commbank analytics
DESCRIPTION
The presentation discusses the concepts, principles and significance of data driven marketing.TRANSCRIPT
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> CommBank Analy-cs < Smart data driven marke-ng
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> 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 § Driving industry best prac-ce (ADMA) § Turning data into ac-onable insights § Execu-ng smart data driven campaigns
April 2011 © Datalicious Pty Ltd 2
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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 Analy-cs Data mining and modelling Customised dashboards Tableau, SpoAire, SPSS, etc Media aMribu-on models Market and compe-tor trends Social media monitoring Customer profiling
Ac-on Campaigns Data usage and applica-on Marke-ng automa-on Alterian, SiteCore, Inxmail, etc Targe-ng and merchandising Internal search op-misa-on CRM strategy and execu-on Tes-ng programs
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> Smart data driven marke-ng
April 2011 © Datalicious Pty Ltd 5
Media AMribu-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
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> Workshop brief § Defining a metrics framework – What to report on, when and why? – Matching strategic and tac-cal goals to metrics – Covering all major categories of business goals
§ Finding and developing the right data – Data sources across channels and goals – Meaningful trends vs. 100% accurate data – Human and technological limita-ons
§ Campaign flow and media aZribu-on – Designing a campaign flow including metrics – Media aZribu-on in a mul--‐channel environment
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> Metrics framework
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
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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
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> Importance of social media Search
WOM, blogs, reviews, ra-ngs, communi-es, social networks, photo sharing, video sharing
April 2011 © Datalicious Pty Ltd
Promo-on
9
Company Consumer
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> Social as the new search
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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%
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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
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New vs. returning visitors
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AU/NZ vs. rest of world
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Prospect vs. customer
High vs. low value
Product affinity
Post code, age, sex, etc
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Exercise: Funnel breakdowns
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> Exercise: Funnel breakdowns § List poten-ally insighaul 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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> Geo-‐demographic segments
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> 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
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Exercise: Conversion metrics
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> Exercise: Conversion metrics
§ Key conversion metrics differ by category – Commerce – Lead genera-on – Content publishing – Customer service
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> Exercise: Conversion metrics
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Source: Omniture Summit, MaZ Belkin, 2007
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> Conversion funnel 1.0
April 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
April 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
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> Conversion funnel design
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Visits
Product Views
Cart Adds
Checkouts
Conversions
Visits
Non-‐Bounces*
Engagements**
Leads**
Conversions
* Non-‐bounce event ** Serialised events, i.e. once per visit
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Exercise: Conversion funnel
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> Exercise: Conversion funnel
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Sen-ment
Reach Influence
> Measuring social media
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Exercise: Metrics framework
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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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€
IR −MIMI
= ROMI + BE
> ROI, ROMI, BE, etc
April 2011 © Datalicious Pty Ltd 34 €
IR −MIMI
= ROMI
€
R − II
= ROI R Revenue I Investment ROI Return on
investment IR Incremental
revenue MI Marke-ng
investment ROMI Return on
marke-ng investment
BE Brand equity
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> Success: ROMI + BE
§ Establish incremental revenue (IR) – Requires baseline revenue to calculate addi-onal revenue as well as revenue from cost savings
§ Establish marke-ng investment (MI) – Requires all costs across technology, content, data and resources plus promo-ons and discounts
§ Establish brand equity contribu-on (BE) – Requires addi-onal sok metrics to evaluate subscriber percep-ons, experience, altudes and word of mouth
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€
IR −MIMI
= ROMI + BE
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> Establishing a baseline
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Switch all adver-sing off for a period of -me (unlikely) or establish a smaller control group that is representa-ve of the en-re popula-on (i.e. search term, geography, etc) and switch off selected channels one at a -me to minimise impact on overall conversions.
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> Process is key to success
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Source: Omniture Summit, MaZ Belkin, 2007
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> Summary and ac-on items
§ Defining a metrics framework – Develop standardised metrics framework – Define addi-onal funnel breakdowns – Establish baseline and incremental – Define addi-onal success metrics – Define conversion funnels
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> Recommended resources § 200501 WAA Key Metrics & KPIs § 200708 WAA Analy-cs Defini-ons Volume 1 § 200612 Omniture Effec-ve Measurement § 200804 Omniture Calculated Metrics White Paper § 200702 Omniture Effec-ve Segmenta-on Guide § 200810 Ronnestam Online Adver-sing And AIDAS § 201004 Al-meter Social Marke-ng Analy-cs § 201008 CSR Customer Sa-sfac-on Vs Delight § Google “Enquiro Search Engine Results 2010 PDF” § Google “Razorfish Ac-onable Analy-cs Report PDF” § Google “Forrester Interac-ve Marke-ng Metrics PDF”
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> Data sources
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
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> Major data categories
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Campaign data TV, print, call center, search, web analy-cs, ad serving, etc
Customer data Direct mail, call center, web analy-cs, emails, surveys, etc
Consumer data Geo-‐demographics, search, social, 3rd party research, etc
Compe-tor data Search, social, ad spend, 3rd party research, news, etc
Campaigns Customers
Compe-tors Consumers
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> Digital data is plen-ful and cheap
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Source: Omniture Summit, MaZ Belkin, 2007
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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
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TV/Print audience
Search audience
Banner audience
> Reach and channel overlap
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> Es-ma-ng reach and overlap
§ Apply average unique visitor count per recorded unique user names to all unique visitor figures in Google Analy-cs, Omniture, etc.
§ Apply ra-o of total banner impressions to unique banner impressions from ad server to paid and organic search impressions in Google AdWords and Google Webmaster Tools.
§ Compare Google Keyword Tool impressions for a specific search term to reach for the same term in Google Ad Planner.
§ Or just add the reach figures for all channels up … April 2011 © Datalicious Pty Ltd 45
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> Google data in Australia
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Source: hZp://www.hitwise.com/au/resources/data-‐centre
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> Search at all stages
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Source: Inside the Mind of the Searcher, Enquiro 2004
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> Search and brand strength
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> Search and the product lifecycle
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Nokia N-‐Series
Apple iPhone
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> Search and media planning
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> Search driving offline crea-ve
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Exercise: Search insights
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> Exercise: Search insights § Iden-fy key category search terms – Data from Google AdWords Keyword Tool – Search for “google keyword tool” – Wordle and IBM Many Eyes for visualiza-ons – Search for “wordle word clouds” and “ibm many eyes”
§ Iden-fy search term trends and compe-tors – Google Trends and Google Search Insights – Search for “google trends” and “google search insights”
§ Search and media planning – DoubleClick Ad Planner by Google – Search for “google ad planner”
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> Cookie based tracking process
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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?
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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
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> 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
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Campaign response data
> Combining data sources
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Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
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> Duplica-on across channels
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Banner Ads
Email Blast
Paid Search
Organic Search
$ Bid Mgmt
Ad Server
Email PlaAorm
Google Analy-cs
$
$
$
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> 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
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> CBA 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
$
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De-‐duplica-on across channels
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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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> Single source of truth repor-ng
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Insights Repor-ng
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April 2011 © Datalicious Pty Ltd 68
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April 2011 © Datalicious Pty Ltd 69
Google: “visualisa-on methods”
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Exercise: Sta-s-cal significance
April 2011 © Datalicious Pty Ltd 70
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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” April 2011 © Datalicious Pty Ltd 71
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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” April 2011 © Datalicious Pty Ltd 72
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> Addi-onal success metrics
April 2011 © Datalicious Pty Ltd 73
Click Through
Add To Cart
Click Through
Page Bounce
Click Through $
Click Through
Call back request
Store Search ? $
$
$ Cart Checkout
Page Views
?
Product Views
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Campaign response data
> Combining data sources
April 2011 © Datalicious Pty Ltd 74
Customer profile data
+ The whole is greater than the sum of its parts
Website behavioural data
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> Behaviours plus transac-ons
April 2011 © Datalicious Pty Ltd 75
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
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Exercise: Customer IDs
April 2011 © Datalicious Pty Ltd 76
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> Exercise: Customer IDs
April 2011 © Datalicious Pty Ltd 77
To reten-on messages To transac-onal data
From suspect to To customer
From behavioural data From awareness messages
Time Time prospect
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> Sample customer level data
April 2011 © Datalicious Pty Ltd 78
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> Atomic labs tag-‐less analy-cs
April 2011 © Datalicious Pty Ltd 79
§ Single point of data capture and processing
§ Real-‐-me queries to enrich website data
§ Mul-ple data export op-ons for web analy-cs
§ Enriching single-‐customer view website behaviour
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April 2011 © Datalicious Pty Ltd 80
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Sen-ment analysis: People vs. machine
April 2011 © Datalicious Pty Ltd 81
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> Al-meter social analy-cs
April 2011 © Datalicious Pty Ltd 82
Social Marke-ng Analy-cs is the discipline that helps companies measure, assess and explain the performance of social media ini-a-ves in the context of specific business objec-ves.
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> Importance of calendar events
April 2011 © Datalicious Pty Ltd 83
Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
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Calendar events to add context
April 2011 © Datalicious Pty Ltd 84
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> Summary and ac-on items
§ Finding and developing the right data – Ensure de-‐duplica-on via central analy-cs – Check reports for sta-s-cal significance – Check data sources and their accuracy – Combine data sources across channels – Start popula-ng a calendar of events
April 2011 © Datalicious Pty Ltd 85
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> Recommended resources § 200311 UK RedEye Cookie Case Study § 200807 Kaushik Tracking Offline Conversion § 200904 Kaushik Standard Metrics Revisited § 201002 Kaushik 8 Compe--ve Intelligence Data Sources § 201005 Google Ad Planner Data Wrong By Up To 20% § 201005 MPI How Sta-s-cally Valid Is Your Survey § 201009 Google Analy-cs How To Tag Links § 200903 Coremetrics Conversion Benchmarks By Industry § 200906 WOM Online The People Vs Machines Debate § 201007 WSJ The Web's New Gold Mine Your Secrets § 201008 Adver-singAge Are Marketers Really Spying On You April 2011 © Datalicious Pty Ltd 86
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> Media aMribu-on
101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010
April 2011 © Datalicious Pty Ltd 87
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Direct mail, email, etc
Facebook TwiMer, etc
> Campaign flow and calls to ac-on
April 2011 © Datalicious Pty Ltd 88
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
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Exercise: Campaign flow
April 2011 © Datalicious Pty Ltd 89
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TV/Print audience
Search audience
Banner audience
> Reach and channel overlap
April 2011 © Datalicious Pty Ltd 90
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Users are segmented before 1st ad is even served
> Ad server exposure test
April 2011 © Datalicious Pty Ltd 91
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
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> Indirect display impact
April 2011 © Datalicious Pty Ltd 92
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> Indirect display impact
April 2011 © Datalicious Pty Ltd 93
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April 2011 © Datalicious Pty Ltd 94
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> Indirect display impact
April 2011 © Datalicious Pty Ltd 95
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> Success aMribu-on models
April 2011 © Datalicious Pty Ltd 96
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
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> First and last click aMribu-on
April 2011 © Datalicious Pty Ltd 97
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
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> CBA first and last touch reports
April 2011 © Datalicious Pty Ltd 98
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April 2011 © Datalicious Pty Ltd 99
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.
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Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
April 2011 © Datalicious Pty Ltd 100
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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> Search call to ac-on for offline
April 2011 © Datalicious Pty Ltd 101
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April 2011 © Datalicious Pty Ltd 102
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> PURLs boos-ng DM response rates
April 2011 © Datalicious Pty Ltd 103
Text
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> 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
April 2011 © Datalicious Pty Ltd 104
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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
April 2011 © Datalicious Pty Ltd 105
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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
April 2011 © Datalicious Pty Ltd 106
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> Jet Interac-ve phone call data
April 2011 © Datalicious Pty Ltd 107
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Closer
SEM Generic
Banner View
TV Ad
> Full path to purchase
April 2011 © Datalicious Pty Ltd 108
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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> Research online, shop offline
April 2011 © Datalicious Pty Ltd 109
Source: 2008 Digital Future Report, Surveying The Digital Future, Year Seven, USC Annenberg School
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> Cross-‐channel impact
April 2011 © Datalicious Pty Ltd 110
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> Offline sales driven by online
April 2011 © Datalicious Pty Ltd 111
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
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Exercise: Offline conversions
April 2011 © Datalicious Pty Ltd 112
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> Exercise: Offline conversions
§ Email click-‐through aker purchase § First online login aker purchase § Unique website or visitor phone number § Call back request or online chat § Unique website promo-on code § Unique printable vouchers § Store locator searches § Make an appointment online
April 2011 © Datalicious Pty Ltd 113
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> Single source of truth repor-ng
April 2011 © Datalicious Pty Ltd 114
Insights Repor-ng
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> Where to collect the data
April 2011 © Datalicious Pty Ltd 115
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
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> Raw aMribu-on data
Web Analy-cs AFFILIATE > SEO > $$$ SEM > SOCIAL > EMAIL > DIRECT > $$$
Ad Server 01/01/2011 12:00 AD IMPRESSION 01/01/2011 12:05 SEO 07/01/2011 17:00 EMAIL 08/01/2011 15:00 $$$
April 2011 © Datalicious Pty Ltd 116
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> Combine purchase paths
April 2011 © Datalicious Pty Ltd 117
Mobile Home Work
Tablet Media Etc
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> Combining data sources
April 2011 © Datalicious Pty Ltd 118
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> Understanding channel mix
April 2011 © Datalicious Pty Ltd 119
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April 2011 © Datalicious Pty Ltd 120
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> Website entry survey
April 2011 © Datalicious Pty Ltd 121
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 aZributed 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.
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> Adjus-ng for offline impact
April 2011 © Datalicious Pty Ltd 122
+15 +5 +10 -‐15 -‐5 -‐10
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April 2011 © Datalicious Pty Ltd 123
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April 2011 © Datalicious Pty Ltd 124
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> ClearSaleing media aMribu-on
April 2011 © Datalicious Pty Ltd 125
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Closer
25%
> Success aMribu-on models
April 2011 © Datalicious Pty Ltd 126
Influencer Influencer $
25% Even AMrib.
Exclusion AMrib.
PaMern AMrib.
25% 25%
Introducer
33% 33% 33% 0%
30% 20% 20% 30%
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Closer
Channel 1
Channel 1
Channel 1
> Path across different segments
April 2011 © Datalicious Pty Ltd 127
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
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Exercise: AMribu-on model
April 2011 © Datalicious Pty Ltd 128
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Closer
25%
> Exercise: AMribu-on models
April 2011 © Datalicious Pty Ltd 129
Influencer Influencer $
25% Even AMrib.
Exclusion AMrib.
Custom AMrib.
25% 25%
Introducer
33% 33% 33% 0%
? ? ? ?
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> Common aMribu-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
April 2011 © Datalicious Pty Ltd 130
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> Media aMribu-on phases § Phase 1: De-‐duplica-on – Conversion de-‐duplica-on across all channels – Requires one central repor-ng plaaorm – Limited to first/last click aZribu-on
§ 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 Analy-cs and Omniture
§ Phase 3: Full purchase path – Direct response tracking including banner exposure – Google Analy-cs and Omniture data collec-on limited – Easier to import addi-onal channels into ad server
April 2011 © Datalicious Pty Ltd 131
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> Summary and ac-on items
§ Campaign flow and media aZribu-on – Draw campaign flow for your company – Check plaaorm cookie expira-on periods – Enable pathing of direct campaign responses – Inves-gate addi-onal pathing op-ons – Inves-gate how to track offline conversions
April 2011 © Datalicious Pty Ltd 132
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> Recommended resources § 200812 ComScore How Online Adver-sing Works § 200905 iProspect Research Study Search And Display § 200904 ClearSaleing American AZribu-on Index § 201003 Datalicious Tying Offline Sales To Online Media § Google: “Forrester Campaign AZribu-on Framework PDF”
April 2011 © Datalicious Pty Ltd 133
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April 2011 © Datalicious Pty Ltd 134
Contact us [email protected]
Learn more
blog.datalicious.com
Follow us twiMer.com/datalicious
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Data > Insights > Ac-on