Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age
 

Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age

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This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013. ...

This presentation was given by Eli Kling, Director - Analytics, AbsolutData at The Business Analytics Conference, AmsterDam, October 2013.

AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools

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Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age Presentation Transcript

  • © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 30, 2014 Multi-Channel Attribution Driving Marketing Spend Planning in the Big Data Age
  • © Absolutdata 2014 Proprietary and Confidential 2 Absolutdata helps forward looking organizations excel through optimal use of data $23MM increase in Customer Loyalty and CRM marketing revenue – A major Hotel chain Contribution of $78MM over the last few years to their margins – A major Retailer $9MM incremental revenue as a result of focused promotional campaigns created – A major Online Retail Discounter $50MM increase in revenue by Market Mix Modeling across 4 geographies – A leading CPG Company 15% revenue growth through Multi Channel Attribution – A large ecommerce company 40% increase in profits through Conjoint based Pricing Optimization – A top SaaS company
  • © Absolutdata 2014 Proprietary and Confidential 3 (IBM Netizza, Hadoop, Hive, etc) Traditional Absolutdata Capabilities New Developed Absolutdata Capabilities Consumer Generated Data Unsolicited customer Feedback Near Real-time data feeds Company Generated Data Business specific data Linkage with Financials Analyzing Data Data Mining Text Mining Visualization Segmentation A/B Testing Predictive Modeling Machine Learning Association Rules Address specific business problems Predict, monitor and control Absolutdata provides the manpower and the technology to make Big Data manageable through our in-house, dedicated resources Big Data Platforms High speed data mining Makes Big Data manageable Absolutdata has the capabilities to help organizations leverage the layers of big data
  • © Absolutdata 2014 Proprietary and Confidential 4 Putting big data into action Marketing attribution for a leading e-commerce company Two other Marketing Mix modeling case studies Ideas for future directions
  • © Absolutdata 2014 Proprietary and Confidential 5 We helped an e-commercecompany change its marketing strategy by undertaking innovative Big Data analytics on On-Line and Off-Line channels and save20% marketing spend. Achieve 50% operations optimization Absolutdata is engaged in this project as a leader in market mixed modeling with expertise in big data 45% 20% 60% 30% Marketing attribution @ segment level Attribution to person level ON – Line Attribution Big Data Bottom up OFF – Line Attribution Not so Big Data Top Down
  • © Absolutdata 2014 Proprietary and Confidential 6 The attribution challenges in the ecommerce environment are more complex than ever  However, despite this, role of offline marketing through different channels such as TV advertising, Radio broadcasting, Print media cannot be ignored. The part played by offline channel is even more enhanced when the target customers are not regular internet users. In this case, offline marketing plays a key role in building brand equity Digital Marketing Sources Traditional Marketing Sources Relationships Networking Cold Calls Referrals Media advertising Trade showsSite visitors Blog Pay-per-click adverts Organic search Social Media Email Campaigns Webinars  Online channels not only act as marketing channels influencing customers through Search activity, Display Ads, Emails etc. but are also gateways to introduce customers to the offered products on the website due to lack of physical presence. This makes online channels very important drivers to track for the e-commerce industry. Hence, there is a plethora of data tracked by companies daily to assess website traffic and to understand users‟ activities on the internet.
  • © Absolutdata 2014 Proprietary and Confidential 7 We would like to measure the direct and indirect impact of our marketing investment at a granularity relevant to planning Weak Relationship Strong Relationship Overall Sales Affiliate Clicks Paid Search Clicks Display Clicks Magazine Online Print Radio TV
  • © Absolutdata 2014 Proprietary and Confidential 8 The solution arrived at combined market mix modelling, cookie attribution and a decision support simulator => multi – channel attribution The challenge While impact of online channels in driving the traffic to the e-commerce website can be easily calculated with readily available supporting data; the role of offline channels in driving day to day business and their impact on online channels is much more complex Methodology  Market Mix Model: to allocate sign ups at an aggregate level to all online & offline channels  Cookie-based Attribution Algorithm: to attribute individual sign ups to all online channels  Reconcile MMM & Cookie Algorithm: to establish sign-up level attribution to all online and offline channels Marketing Channels in Scope Offline Channels: – TV – Radio – Print – PR Online Channels: – Paid Search (Branded/Generic) – Email – Display – Affiliates – Non-Paid Search (SEO)
  • © Absolutdata 2014 Proprietary and Confidential 9 Phase I: Top down marketing mix modeling Phase III: Reconcile MMM & Cookie Attribution Phase IV: Reporting, Simulation and Optimization Phase I: Marketing Mix Modeling Phase II: Cookie- Based Attribution Algorithm Search Clicks Affiliates Display Impressions TV Impacts AffiliatesSecondary Relationships Search Signups Email Signups Print Signups Signups from Other Factors Previous Day’s Baseline Signups +TV GI Signups Display Signups+ + + + + Daily Signups=
  • © Absolutdata 2014 Proprietary and Confidential 10 Secondary attribution provides a refined view of the system Paid Search Clicks Non paid search Cable Total Impact 11.4% 9.0% 2.5% 3.8% -1.0% 2.2% -0.1% 2.6%-3.8% -2.2% Actual TV Attribution taking into account indirect contribution of Search Final Attribution 7.5% 5.7% 11.1% -0.1%
  • © Absolutdata 2014 Proprietary and Confidential 11 Cookie Attribution involves processing a significant Volume of data coming from Varied Sources. Velocity in our case was not a key issue
  • © Absolutdata 2014 Proprietary and Confidential 12  This approach takes into account different – rule-based (first click/first touch/last click/last touch) – and statistics- based approaches • (linear – where each channel gets equal weight • and time-based – where contribution is attributed according to recency)  to come up with a weighted average of contribution  This approach takes into account primarily – the frequency (i.e. number of times a cookie passes through a particular channel) – and recency (i.e. the order in which the cookie passes through different channels)  In order to establish the attribution for each online channel Phase II: Bottom up estimating digital impacts At Absolutdata we use different types of Cookie- Based Attribution Algorithms which help determine the attribution for different online channels based on the path taken by each cookie: Phase III: Reconcile MMM & Cookie Attribution Phase IV: Reporting, Simulation and Optimization Phase I: Marketing Mix Modeling Phase II: Cookie- Based Attribution Algorithm Approach 1: Frequency/ Recency Approach Approach 2: Ensemble Approach  Bayesian Network and Markov Models are statistical techniques used to describe a complex system of transitions between ‘states’. The probability of reaching the interesting end state (signup/visit and )is the basis for the quantification of the channel to contribution Approach 3: Bayesian Network/Markov Model USER 1 30% Search 20% Display 15% Affiliates USER 2 50% Search 50% Display
  • © Absolutdata 2014 Proprietary and Confidential 13 Phase III: Reconcile MMM & cookie attribution Phase III: Reconcile MMM & Cookie Attribution Phase IV: Reporting, Simulation and Optimization Phase I: Marketing Mix Modeling Phase II: Cookie- Based Attribution Algorithm Attribution’s % impact of each media channel drives daily proportions Cookie data captures Unique ID activity and measure recency and frequency Cookie data Cookie data is unique and more detailed but only captures a portion of activity Attribution data Benefits of a top-down/ bottoms-up Data Sources Attribution data captures holistic impact of media but does not link to user data
  • © Absolutdata 2014 Proprietary and Confidential 14 Top down model is proportioned out to people through cookie attribution weights and then aggregated to segments USER 1 30% Search 20% Display 15% Affiliates USER 2 50% Search 50% Display Segment Formed Characteristic of Segment Share of Segments Search & Offline Channels ~20% SEO & Offline Channels ~10% All Digital Channels <10% Search, Display Impressions & offline channels <5% Offline 35% Search 65% Offline 45% SEO 55% Offline 41% Display 15% Search 44% Display 54% Signup Search 46% Signup Signup Signup
  • © Absolutdata 2014 Proprietary and Confidential 15 Phase IV: Reporting, simulation and optimization Phase III: Reconcile MMM & Cookie Attribution Phase IV: Reporting, Simulation and Optimization Phase I: Marketing Mix Modeling Phase II: Cookie- Based Attribution Algorithm  The management takes quarterly decisions on the marketing spend based on the results Absolutdata helped client increase revenue by 15% while maintaining marketing spend Q1 - Pre optimization Q1 - Post optimization Total Cost Q1 - Pre optimization Q1 - Post optimization Revenue Impact Incremental Revenue due to optimized spend Marketing Budget Maintained by the Client
  • © Absolutdata 2014 Proprietary and Confidential 16 Putting big data into action Marketing attribution for a leading e-commerce company Two other Marketing Mix modeling case studies Ideas for future directions
  • © Absolutdata 2014 Proprietary and Confidential 17 The varx is used in a big data situations looking at SKU level data in search for key value items Detailed transactions Aggregate into weekly time series Pricing History Promotions Trackers Time Series Mining {VARx} Impact of category or Item price change on shopping patterns What if scenario explorer tool Co-dependencies between categories/ items (sales) KVI
  • © Absolutdata 2014 Proprietary and Confidential 18 We are also discussion the implantation of marketing mix modeling in combination with brand equity trackers Decision Support Simulator Optimize allocation of media Prioritize contact touch points based on quantified effectiveness ROI KPIs Brand Equity KPIs Media engagement KPIs Media Channels
  • © Absolutdata 2014 Proprietary and Confidential 19 Putting big data into action Marketing attribution for a leading e-commerce company Two other Marketing Mix modeling case studies Ideas for future directions
  • © Absolutdata 2014 Proprietary and Confidential 20 Social media could be harnessed in aid of marketing effectiveness estimations We talked about Volume and Variety Is there a business case for real time attribution (Velocity) ?
  • © Absolutdata 2014 Proprietary and Confidential 21 Omni-channel optimization adds another dimension : True DNA of your customers path 21 Attributing each customer to the right Place and Channel is the first step Combining Physical, Digital, & Mobile platforms A Google search, a review site, a banner ad, a billboard, a store visit, a Facebook post, a Tweet and a magazine QR code scan in a nearby coffee shop … It’s not enough to connect the dots, you need to analyze the touchpoints.
  • © Absolutdata 2014 Proprietary and Confidential 22 Action only according to the true DNA of your customers Data Sources Custom Segments Targeted Message/Offer Personalized to Individuals
  • © Absolutdata 2014 Proprietary and Confidential 23  Founded in 2001, Absolutdata is a pioneer in delivering consulting-oriented advanced analytics services to a global client base  We help clients understand their customers better by statistical data analysis and delivering key analytics that help enhance their own value  Senior management from McKinsey, Kraft, Pfizer, Mitsubishi, Nielsen, GE, and HSBC  450+ employees across San Francisco, Los Angeles, New York, Dubai, Singapore, London and Delhi Mission To help forward looking organizations excel through optimal use of data Services Provided Customer Relationship Management Marketing Effectiveness Data Visualization & Reporting Market Research Big Data Company Overview Corporate Philosophy Absolutdata brings it all together
  • © Absolutdata 2014 Proprietary and Confidential 24 Assessing benefits of different methodologies of bottom-up cookie attribution ATTRIBUTION METHODOLOGIES BENEFITS Incorporates Consumer Path Incorporates Recency Effects Incorporates Frequency Effects Ease of Computation First Click √ First Touch √ Last Click √ Last Touch √ Rules-based Model- driven Frequency + Recency Approach √ √ √ Linear √ √ Markov √ √ √ Time Decay √ √ Bayesian Network √ √ √
  • © Absolutdata 2014 Proprietary and Confidential 25 Approach 1 – Using recency and frequency - theory  For each individual User, the different online channels influencing it will be assigned a points or weights using a frequency and recency and diminishing impact business rules Only those channels visited within one month before the signup date are being considered as “influencing” channels Frequency Rule A more recently visited channel will be given more weight than an older channelRecency Rule Number of interactions (impressions or clicks) with a particular channel will be classified into different stratum of pre-determined weight. e.g. frequency greater than 5 will probably get a weight of 5 only – as more than 5 frequencies might not have additive effect Diminishing Impact Rule USER 1 30% Search 20% Display 15% Affiliates USER 2 50% Search 50% Display
  • © Absolutdata 2014 Proprietary and Confidential 26 Approach 2 – Ensemble approach - Theory Aggregated Attribution Scores for different channels Last Click Last Touch First Click First Touch Linear Time Decay Evaluation of Cost Per Acquisition Estimation of quality of subscribers coming through different channels Simulation Calibration Forecasting Calculate Aggregated Attribution Score Calculate Attribution Through Rule-Based and Model- Based techniques Application Display Search Email Affiliates Rule based Techniques Model based Techniques The different attribution techniques will be prioritized based on Business Understanding Weighted Average based on importance of each techniques
  • © Absolutdata 2014 Proprietary and Confidential 27 Approach 3 – Use of Markov chain and Bayesian networks - Theory $ E1 E2 E3 E4 A user has been to 4 different events (touch/click) as shown below: What fractional credit Wi goes to each Ei Subject to Markov Chain and Bayesian Networks help us to estimate Attribution weights
  • © Absolutdata 2014 Proprietary and Confidential 28 If you need help with Analytics or Research, please write to us: americas.sales@absolutdata.com europe.sales@absolutdata.com asia.sales@absolutdata.com For Media related queries -media.relations@absolutdata.com For all other queries -info@absolutdata.com HEAD OFFICE 314 Marble Arch Tower, 55 Bryanston Street, London W1H 7AA Phone : + 44 207 868 2240 UK OFFICE DLF Cyber City SEZ, Building#14, 4th Floor, Tower B, DLF Phase-III, Sector 24 & 25A, Gurgaon-122002, Phone: +91.124.4953.400 INDIA OFFICE Absolutdata Analytics Middle East JLT Office 1604, Tower BB1 Mazaya Business Avenue Jumeirah Lake Towers Phone: +97150-1577257 DUBAI OFFICE 1851 Harbor Bay Parkway, Suite 125, Alameda, California, USA – 94502 Phone: +1 510 748 9922 Fax: +1 510 217 2387
  • Eli Y. Kling Director - Analytics Phone: +44 (0)7940094976 Email: Eli.Kling@absolutdata.com LinkedIn: Uk.linkedin.com/in/elikling Follow us on: