Connecting the "dots" around your Consumers
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The document discusses the evolution of digital marketing strategies and emphasizes the importance of optimizing customer journeys across various touchpoints and devices. It highlights consumer preferences for personalized experiences while balancing privacy concerns and suggests employing audience data clustering techniques to improve targeting. Key takeaways include recognizing path fragmentation in customer interactions, the necessity of adopting a Customer Data Platform for integrated data management, and the relevance of continuous testing in marketing attribution models.
Connecting the "dots" around your Consumers
- 1. DIGITAL AIR STRIKE Connecting The “Dots” Around Your Consumers Consumer Engagement & Social Intelligence © 2017 Digital Air Strike| Reproduction Prohibited David Corchado Chief Digital Officer dcorchado@digitalairstrike.com
- 2. 2 Ensuring consistency of message across channels Using online data to optimize the offline experience What will be critical in shaping your digital marketing strategy over the next 5 years? Source: Adobe / EconsultancyMarch 2017 Optimizing the customer journey across multiple touchpoints Learning new techniques disciplines and skills for new channels Understanding how mobile users research and buy products Understanding when/where consumers use different devices Using offline data to optimize the online experience 70% 66% 58% 50% 46% 45% 39% Big Brand Marketing Priorities A Quest For The Right Message, In the Right Place
- 3. 3 0 5 10 15 20 25 30 35 40 45 50 None of these Managing health records online Playing a game online Using a social networking app Banking online Making a purchase from a retail website Online Activities Shown: % Prioritize Function over Privacy EU US • 57% of online shoppers are comfortable sharing information as long as it is for their benefit • 64% of respondents said they’d prefer the personalized experience • 73% of consumers prefer to do business with brands that use personal information to make their shopping experiences more relevant Data, Personalization vs. Privacy Source: eMarketer April 2016 Consumers Accept The Risks (if done respectfully)
- 4. 4 Platforms, Evolve, Dissolve, and have Unique Strengths
- 5. 5 Cycle Interact Purchase Evaluate Consider Function Data Collection Privacy Targeting Personalization Analytics Platform Google Facebook Twitter Live Ramp Adobe Device Computer Mobile Wearables Car TVTablet Channel TV Search Social Display Native Affiliate Digital Video CRMOn-Site Tapad Email - 9 Touchpoints - 6 Channels - 4 Devices - 1 Customer Sessions are “Dots” of Singular Behavior The Buyers Path is Fragmented
- 6. 6 D E V I C E A S S O C I A T I O N G R A P H S C r o s s D e v i c e A t t r i b u t i o n T A G M A N A G E M E N T D a t a L a y e r C R O S S P L A T F O R M I N T E G R A T I O N C u s t o m e r D a t a P l a t f o r m ( C D P ) o r E D W - CDP provides for “out of the box” partitioning/segmenting/stitching - Enterprise data warehouse highly flexible but requires DS expertise & domain knowledge 3 Things to Understand the Customer Journey © 2017 Digital Air Strike| Reproduction Prohibited U N I F I E D D A T A L A Y E R E n t e r p r i s e T a g M a n a g e m e n t Foundations of Digital Personalization through 1st Party Data
- 7. 7 Deterministic • Device matching relies on user information captured during login events • 95+% certainty • Proprietary relationships with a telecom providers, publishers, retailers, that provide anonymized bridging data to identify the same consumer across multiple devices • Limited by scale, often walled Probabilistic • Begins with a “truth set” • Lower confidence score • No reliance on cookies / PII • Better over time and with larger sets • Statistical analysis through: • Device Proximity Data • Browsing Patterns • Time-Based Clues ABC’s of Cross Device Targeting DEVICE ASSOCIATION GRAPH
- 8. 8 Before Using Audience Data Remove Redundant “Labels to Minimize Noise Audience data • Data tends to be binary in nature – either a user possesses a given label, or does not possess a given label. • Large numbers of such labels. Thousands, tens of thousands, or hundreds of thousands are common. • Diversity and quantity of of attribute data, demand distilling the essential structure of Internet audiences into coherent and simple clusters with similar attributes. (5% blocked by users) (45% blocked by users)
- 9. 9 Remove Redundancy before Creating “VisitorID’s” Source: Clustering methods. (From Python documentation) • Most methods require an additional estimation procedure, to find the optimal clusters count. • Some are non-deterministic and use random initialization or other randomization during optimization procedure (2 different runs with same input parameters can return different results) • As big data optimization complexity increases dramatically, so in practice we will get different results often. Recommend 2 stage clustering • Non Stability - a small change in input data can bring about a dramatic change in final results. Data can change for reasons like, acquiring data, data obsolescence, or using a subsample..
- 10. 10 Website Analytics Mobile In-App Media Touch points -Offline -CRM, -Loyalty -Email Device Association Graphs Redshift Test Ground for Attribution Models Customer Data Platform Dynamic Creative Media Endpoints Offline Attribution Web Content Email Marketing Data Layer Aggregation Campaign Activation Execution Retargeting Content Personalization “Look-a-Like” Audiences DMP Behavioral / Demographic Facebook Custom Audiences Processing / Segmentation Google -Segmenting -Clustering -Visit Stitching Batch or API Enterprise Tag Management (Data Layer) Customer Data Platform 1st Party Activities 3rd Party Activities
- 11. 11 1 Done by CDP: Algorithms stitch together unique “Visitor ID’s” to create a unified view of customer included map any associated device graphs 2 3Visitor profiles are established that include interest, propensity, devices, & channel preference Bayesian models continuously monitor performance, evaluate which ads need to be optimized out Done through partnerships (DMP): Rules engine displays advertising copy based on “Visitor ID” of customer A/B Testing of Ads (Optimizely). JavaScript aggregates & standardizes data coming from html pages and mobile apps. Done by ETM: Metadata for urls, visitors, timestamps, from different vendors are formatted and sent to system of record. Creating And Serving the Unified View Of the Customer DATA TAG & CAPTURE SERVE & OPTIMIZESEGMENT & STITCH Holistic Omni-Channel Visitor Stitching, Targeting & Personalization
- 12. 12 Social Sites In Apps Desktop Browser Opinion Makers Pinterest, Tumblr, blogs Media Properties Communication Streams Email, messaging, Twitter, feeds Build Compelling Experiences © 2017 Digital Air Strike| Reproduction Prohibited Deploy Content that Pushes the Buyer Forward with Journey Mapping On Any Device
- 13. 13 Key Takeaways Getting Started…. • Use 2 stage clustering to reduce labels and limit noise in your model • Expect path fragmentation: Having true visibility into customer paths is a real eye opener. Making channels work together will not be a linear process. • The true length of time from first ad exposure is typically longer than 30 days depending on product type. Test different attribution models but keep short test cycles <90 days to analyze/revise • Evaluate the vendor ecosystem: • Customer Data Platform: • Unified Data Layer: • Cross Device Association: Establishing a Central Source of Truth © 2017 Digital Air Strike| Reproduction Prohibited
- 14. THANKS © 2017 Digital Air Strike| Reproduction Prohibited David Corchado dcorchado@digitalairstrike.com