digital measurement - how to turn data into actionable insights

[ Digital Measurement ] Analy&cs workshop on how to turn

data into ac&onable insights

[ Company history ]

§  Datalicious was founded in 2007 §  Strong Omniture web analy&cs history §  One-‐stop data agency with specialist team §  Combina&on of analysts and developers § Making data accessible and ac&onable §  Driving industry best prac&ce §  Evangelizing use of data

June 2010 © Datalicious Pty Ltd 2

[ Challenging clients ]


[ Data driven marke:ng ]


Data Pla<orms Data collec:on and processing Web analy:cs solu:ons Omniture, Google Analy:cs, etc Tagless 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

[ Today ]

§  Capturing data – Op&ons, limita&ons, innova&ons

§  Genera&ng insights – Process, metrics, examples

§  Taking ac&on – Media, targe&ng, tes&ng


[ Capturing data ]

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[ Digital data is cheap ]


Source: Omniture Summit, MaS Belkin, 2007

[ Digital data op:ons ]


Source: Accuracy Whitepaper for web analy&cs, Brian CliWon, 2008

+Social

[ On-‐site analy:cs tools ]


Source: Forrester Wave Web Analy&cs, 2009

Google: ”forrester wave

web analy:cs pdf” or

hKp://bit.ly/aTLAKT

[ What pla<orm to use ]


Time, Control

Soph

is&ca&o

n

Stage 1: Data Stage 2: Insights Stage 3: Ac:on

Third par&es control most data, ad hoc repor&ng only, i.e. what happened?

Data is being brought in-‐house, shiW towards insights genera&on and data mining, i.e. why did it happen?

Data is fully owned in-‐house, advanced predic&ve modelling and trigger based marke&ng, i.e. what will happen and making it happen!

[ Governance and data integrity ]



© Datalicious Pty Ltd

[ Free off-‐site analy:cs tools ] §  hSp://www.google.com/trends §  hSp://www.google.com/sktool §  hSp://www.google.com/insights/search §  hSp://www.google.com/webmasters §  hSp://www.google.com/adplanner §  hSp://www.google.com/videotarge&ng §  hSp://www.keywordspy.com §  hSp://www.compete.com §  hSp://www.alexa.com §  hSp://wiki.kenburbary.com June 2010 12

[ Search at all stages ]


Source: Inside the Mind of the Searcher, Enquiro 2004

In Australia Google has a market share of almost 90% of all searches, making it a very large and reliable data sample

[ Search call to ac:on for offline ]


[ Client side tracking process ]


Source: Google Analy&cs, Jus&n Cutroni, 2007

What if: Someone deletes their cookies? Or uses a device that does not support JavaScript? Or uses two computers (work vs. home)? Or two people use the same computer?

[ Tag-‐less data capture ]


Google: “atomic labs” www.atomiclabs.com

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. Google: ”red eye cookie report pdf” or hKp://bit.ly/cszp2o

[ Overes:ma:on of unique visitors ]


Source: White Paper, RedEye, 2007

[ Maximise iden:fica:on points ]


0%

20%

40%

60%

80%

100%

120%

140%

0 4 8 12 16 20 24 28 32 36 40 44 48 Weeks

Probability of iden&fica&on through cookie


Datalicious SuperCookie Persistent Flash cookie that cannot be deleted

[ Mobile page headers ]


Source: Mobile Tracking, Omniture, 2008

MSISDN = Mobile Number

[ Single-‐sign on ]


Facebook Connect gives your company the following data and more with just one click! ID, first name, last name, middle name, picture, affilia&ons, last profile update, &me zone, religion, poli&cal interests, interests, sex, birthday, aSracted 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 and email Need anything else?

[ Research online, shop offline ]


Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School

Google: ”digital future report 2009 pdf” or hKp://bit.ly/ZkLvr

[ Offline sales driven by online ]


Cookie

Website.com Research

Credit Check Fulfilment

Phone Orders

Retail Orders

Online Orders





Online Order Confirma:on

Virtual Order Confirma:on



@

@

@

Cookie Cookie

Adver:sing Campaign

Tying offline conversions back to online campaign and research behavior using standard cookie technology by triggering virtual online order confirma&on pages for offline sales using email receipts.

[ Summary: Capturing data ]

§  Plenty of data sources and plajorms §  Especially search is great free data source § Maintaining data integrity takes effort §  Cookie technology has its limita&ons §  New tag-‐less technologies emerging § Maximise iden&fica&on points §  Offline can be &ed to online


[ Genera:ng insights ]

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[ Corporate data journey ]


Time, Control

Soph

is&ca&o

n

Stage 1

Data Stage 2

Insights Stage 3 Ac:on

Third par&es control most data, ad hoc repor&ng only, i.e. what happened?

Data is being brought in-‐house, shiW towards insights genera&on and data mining, i.e. why did it happen?

Data is fully owned in-‐house, advanced predic&ve modelling and trigger based marke&ng, i.e. what will happen and making it happen!

[ The ideal analyst ] §  Business minded –  Semng realis&c improvement goals

§  Technically savvy –  Bridging gap between business and IT

§  Strong sales skills –  Raising awareness for the value of data

§  Seniority and experience – Needs to be taken serious across organisa&on

§  Posi&on within hierarchy – Able to analyse without loyalty conflict


[ Process is key to success ]



Reach (Awareness)

Engagement (Interest & Desire)

Ac:on (Ac&on)

+Buzz (Sa&sfac&on)

Quan&ta&ve and qualita&ve research data

Website, call center and retail data

[ Defining metrics frameworks ]


Social media data

Media and search data

Social media

[ Key metrics by website type ]



[ Conversion funnel 1.0 ]

June 2010

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

© Datalicious Pty Ltd 31

[ Conversion funnel 2.0 ]

June 2010

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

© Datalicious Pty Ltd 32

[ Addi:onal success metrics ]


Click Through

Add To Cart

Click Through

Bounce Rate

Click Through $

Click Through

Call back requests

Store Searches ? $

$

$ Cart Checkout

Pages Per Visit

?

Video Views

June 2010 © Datalicious Pty Ltd

Exercise: Metrics framework

34

Stage Metrics Data Sources

Reach

Engagement

Ac:on

+Buzz

[ Exercise: Metrics framework ]


Stage Metrics Data Sources

Reach Impressions, Searches

Ad Server, Google

Engagement Video Views, Product Views

Web Analy:cs Pla<orm

Ac:on Orders, Store Searches

Web Analy:cs, Call Center

+Buzz Comments, Men:ons

Social Analy:cs Pla<orm

[ Exercise: Metrics framework ]


Customer data

[ Combining data sets ]


3rd party data

+ The whole is greater than the sum of its parts

Web analy:cs data

[ Behaviours vs. 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 CONTINUOUSLY

[ Store searches vs. actual loca:ons ]


[ Enriching customer profiles ]


Source: Hitwise, 2006

All you need is an address

[ Hitwise Mosaic segment swing ]

australia.com vs. newzealand.com australia.com vs. bulafiji.com



[ Hitwise Mosaic segment swing ]

australia.com vs. newzealand.com australia.com vs. newzealand.com



[ Single source of truth ]


Insights Repor:ng

[ De-‐duplica:on across channels ]


Banner Ads

Email Blast

Paid Search

Organic Search

$ Bid Mgmt

Ad Server

Email Pla<orm

Google Analy:cs

$

$

$

Central Analy:cs Pla<orm

$

$

$


Thinking outside the box

45

[ Search and brand strength ]


[ Search and the product lifecycle ]


Nokia N-‐Series

Apple iPhone

www.google.com/trends

[ Search and media planning ]


www.google.com/adplanner


Fiat 500: Online influencing offline

Google: “slideshare fiat 500 case study” or hKp://bit.ly/lh7bx

[ Search driving offline crea:ve ]



Sen:ment analysis: People vs. machine

Google: “people vs machines debate” or hKp://bit.ly/8VbtB

[ Social metrics and tools ]


Google: ”slideshare

al:meter report” or

hKp://bit.ly/c8uYXT

Source: Social Marke&ng Analy&cs, Al&meter, 2010


Exercise: Sta:s:cal significance

56


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


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?

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

[ Summary: Genera:ng insights ]

§  Right resources and processes are key §  Define a flexible metrics framework § Maintain framework to enable comparison §  Combine data sets for hidden insights §  Establish a single (data) source of truth §  Think outside the box and across channels §  Data does not equal significance


[ Taking ac:on ]

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[ How to drive ROI ] §  Increasing revenue –  Increasing overall amount of sales –  Increasing the average revenue per sale

§  Reducing costs –  Increasing media effec&veness –  Increasing website conversion rates –  Increasing online self-‐service usage

§  Improving customer experience –  Reducing steps necessary to complete a task –  Perceived value or quality of the final solu&on


[ How to drive ROI ]


Media or how to op:mise the channel mix

Targe:ng or how to increasing relevance

Tes:ng or how to maximise conversion

[ Success aKribu: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

June 2010 63 © Datalicious Pty Ltd

[ First vs. last click aKribu:on ]


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

Banner View

TV Ad

Print Ad

[ Path to purchase ]

Banner Click

SEM Generic

Partner Site

Direct Visit

June 2010 65 © Datalicious Pty Ltd

$

SEO Generic $

SEO Branded

Banner Click $

Social Media

Email Update

Direct Visit $

[ Forrester media aKribu:on ]


Google: ”forrester aKribu:on

framework pdf” or

hKp://bit.ly/dnbnzY

Source: Forrester, 2009

[ Customer data journey ]


To reten:on messages To transac:onal data

From suspect to To customer

From behavioural data From awareness messages

Time Time prospect

On-‐site segments

Off-‐site segments

[ Matching segments are key ]


On and off-‐site targe:ng pla<orms should use iden:cal triggers to sort visitors into segments

[ Off-‐site targe:ng pla<orms ]

§  Ad servers – Google/DoubleClick – Eyeblaster – Faciliate – Atlas – Etc

§  Ad Networks – Google – Yahoo – ValueClick – Adconian – Etc


hSp://en.wikipedia.org/wiki/Contextual_adver&sing, hSp://hubpages.com/hub/101-‐Google-‐Adsense-‐Alterna&ves, hSp://en.wikipedia.org/wiki/Central_ad_server, hSp://www.adopera&onsonline.com/2008/05/23/list-‐of-‐ad-‐servers/,

hSp://lists.econsultant.com/top-‐10-‐adver&sing-‐networks.html, hSp://www.clickz.com/3633599, hSp://en.wikipedia.org/wiki/behavioural_targe&ng

[ On-‐site targe:ng pla<orms ] §  Test&Target (Omniture, Offerma&ca, TouchClarity) §  Memetrics (Accenture) §  Op&most (Autonomy) §  KeWa (Acxiom) §  AudienceScience §  Maxymiser §  Amadesa §  Certona §  SiteSpect §  BTBuckets (free) §  Google/DoubleClick Ad Server (free) June 2010 © Datalicious Pty Ltd 72

[ Prospect targe:ng parameters ]


[ Vodafone affinity targe:ng ]


Different type of visitors respond to different ads. By using category affinity targe&ng, response rates are liWed significantly across products.

Message CTR By Category Affinity

Postpay Prepay Broadb. Business

Blackberry Bold - - - + 5GB Mobile Broadband - - + - Blackberry Storm + - + + 12 Month Caps - + - +

[ Affinity targe:ng ]

§  Func&on of behavioural targe&ng – Grouping of visitors into major segments –  Based on content and conversion behaviour –  Ease of use vs. reduced targe&ng ability

§  Most common affini&es used –  Brand affinity –  Image preference –  Price sensi&vity –  Product affinity –  Content affinity


[ Coordinate the experience ]


By coordina:ng the consumer’s end-‐to-‐end experience, companies could enjoy revenue increases of 10-‐20%.

Google: “get more value from digital marke:ng” or hKp://bit.ly/cAtSUN

Source: McKinsey Quarterly, 2010

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 is key ]



Exercise: Targe:ng matrix

78

Phase Segment A Segment B

Awareness

Considera:on

Purchase Intent

Up/Cross-‐Sell

Reten:on

[ Exercise: Targe:ng matrix ]


Phase Segment A Segment B

Awareness Seen this?

Considera:on Great feature!

Purchase Intent Great value!

Up/Cross-‐Sell Add this!

Reten:on Discount?

[ Exercise: Targe:ng matrix ]


Google: “change one word double conversion” or hKp://bit.ly/bpyqFp

[ ClickTale tes:ng case study ]


[ Tes:ng pla<orms ]

§  Test&Target (Omniture, Offerma&ca, TouchClarity) §  Memetrics (Accenture) §  Op&most (Autonomy) §  KeWa (Acxiom) §  Maxymiser §  Amadesa §  SiteSpect §  ClickTale (cheap) §  Unbounce (cheap) §  Google Website Op&miser (free) June 2010 © Datalicious Pty Ltd 82

[ Summary ]

§  There is no magic formula for ROI §  Focus on the en&re conversion funnel § Media aSribu&on is hard but necessary §  Neither first nor last click method works §  Create a coordinated targeted experience §  Content is always king no maSer what §  Test, learn and refine con&nuously



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digital measurement - how to turn data into actionable insights

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