multi-channel analytics by datalicious

> Multi-Channel Analytics <Measuring and optimising

a multi-channel world

© Datalicious Pty Ltd 2

> Workshop overview

About Datalicious Metrics framework Media attribution Channel integration Re-marketing

September 2014


> About Datalicious

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September 2014

> Using data to fatten the funnel

September 2014 © Datalicious Pty Ltd 4

Media Attribution & ModelingMaximise reach, awareness & increase ROI

Testing & OptimisationRemove barriers, drive sales

Boosting ROMI

Targeting & MerchandisingImprove engagement, boost loyalty

“Turning data into actionable insights to widen the conversion funnel”

> Wide range of data services

DataPlatformsData collection and processing

Adobe, Google Analytics, etc

Web and mobile analytics

Tag-less online data capture

Retail and call center analytics

Big data & data warehousing

Single customer view

InsightsAnalyticsData mining and modelling

Tableau, Splunk, SPSS, R, etc

Customised dashboards

Media attribution analysis

Marketing mix modelling

Social media monitoring

Customer segmentation

ActionCampaignsData usage and application

SiteCore, ExactTarget, etc

Targeting and merchandising

Marketing automation

CRM strategy and execution

Data driven websites

Testing programs



> Veda group strategic value add

VedaData

DataliciousTechnology

InivioAnalytics

Products and services for smart data driven marketing in a multi-channel world

September 2014

> Best of breed technologies


> Datalicious product development


SegmentEngage

Analyse

Measure

“Collecting, analysing and actioning data”

dataexchange

> Clients across all industries



> Metrics framework

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September 2014

Awareness Interest Desire Action Satisfaction

> AIDA and AIDAS formulas


Social media

New media

Old media

Reach(Awareness)

Engagement(Interest & Desire)

Conversion(Action)

+Buzz(Delight)

> Simplified AIDAS funnel


Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted

> Marketing is about people


40% 10% 1%

Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted


> Standardised roll-up metrics

Unique browsers,search impressions,TV circulation, etc

Unique visitors,site engagements,

video views, etc

Online sales,online leads, store

locator searches, etc

Facebook comments, Tweets,

ratings, support calls, etc

Response rate, Search response rate,TV response rate, etc

Conversion rate, engagement rate, checkout rate, etc

10%40% 1%

Review rate, rating rate, comment

rate, NPS rate, etc

Peoplereached

Peopleengaged

Peopleconverted

Peopledelighted

> Provide context with figures


40% 10% 1%

New prospects vs. existing customers

Brand vs. direct response campaign

> Provide context with figures Brand vs. direct response campaign New prospects vs. existing customers Competitive activity, i.e. none, a lot, etc Market share, i.e. small, medium, large, et Segments, i.e. age, location, 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 2014 © Datalicious Pty Ltd 18


Google: “google analytics custom variables”


> Conversion funnel 1.0

September 2014

Conversion funnelProduct page, add to shopping cart, view shopping cart, cart checkout, payment details, shipping information, order confirmation, etc

Conversion event

Campaign responses


> Conversion funnel 2.0

September 2014

Campaign responses (inbound spokes)Offline campaigns, banner ads, email marketing, referrals, organic search, paid search, internal promotions, etc

Landing page (hub)

Success events (outbound spokes)Bounce rate, add to cart, cart checkout, confirmed order, call back request, registration, product comparison, product review, forward to friend, etc


> Additional success metrics

September 2014

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 additional metrics closer to the campaign origin


Exercise: Statistical significance


How many survey responses do you need if you have 10,000 customers?

How many email opens do you need to test 2 subject linesif your subscriber base is 50,000?

How many orders do you need to test 6 banner executions if you serve 1,000,000 banners

Google “nss sample size calculator”September 2014


How many survey responses do you need if you have 10,000 customers?

369 for each question or 369 complete responses

How many email opens do you need to test 2 subject linesif 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 executions if you serve 1,000,000 banners?

383 sales per banner execution or 383 x 6 = 2,298 sales

Google “nss sample size calculator”September 2014


> Conversion metrics by category

September 2014

Source: Omniture Summit, Matt Belkin, 2007


> Relative or calculated metrics

Bounce rate Conversion rate Cost per acquisition Pages views per visit Product views per visit Cart abandonment rate Average order value

September 2014


> 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 2014


Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1,people

Level 2,strategic

Level 3,tactical

Funnel breakdowns

> Exercise: Metrics framework

September 2014


Level Reach Engagement Conversion +Buzz

Level 1,people

People reached

People engaged

People converted

People delighted

Level 2,strategic

Display impressions ? ? ?

Level 3,tactical

Interaction rate, etc ? ? ?

Funnel breakdowns Existing customers vs. new prospects, products, etc

> Exercise: Metrics framework

September 2014


> NPS survey and page ratings

September 2014

Page ratings


Google: “google analytics custom events”

> Importance of calendar events


Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless

> Potential calendar events

Press releases Sponsored events Campaign launches Campaign changes Creative changes Price changes Website changes Technical difficultiesSeptember 2014 © Datalicious Pty Ltd 36


> Media attribution

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September 2014


> Duplication across channels

September 2014

Banner Ads

Email Blast

Paid Search

Organic Search

$Bid Mgmt

Ad Server

Email Platform

Google Analytics

$

$

$


> Duplication across channels

September 2014

Display impression

Paid search $

Ad Server

Bid mgmt.

Web analytics

Display click

Ad server cookie

Organic search

Analytics cookie

Analytics cookie

Analytics cookie

Bid mgmt. cookie

Ad server cookie


Central AnalyticsPlatform

$

$

$

> De-duplication across channels

September 2014

Banner Ads

Email Blast

Paid Search

Organic Search

$


Direct mail, email, etc

FacebookTwitter, etc

> Campaign flows are complex

September 2014

POS kiosks, loyalty cards, etc

CRMprogram

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


Exercise: Campaign flow


> Success attribution models

September 2014

Banner Ad

$100

Email Blast

Paid Search$100

Banner Ad

$100

Affiliate Referral

$100

Success

$100

Success

$100

Banner Ad

Paid Search

OrganicSearch$100

Success

$100Last channel

gets all credit

First channel gets all credit

All channels get equal credit

Print Ad$33

Social Media

$33

Paid Search

$33

Success

$100All channels get

partial credit

Paid Search


> First and last click attribution

September 2014

Chart shows percentage of channel touch points that lead to a conversion.

Neither first nor last-click measurementwould provide true picture

Paid/Organic Search

Emails/Shopping Engines

> Ad clicks inadequate measure


Only a small minority of people actually click on ads, the majority merely processes them (if at all) like any other advertising without an immediate response so advertisers cannot rely on clicks as the sole success measure but should instead focus on impressions delivered


> Indirect display impact

September 2014



September 2014


Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking

September 2014

Influencer Influencer $

Display ad clicks

Online leads

Affiliateclicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Lifetime profit

Organic search

Emails, direct mail

Direct site visits

Introducer


Closer

Paid search

Display ad views

TV/print responses

> Full purchase path tracking

September 2014


Display ad clicks

Online leads

Affiliateclicks

Social referrals

Offline sales

Organic search

Social buzz

Retail visits

Lifetime profit

Organic search

Emails, direct mail

Direct site visits

Introducer


> Purchase path example

September 2014


Closer

Channel 1

Channel 1

Channel 1

> Path across different segments

September 2014


Channel 2

Channel 2 Channel 3

Channel 2 Channel 3 Product 4

Channel 3

Channel 4

Channel 4

Introducer

Product A vs. B

Clients vs. prospects

Brand vs. direct resp.


> Understanding channel mix

September 2014


What promoted your visit today? Recent branch visit Saw an ad on television Saw an ad in the newspaper Recommendation from family/friends […]

How likely are you to apply for a loan? Within the next few weeks Within the next few months I am a customer already […]


> Website entry survey

September 2014

Channel% of

Conversions

Straight to Site 27%SEO Branded 15%SEM Branded 9%SEO Generic 7%SEM Generic 14%Display Advertising 7%Affiliate Marketing 9%Referrals 5%Email Marketing 7%

De-duped Campaign Report

}Channel % of Influence

Word of Mouth 32%

Blogging & Social Media 24%

Newspaper Advertising 9%Display Advertising 14%Email Marketing 7%Retail Promotions 14%

Greatest Influencer on Branded Search / STS

Conversions attributed to search terms that contain brand keywords and direct website visits are most likely not the originating channel that generated the awareness and as such conversion credits should be re-allocated.


> Website entry survey example

September 2014

In this retail example, the exposure to retail display ads was the biggest website traffic driver for direct visits as well as visits originating from search terms that included branded keywords – before TV, word of mouth and print ads.


> Adjusting for offline impact

September 2014

+15+5 +10-15-5 -10


> Purchase path vs. attribution

Important to make a distinction between media attribution and purchase path tracking– Not the same, one is necessary to enable the other

Tracking the complete purchase path, i.e. every paid and organic campaign touch point leading up to a conversion is a necessary requirement to be able to actually do media attribution or the allocation or conversion credits back to campaign touch points – Purchase path tracking is the data collection and

media attribution is the actual analysis or modelling

September 2014

> Where to track purchase path


Referral visitsSocial media visits

Organic search visitsPaid search visitsEmail visits, etc

Web Analytics

Banner impressionsBanner clicks

+Paid search clicks

Ad Server

Lacking ad impressionsLess granular & complex

Lacking organic visitsMore granular & complex


> Purchase path data samples

Web Analytics data sampleLAST AD IMPRESSION > SEARCH > $$$| PV $$$AD IMPRESSION > AD IMPRESSION > SEARCH > $$$

Ad Server data sample01/01/2012 11:45 AD IMP YAHOO HOME $3301/01/2012 12:00 AD IMP SMH FINANCE $3301/01/2012 12:05 SEARCH KEYWORD -07/01/2012 17:00 DIRECT $3308/01/2012 15:00 $$$ $100September 2014


Closer

?%

?%

?%

> Media attribution models

September 2014


?%

?% ?%

?% ?% ?%

?%

?%

?%

Introducer

Product A vs. B

Prospects vs. clients



> Full vs. partial purchase path data

September 2014

Display impression

Display impression

Display impression

$

Display impression $

Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔✔

✖ ✖ ✔ ✔

✖ ✔ ✔✔


> Full vs. partial purchase path data

September 2014

Display impression

Display impression

Display impression

$


Display impression


Display impression

Search response

Search response $

Display impression

Display response

Direct visit

✖ ✔ ✔✖

Display impression

Display impression

Email response

Search response

✖ ✔ ✔✔

✖ ✖ ✔ ✔

✖ ✔ ✔✔

5% to 65% variance in conversion attribution

for different channels due to partial purchase path data


> Purchase path for each cookie

September 2014

Mobile Home Work

Tablet Media Etc


0%


September 2014

$100

0% Last click attribution

Even attribution

Weighted attribution

0% 100%

25% 25% 25% 25%

Display impression

Display impression

Display response

Search response

X% X% Y% Z%


> Google Analytics models The First/Last Interaction model plus … The Linear model might be used if your

campaigns are designed to maintain awareness with the customer throughout the entire sales cycle.

The Position Based model can be used to adjust credit for different parts of the customer journey, such as early interactions that create awareness and late interactions that close sales.

The Time Decay model assigns the most credit to touch points that occurred nearest to the time of conversion. It can be useful for campaigns with short sales cycles, such as promotions.

September 2014


Exercise: Attribution models


Closer

?%

?%

?%


September 2014


?%

?% ?%

?% ?% ?%

?%

?%

?%

Introducer

Product A vs. B

Prospects vs. clients



> Media attribution example

September 2014

COST PER CONVERSION

Last click attribution

Even/weighted attribution



September 2014

COST PER CONVERSION

Last click attribution

Even/weighted attribution

?Email

?Direct mail

?Internal

ads?Website content

?TV/Print



September 2014

ROI FULL PURCHASE PATH

TOTA

L CO

NVE

RSIO

N V

ALU

E

Increase spend

Increase spend

Reducespend


> Channel integration

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September 2014


> Tracking offline responses online

Search calls to action for TV, radio, print– Unique search term only advertised 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 identifying 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 attribution model

– Combine raw data from online purchase path, website entry survey and offline sales with offline media placement data in traditional (econometric) media attribution model

September 2014


ChrisBartens.company.com > redirect to > company.com?

utm_id=neND&Demographics=M|35&CustomerSegment=A1&CustomerValue=High&CustomerSince=2001&ProductHistory=A6&NextBestOffer=A7&ChurnRisk=Low[...]

> Personalised URLs for direct mail

September 2014


> Search call to action for offline

September 2014


> Econometric media modelling

September 2014

Use of traditional econometric modelling to measure the impact of communications on sales for offline channels where it cannot be measured directly through smart calls to action online (and thus cookie level purchase path data).


> Tracking offline sales online Email click-through

– Include offline sales flag in 1st email click-through URL after offline sale to track an ‘assisted offline sales’ conversion

First login after 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 activity

Website entry survey for purchase intent– Survey website visitors to at least measure purchase

intent in case actual offline sales cannot be trackedSeptember 2014


Confirmation email, 1st login

> Offline sales driven by online

September 2014

Website research

Phone sales

Retail sales

Online sales

Cookie

Advertising campaign

Fulfilment, CRM, etc

Online sales confirmation

Virtual sales confirmation


http://www.company.com/email-landing-page.html?

utm_id=neNCu&CustomerID=12345&Demographics=M|35&CustomerSegment=A1&CustomerValue=High&ProductHistory=A6&NextBestOffer=A7&ChurnRisk=Low[...]

> Email click-through identification

September 2014


> Login landing and exit pages

September 2014

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[...]

Campaign response data

> Combining data sources


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Transactions plus behaviours


+one-off collection of demographical data

age, gender, address, etccustomer lifecycle metrics and key dates

profitability, expiration, etcpredictive models based on data mining

propensity to buy, churn, etchistorical data from previous transactions

average order value, points, etc

CRM Profile

Updated Occasionally

tracking of purchase funnel stage

browsing, checkout, etctracking of content preferences

products, brands, features, etctracking of external campaign responses

search terms, referrers, etctracking of internal promotion responses

emails, internal search, etc

Site Behaviour

Updated Continuously

> Customer profiling in action


Using website and email responses to learn a little bite more about

subscribers at every touch point to keep

refining profilesand messages.


The study examined data from two of the UK’s busiest ecommerce websites, ASDAand 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 overestimated visitors by up to 7.6 times whilst a cookie-based approach overestimated visitors by up to 2.3 times.

> Unique visitor overestimation

September 2014

Source: White Paper, RedEye, 2007


> Maximise identification points

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 5220%

40%

60%

80%

100%

120%

140%

160%

Weeks

Campaign re

sponse

Email subscr

iption

Online purch

ase

Repeat purch

ase

Confirmation email

Email newsle

tter

Websit

e login

Online bill

payment

−−− Probability of identification through Cookies

September 2014

App download/acce

ss

On-site targeting

Off-sitetargeting

> Combining targeting platforms


CRM


> Re-marketing

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September 2014

> Importance of online experience


The consumer decision process is changing from linear to circular.

Consideration set now grows during online research phase which increases importance of user experience during that phase

Online research

> Increase revenue by 10-20%



APPLY NOW

September 2014

> Network wide re-targeting


Product A

Product B prospect

Product A prospect

Product A customer

Product B Product C

Product C prospect

Product B prospect

Product B customer

Product A prospect

Product C prospect

Product C customer

> Network wide re-targeting


Product B prospect

Product A prospect

Product A customer

Product C prospect

Product B prospect

Product B customer

Product A prospect

Product C prospect

Product C customer

Group wide campaign with approximate impression targets by product rather than hard budget limitations


Closer

Message 1

Message 1

Message 1

> Story telling or ad-sequencing

September 2014


Message 2

Message 2 Message 3

Message 2 Message 3 Message 4

Message 3

Message 4

Message 4

Introducer

Product A

Product B

Product C

> Ad-sequencing in action


Marketing is about telling stories and

stories are not static but evolve over time

Ad-sequencing can help to evolve stories over time the more users engage with ads


> Targeting: Quality vs. quantity

September 2014

30% existing customers with extensive profile including transactional history of which maybe 50% can actually be identified 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


> ANZ home page targeting

September 2014

ANZ home page re-targeting and merchandising combined with landing page optimisation delivered an increase in offer response and conversion rates with an overall project ROI of 578%


Exercise: Re-targeting matrix

Purchase Cycle

Segmentation based on: Search keywords, display ad clicks and website behaviour Data

Points

Default, awareness Default

Research, consideratio

nProduct view, etc

Purchase intent

Checkout, chat, etc

Existing customer

Login, email click, etc

> Exercise: Re-targeting matrix


Purchase Cycle


PointsDefault Product A Product B

Default, awareness

Acquisition message D1

Acquisition message A1

Acquisition message B1 Default


nAcquisition message D2


Acquisition message B2

Product view, etc

Purchase intent

Acquisition message D3


Acquisition message B3

Checkout, chat, etc

Existing customer

Cross-sell message D4

Cross-sell message A4

Cross-sell message B4

Login, email click, etc

> Exercise: Re-targeting matrix



Google: “enable remarketing google analytics”

> Unique phone numbers


2 out of 3 callers hang up as they cannot get their information fast enough.

Unique phone numbers can help improve call experience.



1 unique phone number – Phone number is considered part of the brand– Media origin of calls cannot be established– Added value of website interaction 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

September 2014



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 time stamp enable individual match– Call conversions matched back to search terms

September 2014

Purchase Cycle


PointsDefault Product A Product B

Default, awareness 1300 000 001 1300 000 005 1300 000 009 Default


n1300 000 002 1300 000 006 1300 000 010 Product

view, etc

Purchase intent 1300 000 003 1300 000 007 1300 000 011 Checkout,

chat, etc

Existing customer 1300 000 004 1300 000 008 1300 000 012 Login, email

click, etc

> Website call center integration



> About OptimaHub

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September 2014

Behavioural dataWeb / Apps / Email / Display / Phone / Social / etc

> Combine data across channels

3rd party data vendorsGeo-demographics / income / social influence / etc

+

“The whole is greater than the sum of its parts.”

Transactional dataProduct holding / Lifetime value / CRM profile / etc

Prospects

Customers

Repeat customers



Single Customer ViewWeb / Apps / Errors / Speed / Email / Display / Affiliates / Phone / Social

Maximise Marketing ROIReduce Waste & Churn / Increase Retention, Up- & Cross-Sell

April 2014 © Datalicious Pty Ltd 119

> The big data marketing platform

Wide range of OptimaHub Splunk apps enables identification and tracking of customers across channels on an individual user level

Easy implementation and maintenance if used in conjunction with the SuperTag container tag and mobile app SDKs

Standardised DataCollector data format enables population of pre-built OptimaHub Splunk dashboards without the need for any additional configuration or complex ETL processes

The Splunk big data platform delivers data storage, data mining and analysis as well as data visualisation, reporting and alerts in one (which is unique among BI platforms)

Splunk will scale with your businessJune 2014 © Datalicious Pty Ltd 120

> Core OptimaHub applications

WebAnalytics – Website clickstream analysisAppAnalytics – Mobile app clickstream analysisSocialAnalytics – Social media activity analysisCallAnalytics – Phone call activity analysisRetailAnalytics – Point of sale activity analysisSingleView – Cross-channel single customer viewMediaAttribution – Cross-channel ROI analysisJune 2014 © Datalicious Pty Ltd 121


Access to raw data at any time in standardised format

across all data sources including enhancements

such as social media influencer score

June 2014 © Datalicious Pty Ltd 127OptimaHub SingleView

Single customer view tying events together across channels on an individual user level enabling advanced modeling such as next

best message or next best product

> Holy grail: Next best message


Data source (channel 1)

Data source (channel 2)

Data source (channel N)

DataCollector (standardised data format)

OptimaHub apps (Splunk storage,

analysis, etc)

R-Scripts (predict next best message)

CRM tools (SalesForce, Capsule, etc)

DataExchange (API integrations

synching data)

Campaign tools (Urban Airship, MailChimp, etc)

OptimaHub MediaAttribution

June 2014 © Datalicious Pty Ltd 129OptimaHub MediaAttribution

Compare channel performance using

multiple media attribution models

> Additional OptimaHub apps

ErrorAnalytics – Website JavaScript error analysisSpeedAnalytics – Web page load speed analysisDisplayAnalytics – Display ad view-ability analysisAffiliateAnalytics – Affiliate tracking solution


June 2014 © Datalicious Pty Ltd 131OptimaHub ErrorAnalytics

Analyse client side issues that might be impacting user experience and conversion

such as JavaScript errors

> OptimaHub SuperTag integration

Easy to implement, only requires …– Central install of container tag and mobile SDKs– Switching of call provider (keep same numbers)– Katana1 Splunk cloud hosting available as well

Easy to configure and maintain– User friendly drag and drop user interface – Integrations between reports and applications



Easy implementation and maintenance using the optional SuperTag tag

management platform for websites and mobile apps


SMART DATA DRIVEN MARKETING

August 2014

multi-channel analytics by datalicious

Data & Analytics