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Innovations in marketing effectiveness measurement

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A new and innovative approach in marketing ROI measurement. Goes beyond traditional marketing mix models by 1) developing long-term ad effects measurements, 2) measuring media message and creative, 3) quantifying the interactions or synergies across the marketing mix and 4) measuring the voice-of-the customer through social media

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Innovations in marketing effectiveness measurement

  1. 1. Innovations in marketing effectiveness measurement.
  2. 2. Introducing…  Bottom-Line Analytics is a full service consulting group focusing on marketing effectiveness and brand performance analytics.  Our modeling experts have a total of over 100 years of direct experience with marketing mix modeling.  We are dedicated to the principles of innovation, excellence and uncompromising customer service.  Everything we do is geared towards improving commercial performance. 2
  3. 3. Our experience 3
  4. 4. Standard Marketing Mix Modeling (Econometrics) Incremental Contribution from marketing Develop relationship between sales and drivers Return on Investment per £1 spent Optimise spend, maximise sales 4
  5. 5. …pushing the boundaries TV RADIO NEWSPAPER PAID SEARCH 5 Effectiveness Modeling (econometrics) has not changed a great deal over the last 20 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.
  6. 6. Innovations: Multi-dimensional Media Measurement Synergistic Effects Copy Quality Effects Social Media Effect Long Term Effects 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. 6
  7. 7. 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 7
  8. 8. Media copy quality measurement Media content and copy quality can be separated and measured. This has implications for design, content and message mix. Copy effect can vary – understanding and measuring this is vital Note: we can apply this technique to digital media also. 8
  9. 9. Assess marketing synergies Marketing synergies can be assessed through simultaneous activation of campaigns. The results of combined activation are always greater than the sum of the parts. This is a clear indication of synergies from running truly integrated marketing campaigns (IMCs) Revenue (£) Revenue (£) Revenue (£) +31% +23% +42% Revenue (£) +28% Print Media & Paid Search Synergies Print Media & Online banners Synergies Direct Mail & Email Synergies Outdoor & Online Synergies 9
  10. 10. A breakthrough in measuring social media engagement
  11. 11. Can social media be measured?  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 1 The Growing Importance of Word of Mouth, www.boundless.com 11
  12. 12. Our approach to measuring Social Media  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. 2 Stance Analysis: social cues and attitudes in online interaction, Mason, P , Davis B, In 12 E-Marketing Vol. II . 2005.
  13. 13. Developing the Social Engagement Index (SEI) 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 Net Positive SEI Index 4. Time code by period and aggregate metrics Positive Reviews Negative Reviews HIGH 0 5 7 MEDIUM -5 0 5 LOW -7 -5 0 Positive Scores LOW MEDIUM HIGH Negative Scores Emotional Effect Personalisation
  14. 14. 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” 14 “I REALLY LOVE MY COKE WITH PIZZA” “I LIKE THE TASTE OF SPRITE WITH LEMON” “MY COKE HAS LOST ITS FIZZ AND TASTES AWFUL”
  15. 15. SEI shows superior correlations to brand sales compared with other Social Sentiment Metrics Comparison of correlation to sales for the SEI versus the six leading sentiment metrics 82.9% 2.8% 5.9% 9.9% 14.8% 7.7% -3.2% METRIC 3 POS/NEG RATIO METRIC 2 POS/NEG RATIO METRIC 6 POS/NEG RATIO METRIC 4 POS/NEG RATIO METRIC 1 POS/NEG RATIO METRIC 5 POS/NEG RATIO SOCIAL ENGAGEMENT INDEX POS/NEG RATIO -20% 0% 20% 40% 60% 80% 100% 15
  16. 16. The correlation* to sales over time shows the SEI has Predictive Power 16 ACID TEST: SEIsm has proven linkage with brand sales Correlation = 86.4% Correlation = 84% Correlation = 81.1% CorCroerlaretiloanti o= n8 =3 %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.
  17. 17. 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
  18. 18. 18 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 Net driven by media 67% 8% Sub-model 3% 2% 2% 10% 5% 11% 20% Marketing Contributions SEI Engagement 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
  19. 19. 19 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 The absolute impact from positive & negative consumer reviews +4.4% +16.5% 20% 15% 10% 5% 0% Positive Sentiment Negative Sentiment Marketers need to develop strategies and tactics to immediately mitigate “Negative News” and prevent them from going Viral.
  20. 20. Social channels driving consumer engagement and sales 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 20
  21. 21. Develop In-Market strategies based on “Why” consumers use your brand 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. 21
  22. 22. Visualise social media brand conversations Net Chatter around coolness, funky, style, Décor Alpha_P1 Alpha_P2 Beta_P1 Note: Separate analysis - Adapted Statistical Correspondence Analysis Example: Global Coffee Chain Bubble size represents the buzz/volume of chatter (SEI Conversational Clusters) Beta_P2 Gamma_P1 Net Chatter around in-store Gamma_P2 Net Chatter around value and price customer experience Seating/chairs Latte Net Chatter around taste and product quality Delta_P2 Delta_P1 Good value Coffee Price Food prices Staying in Toilets Richness Amazing taste Like no other Cool brand Funky Stylish Artwork/Decor
  23. 23. Why Impartial and Independent Full Service Analytics Capability Social Media Measurement Marketing Mix Modelling 3.0 Pricing Optimisation Radial Landscape Mapping Key Drivers Analysis Demand Forecasting Customer Satisfaction Modelling Performance Analytics Dashboards Segmentation Analysis Our proprietary approach to social media measurement is unrivalled. Objective approach to media measurement. 23
  24. 24. Michael Wolfe CEO Bottom Line Analytics E: mjw@bottomlineanalytics.com M: 770.485.0270 www.bottomlineanalytics.com Masood Akhtar Partner, Analytics (EMEA) Bottom Line Analytics E: ma@bottomlineanalytics.com M: +44 7970 789 663 www.bottomlineanalytics.com David Weinberger CMO Bottom Line Analytics E: David@bottomlineanalytics.com M: 770.649.0472 www.bottomlineanalytics.com

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