anz analytics
DESCRIPTION
The presentation discusses the concepts, principles and significance of data driven marketing.TRANSCRIPT
> ANZ Analy*cs Workshop < 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)
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> Clients across all industries
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> Wide range of data services
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Data PlaAorms Data collec*on and processing Web analy*cs solu*ons Omniture, Google Analy*cs, etc Tag-‐less online data capture End-‐to-‐end data plaAorms IVR and call center repor*ng Single customer view
Insights Repor*ng Data mining and modelling Customised dashboards Media aNribu*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 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
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Media ANribu*on
Op*mise channel mix
Tes*ng Improve usability
$$$
Targe*ng Increase relevance
Metric
s Framew
ork
Benchm
arking and
tren
ding
Metrics Fram
ework
Benchmarking and trending
> Metrics framework
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Awareness Interest Desire Ac*on Sa*sfac*on
> AIDA and AIDAS formulas
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Social media
New media
Old media
Reach (Awareness)
Engagement (Interest & Desire)
Conversion (Ac.on)
+Buzz (Sa.sfac.on)
> Simplified AIDAS funnel
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People reached
People engaged
People converted
People delighted
> Marke*ng is about people
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40% 10% 1%
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
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Source: White Paper, RedEye, 2007
> 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
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> Maximise iden*fica*on points
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Mobile Home Work
Online Phone Branch
People reached
People engaged
People converted
People delighted
> Addi*onal funnel breakdowns
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40% 10% 1%
New prospects vs. exis.ng customers
Brand vs. direct response campaign
New vs. returning visitors
AU/NZ vs. rest of world
Exercise: Funnel breakdowns
> Exercise: Funnel breakdowns § List poten.ally insighcul funnel breakdowns – Brand vs. direct response campaign – New prospects vs. exis.ng customers – Baseline vs. incremental conversions – Compe..ve ac.vity, i.e. none, a lot, etc – Segments, i.e. age, loca.on, 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 – Devices, i.e. home, office, mobile, tablet, etc
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People reached
People engaged
People converted
People delighted
> Mul*ple metrics data sources
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Quan.ta.ve and qualita.ve research data
Website, call center and retail data
Social media data
Media and search data
Social media
> Importance of calendar events
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Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
Calendar events to add context
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> Conversion funnel 1.0
March 2011
Conversion funnel Product page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping informa.on, order confirma.on, etc
Conversion event
Campaign responses
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> Conversion funnel 2.0
March 2011
Campaign responses (inbound spokes) Offline campaigns, banner ads, email marke.ng, referrals, organic search, paid search, internal promo.ons, etc
Landing page (hub)
Success events (outbound spokes) Bounce rate, add to cart, cart checkout, confirmed order, call back request, registra.on, product comparison, product review, forward to friend, etc
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> Addi*onal success metrics
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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
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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 25
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 26
> Addi*onal success metrics
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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
Funnel breakdowns
> Exercise: Metrics framework
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Level Reach Engagement Conversion +Buzz
Level 1 People
People reached
People engaged
People converted
People delighted
Level 2 Strategic
Display impressions ? ? ?
Level 3 Tac*cal
Interac*on rate, etc ? ? ?
Funnel Breakdowns Exis*ng customers vs. new prospects, products, etc
> Exercise: Metrics framework
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> Exercise: Conversion Funnel
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> Media aNribu*on
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Direct mail, email, etc
Facebook TwiNer, etc
> Complex campaign flows
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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
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Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email PlaAorm
Web Analy*cs
$
$
$
> Cookie expira*on impact
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Banner Ad Click
Email Blast
Paid Search
Organic Search
Bid Mgmt
Ad Server
Email PlaAorm
Google Analy*cs
$
$
$
$
Expira*on
Banner Ad View
> ANZ repor*ng plaAorms
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Central Analy*cs PlaAorm
$
$
$
> De-‐duplica*on across channels
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Banner Ads
Email Blast
Paid Search
Organic Search
$
Exercise: Duplica*on impact
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> 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)
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TV/Print audience
Search audience
Banner audience
> Reach and channel overlap
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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
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> Indirect display impact
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> Indirect display impact
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> Success aNribu*on models
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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 aNribu*on
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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
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Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
> 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
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> Search call to ac*on for offline
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> PURLs boos*ng DM response rates
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Text
> Jet Interac*ve phone call data
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> 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
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> 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
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> Cross-‐channel impact
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> Offline sales driven by online
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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
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Influencer Influencer $
Banner Click Online
SEO Generic
Affiliate Click Offline
SEO Branded
Direct Visit
Email Update Abandon
Direct Visit
Social Media
SEO Branded
Introducer
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Adobe campaign stack does not include organic channels or banner impressions and does not expire on any event, i.e. con*nues as long as the cookie is present.
> Where to collect the data
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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 ad impressions Less granular & complex
Lacking organic visits More granular & complex
> Maximise iden*fica*on points
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Mobile Home Work
Online Phone Branch
> Combining data sources
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> Single source of truth repor*ng
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Insights Repor*ng
> Understanding channel mix
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> Website entry survey
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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 arributed 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
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+15 +5 +10 -‐15 -‐5 -‐10
Closer
25%
> Success aNribu*on models
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Influencer Influencer $
25% Even ANrib.
Exclusion ANrib.
PaNern ANrib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%
Closer
Channel 1
Channel 1
Channel 1
> Path across different segments
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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: ANribu*on model
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Closer
25%
> Exercise: ANribu*on models
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Influencer Influencer $
25% Even ANrib.
Exclusion ANrib.
Custom ANrib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?
> Common aNribu*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
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> Targe*ng and tes*ng
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
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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%
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> New consumer decision journey
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The consumer decision process is changing from linear to circular.
> New consumer decision journey
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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
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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
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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 plaAorms
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ANZ Low Rate MasterCard
ANZ Business Debit Card
On-‐site segments
Off-‐site segments
> Combining technology
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CRM
> SuperTag code architecture
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§ Central JavaScript container tag § One tag for all sites and placorms § 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
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
> Behaviours plus transac*ons
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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
> 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
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> Maximise iden*fica*on points
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Mobile Home Work
Online Phone Branch
> Sample customer level data
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> Sample site visitor composi*on
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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
> Prospect targe*ng parameters
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> Affinity re-‐targe*ng in ac*on
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Different type of visitors respond to different ads. By using category affinity targe.ng, 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 hNp://bit.ly/de70b7
> Ad-‐sequencing in ac*on
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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
Exercise: Targe*ng matrix
Purchase Cycle
Segments: Colour, price, product affinity, etc
Media Channels
Data Points
Default, awareness
Research, considera*on
Purchase intent
Reten*on, up/cross-‐sell
> Exercise: Targe*ng matrix
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Purchase Cycle
Segments: Colour, price, product affinity, etc
Media Channels
Data Points
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, prod views
Purchase intent
A delivers great value!
B delivers great value!
Website, emails, etc
Cart adds, checkouts
Reten*on, up/cross-‐sell
Why not buy B?
Why not buy A?
Direct mails, emails, etc
Email clicks, logins, etc
> Exercise: Targe*ng matrix
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> 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.”
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> ClickTale tes*ng case study
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> Bad campaign worse than none
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Exercise: Tes*ng matrix
Test Segment Content KPIs Poten*al Results
> Exercise: Tes*ng matrix
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Test Segment Content KPIs Poten*al Results
Test #1A New prospects
Conversion form A
Next step, order, etc ? ?
Test #1B New prospects
Conversion form B
Next step, order, etc ? ?
Test #1N New prospects
Conversion form N
Next step, order, etc ? ?
? ? ? ? ? ?
> Exercise: Tes*ng matrix
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> 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 March 2011 © Datalicious Pty Ltd 104
March 2011 © Datalicious Pty Ltd 105
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