Data Driven Marketing - the Key to an Effective Marketing Campaign

[ Data driven marke.ng ] Data to help create highly targeted

and engaging campaigns

[ Quick company history ]

§  Datalicious was founded in 2007 §  Strong Omniture web analy<cs history §  1 of 4 global Omniture Preferred Partners §  Now 360 data agency with specialist team §  Combina<on of analysts and developers §  Evangelizing smart data driven marke<ng § Making data accessible and ac<onable §  Driving industry best prac<ce (ADMA)

September 2010 © Datalicious Pty Ltd 2

[ Clients across all industries ]


[ Using data to reduce waste ]


Media a>ribu.on

Op.mising channel mix

Tes.ng Improving usability

$$$

Targe.ng Increasing relevance

[ The consumer data journey ]


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 ]


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 plaKorms ]


September 2010 © Datalicious Pty Ltd 8 h>p://ww.wesKield.com?data=zimbio,promo.on

[ Search and media planning ]


September 2010 © Datalicious Pty Ltd 10 cookie: zimbio, promo.on, chris.ne, fashion

[ Affinity targe.ng in ac.on ]


Different type of visitors respond to different ads. By using category affinity targe<ng, response rates are li\ed 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 h>p://bit.ly/de70b7

September 2010 © Datalicious Pty Ltd 12 h>p://ww.wesKield.com?data=chris.ne,promo.on

[ Customer profiling in ac.on ]


Using website and email responses to learn a li_le bite more about customers at every touch point in order to keep refining customer profiles and customising future communica<ons.

Phase Segment A/B Channels Data Points

Awareness Seen this? Social, display, search, etc Default

Considera.on Great feature! Social, search, website, etc

Download, product view

Purchase Intent Great value! Search, site, emails, etc

Cart add, checkout, etc

Up/Cross-‐Sell Add this! Direct mail, emails, etc

Email response, login, etc

[ Developing a targe.ng matrix ]

September 2010 14 © Datalicious Pty Ltd

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.”

[ Quality content key to success ]


Campaign response data

[ Combining data sets ]


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

[ Behaviours plus transac.ons ]


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

[ Sample customer level data ]


[ Social media as data source ]


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, a_racted 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

[ Social media data poten.al ]

§  Large Australian consumer brand §  20% of customer emails had social profiles §  Each profile had an average of 8 friends §  2% of profiles had an influencer score §  0.5% of social had a score of over 10 §  For a database of 500,000 that would mean §  Poten<al addi<onal reach of 100,000 friends §  Includes 2,500 influen<al individuals September 2010 © Datalicious Pty Ltd 22

[ Overall volume and influence ]


Data from

[ Influence and media value ]


US

UK

AU/NZ

Data from

[ Google data in Australia ]


Source: h_p://www.hitwise.com/au/datacentre

[ Search at all stages ]


Source: Inside the Mind of the Searcher, Enquiro 2004

[ Search and brand strength ]


[ Search and the product lifecycle ]


Nokia N-‐Series

Apple iPhone

[ Search driving offline crea.ve ]


Direct mail, email, etc

Facebook Twi>er, etc

[ Mapping out campaign flows ]


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

People Reached

People Engaged

People Converted

People Delighted

[ Developing a metrics framework ]


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

[ De-‐duplica.on across channels ]


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email PlaKorm

Google Analy.cs

$

$

$

Central Analy.cs PlaKorm

$

$

$

[ Success a>ribu.on models ]

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


[ Search call to ac.on for offline ]



[ Understanding channel mix ]


[ Target Denim ]


§  51,737 Visitors §  521,857 Unique Page

Views §  11,402 people shared

on Facebook (Most from emails or Facebook)

§  6,821 TVC Views §  82% New Visits (Target

average 73%) §  2,005 Wins §  Average Time on site

is 2.25 minutes (Target average 1.07 minutes)

© Datalicious Pty Ltd

[ Key traffic drivers ]

September 2010 38

NB: Removed data from Friday Feb 11th as due to extreme skew

§  The campaign had a huge first day before paid media began which built momentum early

[ YouTube ] §  13,084 YouTube views, 70

comments, 636 ra<ngs (490 bad, 136 good) –  Silvia Pfeiffer from Vquence found that

males aged 15 – 25 were more likely to comment than any other demographic


Engagement compared to videos of similar length

§  Higher than average engagement from viewers compared to videos of a similar length

§  Honourable men<ons for the week ending the Feb 21st

[ Campaign comparison ]


§  Campaign traffic was almost that of Christmas and much higher than the very successful ‘Colours’ campaign

56% of Visits occur in the first 4 days Site Visits

Christmas = Nov 20 to Dec 30, 2009 Colours = Aug 7 – Sep 16, 2009

§  Looking only at campaign incep<on, it did drive higher daily average traffic; 34k to 32k respec<vely


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Data Driven Marketing - the Key to an Effective Marketing Campaign