Content Analytics for Media Agencies
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This presentation shows different practises carried at Havas Media and needs regarding content analytics.
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Content Analytics for Media Agencies
- 1. HMG Corporate Development Team gloria.andreu@havasmg.com ines.campanella@havasmg.com oscar.munoz@havasmg.com santiago.murillo@havasmg.com
- 2. 5 Continents Billings – $18billion Over 100 Countries 15,000 Professionals 6th global media agency group
- 3. Strong European presence +70 countries 80 Offices 3,900 Media Experts HavasMedia has the leading position in the key European markets: • UK, Germany, France, Italy, Spain
- 4. Our clients 5,000+ customers all over the world 100+ international customers
- 5. Our job • To establish relevant touch points between brands and consumers • Intermediating between brands and media for optimising advertising budget allocation • 80% of global advertising budget is managed by media agencies • Ensuring that brands’messages reaches consumers efficiently (maximising ROI) “Our aim is to be the world’s best company at connecting brands with people using creativity, media and technology”
- 6. From a traditional communication model… Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand • Investment in mass media (TV, Radio, Press, Outdoor, ...) • Communication performancemeasurement made through KPIs based on audience (Gross Rating Points) • Consumer insights are obtained through opinion surveys
- 7. …to a digital world • Consumers are the most influent media (producing content in social media) • Communication performancemeasurement is performed through many different KPIs (visibility, engagement, conversion, recommendation) • Market insights are extended with the analysis of content published in social media by consumers
- 8. Havas Media Communication Process Understand consumers through data analysis • Decision-making drivers • Brand recommendation factors Design communication strategy Cross-media environment (paid, owned and earned) Activate communication strategy • Budget Allocation • Automated real-time communication with consumers Optimise communication processes • KPIs evaluation • Performance measurement through data analysis DATA DRIVEN
- 9. RESEARCH on content analytics at Havas Media
- 10. Awareness Evaluation Purchase Intention Postpurchase Experience 45% Barcelona 30% Madrid 70% - 30% Purchase funnel Marketing mix Gender Location Emotions 50% satisfaction 20% dissatisfaction Purchase Funnel Topics Media Brand Intelligent Consumer Profiling
- 11. Intelligent Consumer Profiling Distribution by gender & location Marketing Mix for the fashion domain Conversation topics when evaluating a product
- 12. Event detection and explanation Monitors 23 KPI − Based on volume, sentiment and relevance − Aggregations by brand, media (ej. Twitter, Facebook) and topics Net Promoter Score NPS = advocates - detractors Time series analysis
- 13. Time series analysis Analysis of relations between social buzz and advertising pressure Advertising Pressure − Audience (GRP) & Investment (€) − Offline (TV, radio, press) y online (display ad.) media Social buzz − 23 KPIs based on volume, sentiment & relevance − Brands, Media & topics Discovery of relations between series − Correlation analysis − Events shared
- 14. Community Analysis Detection of influencers, brand ambassadors, detractors, viralisers, … Social Graph Analysis Fashion Domain
- 15. Social graph analysis Content propagation − Posts most viralised (owned & earned, positive y negative) − Cross-channel analysis of the propagation tree beyond Twitter (blogs, forums, …)
- 16. CHALLENGES for Media Agencies
- 17. Variety and Velocity of data sources
- 18. Volume of data sources 4,895 brands 1.3 B records 230 TB 8,940 brands 116.182 M records 178 TB 1,913 brands 6,108 M records 61 TB 301 brands 1,411 M records 13 TB — —
- 19. Consumer Connected Platform https://vimeo.com/88775576 − Tracking of consumers across online and offline touch points generating consumer profiles − Real time automation of communication processes ✴Online ads, push messages sent to the smartphone, email recommendations, …
- 21. Conclusions & Challenges for media agencies • Understand consumer through audience measurement and opinion surveys • Segmentation of consumer groups • Small tabular data • Batch budget allocation • Understand consumers through Content Analysis, Web Analytics & social media interactions - Insights must go beyond polarity detection - Multilingualismbeyond main European languages - Accuracy of the algorithms is a key issue • Segmentation of individual consumers • Big Linked Data - Integration of multiple heterogeneous & unstructured data sources at scale • Communication with consumer activated in real time - Meaningful recommendations beyond display advertising - Online-offline integration through mobile devices
- 22. THANK YOU Óscar Muñoz-García @omunozgarcia oscar.munoz@havasmg.com
Notes de l'éditeur
- Good morning everyone. My name is Óscar Muñoz I work for Havas Media Group and I will present our practises and needs regarding content analytics
- First, let me start with a brief introduction to our company: Havas Media Group is a media agency group focussed in the marketing and advertisement industry. It is the 6th global media agency group and is present in the 5 continents in more than 100 countries. With more than $18 billion billings more than 15,000 professionals work at Havas Media .
- We have a strong European presence. With 80 offices in more than 70 countries and near 4 thousand media experts we have the leading position in the key European markets, namely, UK, Germany, France, Italy and Spain.
- Regarding our clients, we have more than 5,000 customers all over the world, from which more than 100 are very relevant international companies.
- As and advertising media agency, our job is to establish relevant touch points between brands and consumers. That is, we intermediate between brands and media for optimising budget allocation, ensuring that brand’s messages reaches consumers efficiently. In summary, we optimise brand’s Return Of Investment in advertising.
- Up to a few years ago, our work was an easy task, the more investment in mass media, the more visibility for the brand. In this scenario the performance of communication was measured through KPIs based on the audience of the spots (the so called rating points). If we needed to perform any market study, we had to made opinion surveys, since we did not have another way of capturing data about consumers.
- But since some years ago, the advertising panorama has changed drastically, with the advent of Web advertising, social media, and mobile devices. Nowadays consumers are the most influent media because of the content produced in social media by them. In this context, communication performance measurement is getting more and more complex since we have to take into account dozens of KPIs that measure more dimensions, namely visibility, engagement, conversion and recommendation. Therefore the market insights that we had are extended with the analysis of content published in social media by consumers.
- For dealing with this complexity, in Havas Media, we have designed a communication process, in which data is the most important part of every activity, either for market research in the understanding phase, for automated communication with consumers in the activation phase or for campaign performance measurement in the optimisation phase.
- Next, I will show you different the different research that we have carried out at Havas Media regarding content analytics.
- The first use case is focussed in segmenting consumers along different socio-demographic and psychographic categories with the aim of performing market research. The first variable that we analyse is the purchase funnel, which is related to the different stages that consumers transit when making purchase decisions. Then we classify opinions about products and brands according to the MM framework, which shows if consumer opinions are related to the price of the product, the point of sale, specific promotions, or the product characteristics. We also segment consumers according to socio-demographic variables, like gender and location. In addition we perform sentiment analysis of content, but going beyond polarity detection. UPM has developed this classifier for us, which is able to detect a rich set of human emotions about brands, including satisfaction, dissatisfaction, trust, fear, love and hate. Finally, we also identify conversation topics in an unsupervised manner helping us to summarise huge volume of conversations.
- In this slide we see some example dashboards outputted by the tool. The one on the top-left corner shows distributions by gender and location of consumers that talk about the brands Zara, Mango and H&M The one on the top-right corner shows the different conversation topics corresponding to consumers that evaluate the brand H&M. We can see that H&M products are mostly compared with Zara (more than with other brands). The one in the bottom shows the marketing mix elements and their volume corresponding to the fashion domain. In this domain the most important marketing attributes are the point of sale, the price and the design.
- The second use case is related with the analysis of time series generated by aggregating content analytics results. We perform atypical events’ detection, and summarise the conversation of consumers within the day of the atypical event comparing it with the previous and the next day. As an example, in this slide you can see the evolution of a KPI called “Net Promoter Score” which shows the difference between brand’s advocates and brand detractors. EXPLICAR EJEMPLO DE RENAULT In summary, we have developed 23 based on volume of posts, sentiment and relevance, We perform KPI aggregation by brand, media and topics.
- In addition, we analyse the relations between social buzz and advertising pressure. We correlate time series that measure the evolution on investment in different media with series of social buzz. Discovering relations between series based in correlation analysis and the events shared by these series, checking whether a given ad campaign had influence over the social buzz.
- The last use case is about social graph analysis. We perform community analysis clustering users into communities, Detecting who are the influencers for a given brand or domain
- We also perform analysis of content propagation, Not also attending to a particular media like Twitter, but performing a cross-chanel analysis, being able, for example to detect opinion propagation from blogs, news, etc. to social networks.
- Finally, I will show you the different challenges that we are trying to face at Havas Media
- The first challenge regards the Variety and Velocity of the different data sources that we have to manage, analyse and integrate. Regarding structure we have to handle data sources that go from structured ones to highly unstructured ones. Regarding format all of these data sources are heterogeneous, having many formats for each kind of data source. Regarding velocity the data is produced in an speed that currently we are not able to process at real time
- Another challenge is related to the volume of the data sources. We have to process billions of records and hundreds of terabytes.
- Our goal is to integrate all of this data sources in order to track consumers across online and offline touch points gathering information about them with the aim of perform real time automation of communication process Serving online ads, push messages sent to the smartphone, email recommendations, etc. Let me illustrate this view with a short video
- The idea here is to integrate the data gathered from web sites with the content extracted from social media and the events captured in the real world through mobile devices. From each media we can extract different knowledge about consumers. That, once integrated, help use to produce different outcomes like the ones presented in the slide.
- As a conclusion, Media Agencies are moving from communicating in mass media to a communicating in a digital eco-system in which social media and mobile devices are more and more important. In this context, our challenges are the following: Understand consumers through Content Analysis, Web Analytics & social media interactions Insights must go beyond polarity detection Multilingualism beyond main European languages Accuracy of the algorithms is a key issue Segmentation of individual consumers Big Linked Data Integration of multiple heterogeneous & unstructured data sources at scale Communication with consumer activated in real time Meaningful recommendations beyond display advertising Online-offline integration through mobile devices