We summarise different innovation projects we are working on Havas Media oriented to understand consumer behavior by analysing content extracted from social media, and for activating the brands' communication strategies in real time in an omni-channel environment that takes into account the different touch points between brands and consumers.
Tuesday, 2 September 2014
Content Analytics for Media Agencies
This presentation shows different practises carried at Havas Media and needs regarding content analytics.
We summarise different innovation projects we are working on Havas Media oriented to understand consumer behavior by analysing content extracted from social media, and for activating the brands' communication strategies in real time in an omni-channel environment that takes into account the different touch points between brands and consumers.
We summarise different innovation projects we are working on Havas Media oriented to understand consumer behavior by analysing content extracted from social media, and for activating the brands' communication strategies in real time in an omni-channel environment that takes into account the different touch points between brands and consumers.
Friday, 18 July 2014
A classification of user-generated content into consumer decision journey stages
In the last decades, the availability of digital user-generated documents from social media has dramatically increased. This massive growth of user-generated content has also affected traditional shopping behaviour.
Customers have embraced new communication channels such as microblogs and social networks that enable them not only just to talk with friends and acquaintances about their shopping experience, but also to search for opinions expressed by complete strangers as part of their decision making processes. Uncovering how customers feel about specific products or brands and detecting purchase habits and preferences has traditionally been a costly and highly time-consuming task which involved the use of methods such as focus groups and surveys. However, the new scenario calls for a deep assessment of current market research techniques in order to better interpret and profit from this ever-growing stream of attitudinal data.
With this purpose, we present a novel analysis and classification of user-generated content in terms of it belonging to one of the four stages of the Consumer Decision Journey (i.e. the purchase process from the moment when a customer is aware of the existence of the product to the moment when he or she buys, experiences and talks about it). Using a corpus of short texts written in English and Spanish and extracted from different social media, we identify a set of linguistic patterns for each purchase stage that will be then used in a rule-based classifier.
Additionally, we use machine learning algorithms to automatically identify business indicators such as the Marketing Mix elements. The classification of the purchase stages achieves an average precision of 74%. The proposed classification of texts depending on the Marketing Mix elements expressed achieved an average precision of 75% for all the elements analysed.
Related paper:
Customers have embraced new communication channels such as microblogs and social networks that enable them not only just to talk with friends and acquaintances about their shopping experience, but also to search for opinions expressed by complete strangers as part of their decision making processes. Uncovering how customers feel about specific products or brands and detecting purchase habits and preferences has traditionally been a costly and highly time-consuming task which involved the use of methods such as focus groups and surveys. However, the new scenario calls for a deep assessment of current market research techniques in order to better interpret and profit from this ever-growing stream of attitudinal data.
With this purpose, we present a novel analysis and classification of user-generated content in terms of it belonging to one of the four stages of the Consumer Decision Journey (i.e. the purchase process from the moment when a customer is aware of the existence of the product to the moment when he or she buys, experiences and talks about it). Using a corpus of short texts written in English and Spanish and extracted from different social media, we identify a set of linguistic patterns for each purchase stage that will be then used in a rule-based classifier.
Additionally, we use machine learning algorithms to automatically identify business indicators such as the Marketing Mix elements. The classification of the purchase stages achieves an average precision of 74%. The proposed classification of texts depending on the Marketing Mix elements expressed achieved an average precision of 75% for all the elements analysed.
Related paper:
Tuesday, 24 June 2014
¿Cómo puede ayudar el Big Data a dirigir las campañas de comunicación?
Presentación que trata sobre diferentes proyectos de innovación en los que estamos trabajando en Havas Media orientados a entender el comportamiento de los consumidores mediante el análisis de Big Data procedente de medios sociales, y a activar la estrategia de comunicación de marca en tiempo real en un entorno omnicanal que tenga encuenta todos los puntos de contacto entre marcas y consumidores.
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