Download - ADMA Multi-Channel Marketing
> Mul&-‐Channel Marke&ng < Effec%ve marke%ng measurement in a mul%-‐channel environment
> About Datalicious § Datalicious was founded in November 2007 § Official Adobe & Google Analy%cs partner § 360 data agency with team of data specialists § Combina%on of analysts and developers § Blue chip clients across all industry ver%cals § Carefully selected best of breed partners § Driving industry best prac%ce with ADMA § Turning data into ac%onable insights § Execu%ng smart data driven campaigns September 2012 © Datalicious Pty Ltd 2
> Smart data driven marke&ng
September 2012 © Datalicious Pty Ltd 3
Media A=ribu&on & Modeling
Op&mise channel mix, predict sales
Tes&ng & Op&misa&on Remove barriers, drive sales
Boos&ng ROMI
Targe&ng & Merchandising Increase relevance, reduce churn
“Using data to widen the funnel”
> Mul&-‐channel measurement
§ Metrics standardisa%on § Media aRribu%on § Channel integra%on
September 2012 © Datalicious Pty Ltd 4
> Metrics standardisa&on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
September 2012 © Datalicious Pty Ltd 5
August 2011 © Datalicious Pty Ltd 6
Awareness Interest Desire Ac&on Sa&sfac&on
> AIDA and AIDAS formulas
September 2012 © Datalicious Pty Ltd 7
Social media
New media
Old media
Reach (Awareness)
Engagement (Interest & Desire)
Conversion (Ac%on)
+Buzz (Delight)
> Simplified AIDAS funnel
September 2012 © Datalicious Pty Ltd 8
People reached
People engaged
People converted
People delighted
> Marke&ng is about people
September 2012 © Datalicious Pty Ltd 9
40% 10% 1%
People reached
People engaged
People converted
People delighted
September 2012 © Datalicious Pty Ltd 10
> Standardised roll-‐up metrics
Unique browsers, search impressions, TV circula&on, etc
Unique visitors, site engagements, video views, etc
Online sales, online leads, store locator searches, etc
Facebook comments, Tweets,
ra&ngs, support calls, etc
Response rate, Search response rate, TV response rate, etc
Conversion rate, engagement rate, checkout rate, etc
10% 40% 1%
Review rate, ra&ng rate, comment rate, NPS rate, etc
People reached
People engaged
People converted
People delighted
> Provide context with figures
September 2012 © Datalicious Pty Ltd 11
40% 10% 1%
New prospects vs. exis%ng customers
Brand vs. direct response campaign
September 2012 © Datalicious Pty Ltd 12
> Provide context with figures § Brand vs. direct response campaign § New prospects vs. exis%ng customers § Compe%%ve ac%vity, i.e. none, a lot, etc § Market share, i.e. small, medium, large, et § 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 September 2012 © Datalicious Pty Ltd 13
> Addi&onal success metrics
September 2012 © Datalicious Pty Ltd 14
Click Through
Add To Cart
Click Through
Page Bounce
Click Through $
Click Through
Call back request
Store Search ? $
$
$ Cart Checkout
Page Views
?
Product Views
Use addi&onal metrics closer to the campaign origin
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” September 2012 © Datalicious Pty Ltd 15
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” September 2012 © Datalicious Pty Ltd 16
> Rela&ve or calculated metrics
§ Bounce rate § Conversion rate § Cost per acquisi%on § Pages views per visit § Product views per visit § Cart abandonment rate § Average order value
September 2012 © Datalicious Pty Ltd 17
> Align metrics across channels § Paid search response rate
= website visits / paid search impressions § Organic search response rate
= website visits / organic search impressions § Display response rate
= website visits / display ad impressions § Email response rate
= website visits / emails sent § Direct mail response rate
= (website visits + phone calls) / direct mail pieces sent § TV response rate
= (website visits + phone calls) / (TV ad reach x frequency)
September 2012 © Datalicious Pty Ltd 18
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
> Develop a metrics framework
September 2012 © Datalicious Pty Ltd 19
> Importance of calendar events
September 2012 © Datalicious Pty Ltd 20
Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
September 2012 © Datalicious Pty Ltd 21
> Poten&al calendar events
§ Press releases § Sponsored events § Campaign launches § Campaign changes § Crea%ve changes § Price changes § Website changes § Technical difficul%es
September 2012 © Datalicious Pty Ltd 22
> Media a=ribu&on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
September 2012 © Datalicious Pty Ltd 23
> Duplica&on across channels
September 2012 © Datalicious Pty Ltd 24
Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email Plajorm
Google Analy&cs
$
$
$
> Duplica&on across channels
September 2012 © Datalicious Pty Ltd 25
Display impression
Paid search $
Ad Server
Bid mgmt.
Web analy&cs
Display click
Ad server cookie
Organic search
Analy&cs cookie
Analy&cs cookie
Analy&cs cookie
Bid mgmt. cookie
Ad server cookie
Central Analy&cs Plajorm
$
$
$
> De-‐duplica&on across channels
September 2012 © Datalicious Pty Ltd 26
Banner Ads
Email Blast
Paid Search
Organic Search
$
Direct mail, email, etc
Facebook Twi=er, etc
> Campaign flows are complex
September 2012 © Datalicious Pty Ltd 27
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
> Success a=ribu&on models
September 2012 © Datalicious Pty Ltd 28
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 a=ribu&on
September 2012 © Datalicious Pty Ltd 29
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
> Indirect display impact
September 2012 © Datalicious Pty Ltd 30
Closer
Paid search
Display ad views
TV/print responses
> Full purchase path tracking
September 2012 © Datalicious Pty Ltd 31
Influencer Influencer $
Display ad clicks
Online leads
Affiliate clicks
Social referrals
Offline sales
Organic search
Social buzz
Retail visits
Life&me profit
Organic search
Emails, direct mail
Direct site visits
Introducer
Closer
Paid search
Display ad views
TV/print responses
> Full purchase path tracking
September 2012 © Datalicious Pty Ltd 32
Influencer Influencer $
Display ad clicks
Online leads
Affiliate clicks
Social referrals
Offline sales
Organic search
Social buzz
Retail visits
Life&me profit
Organic search
Emails, direct mail
Direct site visits
Introducer
> Purchase path example
September 2012 © Datalicious Pty Ltd 33
> Where to track purchase path
September 2012 © Datalicious Pty Ltd 34
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
> Purchase path data samples
Web Analy&cs data sample LAST AD IMPRESSION > SEARCH > $$$| PV $$$ AD IMPRESSION > AD IMPRESSION > SEARCH > $$$
Ad Server data sample 01/01/2012 11:45 AD IMP YAHOO HOME $33 01/01/2012 12:00 AD IMP SMH FINANCE $33 01/01/2012 12:05 SEARCH KEYWORD -‐ 07/01/2012 17:00 DIRECT $33 08/01/2012 15:00 $$$ $100
September 2012 © Datalicious Pty Ltd 35
> Understanding channel mix
September 2012 © Datalicious Pty Ltd 36
September 2012 © Datalicious Pty Ltd 37
September 2012 © Datalicious Pty Ltd 38
What promoted your visit today? q Recent branch visit q Saw an ad on television q Saw an ad in the newspaper q Recommenda%on from family/friends q […] How likely are you to apply for a loan? q Within the next few weeks q Within the next few months q I am a customer already q […]
> Website entry survey
September 2012 © Datalicious Pty Ltd 39
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
September 2012 © Datalicious Pty Ltd 40
+15 +5 +10 -‐15 -‐5 -‐10
0%
> Media a=ribu&on models
September 2012 © Datalicious Pty Ltd 41
$100
0% Last click a=ribu&on
Even a=ribu&on
Weighted a=ribu&on
0% 100%
25% 25% 25% 25%
Display impression
Display impression
Display response
Search response
X% X% Y% Z%
> Media a=ribu&on example
September 2012 © Datalicious Pty Ltd 42
COST PER CONVERSION
Last click aRribu%on
Even/weighted aRribu%on
> Media a=ribu&on example
September 2012 © Datalicious Pty Ltd 43
COST PER CONVERSION
Last click aRribu%on
Even/weighted aRribu%on
? Direct mail
? Internal ads ?
Website content
? TV/Print
> Channel integra&on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
September 2012 © Datalicious Pty Ltd 44
> Tracking offline responses online
§ Search calls to ac%on for TV, radio, print – Unique search term only adver%sed in print so all responses from that term must have come from print
§ PURLs (personalised URLs) for direct mail – Brand.com/customer-‐name redirects to new URL that includes tracking parameter iden%fying response as DM
§ Website entry survey for direct/branded visits – Survey website visitors that have come to site directly or via branded search about their media habits, etc
§ Combine data sets into media aRribu%on model – Combine raw data from online purchase path, website entry survey and offline sales with offline media placement data in tradi%onal (econometric) media aRribu%on model
September 2012 © Datalicious Pty Ltd 45
> Search call to ac&on for offline
September 2012 © Datalicious Pty Ltd 46
ChrisBartens.company.com > redirect to > company.com?
CampaignID=DM:123& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& CustomerSince=2001& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]
> Personalised URLs for direct mail
September 2012 © Datalicious Pty Ltd 47
> Econometric media modelling
September 2012 © Datalicious Pty Ltd 48
Use of tradi%onal econometric modelling to measure the impact of communica%ons on sales for offline channels where it cannot be measured directly through smart calls to ac%on online (and thus cookie level purchase path data).
> Cross-‐channel impact
September 2012 © Datalicious Pty Ltd 49
> Tracking offline sales online § Email click-‐through
– Include offline sales flag in 1st email click-‐through URL auer offline sale to track an ‘assisted offline sales’ conversion
§ First login auer purchase – Similar to the above method, however offline sales flag happens via JavaScript parameter defined on 1st login
§ Unique phone numbers – Assign unique website numbers to responses from specific channels, search terms or even individual visitors to match offline call center results back to online ac%vity
§ Website entry survey for purchase intent – Survey website visitors to at least measure purchase intent in case actual offline sales cannot be tracked
September 2012 © Datalicious Pty Ltd 50
Confirma&on email, 1st login
> Offline sales driven by online
September 2012 © Datalicious Pty Ltd 51
Website research
Phone sales
Retail sales
Online sales
Cookie
Adver&sing campaign
Fulfilment, CRM, etc
Online sales confirma&on
Virtual sales confirma&on
hRp://www.company.com/email-‐landing-‐page.html?
CampaignID=DM:123& CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]
> Email click-‐through iden&fica&on
September 2012 © Datalicious Pty Ltd 52
> Login landing and exit pages
September 2012 © Datalicious Pty Ltd 53
Customer data exposed in page or URL on login or logout
CustomerID=12345& Demographics=M|35& CustomerSegment=A1& CustomerValue=High& ProductHistory=A6& NextBestOffer=A7& ChurnRisk=Low [...]
> About Datalicious
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
September 2012 © Datalicious Pty Ltd 54
> Short but sharp history § Datalicious was founded in November 2007 § Official Adobe & Google Analy%cs partner § 360 data agency with team of data specialists § Combina%on of analysts and developers § Blue chip clients across all industry ver%cals § Carefully selected best of breed partners § Driving industry best prac%ce with ADMA § Turning data into ac%onable insights § Execu%ng smart data driven campaigns September 2012 © Datalicious Pty Ltd 55
> Smart data driven marke&ng
September 2012 © Datalicious Pty Ltd 56
Media A=ribu&on & Modeling
Op&mise channel mix, predict sales
Tes&ng & Op&misa&on Remove barriers, drive sales
Boos&ng ROMI
Targe&ng & Merchandising Increase relevance, reduce churn
“Using data to widen the funnel”
> Wide range of data services
September 2012 © Datalicious Pty Ltd 57
Data Plajorms Data collec&on and processing Adobe, Google Analy&cs, etc Web and mobile analy&cs Tag-‐less online data capture Retail and call center analy&cs Data warehouse solu&ons Single customer view
Insights Analy&cs Data mining and modelling Tableau, Splunk, SPSS, etc Customised dashboards Media a=ribu&on analysis Media mix modelling Social media monitoring Customer segmenta&on
Ac&on Campaigns Data usage and applica&on Alterian, SiteCore, Inxmail, etc Targe&ng and merchandising Marke&ng automa&on CRM strategy and execu&on Data driven websites Tes&ng programs
> Over 50 years of experience
September 2012 © Datalicious Pty Ltd 58
Chris%an Bartens Founder & Director § Bachelor of Business
Management with marke%ng focus
§ Web analy%cs and digital marke%ng work experience
§ Space2go, E-‐Lou, Tourism Australia
§ SuperTag founder, ADMA Analy%cs Chair, I-‐COM Board Member
LinkedIn profile
Elly Gillis General Manager § Bachelor of
Communica%ons with print and digital focus
§ Digital marke%ng and project management work experience
§ M&C Saatchi, Mark, Holler, Tequila, IAG, OneDigital, Telstra
§ Australian gold medal in surf boat rowing
LinkedIn profile
Michael Savio Head of Insights § Bachelor of Arts &
Science with applied mathema%cs focus
§ CRM and marke%ng research and analy%cs work experience
§ ANZ Bank, Australian Bureau of Sta%s%c, DBM Consultants
§ ADMA lecturer on marke%ng tes%ng
LinkedIn profile
Chaoming Li Head of Data § Bachelor of
Technology with microelectronics focus
§ Souware and website development work experience
§ Standards Australia, DF Securi%es, Globiz, Etang
§ Developing his own CMS pla|orm
LinkedIn profile
> Best of breed partners
September 2012 © Datalicious Pty Ltd 59
> Clients across all industries
September 2012 © Datalicious Pty Ltd 60
> Great customer feedback “[…] Datalicious quickly earned our respect and confidence […] understand our business needs, deliver value, push our thinking […]. Likeable, transparent and trustworthy. I would be happy to recommend Datalicious to anyone.” Murray Howe, Execu%ve Manager, Suncorp Group "[…] Datalicious brought with them best prac>ce analy>cs to demonstrate the true value of our marke>ng dollars […] have become a criBcal business partner […] provided great insights which have driven key business decisions.” Trang Young, Senior Marke%ng Manager, E*Trade Australia “The Datalicious guys are great to work along side […] 'no stone unturned' approach to finding solu>ons to challenges […] knowledge and passion for web analy>cs and best of breed web opBmizaBon was second to none” Steve Brown, Senior Business Analyst, Vodafone “[…] The Vodafone implementa>on of SiteCatalyst is one of the most impressive I have seen and ranks in the top 10 […]. It is an amazing founda>on for taking ac>on on the data and improving ROI.” Adam Greco, Consul%ng Lead, Omniture
September 2012 © Datalicious Pty Ltd 61
> Great customer feedback "[…] Datalicious understand the value of informa>on and how to leverage it using best of breed soEware. I would recommend the team without hesita>on [...]." James Fleet, Marke%ng Director, Appliances Online "[...] Datalicious have been inBmately involved in building our analyBcs soluBon. Most importantly their knowledge of best prac>ce combined with innova>ve solu>ons has allowed our business to remain nimble and current. They are also nice guys." Tzvi Balbin, Group Digital Marke%ng Lead, Catch of the Day "[...] Datalicious are helping us to move from a last click campaign measurement model to a more accurate media aFribu>on approach. [...] potenBal to significantly change our media planning [...]. Highly recommended." Keith Mirgis, Senior Digital & Social Media Marke%ng Manager, Telstra "We engaged Datalicious to support a strategic change in our business [...] understand our customers [and their transac>ons] beFer to ensure we retained as many as possible [...]" Natalie Farrell, Direct Marke%ng Manager, Luxo~ca
September 2012 © Datalicious Pty Ltd 62
> About SuperTag
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
September 2012 © Datalicious Pty Ltd 63
> SuperTag tag management
64
SuperTag
Conversion Tracking
Conversion De-‐duping
Media A=ribu&on
Behavioral Targe&ng
A/B Tes&ng Heat Maps
Live Chat
Web Analy&cs
Any JavaScript
Easily implement and update any JavaScript based tag on any site without IT involvement De-‐duplicate conversions for CPA deals and align repor%ng figures across pla|orms Collect accurate mul%-‐channel media aRribu%on data to provide advanced insights Enable advanced features such as targe%ng, tes%ng and chat to op%mise user experience
September 2012 © Datalicious Pty Ltd
> Unique SuperTag architecture
65
T
B
B
B
§ One tag for all sites & pla|orms § Hosted internally or externally § Easy tag implementa%on/updates § Tag/business rule tes%ng on live site § Facilitates A/B & MV test execu%on § Implementa%on of heat-‐maps § Re-‐targe%ng across channels & tools § Live chat & phone number targe%ng § Collect raw page & impression data
T T
SuperTag
September 2012 © Datalicious Pty Ltd
Injec%ng JavaScript tags into the page based on business rules using the SuperTag top and boRom containers. The SuperTag top and boRom containers are JavaScript func%ons called in the page code just auer the opening <body> tag and just before the closing </body> tag on all page across all domains.
superT.t()
superT.b()
> Overcoming team barriers
66
Marke&ng SuperTag Technology
Easy to use online user interface enabling marketers to manage tags without intensive technology support
September 2012 © Datalicious Pty Ltd
> Cross-‐plajorm integra&on
67
Web Analy&cs Heat Maps Targe&ng Live Chat Tes&ng
SuperTag
CRM/eDMs Paid Search Ad Servers Affiliates DFPs
Centralised uniform business rules to trigger conversions and segment visitors across mul%ple marke%ng pla|orms
September 2012 © Datalicious Pty Ltd
2012 © Supertag Pty Ltd 68 Same code on all pages, no customisa&on required
September 2012 © Datalicious Pty Ltd 69 Easy to use drag & drop interface to manage tags
§ Adobe SiteCatalyst § Adobe Test&Target § Google Analy&cs § Google DoubleClick § Microsot Atlas § MediaMind § ClearSaleing § ClickTale § Olark § Etc
September 2012 © Datalicious Pty Ltd 70 Turn any page element into tests or data variables
SuperTag JS hos&ng on client CDN
> SuperTag deployment op&ons
71
Client website
SuperTag JS hos&ng on client server
Email/FTP SuperTag JS publishing
Manual JavaScript
management
Client website
Client website
SuperTag JS management app.supert.ag
Real-‐&me SuperTag JS publishing
SuperTag JS hos&ng on CDN c.supert.ag
CDN = Content delivery network
Dedicated Github client JS archive
September 2012 © Datalicious Pty Ltd
JavaScript
JavaScript
JavaScript Tags
Tags
Tags
> CookieBouncer extension
September 2012 © Datalicious Pty Ltd 72
Cookie Bouncer
Search Domain A Prod view
Domain A Checkout
Domain B Payment
Domain A Conversion
Search Domain A Prod view
Domain A Checkout
Domain A Payment
Domain A Conversion
Campaign = search
Campaign = Search
Campaign = Domain A
Campaign = Domain B
Campaign = Search
Cookie
Cookie Cookie
Cookie Cookie
> DataCollector extension
August 2012 © Datalicious Pty Ltd 73
SuperTag app genera&ng JS app.supert.ag
Full SuperTag JS hosted by client or on c.supert.ag
Client pages referencing SuperTag JS
Browser execu&ng SuperTag JS
SuperTag DataCollector(s) d.supert.ag
Splunk saved searches and dashboards
JavaScript JavaScript
Requests
Tags
Logs
Lookup tables
Databases, files, etc
Splunk regex builder and data extracts
3rd party data plajorms (i.e. CRM, POS, IP)
Dedicated client Splunk server(s)
Display ads loading sta&c 1x1 pixel
Requests
September 2012 © Datalicious Pty Ltd 74 Detailed Splunk dashboards for web analy&cs
September 2012 © Datalicious Pty Ltd 75 Detailed Splunk dashboards for media a=ribu&on
> ExactTarget email extension
September 2012 © Datalicious Pty Ltd 76
SuperTag app genera&ng JS app.supert.ag
Full SuperTag JS hosted by client or on c.supert.ag
Client pages referencing SuperTag JS
SuperTag ExactTarget integra&on
JavaScript Tags
Requests
Data Databases, files, etc
ExactTarget business rules and condi&ons
Addi&onal client data
(i.e. CRM, POS)
Dedicated ExactTarget
DataExtension
Relevant ExactTarget
email campaign
Data
List Targeted ExactTarget campaign list
Emails
Clicks, data
> Unique selling points (USPs) § Superior pla|orm architecture for more flexibility – Turn any page element into variables for data collec%on or business rules for tag execu%on
– Cross-‐pla|orm integra%on and data exchange – Splunk integra%on for advanced data mining
§ Superior tes%ng, deployment and audit features – Tes%ng of tags & business rules on the live website – Complete audit trail of all tag changes and tests
§ No lock-‐in, stop using the SuperTag at any %me – External and internal JavaScript hos%ng available – Perpetual JavaScript usage rights & Github archive
§ All inclusive pricing structure incen%vizing use September 2012 © Datalicious Pty Ltd 77
> Blue chip SuperTag clients
September 2012 © Datalicious Pty Ltd 78
> Great customer feedback
"Managing third party tags has never been easier [...] simplicity of seMng business rules [...] reducBon in CPA [...].“ Jason Lima, Online Marke%ng, IMB "[...] SuperTag tool is so easy to use [...]. Live tes>ng is par>cularly useful [...] highly recommended [...]." Helene Cameron-‐Heslop, Analyst, Appliances Online "SuperTag speeds up tag implementa>on and gives us increased flexibility [...] manage media and website analyBcs [...].” Alex Crompton, Head of Digital, Aussie
79 September 2012 © Datalicious Pty Ltd
September 2012 © Datalicious Pty Ltd 80
Contact us [email protected]
Learn more
blog.datalicious.com
Follow us twi=er.com/datalicious
Data > Insights > Ac&on
September 2012 © Datalicious Pty Ltd 81