p&o analytics

> P&O Analy+cs Workshop < Smart data driven marke-ng

> 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

June 2011 © Datalicious Pty Ltd 2

> Smart data driven marke+ng


Media A@ribu+on & Modeling

Op+mise channel mix, predict sales

Tes+ng & Op+misa+on Remove barriers, drive sales

Boost ROMI

Targeted Direct Marke+ng Increase relevance, reduce churn

> Wide range of data services


Data PlaIorms Data collec+on and processing Web analy+cs solu+ons Omniture, Google Analy+cs, etc Tag-‐less online data capture End-‐to-‐end data plaIorms IVR and call center repor+ng Single customer view

Insights Analy+cs Data mining and modelling Customised dashboards Tableau, SpoIire, SPSS, etc Media a@ribu+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

> Clients across all industries


> Today

§  Data Roadmap Prerequisites: 1.  How do you want to differen-ate your

promo-on ac-vity to different segments of consumers/web users/customers? (What would these segments be?) OUTPUT: Dra[ Targe-ng Matrix

2.  What metrics are available at different points in the consumer path to purchase? OUTPUT: Dra[ Metrics Framework



Clive Humby: Data is the new oil

> 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, shi[ 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!

“Followers”

“Leaders”

“Laggards”


Oil and data come at a price

> Google Ngram: Privacy


June 2011 © Datalicious Pty Ltd

Collec+ng data for the sake of it or to add value to customers?

11

> Data driven marke+ng to …

§  Improve media planning and targe-ng §  Op-mise media placements across channels §  Increase campaign/content engagement §  Increase website/call center conversion §  Iden-fy profitable product bundles/prices §  Improve targe-ng and increase up/cross-‐sell §  Improve travel agent engagement/training §  And much more …


Marke+ng

Mix

Product

Price

Place

Promo+on

Physical Evidence

People

Process

Partners

> Targe+ng matrix

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010

November 12 © Datalicious Pty Ltd 14

The right message Via the right channel To the right person At the right -me

Targe+ng


Capture internet traffic Capture 50-‐100% of fair market share of traffic

Increase consumer engagement Exceed 50% of best compe-tor’s engagement rate

Capture qualified leads and sell Convert 10-‐15% to leads and of that 20% to sales

Building consumer loyalty Build 60% loyalty rate and 40% sales conversion

Increase online revenue Earn 10-‐20% incremental revenue online

> Increase revenue by 10-‐20%


> New consumer decision journey


The consumer decision process is changing from linear to circular.

> New consumer decision journey


The consumer decision process is changing from linear to circular.

Change increases the importance of experience during research phase.

Online research

Exercise: Customer journey


> 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 plaIorms



Take a closer look at our cash flow solu+ons


0

> Affinity re-‐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

> Ad-‐sequencing in ac+on


Marke-ng is about telling stories and

stories are not sta-c but evolve over -me

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

> Prospect targe+ng parameters


> Sample site visitor composi+on


30% exis+ng customers with extensive profile including transac-onal history of which maybe 50% can actually be iden-fied 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

> Search call to ac+on for offline



Include in press

> PURLs boos+ng DM response rates


Text

> 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


> 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


> Jet Interac+ve phone call data


> 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


Calls to ac+on can help shape the customer experience not just evaluate responses

Campaign response data

> Combining data sources


Customer profile data

+ The whole is greater than the sum of its parts

Website behavioural data

> Transac+ons plus behaviours


+ 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

> Customer profiling in ac+on


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

subscribers at every touch point to keep

refining profiles and messages.

> Online form best prac+ce


Maximise data integrity Age vs. year of birth Free text vs. op-ons

Use auto-‐complete wherever possible

Geo-‐demographic data

> Enhancing data sources


3rd party data

+ The whole is greater than the sum of its parts

Customer profile data

> Geo-‐demographic segments


> Quality content is key

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.”


Exercise: Targe+ng matrix


> Exercise: Targe+ng matrix


Purchase Cycle

Segments: Colour, price, product affinity, etc Media

Channels Data Points

X Y

Default, awareness

Research, considera+on

Purchase intent

Reten+on, up/cross-‐sell

Purchase Cycle

Segments: Colour, price, product affinity, etc Media

Channels Data Points

X Y

Default, awareness

Have you seen A?

Have you seen B?

Display, search, etc Default

Research, considera+on

A has great features!

B has great features!

Search, website, etc

Ad clicks, prod views

Purchase intent

A delivers great value!

B delivers great value!

Website, emails, etc

Cart adds, checkouts

Reten+on, up/cross-‐sell

Why not buy B?

Why not buy A?

Direct mails, emails, etc

Email clicks, logins, etc

> Exercise: Targe+ng matrix


> Metrics framework

101011010010010010101111010010010101010100001011111001010101010100101011001100010100101001101101001101001010100111001010010010101001001010010100100101001111101010100101001001001010


Awareness Interest Desire Ac+on Sa+sfac+on

> AIDA and AIDAS formulas


Social media

New media

Old media

Reach (Awareness)

Engagement (Interest & Desire)

Conversion (Ac-on)

+Buzz (Sa-sfac-on)

> Simplified AIDAS funnel


People reached

People engaged

People converted

People delighted

> Marke+ng is about people


40% 10% 1%

People reached

People engaged

People converted

People delighted

> Addi+onal funnel breakdowns


40% 10% 1%

New prospects vs. exis-ng customers

Brand vs. direct response campaign


New vs. returning visitors


AU/NZ vs. rest of world

> Poten+al 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 June 2011 © Datalicious Pty Ltd 55

Exercise: Metrics framework


Level Reach Engagement Conversion +Buzz

Level 1, people

Level 2, strategic

Level 3, tac+cal

Funnel breakdowns

> Exercise: Metrics framework


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


> Establishing a baseline


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.

> Importance of calendar events


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


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