More Related Content Similar to ADMA Marketing Data Strategy Workshop Similar to ADMA Marketing Data Strategy Workshop (20) More from Datalicious (20) 10. > 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 11. > 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 13. > 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 14. May 2011 © Datalicious Pty Ltd 7 Clive Humby: Data is the new oil 15. > 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 16. >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 18. May 2011 © Datalicious Pty Ltd 11 Oil and data come at a price 20. May 2011 © Datalicious Pty Ltd Collecting data for the sake of itor to add valueto customers? 13 27. Make opt-outs a KPI not just opt-ins= Data benefits and privacy policy May 2011 © Datalicious Pty Ltd 14 30. Targeting The right message Via the right channel To the right person At the right time May 2011 © Datalicious Pty Ltd 17 32. > New consumer decision journey May 2011 © Datalicious Pty Ltd 19 The consumer decision process is changing from linearto circular. 33. > New consumer decision journey May 2011 © Datalicious Pty Ltd 20 The consumer decision process is changing from linear to circular. Online research Change increases the importance of experience during research phase. 35. > Coordination across channels May 2011 © Datalicious Pty Ltd 22 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 38. November 2010 © Datalicious Pty Ltd 25 Take a closer look at our cash flow solutions 39. > 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 40. > Ad-sequencing in action May 2011 © Datalicious Pty Ltd 27 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 43. > Sample site visitor composition May 2011 © Datalicious Pty Ltd 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 44. > Search call to action for offline May 2011 © Datalicious Pty Ltd 31 76. Plus regression analysis of cause and effectMay 2011 © Datalicious Pty Ltd 37 Calls to action can help shape the customer experience not just evaluate responses 77. > The consumer data journey May 2011 © Datalicious Pty Ltd 38 To retention messages To transactional data From suspect to To customer prospect Time Time From behavioural data From awareness messages 78. Campaign response data > Combining data sources May 2011 © Datalicious Pty Ltd 39 Website behavioural data + The whole is greater than the sum of its parts Customer profile data 79. > 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 80. > Customer profiling in action May 2011 © Datalicious Pty Ltd 41 Using website and email responses to learn a little bite more about subscribers at every touch point to keep refining profiles and messages. 81. > Online form best practice May 2011 © Datalicious Pty Ltd 42 Maximise data integrity Age vs. year of birth Free text vs. options Use auto-complete wherever possible 85. >Exercise: Customer IDs May 2011 © Datalicious Pty Ltd 46 To retention messages To transactional data From suspect to To customer prospect Time Time From behavioural data From awareness messages 86. 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 89. May 2011 © Datalicious Pty Ltd 50 Event sponsor presentation 92. 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 95. 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 96. Last bought from YOU 25-36 mo., $25-$49 Response Rate = 0.50% transcape 35,000 matches 50,000 Buyers 1 .4 million names 97. 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 110. > 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 71 112. > Develop a testing matrix May 2011 © Datalicious Pty Ltd 73 113. > Develop a testing matrix May 2011 © Datalicious Pty Ltd 74 114. > AIDA and AIDAS formulas May 2011 © Datalicious Pty Ltd 75 Old media New media Social media 116. > Marketing is about people May 2011 © Datalicious Pty Ltd 77 40% 10% 1% 117. > Additional funnel breakdowns May 2011 © Datalicious Pty Ltd 78 Brand vs. direct response campaign 40% 10% 1% New prospects vs. existing customers 118. May 2011 © Datalicious Pty Ltd 79 New vs. returning visitors 119. May 2011 © Datalicious Pty Ltd 80 AU/NZ vs. rest of world 130. > Developing a metrics framework May 2011 © Datalicious Pty Ltd 82 131. > Developing a metrics framework May 2011 © Datalicious Pty Ltd 83 132. > Establishing a baseline May 2011 © Datalicious Pty Ltd 84 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. 133. > Importance of calendar events May 2011 © Datalicious Pty Ltd 85 Traffic spikes or other data anomalies without context are very hard to interpret and can render data useless 134. >Out-sourcing or in-sourcing? May 2011 © Datalicious Pty Ltd 86 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 135. May 2011 © Datalicious Pty Ltd 87 Contact mecbartens@datalicious.com Learn moreblog.datalicious.com Follow metwitter.com/datalicious Editor's Notes ProsConsumers multi-taskIncreased recollection levelsAbility to track offline channelsConsPaid search competitionDifficult to get natural rankings
![> Short but sharp history ,[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)