PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION

Transcription

1 PREDICTIVE MARKETING, DIGITAL ATTRIBUTION, OPTIMIZATION, AND DATA-DRIVEN PERSONALIZATION A m a r t y a B h a t t a c h a r j y & S u n e e l G r o v e r P r i n c i p a l S o l u t i o n A r c h i t e c t s, S A S

2 Module C: Sunday - 1:30pm-2:45pm Overcoming the Challenges of Algorithmic Digital Attribution S u n e e l G r o v e r P r i n c i p a l S o l u t i o n s A r c h i t e c t, S A S

3 Agenda Making the business case for algorithmic attribution Identifying and preparing important digital data sources for analysis Algorithmic applications and demonstration Taking action on the results Q&A

4 T h e C a s e f o r A l g o r i t h m i c A t t r i b u t i o n

5 Is This Your Organization s Problem?

6 The Business Problem Everybody claims they should have more marketing budget because they are responsible for the sale of $500 USD. Receives Direct Mail Clicked on Display Ad Opens with Promo Website Visit via Organic Search Direct Visit to Website Places an Order for $500 USD THE PATH TO PURCHASE

7 The holy grail of measurement is to analyze the impact and true value of all company-generated marketing interactions across the complex customer journey. 1. Allocates credit more accurately 2. Provides invaluable insights to channel interactions 3. Empowers marketers to spend more wisely

8 Bottom Up Approach Marketing Attribution Modeling: Approaches Sales Top Down Approach Radio TV Channel 1 Channel 2 Offline marketing Spend Catalog Magazine Sales Agents Paid Search Online Display Google Yahoo Bing Online marketing Spend Social Organic Search

9 Attribution Approach Methodologies Top Down Key Differences Bottom Up X Sales/ROI lift analysis X X Considers halo effects Considers the impact of all marketing channels Behavior analysis Customer journey analysis Needs user level data X X X

10 Rules-Based Methods: Last-Click 100 % 100 % PAID SEARCH ONLINE AD DISPLAY SOCIAL ORGANIC DIRECT VISIT PAID SEARCH ONLINE AD DISPLAY SOCIAL ORGANIC DIRECT VISIT Last Click (Interaction) Attribution Model Credit: 100% allocated to Direct Visit ROI on marketing channels = 0% Last Non-Direct Click (Interaction) Attribution Model All direct visits are ignored ROI on marketing channels = 100 % for Organic Search

11 Rules-Based Methods: Linear & Time-Decay 16.6% 40% 16.6% 16.6% ONLINE AD DISPLAY 16.6% ORGANIC 16.6% DIRECT VISIT 2.5% 7.5% ONLINE AD DISPLAY 20% ORGANIC 15% DIRECT VISIT PAID SEARCH 16.6% SOCIAL PAID SEARCH 5% REFFERAL 10% SOCIAL Linear (Interaction) Attribution Model Credit/ROI: Equal amounts assigned to every channel Time Decay (Interaction) Attribution Model Assigns the most credit to interactions that occur nearest to the time of conversion.

12 Rules-Based Methods: Position Based / Custom 30% 30% 10% ONLINE AD DISPLAY 10% ORGANIC 10% DIRECT VISIT PAID SEARCH 10% SOCIAL Position Based (Interaction) Attribution Model Assigns the most credit to interactions that occur in the beginning and end of the purchase cycle. Custom Based (Interaction) Attributions Models User applies custom weights to specific channels within the customer journey

13 Rules-Based Methods: Conclusion

14 What We See Our Clients Doing

15 Example: Buy Attribution Technology

16 Example: Buy Attribution Serv ices

17 Algorithmic Methods: Defined 1. Assigns data-driven conversion credit across all touch points preceding the conversion, using data science to dictate where credit is due. 2. Algorithmic attribution starts at the event level and analyzes both converting and non-converting paths across all channels. 3. Allows the data to point out the hidden correlations and insights within marketing efforts.

18 Why Build Attribution Models In-House? 1. Do you want to do algorithmic attribution analysis yourself and maintain a transparent (white-box) understanding of how your digital media approaches are performing? 2. Do you trust a third-party vendor s approach, and accept less transparency?

19 Algorithmic Attribution: The Approach

20 Case Study: Hospitality Industry The Challenge: Unable to scale digital analytics for algorithmic attribution to measure drivers of conversion and advertising effectiveness Business needs to understand: Drivers of resort hotel bookings online Marketing channel attribution to bookings with statistical validation Insights to allocate future digital media ad spend Current Limitations: Clickstream and display ad-serving very large in size Rules-based attribution methods largely inaccurate Approach Step 1: Modeling table development assets and code for preprocessing clickstream & adserving data at scale Modeling project and code for algorithmic attribution at scale Deployment of modeling insights (reporting, scoring) Technical Summary: 90 day est. file size for Adobe HIT data: 2.5 TB 90 day est. file size for Google Network (Display) data: 3.0 TB Asset development and workflow must be capable of processing on Hadoop platform Approach Step 2: Obtain broader time-series for Adobe HIT and Google Network data and read into prototype project

21 P r e p a r i n g D i g i t a l Data for A n a l y s i s

22 The Typical Marketing A nalysis Challenge *&%#*$@! 80% 20%

23 Digital Data Mining & Hit (Clickstream) Data 4 11 ID ID

24 Big Digital Data Generation

25 Examples of Clickstream Attributes Standard Attributes visid_high visid_low referrer page_event hit_time_gmt page_url Custom Traffic Attributes prop1 prop75 Custom Conversion Attributes evar1 evar75 And there s much more!

26 Examples of Display Media Attributes And there s much more!

27 Points of Adv ice 1. Obtain data dictionary and marketing channel processing documentation from your clickstream technology vendor 2. Obtain data dictionary from your display media serving technology vendor

28 Preparing the Analytical Base Table (ABT) Data needs to be converted to one row per subject ID PREDICTORS A B C D E F G H I J K L ID ID Each HIT ultimately becomes part of a column (variable / input) Each ID becomes a row (observation / case)

29 Digital Data Preparation: Summary Analytical Data Preparation Model Development Model Deployment Data Access (Leveraging Hadoop or Big Data Lake Platform) Data Exploration & Data Quality (Statistical Visualization & Exploration) Data Quality (Recoding, Missing Values, Transformations) Joins, Summarization, Aggregation, Transposition (Data Manipulation & Reshaping)

30 Digital Data Preparation: Visualized Analytic Data Preparation & Modeling Table Creation Modeling Table

31 A l g o r i t h m i c A p p l i c a t i o n s & Ta k i n g A c t i o n

32 Teams Joining Forces & Potential Issues Data Scientists (Statisticians, Data Miners, etc.) Web & Digital Analytics (Digital Ninjas, Web Analysts, etc.) 1. Mature advanced analytic practices 2. Powerful databases & tools 3. Growing experience with digital data 1. Reporting & BI-centric analytic practices 2. Digital marketing domain expertise 3. Growing awareness of predictive analytics LOGO HERE

33 Is T h e r e O n e A l g o r i t h m T h a t S o l v e s A t t r i b u t i o n? LOGO HERE

34 Big Digital Data O p timize With Champion-Challenger Modeling Champion Challenger Modeling Assess Select Champion Attribution Scoring & Channel Weighting Tournament of Algorithmic Alternatives LOGO HERE

35 The Science Of Predictive Analytics LOGO HERE

36 The Need For Interpretability LOGO HERE

37 Approachable Advanced Analytics Platform Predictive Marketers (Business/Digital Analysts, Data Miners, etc.) 1. Self-Service Visual Analytics 2. Powerful BIG data tools 3. Works with online & offline data easily LOGO HERE

38 D e m o : D i g i t a l M e d i a M i x A n a l y s i s & O p t i m i z a t i o n FVi86GuTpnzE4I1lmYOYP_f6e LOGO HERE

39 D a t a S c i e n c e ( M i n i n g ) P l a t f o r m : A l g o r i t h m S e l e c t i o n LOGO HERE

40 Data Science (Mining) Platform: Results View Selected Variable Importance Variable Importance Scoring LOGO HERE

41 A lgorithmic A ttribution Modeling: Summary Analytical Data Preparation Model Development Model Deployment Model Building (Scalable Data Mining) Champion-Challenger Modeling Enablement (Increased Precision & Accuracy) Scoring Large Data (Model Deployment at Speed) To Drive Digital Media Strategy LOGO HERE

42 Why Aren t More Organizations Doing This? 1. Large data volumes require the use of modern big data platforms. 2. The talent required to unlock the marketing value in that data is scarce. 3. Organizations are re-thinking how they collect, analyze, and take action on important digital data sources. LOGO HERE

43 QUESTIONS? LOGO HERE