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  • ProsConsumers multi-taskIncreased recollection levelsAbility to track offline channelsConsPaid search competitionDifficult to get natural rankings

ADMA Marketing Data Strategy Workshop ADMA Marketing Data Strategy Workshop Presentation Transcript

  • > Marketing Data Strategy <
    Smart data driven marketing
  • > Short but sharp history
    • Datalicious was founded late 2007
    • Strong Omniture web analytics history
    • Now 360 data agency with specialist team
    • Combination of analysts and developers
    • Carefully selected best of breed partners
    • Driving industry best practice (ADMA)
    • Turning data into actionable insights
    • Executing smart data driven campaigns
    May 2011
    © Datalicious Pty Ltd
    2
  • > Smart data driven marketing
    May 2011
    © Datalicious Pty Ltd
    3
    Media Attribution & ModelingOptimise channel mix, predict sales
    Targeted Direct Marketing Increase relevance, reduce churn
    Testing & OptimisationRemove barriers, drive sales
    Boost ROAS
  • > Wide range of data services
    May 2011
    © Datalicious Pty Ltd
    4
    Insights
    Analytics
    Data mining and modelling
    Customised dashboards
    Tableau, Spotfire, SPSS, etc
    Media attribution models
    Market and competitor trends
    Social media monitoring
    Customer profiling
    Action
    Campaigns
    Data usage and application
    Marketing automation
    Alterian, SiteCore, Inxmail, etc
    Targeting and merchandising
    Internal search optimisation
    CRM strategy and execution
    Testing programs
    Data
    Platforms
    Data collection and processing
    Web analytics solutions
    Omniture, Google Analytics, etc
    Tag-less online data capture
    End-to-end data platforms
    IVR and call center reporting
    Single customer view
  • > Clients across all industries
    May 2011
    © Datalicious Pty Ltd
    5
  • > Data driven marketing
    What is data driven marketing?
    Self assessment: Your capabilities
    Strategies for effective data collection
    Campaign development and data integrity
    Effective multi-channel campaign execution
    Analysis and performance measurement
    In-sourcing or outsourcing
    May 2011
    © Datalicious Pty Ltd
    6
  • May 2011
    © Datalicious Pty Ltd
    7
    Clive Humby: Data is the new oil
  • > Major data categories
    May 2011
    © Datalicious Pty Ltd
    8
    Campaign dataTV, print, call center, search, web analytics, ad serving, etc
    Customer data
    Direct mail, call center, web analytics, emails, surveys, etc
    Consumer data
    Geo-demographics, search, social, 3rd party research, etc
    Competitor data
    Search, social, ad spend, 3rd party research, news, etc
    Campaigns
    Customers
    Competitors
    Consumers
  • >Corporate data journey
    May 2011
    © Datalicious Pty Ltd
    9
    Stage 1Data
    Stage 2Insights
    Stage 3Action
    Data is fully owned in-house, advanced predictive modelling and trigger based marketing, i.e. what will happen and making it happen!
    Sophistication
    Data is being brought in-house, shift towards insights generation and data mining, i.e. why did it happen?
    Third parties control most data, ad hoc reporting only, i.e. what happened?
    Time, Control
  • May 2011
    © Datalicious Pty Ltd
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  • May 2011
    © Datalicious Pty Ltd
    11
    Oil and data come at a price
  • > Google Ngram: Privacy
    May 2011
    © Datalicious Pty Ltd
    12
  • May 2011
    © Datalicious Pty Ltd
    Collecting data for the sake of itor to add valueto customers?
    13
  • > Privacy vs. data benefits policy
    • Do not hide behind small print
    • Use plain English in your privacy policy
    • Explain exactly what data you are recording
    • Explain why you are recording the data
    • Explain the benefits for the consumer
    • Provide opt-out and feedback options
    • Make opt-outs a KPI not just opt-ins
    = Data benefits and privacy policy
    May 2011
    © Datalicious Pty Ltd
    14
  • Exercise: Marketing mix
    May 2011
    © Datalicious Pty Ltd
    15
  • Targeting
    The right message
    Via the right channel
    To the right person
    At the right time
    May 2011
    © Datalicious Pty Ltd
    17
  • > Increase revenue by 10-20%
    May 2011
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  • > New consumer decision journey
    May 2011
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    The consumer decision process is changing from linearto circular.
  • > New consumer decision journey
    May 2011
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    The consumer decision process is changing from linear to circular.
    Online research
    Change increases the importance of experience during research phase.
  • May 2011
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  • > Coordination across channels
    May 2011
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    TV, radio, print, outdoor, search marketing, 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
  • > Combining targeting platforms
    May 2011
    © Datalicious Pty Ltd
    23
  • November 2010
    © Datalicious Pty Ltd
    24
  • November 2010
    © Datalicious Pty Ltd
    25
    Take a closer look at our cash flow solutions
  • > Affinity re-targeting in action
    May 2011
    © Datalicious Pty Ltd
    26
    Different type of visitors respond to different ads. By using category affinity targeting, response rates are lifted significantly across products.
    Google: “vodafone omniture case study”or http://bit.ly/de70b7
  • > Ad-sequencing in action
    May 2011
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    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
  • > Prospect targeting parameters
    May 2011
    © Datalicious Pty Ltd
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  • November 2010
    © Datalicious Pty Ltd
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  • > Sample site visitor composition
    May 2011
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    30
    30% new visitors with no previous website history aside from campaign or referrer data of which maybe 50% is useful
    30% repeat visitors with referral data and some website history allowing 50% to be segmented by content affinity
    10% serious prospects with limited profile data
    30% existing customers with extensive profile including transactional history of which maybe 50% can actually be identified as individuals
  • > Search call to action for offline
    May 2011
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  • May 2011
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  • > PURLs boosting DM response rates
    May 2011
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    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 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
    May 2011
    © Datalicious Pty Ltd
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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 time stamp enable individual match
    • Call conversions matched back to search terms
    May 2011
    © Datalicious Pty Ltd
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  • > Jet Interactive phone call data
    May 2011
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  • > Potential calls to action
    • 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 promotional codes, vouchers
    • Geographic location (Facebook, FourSquare)
    • Plus regression analysis of cause and effect
    May 2011
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    Calls to action can help shape the customer experience not just evaluate responses
  • > The consumer data journey
    May 2011
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    To retention messages
    To transactional data
    From suspect to
    To customer
    prospect
    Time
    Time
    From behavioural data
    From awareness messages
  • Campaign response data
    > Combining data sources
    May 2011
    © Datalicious Pty Ltd
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    Website behavioural data
    +
    The whole is greater than the sum of its parts
    Customer profile data
  • > Transactions plus behaviours
    May 2011
    © Datalicious Pty Ltd
    40
    CRM Profile
    Site Behaviour
    one-off collection of demographical data age, gender, address, etc
    customer lifecycle metrics and key datesprofitability, expiration, etc
    predictive models based on data miningpropensity to buy, churn, etc
    historical data from previous transactionsaverage order value, points, etc
    tracking of purchase funnel stagebrowsing, checkout, etc
    tracking of content preferencesproducts, brands, features, etc
    tracking of external campaign responses
    search terms, referrers, etc
    tracking of internal promotion responses
    emails, internal search, etc
    +
    Updated Occasionally
    Updated Continuously
  • > Customer profiling in action
    May 2011
    © Datalicious Pty Ltd
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    Using website and email responses to learn a little bite more about subscribers at every
    touch point to keep
    refining profiles
    and messages.
  • > Online form best practice
    May 2011
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    42
    Maximise data integrity
    Age vs. year of birth
    Free text vs. options
    Use auto-complete
    wherever possible
  • Exercise: Enriching profiles
    May 2011
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  • > Exercise: Enriching profiles
    May 2011
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    CRM Profile
    Site Behaviour
    +
    ?
    ?
  • Exercise: Customer IDs
    May 2011
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  • >Exercise: Customer IDs
    May 2011
    © Datalicious Pty Ltd
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    To retention messages
    To transactional data
    From suspect to
    To customer
    prospect
    Time
    Time
    From behavioural data
    From awareness messages
  • Geo-demographic data
    > Enhancing data sources
    May 2011
    © Datalicious Pty Ltd
    47
    Customer profile data
    +
    The whole is greater than the sum of its parts
    3rd party data
  • > Geo-demographic segments
    May 2011
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  • May 2011
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  • May 2011
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    Event sponsor presentation
  • transcape
    data solutions
  • Magazine Subscribers
    Mail Order Catalog Buyers
    E-commerce customers
  • transcape
    Buyer File
    1
    Buyer File
    2
    Buyer File
    7
    Buyer File
    3
    Buyer File
    6
    Buyer File
    4
    Buyer File
    5
    "IMP have been working with Alliance Data ever since they launched and have using their Australian & NZ datawith great success across a range of products"
    Victoria Coleman
    Media Manager
    International Masters Publishers
  • transcape
    Selectable by:
    Recency
    Money
    Frequency
  • transcape
    Gender
    Age
    Income
    Selectable by:
    Female
    Male
  • RFM Segmentation (house file)
    0-6 mo.
    7-12 mo.
    13-24 mo.
    25-36 mo.
    37mo.+
    <$10
    0.10%
    1.20%
    0.30%
    0.50%
    0.70%
    $10-$24
    1.50%
    0.90%
    0.70%
    0.40%
    0.20%
    $25-$49
    1.80%
    1.20%
    1.00%
    0.50%
    0.30%
    $50-$99
    2.00%
    1.70%
    1.20%
    0.80%
    0.40%
    2.50%
    2.10%
    1.50%
    1.10%
    0.50%
    $100-$249
    $250+
    3.00%+
    2.20%
    2.00%
    1.40%
    0.70%
    450,000 Buyers
    50,000 Buyers
  • Last bought from YOU
    25-36 mo., $25-$49
    Response Rate = 0.50%
    transcape
    35,000 matches
    50,000 Buyers
    1 .4 million names
  • 0.50%
    0.90%
    Response Rate =
    Last bought from you
    25-36 mo., $25-$49
    50,000
    35,000
    20,000
    Universe =
    Have also bought elsewhere
    1x
    2x
    3x
    1+
    Frequency =
    Recency
    Value
    0-12 mo.
    25+ mo.
    12-24 mo.
    0.30%
    <$25
    0.10%
    0.50%
    $25-49
    0.70%
    0.50%
    0.30%
    0.70%
    0.90%
    $50-$99
    0.50%
    $100+
    0.90%
    1.10%
    0.70%
    Further optimise your house file segments
  • Transactional Data
    Demographic Data
    Geographic Data
  • transcape
    data solutions
    Thank you!
  • Exercise: Targeting matrix
    May 2011
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  • > Exercise: Targeting matrix
    May 2011
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  • > Exercise: Targeting matrix
    May 2011
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  • May 2011
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  • May 2011
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  • May 2011
    © Datalicious Pty Ltd
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  • May 2011
    © Datalicious Pty Ltd
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  • Exercise: Marketing automation
    May 2011
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  • May 2011
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  • > Quality content is key
    Avinash Kaushik: “The principle of garbage in, garbage out applies here. [… what makes a behaviour targeting platform tick, 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.”
    May 2011
    © Datalicious Pty Ltd
    71
  • Plan to fail …
    May 2011
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  • > Develop a testing matrix
    May 2011
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  • > Develop a testing matrix
    May 2011
    © Datalicious Pty Ltd
    74
  • > AIDA and AIDAS formulas
    May 2011
    © Datalicious Pty Ltd
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    Old media
    New media
    Social media
  • > Simplified AIDAS funnel
    May 2011
    © Datalicious Pty Ltd
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  • > Marketing is about people
    May 2011
    © Datalicious Pty Ltd
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    40%
    10%
    1%
  • > Additional funnel breakdowns
    May 2011
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    Brand vs. direct response campaign
    40%
    10%
    1%
    New prospects vs. existing customers
  • May 2011
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    New vs. returning visitors
  • May 2011
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    AU/NZ vs. rest of world
  • > Potential funnel breakdowns
    • Brand vs. direct response campaign
    • New prospects vs. existing customers
    • Baseline vs. incremental conversions
    • Competitive activity, i.e. none, a lot, etc
    • 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
    May 2011
    © Datalicious Pty Ltd
    81
  • > Developing a metrics framework
    May 2011
    © Datalicious Pty Ltd
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  • > Developing a metrics framework
    May 2011
    © Datalicious Pty Ltd
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  • > Establishing a baseline
    May 2011
    © Datalicious Pty Ltd
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    Switch all advertising off for a period of time (unlikely) or establish a smaller control group that is representative of the entire population (i.e. search term, geography, etc) and switch off selected channels one at a time to minimise impact on overall conversions.
  • > Importance of calendar events
    May 2011
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    Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless
  • >Out-sourcing or in-sourcing?
    May 2011
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    Year 1Platforms
    Year 2Training
    Year 3Support
    Reduce vendor reliance to absolute minimum but consider the value of support agreements for both maintenance as well as updates on market innovations and new features.
    Degree of in-house control and sophistication
    Start taking control of technology and data, shift vendor focus to enhancements and the provision of training
    for internal resources
    Engage third parties with more experience to get started and to implement technology
    Time, Control
  • May 2011
    © Datalicious Pty Ltd
    87
    Contact mecbartens@datalicious.com
    Learn moreblog.datalicious.com
    Follow metwitter.com/datalicious
  • Data > Insights > Action