicious Pty Ltd 55
How many survey responses do you need if you have 10,000 customers?
369 for each ques.on or 369 complete responses
How many email opens do you need to test 2 subject lines if your subscriber base is 50,000? And email sends? 381 per subject line or 381 x 2 = 762 email opens
How many orders do you need to test 6 banner execu.ons if you serve 1,000,000 banners?
383 sales per banner execu.on or 383 x 6 = 2,298 sales
Google “nss sample size calculator” November 2010 © Datalicious Pty Ltd 56
> Addi.onal success metrics
November 2010 © Datalicious Pty Ltd 57
Click Through
Add To Cart
Click Through
Page Bounce
Click Through $
Click Through
Call back request
Store Search ? $
$
$ Cart Checkout
Page Views
?
Product Views
November 2010 © Datalicious Pty Ltd 58
Quick win: Addi.onal metrics
> Importance of calendar events
November 2010 © Datalicious Pty Ltd 59
Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
Calendar events to add context
November 2010 © Datalicious Pty Ltd 60
November 2010 © Datalicious Pty Ltd 61
Quick win: Event calendar
> Quick wins and geung started
§ Central analy-cs plaiorm § Addi-onal data into ad server § Unique phone numbers § Search call to ac-on § Addi-onal metrics § Event calendar
November 2010 © Datalicious Pty Ltd 62
> Targe.ng
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
November 2010 © Datalicious Pty Ltd 63
Capture internet traffic Capture 50-‐100% of fair market share of traffic
Increase consumer engagement Exceed 50% of best compe-tor’s engagement rate
Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to sales
Building consumer loyalty Build 60% loyalty rate and 40% sales conversion
Increase online revenue Earn 10-‐20% incremental revenue online
> Increase revenue by 10-‐20%
November 2010 © Datalicious Pty Ltd 64
> New consumer decision journey
November 2010 © Datalicious Pty Ltd 65
The consumer decision process is changing from linear to circular.
> New consumer decision journey
November 2010 © Datalicious Pty Ltd 66
The consumer decision process is changing from linear to circular.
Change increases the importance of experience during research phase.
Online research
> The consumer data journey
November 2010 © Datalicious Pty Ltd 67
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
> The consumer data journey
November 2010 © Datalicious Pty Ltd 68
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
> Coordina.on across channels
November 2010 © Datalicious Pty Ltd 69
Off-‐site targe.ng
On-‐site targe.ng
Profile targe.ng
Genera.ng awareness
Crea.ng engagement
Maximising revenue
TV, radio, print, outdoor, search marke-ng, display ads, performance networks, affiliates, social media, etc
Retail stores, in-‐store kiosks, call centers, brochures, websites, mobile apps, online chat, social media, etc
Outbound calls, direct mail, emails, social media, SMS, mobile apps, etc
Off-‐site targe-ng
On-‐site targe-ng
Profile targe-ng
> Combining targe.ng pla>orms
November 2010 © Datalicious Pty Ltd 70
November 2010 © Datalicious Pty Ltd 71
November 2010 © Datalicious Pty Ltd 72
Take a closer look at our cash flow solu.ons
November 2010 © Datalicious Pty Ltd 73
November 2010 © Datalicious Pty Ltd 74
+ Add website behaviour to submiKed contact form data
November 2010 © Datalicious Pty Ltd 75
Take a closer look at our cash flow solu.ons
November 2010 © Datalicious Pty Ltd 76
Save .me and get your business insurance online.
November 2010 © Datalicious Pty Ltd 77
Our Flexi-‐Premium car insurance can help you save.
November 2010 © Datalicious Pty Ltd 78
Our Flexi-‐Premium car insurance can help you save.
Save with our combine car and life insurance offer.
November 2010 © Datalicious Pty Ltd 79
November 2010 © Datalicious Pty Ltd 80
November 2010 © Datalicious Pty Ltd 81 It’s no accident we’re cheaper
On-‐site segments
Off-‐site segments
> Combining technology
November 2010 © Datalicious Pty Ltd 82
CRM
> Extended targe.ng pla>orm
November 2010 © Datalicious Pty Ltd 83
Brand
Network
Partners
Publishers
> SuperTag code architecture
November 2010 © Datalicious Pty Ltd 84
§ Central JavaScript container tag § One tag for all sites and plaiorms § Hosted internally or externally § Faster tag implementa-on/updates § Eliminates JavaScript caching § Enables code tes-ng on live site § Enables heat map implementa-on § Enables redirects for A/B tes-ng § Enables network wide re-‐targe-ng § Enables live chat implementa-on
Campaign response data
> Combining data sets
November 2010 © Datalicious Pty Ltd 85
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
> Behaviours plus transac.ons
November 2010 © Datalicious Pty Ltd 86
one-‐off collec-on of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira.on, etc predic-ve models based on data mining
propensity to buy, churn, etc historical data from previous transac-ons
average order value, points, etc
CRM Profile
Updated Occasionally
+ tracking of purchase funnel stage
browsing, checkout, etc tracking of content preferences
products, brands, features, etc tracking of external campaign responses
search terms, referrers, etc tracking of internal promo-on responses
emails, internal search, etc
Site Behaviour
Updated Con.nuously
The study examined data from two of the UK’s busiest ecommerce websites, ASDA and William Hill. Given that more than half of all page impressions on these sites are from logged-‐in users, they provided a robust sample to compare IP-‐based and cookie-‐based analysis against. The results were staggering, for example an IP-‐based approach overes-mated visitors by up to 7.6 -mes whilst a cookie-‐based approach overes.mated visitors by up to 2.3 .mes.
> Unique visitor overes.ma.on
November 2010 © Datalicious Pty Ltd 87
Source: White Paper, RedEye, 2007
Datalicious SuperCookie Persistent Flash cookie that cannot be deleted
November 2010 © Datalicious Pty Ltd 88
> Maximise iden.fica.on points
20%
40%
60%
80%
100%
120%
140%
160%
0 4 8 12 16 20 24 28 32 36 40 44 48
Weeks
−−− Probability of iden-fica-on through Cookies
November 2010 89 © Datalicious Pty Ltd
November 2010 © Datalicious Pty Ltd 90
Quick win: Iden.fying users
> Maximise iden.fica.on points
November 2010 © Datalicious Pty Ltd 91
Mobile Home Work
Online Phone Branch
> Sample customer level data
November 2010 © Datalicious Pty Ltd 92
> Facebook Connect single sign on
November 2010 © Datalicious Pty Ltd 93
Facebook Connect gives your company the following data and more with just one click Email address, first name, last name, gender, birthday, interests, picture, affilia-ons, last profile update, -me zone, religion, poli-cal interests, ajracted to which sex, why they want to meet someone, home town, rela-onship status, current loca-on, ac-vi-es, music interests, tv show interests, educa-on history, work history, family, etc Need anything else?
(influencers only)
(all contacts)
Appending social data to customer profiles Name, age, gender, occupa.on, loca.on, social profiles and influencer ranking based on email
November 2010 © Datalicious Pty Ltd 94
> Sample site visitor composi.on
November 2010 © Datalicious Pty Ltd 95
30% exis.ng customers with extensive profile including transac-onal history of which maybe 50% can actually be iden-fied as individuals
30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful
10% serious prospects with limited profile data
30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
> Poten.al home page layout
November 2010 © Datalicious Pty Ltd 96
Branded header
Rule based offer
Customise content delivery on the fly based on referrer data, past content consump-on or profile data for exis-ng customers.
Targeted offer Popular
links, FAQs
Targeted offer
Login
> Prospect targe.ng parameters
November 2010 © Datalicious Pty Ltd 97
> Affinity re-‐targe.ng in ac.on
November 2010 © Datalicious Pty Ltd 98
Different type of visitors respond to different ads. By using category affinity targe-ng, response rates are lioed significantly across products.
Message CTR By Category Affinity
Postpay Prepay Broadb. Business
Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +
Google: “vodafone omniture case study” or hKp://bit.ly/de70b7
> Ad-‐sequencing in ac.on
November 2010 © Datalicious Pty Ltd 99
Marke-ng is about telling stories and
stories are not sta-c but evolve over -me
Ad-‐sequencing can help to evolve stories over -me the more users engage with ads
November 2010 © Datalicious Pty Ltd 100
Quick win: Basic re-‐targe.ng
> Poten.al newsleKer layout
November 2010 © Datalicious Pty Ltd 101
Closest stores, offers etc
Rule based branded header
Data verifica.on
Rule based offer
Profile based offer
Using profile data enhanced with website behaviour data imported into the email delivery plaiorm to build business rules and customise content delivery.
NPS
> Customer profiling in ac.on
November 2010 © Datalicious Pty Ltd 102
Using website and email responses to learn a lijle bite more about
subscribers at every touch point to keep
refining profiles and messages.
> Poten.al landing page layout
November 2010 © Datalicious Pty Ltd 103
Rule based branded header
Campaign message match
Targeted offer
Passing data on user preferences through to the website via parameters in email click-‐through URLs to customise content delivery.
Call to ac.on
November 2010 © Datalicious Pty Ltd 104
> Poten.al call center interface
November 2010 © Datalicious Pty Ltd 105
Customers can also be iden-fied offline and given most call center plaiorms are now web-‐based it would be possible to use online targe-ng plaiorms to shape the call experience.
Call center menu op.ons
Customer contact history
Targeted offer Call script
Exercise: Targe.ng matrix
November 2010 © Datalicious Pty Ltd 106
November 2010 © Datalicious Pty Ltd 107
Purchase cycle
Segment A Segment B Media
channels Data points
Default, awareness
Research, considera.on
Purchase intent
Reten.on, up/Cross-‐Sell
November 2010 © Datalicious Pty Ltd 108
Purchase cycle
Segment A Segment B Media
channels Data points
Colour, price, product affinity, etc
Default, awareness
Have you seen A?
Have you seen B?
Display, search, etc Default
Research, considera.on
A has great features!
B has great features!
Search, website, etc
Ad clicks, product views
Purchase intent
A delivers great value!
B delivers great value!
Website, emails, etc
Cart adds, checkouts, etc
Reten.on, up/Cross-‐Sell
Why not buy B?
Why not buy A?
Direct mails, emails, etc
Email clicks, logins, etc
> Quality content is key
Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour
targe;ng pla<orm ;ck, and produce results, is not its intelligence, it is your ability to actually feed it the right content which it can then target […. You feed your BT system crap and it will quickly and efficiently target crap to your
customers. Faster then you could ever have yourself.”
November 2010 © Datalicious Pty Ltd 109
> ClickTale tes.ng case study
November 2010 © Datalicious Pty Ltd 110
> Bad campaign worse than none
November 2010 © Datalicious Pty Ltd 111
Awareness Interest Desire Ac.on Sa.sfac.on
> AIDA and AIDAS formulas
November 2010 © Datalicious Pty Ltd 112
Social media
New media
Old media
Reach (Awareness)
Engagement (Interest & Desire)
Conversion (Ac-on)
+Buzz (Sa-sfac-on)
> Simplified AIDA funnel
November 2010 © Datalicious Pty Ltd 113
People reached
People engaged
People converted
People delighted
> Standardised global metrics
November 2010 © Datalicious Pty Ltd 114
40% 10% 1%
Quan-ta-ve and qualita-ve research data
Website, call center and retail data
Social media data
Media and search data
Social media
November 2010 © Datalicious Pty Ltd 115
Quick win: Global metrics
> Keys to effec.ve targe.ng
1. Define success metrics 2. Define and validate segments 3. Develop targe-ng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targe-ng and automate 7. Keep tes-ng and refining 8. Communicate results November 2010 © Datalicious Pty Ltd 116
> Quick wins and geung started
§ Iden-fica-on of individual users § Simple home page re-‐targe-ng § Simple ad server re-‐targe-ng § Global targe-ng matrix § Standardised metrics
November 2010 © Datalicious Pty Ltd 117
> Resources
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
November 2010 © Datalicious Pty Ltd 118
> The consumer data journey
November 2010 © Datalicious Pty Ltd 119
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
> The consumer data journey
November 2010 © Datalicious Pty Ltd 120
To reten.on messages To transac.onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
> Forrester on web analy.cs
November 2010 © Datalicious Pty Ltd 121
> Forrester on tes.ng/targe.ng
November 2010 © Datalicious Pty Ltd 122
> Forrester on media aKribu.on
November 2010 © Datalicious Pty Ltd 123
> Es.mate resource costs
November 2010 © Datalicious Pty Ltd 124
Exercise: Return on investment
November 2010 © Datalicious Pty Ltd 125
November 2010 © Datalicious Pty Ltd 126
Contact us [email protected]
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Data > Insights > Ac.on