Hitting Reboot: Marketing Response Modeling 2.0

Transcription

1 Hitting Reboot: Marketing Response Modeling 2.0 Digital Media in the Pre-Purchase Moment September 28 th 2012 CMM NYU Stern Copyright 2012 Middlegame Marketing Sciences, LLC Atlanta, GA USA

2 Organization of Discussion Background for Marketing Response Modeling 2.0 Case Study Demonstration Solution Summary Current Work with CMM using the Framework page: 3

3 Circular Consumer Decision Process Today, consumers narrow the considered set of potential brands/products for purchase during an extended evaluation phase. After purchase, they enter into an open-ended relationship with the brand. Many share their experience online. If consumers bond with the brand, they repurchase without cycling through the earlier stages. consider bond evaluate the loyalty loop advocate enjoy buy new media make the evaluate and advocate stages increasing relevant to the process, but 75% of 2010 media budgets were targeted at consider or buy. Source: Edelman (2010) "Branding in the Digital Age" in Harvard Business Review. page: 4

4 Revised Role of Marketing The social and collaborative nature of the web becomes the determining factor in the shopper decision making process introducing the Zero Moment of Truth (ZMOT). To maintain or build market share, brands must optimize/ synergize advertising and in-store messaging with the information consumers are looking for during ZMOT. 83% of moms do an online search after seeing a TV commercial for products that interest them 79% of consumers say they use a smartphone to help with shopping stimulus shelf experience ZMOT traditional advertising 70% of Americans look at product reviews before making a purchase first moment of truth influence repurchase or other potential consumers second moment of truth Source: Lecinski (2011) "Winning the Zero Moment of Truth" by Google available at vook.com. page: 5

5 Updated Analytics for the New Landscape To address the revised role of marketing, Vector Autoregression (VAR) provides a proven tool for explaining the "network" of direct and indirect effects of paid, owned, earned, and shared media inherent in the circular consumer decision process. Variables in VAR can be defined as both exogenous and endogenous to the circular consumer decision process for modeling. Subsequent Impulse Response Functions (IRFs) based on Pauwels (2004) trace the attribution of marketing actions across the dynamic network including the opportunity for the evaluate and advocate stages to influence repurchase or other potential consumer behavior on an ongoing basis. Attribution can also be translated into typical "marketing mix modeling" results including contribution, effectiveness, and efficiency. page: 7 Reference: Pauwels (2004) "How Dynamic Consumer Response Shapes Long Term Marketing Effectiveness" in Marketing Science. Source: Stacey et. al (2012) "Beyond Likes and Tweets" a Stern Center for Measurable Marketing working paper.

6 Marketing Attribution Across Moments VAR allows MPG to evaluate and test the causality of marketing from both a direct and indirect perspective as well as understand the immediate and longer-term contribution of any element in the "network". Subsequent scenarios can then be generated to evaluate the risk associated with future potential decisions. stimulus traditional advertising keywords: love, ads, stores, shop, visit, etc. direct effect loyalty loop second moment consumer usage Traditional Advertising Contribution to Store Traffic zero moment social media store traffic indirect Google search transaction rate direct indirect effects page: 9 website traffic in-store promotion transaction price first moment immediate longer-term Note: Hypothetical example of the moments as the description of the revised role of marketing.

7 Case Study Demonstration Hitting Reboot: Marketing Response Modeling 2.0 September 28 th 2012 CMM NYU Stern page: 10

8 Case Study Overview Client: national retailer with a significant presence as both a traditional "bricks and mortar" and online merchant operating in 20 markets and/or geographic regions. Overall Goal: help plan the 2011 holiday media schedule. Key Performance Indicators (KPIs): store/website traffic/visits, customer transactions, and retail sales focus on declining store traffic trend here to maintain confidentiality. Marketing Stimulus: television (branded and promotional), radio, direct mail, banner, search engine, and affiliate good mix of traditional and new (digital) media. Consumer Decision Process: Google searches, brand conversations in social media weighted by sentiment (proxy for advocate), and likelihood to shop conversations in social media weighted by sentiment (proxy for evaluate). Environmental Factors: economic conditions, overall shopper traffic (ShopperTrak Retail Traffic Index ), seasonality, key holidays. page: 11 Note: There was also very limited and insignificant and print (magazines and newspapers) activity.

9 Latest Client Trends The holiday season during December 2010 saw a significant decline in store traffic versus the same period in 2009 despite increased impressions. However, the build-up of marketing impressions pre-holiday 2009 were far more developed. 15 concern millions 10 5 store traffic 0 website visits millions > 2010 < 2010 traditional media impressions new media impressions Weeks ending 07/23/11 page: 12 Note: Variability is consistent with study, but scale is transformed to maintain confidentiality of client data.

10 Model Design The VAR methodology allows us to analyze the data in the form of the ZMOT model where direct and indirect effects of the marketing mix are tested and defined based on statistical significance. marketing stimulus direct effect television loyalty loop radio direct mail banner ads indirect effect ZMOT Google search advocate conversations evaluate conversations store traffic direct effect competitive interaction search engine website visits affiliate loyalty loop direct effect page: 13

11 Model Fit The VAR system simultaneously estimates the values of all endogenous variables that represent the circular consumer decision process. Mean Absolute Percent Error (MAPE) ranges between 13.4 and 3.5 and generally get better over time as more data is introduced to VAR. perfect fit is the 45 angle MAPE Results 12.6 standardized predictions store traffic website visits google search advocate evaluate -4 standardized actual results 0 Prior 52 Weeks Current 52 Weeks Note: Standardized scores are the predicted/actual minus the actual mean divided by the actual standard deviation. page: 14

12 Measuring Response Promotional (Discount) Ads The results of the VAR system can be explained as Impulse Response Functions (IRFs) that depict the reaction of the dynamic system over time in response to a change in stimulus. For promotional advertising, the initial impulse is captured as a traditional immediate effect while the carry-over displays the longer-term effect. Impressions 2009Q4 Impulse Response Functions of 10/25/09 Promotional Television millions 750 promotional television /25/09 11/01/09 11/08/09 11/15/09 11/22/09 11/29/09 12/06/09 12/13/09 12/20/09 12/27/09 01/03/10 01/10/10 standardized response real time-series methods generate the persistence (carry-over) effects and not supposed a priori knowledge in the form of analyst predefined "ad-stocks" store traffic website visits google search advocate conversations evaluate conversations Note: Standardized response is the impact (positive or negative) in standard deviations. page: 15

13 Measuring Response Branded (Equity) Ads The results of the VAR system can be explained as Impulse Response Functions (IRFs) that depict the reaction of the dynamic system over time in response to a change in stimulus. For branded advertising, the initial impulse is captured as a traditional immediate effect while the carry-over displays the longer-term effect. millions Impressions 2009Q branded television standardized response /01/09 11/08/09 11/15/09 11/22/09 11/29/09 12/06/09 12/13/09 12/20/09 12/27/09 01/03/10 01/10/10 01/17/10 01/24/10 Impulse Response Functions of 11/01/09 Branded Television similar, but more persistent effects than with promotional television despite less SOV in the impressions weight store traffic website visits google search advocate conversations evaluate conversations Note: Standardized response is the impact (positive or negative) in standard deviations. page: 16

14 Tracing Direct and Indirect Effects The same results for the branded television activity (+0.412MM store visits) can be broken down into the direct and indirect components of response. Approximately 48% of the increase in store traffic was due to the direct effects while the remainder 52% of indirect effects were led by conversion through advocate conversations. 13 week period starting 11/01/ MM +3.9% advocate conversations MM MM store traffic MM branded television Google search MM 143.6MM impressions 11/01/ % +0.2% evaluate conversations MM website visits +0.7% Note: Variability is consistent with study, but scale is transformed to maintain confidentiality of client data. page: 17

15 Comparing Total Marketing Efficiencies Search engine marketing was consistently the most efficient at generating contribution per impression and per allocation of the marketing budget. However, there are only a limited number of keywords available beyond those already owned by the client. Other marketing stimulus need to be considered growth factors. Contribution per Impression Indices Contribution per $000 Spend Indices branded television promoted television radio direct mail banner ads search engine affiliate page: 18 store traffic website visits

16 Planning 2011Q4 with Scenarios Leveraging an understanding of efficiency in immediate and longer-term effects, four scenarios for the 2011Q4 allocation of the marketing budget were created. The VAR system was able to subsequently explain the expected outcomes of each scenario through a dynamic (evolving) forecast across the entire consumer decision process. Scenario Forecast for 13 Weeks Ending 01/07/12 10% 2009 Repeated 2009 OCT/NOV 2010 DEC 2010 Less New Media 2010 More New Media change in traffic/visits versus % 0% -5% share of new media -0.95% -0.86% -0.34% -3.23% 0.58% 5.33% -10% -7.80% -7.45% store traffic website visits Note: Variability is consistent with study, but scale is transformed to maintain confidentiality of client data. page: 19

17 Solution Summary Hitting Reboot: Marketing Response Modeling 2.0 September 28 th 2012 CMM NYU Stern page: 20

18 Solution Summary Provides as methodology for attributing revised role of marketing within the circular consumer decision process. Generates similar metrics to a typical "marketing mix modeling" exercise, i.e. contribution or efficiency as well as return on investment. Traces the attribution of marketing actions throughout the network of consumer responses with a recursive "feedback loop" explaining both immediate and longer-term effects. Demonstrates the importance of social media and ZMOT in the effectiveness of paid marketing based on the indirect effects that are derived. Produces multiple what if? scenario forecasts based on the dynamic flow of an integrated time series "vector". page: 21

19 Current Work with CCM The CCM has organized a series of partners to provide the opportunity to explain the contribution of marketing to the current presidential election where television spending alone through September 9 th was $450MM. Subsequent scenarios in October will forecast the potential impact on social conversations and likely voters. traditional direct effect marketing stimulus media spending campaign visits evaluate conversations advocacy conversations feedback loop indirect effect feedback loop Obama Romney competitive interaction page: 22 traditional direct effect

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