ad tech campaign measurement
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> Campaign Measurement < Digital Campaign Measurement
ad:tech 2011 Workshop
> Short but sharp history
§ Datalicious was founded late 2007 § Strong Omniture web analyBcs history § Now 360 data agency with specialist team § CombinaBon of analysts and developers § Carefully selected best of breed partners § Evangelizing smart data driven markeBng § Making data accessible and acBonable § Driving industry best pracBce (ADMA)
March 2011 © Datalicious Pty Ltd 2
> Clients across all industries
March 2011 © Datalicious Pty Ltd 3
> Wide range of data services
March 2011 © 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 Campaigns Data usage and applica?on Marke?ng automa?on Alterian, 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
March 2011 © Datalicious Pty Ltd 5
Media AKribu?on
Op?mise channel mix
Tes?ng Improve usability
$$$
Targe?ng Increase relevance
Stan
dardised
Metric
s Be
nchm
arking and
tren
ding
Standardised Metrics
Benchmarking and trending
> Metrics framework
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Awareness Interest Desire Ac?on Sa?sfac?on
> AIDA and AIDAS formulas
March 2011 © Datalicious Pty Ltd 7
Social media
New media
Old media
Reach (Awareness)
Engagement (Interest & Desire)
Conversion (AcBon)
+Buzz (SaBsfacBon)
> Simplified AIDAS funnel
March 2011 © Datalicious Pty Ltd 8
People reached
People engaged
People converted
People delighted
> Marke?ng is about people
March 2011 © Datalicious Pty Ltd 9
40% 10% 1%
People reached
People engaged
People converted
People delighted
> Addi?onal funnel breakdowns
March 2011 © Datalicious Pty Ltd 10
40% 10% 1%
New prospects vs. exisBng customers
Brand vs. direct response campaign
New vs. returning visitors
AU/NZ vs. rest of world
Exercise: Funnel breakdowns
> Exercise: Funnel breakdowns
§ List potenBally insighXul funnel breakdowns – Brand vs. direct response campaign – New prospects vs. exisBng customers – Baseline vs. incremental conversions – CompeBBve acBvity, i.e. none, a lot, etc – Segments, i.e. age, locaBon, influence, etc – Channels, i.e. search, display, social, etc – Campaigns, i.e. this/last week, month, year, etc – Products and brands, i.e. iphone, htc, etc – Offers, i.e. free minutes, free handset, etc
March 2011 © Datalicious Pty Ltd 14
People reached
People engaged
People converted
People delighted
> Mul?ple metrics data sources
March 2011 © Datalicious Pty Ltd 15
QuanBtaBve and qualitaBve research data
Website, call center and retail data
Social media data
Media and search data
Social media
> Importance of calendar events
March 2011 © Datalicious Pty Ltd 16
Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
Calendar events to add context
March 2011 © Datalicious Pty Ltd 17
> Conversion funnel 1.0
March 2011
Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informaBon, order confirmaBon, etc
Conversion event
Campaign responses
© Datalicious Pty Ltd 18
> Conversion funnel 2.0
March 2011
Campaign responses (inbound spokes) Offline campaigns, banner ads, email markeBng, referrals, organic search, paid search, internal promoBons, etc
Landing page (hub)
Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registraBon, product comparison, product review, forward to friend, etc
© Datalicious Pty Ltd 19
> Addi?onal success metrics
March 2011 © Datalicious Pty Ltd 20
Click Through
Add To Cart
Click Through
Page Bounce
Click Through $
Click Through
Call back request
Store Search ? $
$
$ Cart Checkout
Page Views
?
Product Views
Exercise: Sta?s?cal significance
March 2011 © Datalicious Pty Ltd 21
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” March 2011 © Datalicious Pty Ltd 22
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” March 2011 © Datalicious Pty Ltd 23
> Addi?onal success metrics
March 2011 © Datalicious Pty Ltd 24
Click Through
Add To Cart
Click Through
Page Bounce
Click Through $
Click Through
Call back request
Store Search ? $
$
$ Cart Checkout
Page Views
?
Product Views
Exercise: Metrics framework
Level Reach Engagement Conversion +Buzz
Level 1 People
Level 2 Strategic
Level 3 Tac?cal
> Exercise: Metrics framework
March 2011 © Datalicious Pty Ltd 26
Level Reach Engagement Conversion +Buzz
Level 1 People
People reached
People engaged
People converted
People delighted
Level 2 Strategic
Search impressions, UBs, etc
? ? ?
Level 3 Tac?cal
Keyword rank, click-‐through, etc
? ? ?
> Exercise: Metrics framework
March 2011 © Datalicious Pty Ltd 27
> Media aKribu?on
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March 2011 © Datalicious Pty Ltd 28
Direct mail, email, etc
Facebook TwiKer, etc
> Complex campaign flows
March 2011 © Datalicious Pty Ltd 29
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
= Paid media
= Viral elements
Call center, retail stores, etc
= Sales channels
Display ads, affiliates, etc
> Duplica?on across channels
March 2011 © Datalicious Pty Ltd 30
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email Pla>orm
Google Analy?cs
$
$
$
> Cookie expira?on impact
March 2011 © Datalicious Pty Ltd 31
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
March 2011 © Datalicious Pty Ltd 32
Banner Ads
Email Blast
Paid Search
Organic Search
$
Exercise: Duplica?on impact
March 2011 © Datalicious Pty Ltd 33
> Exercise: Duplica?on impact § Double-‐counBng 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 reporBng but once de-‐duplicated they each only contributed 50% of conversions
– What are the iniBal CPA values and what is the true CPA? § SoluBon: $50 iniBal CPA and $100 true CPA
– $5,000 / 100 = $50 iniBal CPA and $5,000 / 50 = $100 true CPA (which represents a 100% increase)
March 2011 © Datalicious Pty Ltd 34
TV/Print audience
Search audience
Banner audience
> Reach and channel overlap
March 2011 © Datalicious Pty Ltd 35
Users are segmented before 1st ad is even served
> Ad server exposure test
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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
> Indirect display impact
March 2011 © Datalicious Pty Ltd 37
> Indirect display impact
March 2011 © Datalicious Pty Ltd 38
> Indirect display impact
March 2011 © Datalicious Pty Ltd 39
> Success aKribu?on models
March 2011 © Datalicious Pty Ltd 40
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
March 2011 © Datalicious Pty Ltd 41
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
Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
March 2011 © Datalicious Pty Ltd 42
Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
> Search call to ac?on for offline
March 2011 © Datalicious Pty Ltd 43
March 2011 © Datalicious Pty Ltd 44 Offline response tracking and improved experience
March 2011 © Datalicious Pty Ltd 45
March 2011 © Datalicious Pty Ltd 46 hKp://www.suncorp.com.au?campaign=workshop
> PURLs boos?ng DM response rates
March 2011 © Datalicious Pty Ltd 47
Text
> 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 promoBonal codes, vouchers § Geographic locaBon (Facebook, FourSquare) § Plus regression analysis of cause and effect
March 2011 © Datalicious Pty Ltd 48
> Jet Interac?ve phone call data
March 2011 © Datalicious Pty Ltd 49
> 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 interacBon 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
March 2011 © Datalicious Pty Ltd 50
> 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 Bme stamp enable individual match – Call conversions matched back to search terms
March 2011 © Datalicious Pty Ltd 51
> Cross-‐channel impact
March 2011 © Datalicious Pty Ltd 52
> Offline sales driven by online
March 2011 © Datalicious Pty Ltd 53
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
Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
March 2011 © Datalicious Pty Ltd 54
Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
> Adobe stacking/par?cipa?on
March 2011 © Datalicious Pty Ltd 55
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 ater the fact.
> Where to collect the data
March 2011 © Datalicious Pty Ltd 56
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
> Combining data sources
March 2011 © Datalicious Pty Ltd 57
> Single source of truth repor?ng
March 2011 © Datalicious Pty Ltd 58
Insights Repor?ng
> Understanding channel mix
March 2011 © Datalicious Pty Ltd 59
> Website entry survey
March 2011 © Datalicious Pty Ltd 61
Channel % of Conversions
Straight to Site 27%
SEO Branded 15%
SEM Branded 9%
SEO Generic 7%
SEM Generic 14%
Display AdverBsing 7%
Affiliate MarkeBng 9%
Referrals 5%
Email MarkeBng 7%
De-‐duped Campaign Report
} Channel % of Influence
Word of Mouth 32%
Blogging & Social Media 24%
Newspaper AdverBsing 9%
Display AdverBsing 14%
Email MarkeBng 7%
Retail PromoBons 14%
Greatest Influencer on Branded Search / STS
Conversions aoributed to search terms that contain brand keywords and direct website visits are most likely not the originaBng channel that generated the awareness and as such conversion credits should be re-‐allocated.
> Adjus?ng for offline impact
March 2011 © Datalicious Pty Ltd 62
+15 +5 +10 -‐15 -‐5 -‐10
Closer
25%
> Success aKribu?on models
March 2011 © Datalicious Pty Ltd 63
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
March 2011 © Datalicious Pty Ltd 64
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
Exercise: AKribu?on model
March 2011 © Datalicious Pty Ltd 65
Closer
25%
> Exercise: AKribu?on models
March 2011 © Datalicious Pty Ltd 66
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 sBmulate repeat purchases
§ Allocate more conversion credits to more recent touch points for brands with a direct response focus
§ Allocate more conversion credits to iniBaBng touch points for new and expensive brands and products to insert them into the mindset
March 2011 © Datalicious Pty Ltd 67
> Media aKribu?on phases § Phase 1: De-‐duplicaBon – Conversion de-‐duplicaBon across all channels – Requires one central reporBng plaXorm – Limited to first/last click aoribuBon
§ 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 AnalyBcs and Omniture
§ Phase 3: Full purchase path – Direct response tracking including banner exposure – Cannot be done in Google AnalyBcs or Omniture – Easier to import addiBonal channels into ad server
March 2011 © Datalicious Pty Ltd 68
> Targe?ng
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Capture internet traffic Capture 50-‐100% of fair market share of traffic
Increase consumer engagement Exceed 50% of best compeBtor’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%
March 2011 © Datalicious Pty Ltd 70
> New consumer decision journey
March 2011 © Datalicious Pty Ltd 71
The consumer decision process is changing from linear to circular.
> New consumer decision journey
March 2011 © Datalicious Pty Ltd 72
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
March 2011 © Datalicious Pty Ltd 73
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
March 2011 © Datalicious Pty Ltd 74
Off-‐site targe?ng
On-‐site targe?ng
Profile targe?ng
Genera?ng awareness
Crea?ng engagement
Maximising revenue
TV, radio, print, outdoor, search markeBng, 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 targeBng
On-‐site targeBng
Profile targeBng
> Combining targe?ng pla>orms
March 2011 © Datalicious Pty Ltd 75
March 2011 © Datalicious Pty Ltd 76
March 2011 © Datalicious Pty Ltd 77
Take a closer look at our cash flow solu?ons
March 2011 © Datalicious Pty Ltd 78
March 2011 © Datalicious Pty Ltd 79
+ Add website behaviour to submiKed contact form data
March 2011 © Datalicious Pty Ltd 80
Take a closer look at our cash flow solu?ons
March 2011 © Datalicious Pty Ltd 81
Save ?me and get your business insurance online.
March 2011 © Datalicious Pty Ltd 82
Our Flexi-‐Premium car insurance can help you save.
March 2011 © Datalicious Pty Ltd 83
Our Flexi-‐Premium car insurance can help you save.
Save with our combine car and life insurance offer.
March 2011 © Datalicious Pty Ltd 84
March 2011 © Datalicious Pty Ltd 85
March 2011 © Datalicious Pty Ltd 86 It’s no accident we’re cheaper
On-‐site segments
Off-‐site segments
> Combining technology
March 2011 © Datalicious Pty Ltd 87
CRM
> SuperTag code architecture
March 2011 © Datalicious Pty Ltd 88
§ Central JavaScript container tag § One tag for all sites and plaXorms § Hosted internally or externally § Faster tag implementaBon/updates § Eliminates JavaScript caching § Enables code tesBng on live site § Enables heat map implementaBon § Enables redirects for A/B tesBng § Enables network wide re-‐targeBng § Enables live chat implementaBon
Campaign response data
> Combining data sets
March 2011 © Datalicious Pty Ltd 89
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
> Behaviours plus transac?ons
March 2011 © Datalicious Pty Ltd 90
one-‐off collecBon of demographical data age, gender, address, etc customer lifecycle metrics and key dates profitability, expira?on, etc predicBve models based on data mining
propensity to buy, churn, etc historical data from previous transacBons
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 promoBon 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 overesBmated visitors by up to 7.6 Bmes whilst a cookie-‐based approach overes?mated visitors by up to 2.3 ?mes.
> Unique visitor overes?ma?on
March 2011 © Datalicious Pty Ltd 91
Source: White Paper, RedEye, 2007
Datalicious SuperCookie Persistent Flash cookie that cannot be deleted
March 2011 © Datalicious Pty Ltd 92
> 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 idenBficaBon through Cookies
March 2011 93 © Datalicious Pty Ltd
> Maximise iden?fica?on points
March 2011 © Datalicious Pty Ltd 94
Mobile Home Work
Online Phone Branch
> Sample customer level data
March 2011 © Datalicious Pty Ltd 95
> Sample site visitor composi?on
March 2011 © Datalicious Pty Ltd 96
30% exis?ng customers with extensive profile including transacBonal history of which maybe 50% can actually be idenBfied 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
March 2011 © Datalicious Pty Ltd 97
Branded header
Rule based offer
Customise content delivery on the fly based on referrer data, past content consumpBon or profile data for exisBng customers.
Targeted offer Popular
links, FAQs
Targeted offer
Login
> Prospect targe?ng parameters
March 2011 © Datalicious Pty Ltd 98
> Affinity re-‐targe?ng in ac?on
March 2011 © Datalicious Pty Ltd 99
Different type of visitors respond to different ads. By using category affinity targeBng, response rates are lited 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
March 2011 © Datalicious Pty Ltd 100
MarkeBng is about telling stories and
stories are not staBc but evolve over Bme
Ad-‐sequencing can help to evolve stories over Bme the more users engage with ads
> Poten?al newsleKer layout
March 2011 © 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 plaXorm to build business rules and customise content delivery.
NPS
> Customer profiling in ac?on
March 2011 © Datalicious Pty Ltd 102
Using website and email responses to learn a liole bite more about
subscribers at every touch point to keep
refining profiles and messages.
> Poten?al landing page layout
March 2011 © 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
March 2011 © Datalicious Pty Ltd 104
> Poten?al call center interface
March 2011 © Datalicious Pty Ltd 105
Customers can also be idenBfied offline and given most call center plaXorms are now web-‐based it would be possible to use online targeBng plaXorms to shape the call experience.
Call center menu op?ons
Customer contact history
Targeted offer Call script
Exercise: Targe?ng matrix
March 2011 © Datalicious Pty Ltd 106
March 2011 © 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
March 2011 © 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.”
March 2011 © Datalicious Pty Ltd 109
> ClickTale tes?ng case study
March 2011 © Datalicious Pty Ltd 110
> Bad campaign worse than none
March 2011 © Datalicious Pty Ltd 111
> Keys to effec?ve targe?ng
1. Define success metrics 2. Define and validate segments 3. Develop targeBng and message matrix 4. Transform matrix into business rules 5. Develop and test content 6. Start targeBng and automate 7. Keep tesBng and refining 8. Communicate results March 2011 © Datalicious Pty Ltd 112
March 2011 © Datalicious Pty Ltd 113
Contact us cbartens@datalicious.com
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