Shows approach which expands the breadth of what marketing-mix models c
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The document discusses the limitations of traditional marketing-mix modeling, emphasizing the need for a paradigm shift to address issues such as short-term focus, neglect of customer perspectives, and the inadequate measurement of multi-touch marketing attribution. It advocates for next-generation models that incorporate customer sentiment and employ advanced econometric methods to capture true marketing impacts, thereby enhancing long-term advertising effectiveness and return on investment. Additionally, it highlights the importance of measuring social media engagement through innovative techniques like the social engagement index (SEI) to better understand consumer relationships with brands.
![Percent Contributions Single Eqtn. OLS 2SLS SUR Nested NNet
Digital Website Page Views [lag 3] 4.27 0.83 0.89 0.56
Display Ads 3.44 3.19 0.88
Digital.Pd.Search 0.51
Mass.TV 0.44 0.44 0.44 0.43
Mass.Print 0.09 0.16 0.17
Trend (4.05) (4.05) (4.06) (0.85)
Final LongLTVariable .KalmanFilter 28.40 28.40 28.34 4.49
Base 70.94 70.85 71.04 76.91
Total 100.00 100.00 100.00 100.00
Synergy 16.90
6.0 5.9 6.1 1.8
Conventional single –equation models are
biased with respect to multi-touch attribution
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Single Eqtn. OLS 2SLS SUR Nested NNet
Model Percent Contributions
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Mass.TV
Digital.Pd.Search
Display Ads
Digital Website Page Views [lag 3]
Multi-Equation approaches are more balanced and overcome MT Attribution Bias
Single equation solutions often biased in
favor of activity closest to sales conversion](https://image.slidesharecdn.com/next-170412140330/85/Shows-approach-which-expands-the-breadth-of-what-marketing-mix-models-c-18-320.jpg)
Shows approach which expands the breadth of what marketing-mix models c
- 2. Content • Marketing-Mix Modeling at the Crossroad & its Shortfalls 4-5 • Next-Generation Marketing-Mix Models 5 – It starts with the basics of blocking and tackling 6 – No Silos for marketing and Marketing Synergies 7-9 – Measuring the Long-Term Effects of Advertising 10-12 – Addressing Multi-Touch Marketing Attribution & Digital ROI 17-22 – Building in the Voice-of-the-Customer into MM Models 23-36 – Simulation and Real-Time Marketing-Mix Models 37-38 • Analytics Portfolio 39 • Global-Analytics Partners 40-42 • Contact Us 43 2
- 3. Marketing-Mix Modeling at a Crossroad: Marketing- Mix Models Falling Short • Marketing-mix models have been referred to as the de facto standard for marketing measurement. • Yet, with all the dramatic change in the marketing landscape, there has been little to no change in how these models calibrate and measure marketing ROI over the past 30 years. The tools have failed to adapt and this has stimulated a chorus of criticisms, wondering if Marketing-Mix Modeling is obsolete • In a recent AdAge article entitled “Marketing-Mix Models Get Pushback As Media Landscape Changes” (Apr. 2013), we hear a growing chorus of critics. • “Some critics believe the models have been wrong all along, and work even worse after three decades of change in the media landscape. They say the models underestimate the impact of advertising, particularly of broad-reach network TV; overstate the value of price promotion, mislead marketers into buying thinly rated programming; wrongly downplay risks of going dark for weeks on end; and fail to account for how online search has made all advertising more effective”. • Marketing-Mix Modeling has been criticized for its focus only on short-term marketing response and, for the most part, not adapting to more advanced methods for measuring long-term marketing impacts and customer loyalty and repeat-purchase dynamics. Because MM models mostly focus only on short-term effects of adverting, this has relegated the ad investment to negative ROI in about 85% of cases. Without consideration of the larger long-term effects of advertising, there is a major under-estimation of the true value of advertising and marketing and this has caused an incorrect focus on short-term only marketing. • There seems to be a consensus that a new paradigm needs to be developed for marketing measurement. However, most MMM vendors have not put forth answers to some of these known short-comings. 3
- 4. Marketing-Mix Modeling at a Crossroad A lot of the underlying method of MM models relies on statistical assumptions which assume complete independence of the marketing drivers. Marketing can not be relegated to silos. All of this ignores the synergistic and more complex symphony represented by multiple marketing activities acting together. Marketing is all about a brand’s “relationship” with customers. Yet, through all of this, “the voice-of- the-customer” remains silent in the MM modeling exercise. The proper step for MM modeling requires developing a means for measuring the various aspects of the customer-brand-experience and clearly understanding the brand value proposition directly from the customer’s perspective MM modeling focuses pretty much exclusively on marketing channels. Media effectiveness is seen through the lenses of TV, radio, print or digital channels. All of this ignores that marketing is really about “message and communication”. MM Modeling needs to change its focus and measurement towards the effectiveness of ad message and creative. This is a major missing piece that limits MM Modeling from being an effective and powerful tool for forming marketing communications strategies. MM modeling has also been criticized for its inability to accurately address the issue of digital multi- touch attribution. Single equation econometric models often yield biased solutions, with extreme solutions favoring the media or activity closest in proximity to the sales conversion and giving no or little credit to key touch-points along the customer journey path. This means that more advanced “multi-equation” econometric solutions need to be employed which will better account for and accommodate the actual pathways and media touch points of the customer journey. We think that it is time for something different: a paradigm shift. 4
- 5. Incremental Contribution from marketing Return on Investment per £1 spent Optimize spend, maximise sales Develop relationship between sales and drivers Next-Gen Marketing Mix starts with blocking & tackling 5
- 6. …pushing the boundaries. Next Generation Marketing-Mix Models TV RADIO NEWSPAPER PAIDSEARCH 6 Effectiveness Modeling (econometrics) has not changed a great deal over the last 30 years. We fundamentally believe that marketing and media channels do not operate in silos; but most statistical models treat them as such. We employ advanced non-linear methods which account for direct and indirect effects from marketing drivers.
- 7. Marketing Synergies - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Separate Impact Simultaneous Activation Marketing Synergy Radio Digital Radio and Digital Media are most effective when executed together. There is about a +45% synergy or additional lift due to simultaneous activation. 7
- 8. Marketing Synergies - 1,000 2,000 3,000 4,000 5,000 6,000 Separate Impact Simultaneous Activation Synergy TV Digital TV and Digital Media are most effective when executed together. There is about a +66% synergy or additional lift due to simultaneous activation. 8
- 9. Synergistic Effects Copy Quality Effects Social Media Effect Long Term Effects Innovations: Multi-dimensional Media Measurement Bottom Line Analytics is at the forefront of innovations in media measurement. We have developed ways to measure media synergies, copy quality, long term ad effects and social media engagement. 9
- 10. Measure long term ad effects Most advertising creates an initial short term lift in sales and a prolonged long term impact. This is generated through repeat purchase and customer loyalty. Long Term Effect 10
- 11. Many MM Vendors do not know how to measure Long-Term ad effects and counsel clients to simply multiply short-term media by 2. This is wrong! For this brand, where 90% of sales were repeat buys, Long-Term ad effects were 15x 66.05% 7.29% 1.82% 0.50% 0.21% 0.02% 1.45% 22.67% 33.95% Incremental Contributions to Total Neutrogena Sales Baseline Dist.@Min Mdsg.Ftr Mdsg.Displ Mdsg.F + D ST Adv.Spend ST Adv.Creative Long-Term Adv 11
- 12. The financials of advertising dramatically change from -$100K to 2 .7 million dollars! ($500) $0 $500 $1,000 $1,500 $2,000 $2,500 $3,000 Short-Term Effect Only With Long-Term Effects ($100) $2,720 HBA Brand Annual Ad Net Returns * Assumes 20% EBITDA/Sales Margin 12
- 13. Media copy quality measurement 0 50 100 150 200 250 300 Radio Ad 1 TV Ad 2 Digital Ad 3 TV Ad 4 GRPs/Spend Creative 13 Media content and copy quality can be separated and measured. This has implications for design, content and message mix. 60 to 80 percent of short- term advertising is message or creative and not spend/GRPs Note: we can apply this technique to digital media also.
- 14. In the ideal world, spend on an ad-by-ad basis would align perfectly with ad sales lift, but this is not the case. The true situation is most often highly inefficient and wasteful! Correl.=0.298 0 20 40 60 80 100 120 140 160 3 5 7 9 11 13 15 SPENDPERADSQRT Ad SALES LIFT SQRT 12
- 15. However, when we use copy test scores from ABX, it has proven to align and correlate extremely well with individual ad sales lifts. This provides a great resource for allocating marketing funds across individual ads! Correl.= 0.743 4 5 6 7 8 9 10 11 12 13 3 5 7 9 11 13 15 ABXINDEXCREATIVESCORESQRT AD SALES LIFT SQRT Using the ABX metric to allocate media budget by ad would have generated + $21MM (2%) in incremental revenue for Neutrogena 13
- 16. Effective Measurement of Digital Media Annual Marketing Contributions 86.2% 0.0% 2.9% 0.7% 0.9% 1.9% 1.2% 1.2% 3.0% 0.2% 1.8% 13.8% Baseline Display Premium Display Network Paid Search SEO Branded TCP TV GRPs Sponsorship (ITV Weather ) Cinema Ad Radio Press GDP Effect 1.4 million in marketing spend generated almost 13.4 million pounds in revenue sales. Total media accounts for about 12% of total sales. Radio, Digital Display and TV were the largest drivers of car sales. 16
- 17. Addressing the issue or “multi-touch” marketing attribution bias • A common and well-known criticism of current MM models is their failure to accurately cover and reflect the influence of different channels, especially digital ones, that reflect the customer journey towards sales conversions. Frequently, what we find is a bias that favors the specific channel closest to the sales conversion, attributing most of the impact to that single last-touch point. • MTA or multi-touch attribution covers the challenge of attributing accurate impact of our marketing and advertising efforts across multiple devices (desktop, laptop, mobile, TV) and/or channels. • On the next slide, we illustrate how different model methodologies generate results from the same data case study. • Our findings reveal that common “single equation” econometrics yields extreme results, assigning near full credit to one single channel. • By using more advanced and multi-equation econometric methods, we are able to develop models which simulate a path-solution rather then point-in time responses. These more advanced multi-equation models allow for each marketing channel to assume its more accurate impact based on the true consumer purchase path. 17
- 18. Percent Contributions Single Eqtn. OLS 2SLS SUR Nested NNet Digital Website Page Views [lag 3] 4.27 0.83 0.89 0.56 Display Ads 3.44 3.19 0.88 Digital.Pd.Search 0.51 Mass.TV 0.44 0.44 0.44 0.43 Mass.Print 0.09 0.16 0.17 Trend (4.05) (4.05) (4.06) (0.85) Final LongLTVariable .KalmanFilter 28.40 28.40 28.34 4.49 Base 70.94 70.85 71.04 76.91 Total 100.00 100.00 100.00 100.00 Synergy 16.90 6.0 5.9 6.1 1.8 Conventional single –equation models are biased with respect to multi-touch attribution 18 (5.00) (4.00) (3.00) (2.00) (1.00) - 1.00 2.00 3.00 4.00 5.00 6.00 Single Eqtn. OLS 2SLS SUR Nested NNet Model Percent Contributions Trend Mass.Print Mass.TV Digital.Pd.Search Display Ads Digital Website Page Views [lag 3] Multi-Equation approaches are more balanced and overcome MT Attribution Bias Single equation solutions often biased in favor of activity closest to sales conversion
- 19. Single Equation Regression Model Sales Web Page View TV Print BaseTrend Lag 3 1) Sales is attributed to Web Page Views, TV, Base and Trend 2) Numerous variables are not significant + multicollinear 19 Long Term Media 3) Very high attribution on 1 variable (last touch?) Competitor Ads Display Ads Paid Search 19 The most common approach to MM modeling is single-equation models, which have a high likelihood of generating biased attribution due to “last-touch attribution bias”.
- 20. Two Stage Least-Squares Model Sales Web Page View TV Print BaseTrend Lag 3 Lag 1 1) Uses two equations, also referred to as instrumental variable approach 2) Sales and Web Page Views have a reciprocal relationship which is lagged 3) Display ads are indirectly contributing to sales via Web Page Views 4) It appears that competitor ads are also driving some traffic to own Home Page. 5) Direct & Indirect Effects Paid Search not influencing sales or web page views. 20 Paid Search Display Ads Competitor Ads Long Term Media 20Multi-equation models are better suited for discovering the nature and direction of complex indirect or reciprocal relationships and interactions within the data models
- 21. Seemingly Unrelated Regressions (SUR) 21 Display Ads Sales Web Page View TV Long Term Media Print Trend This variables is not significantPaid Search Lag 1Lag 3 1) Like SEM for time series modeling. SUR is a true Multi- equation system 2) When errors are correlated, solution is a path rather than discrete data variables. This path can be assumed to be the attribution path. 3) But when the regression errors really are unrelated, then we are just generating single eqtn. OLS results Competitor Ads 21
- 22. Nested Neural-Network Model 22 Sales TV Print Display Ads Paid Search Long Term Media All MarCom Competitor Ads Web Page Views Trend 1) All MarCom Variables pooled into meta-variable and dynamically weighted 2) Good for discovery of non- linearity, interaction and synergistic effects without a priori knowledge 3) Have undeserved reputation for being black-box & can be trained to be stupid.
- 23. Measuring the “Voice-of-the- Customer” via Social Media A breakthrough in measuring social media engagement 23
- 24. Can social media be measured? 1 The Growing Importance of Word of Mouth, www.boundless.com 24 Social Media really isn’t Media as we know it. It doesn’t have “inventory” and it’s not meant to deliver “ads” like traditional “media” Marketing was once seen as a one way relationship, with firms broadcasting their offerings and value proposition. • Now Marketing is seen more as a conversation between marketers and customers.1 • Social media is a key and critical channel for this two-way communication Current social media metrics are expressed in terms of “sentiment” • Positive and negative commentaries about brands • These metrics do not seem to explain or predict purchase behavior Many have given up and say social media can not be measured
- 25. If we remember that social media is a form of word-of-mouth, then words matter! • The semantics, linguistics and context of the conversation matters Our Social Media analysis is based on Stance-Shift Analysis • Uses the Social Media conversations about your Brand as input • Apply linguistic principles of sentiment and tonality • Results in an engagement score that is a translation of a customer’s “personal” and “emotional” relationship with brands, as revealed through language & semantics….Social Engagement Index (SEI) • Academically published, peer reviewed & validated.2 Stance-Shift Analysis translates the consumer’s qualitative emotions into quantitative metrics. Our approach to measuring Social Media 2 Stance Analysis: social cues and attitudes in online interaction, Mason, P , Davis B, In E-Marketing Vol. II . 2005. 25
- 26. Developing the Social Engagement Index (SEI) Net Positive SEI Index 1. Mine all brand related social media reviews and commentary. 2. Parse into positive & negative review groups 3. Apply Social Engagement Index algorithm to “score” reviews 4. Time code by period and aggregate metrics Positive Reviews Negative Reviews Positive Scores Negative Scores LOW MEDIUM HIGH HIGH 0 5 7 MEDIUM -5 0 5 LOW -7 -5 0 Emotional Effect Personalisation 26
- 27. SOCIAL ENGAGEMENT INDEX (SEI) Conversations are scored on personal and emotional content “I HAD A DIET COKE FOR LUNCH TODAY” “THE WARM DIET COKE WAS RATHER BLAND” 27 “I REALLY LOVE MY COKE WITH PIZZA” “I LIKE THE TASTE OF SPRITE WITH LEMON” “MY COKE HAS LOST ITS FIZZ AND TASTES AWFUL”
- 28. SEI shows superior correlationsto brand sales compared with other SocialSentiment Metrics 82.9% 14.8% 9.9% 7.7% 5.9% 2.8% -3.2% -20% 0% 20% 40% 60% 80% 100% SOCIAL ENGAGEMENT INDEX POS/NEG RATIO METRIC 5 POS/NEG RATIO METRIC 1 POS/NEG RATIO METRIC 4 POS/NEG RATIO METRIC 6 POS/NEG RATIO METRIC 2 POS/NEG RATIO METRIC 3 POS/NEG RATIO Comparison of correlation to sales for the SEI versus the six leading sentiment metrics 28
- 29. The correlation* to sales over time shows the SEI has Predictive Power 29 ACID TEST: SEIsm has proven linkage with brand sales Correlation = 86.4% Correlation = 84% Correlation = 81.1% Correlation = 83% Correlation = 83% * Lead lag analysis has confirmed that causation is only one way – the SEI to a large degree is able to drive hard commercial metrics.
- 30. Applications of the SEISM Packaged inside a media mix model, the SEI acts as the key indicator for social media ‘word of mouth’. We are able to determine the return on investment for social media and provide steer around the most effective channels and spend. SEI to help uncover market insights The SEI is also the primary tool used to understand the degree of brand engagement as it transpires through the use of language. • Understand drivers to positive engagement. • Measure the efficacy of individual campaigns. • Develop content strategy that has cut through. • Enhance the execution of sporting events. • Assess brand perception in a competitive sense. • Understand consumer discourse and manage crises. SEI to measure social media ROI 30
- 31. 31 SEI to measure social media ROI We find that conventional advertising has both a “direct” and “indirect” impact on sales due to its influence on social media conversations and the SEI. The large contribution from the SEI support the notion that this is a “word-of-mouth” effect 67% 8% 3% 2% 2% 10% 5% 11% 20% Marketing Contributions Base Sales Direct Alpha Brand Mass Media Direct Alpha Brand Digital Media Direct Social Media Social Media on SEI Mass Media on SEI Digital Media on SEI SEI Base Net driven by media SEI Engagement Sub-model
- 32. 32 The impact of Social Media sentiment A key insight we uncovered across clients is the difference between “positive” and “negative” brand conversations Negative-toned conversation have a significantly greater net impact on brand sales +4.4% +16.5% 0% 5% 10% 15% 20% Positive Sentiment Negative Sentiment The absolute impact from positive & negative consumer reviews Marketers need to develop strategies and tactics to immediately mitigate “Negative News” and prevent them from going Viral.
- 33. Much like other marketing and media metrics, we can deconstruct the different elements of the SEI metric into the channels driving social engagement and brand sales. Source: Nielsen BuzzMetrics data as of November 27, 2011 Social channels driving consumer engagement and sales 33
- 34. Most Important Drivers to Positive SEI. Using this insight, the client developed a ‘bring a friend, and get one coffee free’ to drive store level sales. Positive SEI 3.93 = 100 Place2HangOut >5.46= 211 9.1% Place2HangOut <5.46 = 83 91.9% ToMeetPeople> 9.43 = 325 2.6% ToMeetPeople< 9.63 = 188 6.5% Atmosphere >14.0 = 466 0.6% Atmosphere <14.0 = 288 1.9% To Meet People >5.4 = 229 3.8% To Meet People <5.4 = 85 85.5% Beverage A >6.4 = 271 7.7% Beverage A <6.4 = 74 77.8% Place2HangOut >3.6 = 126 5.9% Place2HangOut <3.6 = 76 71.9% Beverage B >5.2 = 211.1 1.6% Beverage B <5.2 = 67 70.3% Note: Separate analysis - Classification & Regression Trees (CART) The tree starts with an average SEI score of 100; and each level indicates a higher or lower SEI based on an SEI score for a topic. The percent represents the percent of the sample in each segment. Develop In-Market strategies based on “Why” consumers use your brand 34
- 35. Alpha_P1 Beta_P1 Note: Separate analysis - Adapted Statistical Correspondence Analysis Example: Global Coffee Chain Bubble size represents the buzz/volume of chatter (SEI Conversational Clusters) Alpha_P2 Beta_P2 Gamma_P1 Gamma_P2 Net Chatter around value and price Net Chatter around coolness, funky, style, Décor Net Chatter around taste and product quality Net Chatter around in-store customer experience Delta_P2 Delta_P1 Good value Coffee Price Food prices Staying in Seating/chairs Toilets Richness Latte Amazing taste Like no other Cool brand Funky Stylish Artwork/Decor Visualize social media brand conversations 35
- 36. Introducing… Bottom-Line Analytics & GAP is a full service consulting group focusing on marketing effectiveness and brand performance analytics. Our modeling experts have a total of over 150 years of direct experience with marketing mix modeling with direct experience in over 40 countries We are dedicated to the principles of innovation, excellence and uncompromising customer service. Everything we do is geared towards improving commercial performance. 36 &
- 37. Play out marketing What-if scenarios An interactive dashboard allows you to simulate different marketing mix/spend scenarios and assess the resultant impact on sales and profitability. 1. Set marketing budgets. 2. Set your spend levels across media channels 3. Assess the resultant impact on sales & profit 37
- 38. Continuous model updates allow for real-time simulation and planning An interactive dashboard allows you to simulate different marketing mix/spend scenarios and assess the resultant impact on sales and profitability.
- 39. Why Impartial and Independent Full Service Analytics Capability VOC Measurement With Social Media Marketing Mix Modelling 3.0 Ad Copy ROI Measurement Multi-Touch Attribution Models Marketing Synergies Long-Term Ad Effect Pricing Optimisation Radial Landscape Mapping Key Drivers Analysis Demand Forecasting Customer Satisfaction Modelling Performance Analytics Dashboards Segmentation Analysis Marketing Decision Support Tools Our proprietary approach to social media measurement is unrivalled. Objective approach to media measurement. 39 &
- 40. Full Service Analytics Capability Social Media ROI Marketing Mix Modelling Pricing Optimization Radial Landscape Mapping Key Drivers Analysis Demand Forecasting Customer Satisfaction Modelling Digital Performance Analytics Dashboards Segmentation Analysis Decision-Support Systems 40 BLA is a trusted advisor to a wide array of clients We believe in the continuous innovative application of analytics to advance customer centric decision making for improved business performance.
- 41. It’s all About Results Company Results Coca-Cola Brought marketing ROI modeling to company for first time in 1996. In first year developed models for Coca-Cola, Coke Light, Fanta and Sprite in 12 Countries. Year two sales gains over prior year exceeded $300 million. Starbucks Developed measure of customer-brand experience using social media. Discovered that Starbucks main strength lies in its in-store experience. Successfully developed brand positioning for Frappucino and Via Coffee. Sales growth improved from +7 to +11 percent McDonald's Identified significant upside growth opportunity to drive higher restaurant sales by investing significantly more in "dollar-value meals" one year after launch in 2005. Per recommendation, major & higher marketing investment in dollar value meals made McD's the growth leader in its competitive segment for 2 years thereafter. L'0real Developed models which measured the ROI across 12 different "Celebrity Spokespersons" in L'Oreal Commercials. Recommended reducing number from 12 to 5 Celebrities, leading to growth improvement from +3 to +5%. Hyatt Hotels Developed SEI to quantify measure of "customer satisfaction" derived from measures of Trip Advisor hotel reviews across 300 different properties. This lead to a 5% improvement in customer satisfaction in subsequent year and a +6% growth in total bookings AT&T Identified and quantified impact from the launch of iPhone. By identifying which ad copy messages were most effective, AT&T managed to increase it's wireless telecom market share from 28 to 30%. Johnson and Johnson Developed analytic system for measuring and evaluating ad copy for Splenda brand. Enabled brand to reduce ad production from 8 to 4 commercial executions, saving $6 million 41
- 42. Global Analytics Partners 37 Global Analytics Partners is a consortium of advanced analytics, marketing technology & strategy firms bringing together extensive global experience in all phases of marketing science, decision support & advanced analytics. Collectively, we have the scale and the tools to assume any challenge & have over 150 years of direct experience covering over 40 International markets
- 43. Bangalore, IN Office: No. 141, 2nd Cross, 2nd Main,Domlur, 2nd Stage, Bangalore 560071Phone: +91 80 40917572, +91 80 40916116 info@therainman.in Contact Us US Office: Suite 100, 1780 Chadds Lake Dr, NE Marietta, Georgia, 30068-1608 Atlanta, USA mjw@bottomlineanalytics.com