Making sense of consumer data in the digital world
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Making sense of consumer data in the digital world

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  • Wasn ’ t always this easy to identify prospects 12 years ago relatively simple
  • Data was relatively push or pull Uncomplicated data processes
  • Internet has changed all this No longer push or pull, direct or ATL Consumers are now far more powerful than ever Through Social Media they have a voice
  • Social media means that consumers can interact with brands outside of the brand ’ s traditional channels…… The Guardian has sold over 150k Apps to iPhone users, but this is all they know….Apple keeps all behavioural data, so whilst Guardian has received additional revenue and exposure, it doesn ’ t know which of it ’ s readers are also consuming the news via their iPhone.
  • Historically marketeers strived to find that key piece of data; trigger, behavioural to signify consumer engagement Difficult to identify unless you asked the consumer directly: low response rates, high costs Even though direct marketing was targeted…..was also more than a hint of blanket mailing.
  • Plus they leave data trails that are complex and often disjointed. Simple page query on a website can result in text string of over 3k characters.
  • Not just data…..technology and vendors Is SCV thing of the past? Series of data marts that speak to each other but not all in one box
  • Consequently marketing terminology has also changed We ’ re still coming to terms with what these mean and how they fit into the comms process
  • Consumers expectations have changed….based on experiences with other brands, they now expect (and demand) a level of service from all brands and also have the amplification to voice their dissatisfaction.
  • Marketeers are having to quickly integrate Social Media channels and online data into their strategies to acquire, grow and retain customers. New sources of data mean that messages can be quickly and easily amplified by consumers….but including consumers as part of the marketing process is a two-way exchange. Whilst they expect their information and data to be handled with respect and diligence as a matter of course; they demand that the information they provide is listened to, processed and acted upon at the relevant time intelligently.

Making sense of consumer data in the digital world Making sense of consumer data in the digital world Presentation Transcript

  • Matt Jarman
  • A few years ago there was ‘The Line’… Long lunches, beautiful ads, sexy models, big budgets… Sales? ROI? The econometric model says we need more TV… “We’re brand builders, darling.” Targeted comms, just smaller budgets (& lunches)… Response Rates may be low but it’s profitable… “We use data-driven insight & analysis”.
  • …consumers interacted directly with brands “ I buy in shop, direct or through an intermediary I’ll ask friends or read reviews in magazines I watch TV, read the paper, have a mobile Identity theft is losing my wallet My bank, the Government & data companies know me Brands or data companies survey me I can opt out or ignore comms ”
  • The information age has transformed this… Explosion of channels Google, Facebook, Apple etc. are the new data giants Technology, software, social change? We’re all fuelling this data explosion More data for marketeers then ever before… Picture: hardknoxlife.files.wordpress.com/2008/08/digitalrevolution.gif
  • …consumers don’t just engage directly with a brand 955m Facebook users by 2011 ½ of all mobiles to be smartphones by 2015 400m Tweets a day c25bn App downloads via iTunes Twitter has 13bn API calls per day Picture: hardknoxlife.files.wordpress.com/2008/08/digitalrevolution.gif
  • Alexsandr Orlov… 810k people who ‘like’ him on Facebook 53k Twitter followers 4m views on YouTube channel & 5k followers A great example of an integrated awareness campaign But is it just multi-channel ATL branding? What actionable, consumer data can CTM identify through these social media? (Why do people ‘like’ or ‘follow’ a fictional meerkat?)Picture:http://blogs.sundaymercury.net/telly-talk/meerkat.jpg
  • Now we have what we’ve always craved: Contextual Data Likes, dislikes, real-time location, preferences, interest… Consumers openly supply data to brands & friends Brand interaction can be tracked Active Word of Mouth can be measured Brand Engagement Consumer influence can be gauged Tracking technology continually getting smarter
  • Digital trails are long and complex… Network Comment Engagement Location Sentiment Referral Personal Interest IP Address Affiliation Purchase Click-Stream Cookie Age Preferences Open Rates Time/Date Income Click-Thrus Screen Geo-dems Likes Trigger BehaviourPicture: http://images.publicradio.org/content/2008/08/06/20080806_airplane_33.jpg
  • …customer records are becoming more complex & diverse Personal Data Personal Data Demographic & Lifestyle Demographic & Lifestyle Transactional Information Transactional Information Comms Data Comms Data Behavioural (Web & Comms) Predictive (Web & Comms) UGI & CVI Social Media Engagement SentimentPicture: http://images.publicradio.org/content/2008/08/06/20080806_airplane_33.jpg
  • CACI’s Consumer Digital Data Model Web Brand ESP P Cookies Social WEBSTE Media Beacons WEBSITE P A A WEB ANALYTICS Flash CookiesIDENTIFIABLE CONSUMER CONSUMER 3RD PARTY 3rd PARTY ANONYMOUSCONSUMERS VOLUNTEERED DERIVED VOLUNTEERED DERIVED CONSUMERS Personal data provided to an Data derived about consumers Data acquired on consumers Data collected & aggregated organisation directly by or customers based on via 3rd party Social Media sites on anonymous consumers consumers or customers observed behaviour (and intermediaries) based on observed behaviour • Contact Details • IP Address • Personal details • Web behaviour • Preferences • Cookie data • Network Size • Click-stream data • Purchases • Browsing history • Location • Location/ IP Address • Location • Screen Resolution • Brand affiliation • Shopping Basket • Personal Data • Email Behaviour • Comment data • Privacy Options • Email Provider • Sentiment • Interests • Reviews • Preferences • Brand sentiment • Aggregated • Contextual data demographics & • Brand Engagement geo-demographics • Likes/ Dislikes Picture: http://www.ncdd.nl/images/digitaal.jpg
  • Multiple components: Some new, some legacy Facebook Ad server Twitter Store/Branch Online Sales Comment/ Partner Review Email, Telephone, Web SMS, Mail Competitions Registration Web Application CMS Analytics Transactional Data Store Data Matching Data Integration Services Cleansing CONSUMER CONSUMER 3rd PARTY 3rd PARTY TYPE OF DATA VOLUNTEERED DERIVED VOLUNTEERED DERIVED
  • Marketing language is rapidly changing… Open APIs Networks Amplification Communities Social Conversational Networks Marketing Tradigital Augmented Marketing Reality Sentiment Monitoring Picture:chrisbennetts.info/wp-content/uploads/2010/07/bravenewworld-heads.jpg
  • …consumer expectations have increased FROM TO 6 week reaction 0.06 second reaction Single Channel Multiple Channels One to Many One to One to Many Push Pull Data assimilation Customer dialogue Muted Loud Picture:bergoiata.org/fe/sun-sky/New%20Horizon.jpg
  • Marketing models have to (and are) adapting… PUSH Acquisition Growth Retention PULL Engage Brands need to record, link, monitor and utilise relevant data collected from inbound, Influence outbound and intra-consumer conversations, across all relevant channels to drive better conversations and therefore have deeper customer relationships Amplify Picture:reptilia.org/images/sce/VeildSkin.jpg
  • But, familiar challenges still exist Data held in organisational silos A lot is known about a few Data rich; insight poor Brand centric data, not user centric-data Organisational planning still product, not consumer focussed Picture:2.bp.blogspot.com/_gHLi5No8dSY/SNKa6PxB8kI/AAAAAAAABFk/RvH2E8AwQCo/s400/red-traffic-light.jpg
  • Where next? • Consumer digital is increasing and will only increase • Whether social, web behaviour, email behaviour, Open ID or all – no brand is impervious & you’ll already be collecting this data • CACI can help you to: – Identify what data to collect, distil, and use – How to bring the disparate data components together – Append additional, user-centric data – Bespoke online analysis – Technology vendor selection & implementation