Overblog
Suivre ce blog
Administration Créer mon blog
18 mars 2014 2 18 /03 /mars /2014 09:43

What is buzz marketing? In the strict sense of the term, buzz marketing is creating noise around a product, service, company or brand. For example you can recruit consumers, preferably proactive volunteers who influence their peers, and help them to try your products in good condition before pushing them to talk about their experience. The buzz is one of the most powerful forces in the market, and knowing how to master this important marketing channel is critical. Word of mouth is more credible than most sincere seller; it affects more people, faster than advertising, direct mail or even a website. It is this credibility which gives to the Word of mouth part of its power. But be careful, it also obtains its credibility because it can be negative, in which case the marketing finds it is not easy to control. The buzz became a basic weapon in the marketing kit, and it is used more frequently. The best buzz relates to products or services, which consumers like to talk. Make available a great product, and your customers are happy to tell their friends, colleagues and family, and generate word of mouth you want. Buzz or rumors: what's the difference? The rumor is information with hidden or unknown origin that spreads widely without being checked. The buzz breaks out without any advertising investment. A rumor is a "subject", while the buzz is a "medium". The buzz targets to capture the attention of customers and media, and ensures that speaking about your brand is fun, exciting and rewarding. This requires knowing how to launch and sustain conversations. As with any advertising campaign, the buzz campaign is based on a strong idea. This idea must meet a need expressed or unconscious, it must be attractive and original, catch the attention, generating a need or pleasure. To start a buzz we must follow the following steps. 1) Identify the key people (influential in their communities) may be vectors of the message. 2) Valuing these people through a personal experience that flatters their ego to make them eager to spread the message. 3) Encourage the message out by providing, information and means for feeding the buzz. Vectors can be a network, a group of related or maintained people. In this type of approach it is necessary to identify and exploit different types of actors. Innovators, these people have an open mind to accept new ideas far away from traditional aspects of fashion. Marketing should not give them much attention because they are a minority. The "early adopters", they are always in search of novelty, they are attracted to risk. They adopt or create novelty and transmit to the bees (early majority). But beware, they are attracted by exclusive rights, specific offers, they like to feel among the privileged. Bees, they are the heart of the buzzing and they can supply large-scale chain information within the targeted community. We need to get the bees to talk about their experiences, and share their findings with others. Key people for exchanges are the "connectors" that have a complete address book or "mavens" who are experts in the field, and are opinion leaders. There is also the general public, which when touched can cause a snowball effect and finally the laggards that we have not to care of, because they are attached to traditional things and are not open to new ideas. We should distinguish from the traditional buzz and buzz digital. Traditional buzz techniques are for example, product placement, distribution of samples, animation events of discovery, especially in the street and sponsorship. The key element in the traditional buzz is the contact and the relationship between vectors and products, so that the vector can observe and keep in touch with the people targeted. What’s the vector has to do, is talk about the product, idea, service: this is how to buzz. The digital buzz, also called viral marketing is a technique that allows using the internet very rapid dissemination of ideas, news and product information. Two methods can be used for example to spread an idea we can send a funny or surprising message and it will circulate rapidly among Internet actors, or offer to Internet actors via a good grip to visit an interesting site, and invited them to register to be part of a valuable information campaign / action. To conduct buzz operations we should at least have operational tools (document repository, database, campaign management, survey…) and decision applications (customers profiling, segmentation, reporting on actions ...). If operations are developed on the web, especially via social media, Big Data analytical applications (clickstream analysis on the web, networks, texts, sentiment analysis...) can be very powerful to understand the market, identify the various stakeholders and to drive actions. To go further on Teradata Aster Big Data solutions and references, in particular on the Buzz Marketing domain, you can usefully consult the following link: http://www.asterdata.com/solutions/social-network-analysis.php

Published by bruley - dans Management
commenter cet article
11 mars 2014 2 11 /03 /mars /2014 09:23

~~IT market studies expert indicate that the advanced analysis of various data and the management of an increasing volume of data, are among the five priorities large enterprises CIOs and companies at the forefront of the world of internet. Big Data is thus increasingly important, and a growing number of companies complete their decision-making infrastructure, with new analytical platforms to improve their efficiency and profitability. First experiences Big Data observations show that many different technologies are used, even if an emerging technology based on the open source project Apache Hadoop, is often present in the infrastructure of the pioneers of Big Data. Hadoop's popularity seems to lie in its ability to handle large volumes of data with a standard low cost server infrastructure. But be careful all the experts say that there is no universal solutions for Big Data, users should be determined according to their needs, the appropriate technology mix to put in place, and set precisely where each (including Hadoop) can add value in their decision-making architecture. First, consider the data that are to be processed. There are those in which the data model is established and is stable over time. Here we will find everything about the classic BI, reporting, financial analysis, automated decisions related to the operational and analysis of spatial data. There are those that can be stored in a more or less raw mode, which will be modeled in different ways according to the needs of iterative analyzes. This may involve, for example, the analysis of user clicks during their web browsing, data from sensors, CDR in telecommunications. There are those who are simply defined by a format. It is for example: images, videos, and audio recordings. We must also consider what you want to do with the data. If the idea is to use structured data (reporting, BI, data mining), a classical appliance will be ideal, you can possibly complete with a cheap storage Hadoop solution appliance or with "the Teradata Extreme Data Appliance” for the data which present less interest in being included in the model of the warehouse business. For other data (web log, sensors, CDR, images, videos, ...) must be used according to the treatments described in the type Hadoop solutions and / or Teradata Aster, which can be implemented at low cost ( storage, application development, operation, integration with structured data warehouse) MapReduce programs. Note that the Teradata Aster uses a patented technology named SQL-MapReduce that allows implementing MapReduce programs without having to learn a new programming language. This solution also offers the performance, scalability to handle large volumes of data, and process data with relational data pertaining to various formats. Compared with Hadoop this solution offers substantial benefits in terms of development costs and application response time. Measured by an increase in revenue, market share gains and cost reduction, data analyses have always played a key role in business success. Today, the development of the Internet and automated business processes, makes crucial Big Data operations, and bring business leaders to rely more and more on their data analysis means. In this context, the teams are then conducted to supplement their existing infrastructure decision-making, with new solutions that implement complex algorithms. The pioneers who have already operated Big Data successfully, all say there is no magic bullet, even Hadoop, and it is for that Teradata offers different platforms implementing the Teradata database, Aster MapReduce and Hadoop solutions. To go further on the subject you can effectively discover the latest Teradata Aster Big Analytics Appliance that integrates in a single platform solution Hadoop and Aster (Hortonworks distribution): http://www.asterdata.com/resources/downloads/datasheets/Teradata-As...

Published by bruley
commenter cet article
6 mars 2014 4 06 /03 /mars /2014 10:04

A social network is a social structure that links actors (individuals and organizations), and highlights how actors are connected, from a simple relationship to family ties. We are all part of many networks that correspond to the dimensions of our life (family, study, work, leisure activities). Our membership, our activities, our place in these networks are for marketers a valuable source of information, knowledge and opportunities for actions to promote their offers, according to the principle that the behavior of individuals is in part related to the structures in which they operate.

The internet has facilitated the development, the life of social networks, and marketers have acquired the appropriate analytical techniques to exploit them. In fact, a network can be represented as a graph and be mathematically analyzed. In these approaches, the actors are nodes and relationships are links, forming a model where all significant relationships can be analyzed via the construction of a matrix to represent the network. We can then obtain a graph using mathematical treatments performed on matrices, and search the presence of a clique, a chain, a cycle that characterizes the network. Finally using algorithm it is possible to calculate the degrees of strength and the density between social entities, for example, determine the social capital of actors.

There are many measures of connections, distances, power, prestige: the number of nodes, number of links present versus the number of links possible, the sum of links to other members, the degree of density, degree of cohesion, betweeness, path length, the degree to which any member of the network can reach other members of the network, structural holes, etc.. Thus we can characterize both a network and each actor, for example, to identify key people who have an important role in communication and influence.

The analysis of social networks is valuable for example for controlling the flow of information, improve / enhance communication, improve the resilience of a network, find community, or to trust a community. For marketers this is an opportunity to better understand, target, approach clients, prospects, suspects, to sell them more, for better lead communities, to innovate, differentiate themselves from the competition and develop a competitive advantage.

Companies, such as Myspace, LinkedIn or Mzinga, have understood the importance of this type of approach and already widely practiced social network analysis, launching new products, improving the experiences of their customers and satisfying them better. Mzinga in particular, whose business is to provide a means for facilitating communities of clients, provides tools for network analysis. Thus the 14,000 communities, with 40 million people who are managed with Mzinga tools, can be analyzed by their leaders and allow them to optimize their operations.

But be careful, for analysing social networks you must use others solutions than traditional analytical approaches based on relational databases and BI tools. The companies mentioned above already practicing this type of analysis, have had to develop by their own their analytical applications. They use particularly new kind Hadoop solutions and / or Teradata Aster and implement MapReduce programs that involve specific facilities and specialists in this type of analysis. To go further on the experiences of the companies mentioned above, you can access useful information about them via the link below:

http://www.asterdata.com/customers/index.php

Published by bruley
commenter cet article
26 février 2014 3 26 /02 /février /2014 12:21

LinkedIn was founded in 2003, is currently revenue 243 million and employs 1797 people. This is not what we call a large company. However, LinkedIn has 175 million members in 200 countries including 50% outside the U.S., two new members join the network every second, and analysts said that all "executive" of the Global 500 are members. Under these conditions, LinkedIn is facing a high volume of data to process. Indeed their information system must support 2 billion a year of research carried out by members, dealing daily data and 75 T0 10 billion lines.

By analyzing all data LinkedIn is able to establish for example the list of the words used by members to describe their capabilities, and these words differ from one country to another. United States and Canada are extended highlights of the experience, while in Italy, France or Germany we say innovative, as in Brazil and Spain they are dynamic and that in Great Britain they highlights their motivation.

LinkedIn is certainly one of the companies involved in the development of what is now known in the business world as the "Science of Data", which is based on know-how from computer science, mathematics, data analysis and business management. Specifically the process is to collect quickly raw data, explore and analyze, translate this data into actionable information, and therefore reduce the overall time between the discovery of relevant facts, the characterization of business opportunity and triggering actions.

But what LinkedIn does with its data? The company classically realizes analysis to better understand and carry out its activities, but above all it creates products / services based on the information it generates, either at the global level as with most used words seen above, either at the individual level with systems recommendations (the people you may know, the jobs that ...). The data allow for example to identify people of influence, viral process and social trends, test new products / services, new sites to maximize the business impact of connection and use of the site by members, to understand service use over time based on subscription levels, the connection means (PC, mobile, ...), providing detailed reports of analysis of advertising revenue, to assess the impact of action of viral marketing, to optimize recommendation engine, to create specialized functions for services to business (marketing, recruitment, ...).

To obtain these interesting results of the operation of its data, LinkedIn had to develop its own management application’s data flow, storage, research, network analysis, etc., and of course their own dashboards. For that the company went to get on the market tools or solutions they need, and we can mainly list: Teradata Aster, Hadoop, Azkaban, Kafka, Project Voldemort, Pig, Pithon, Prefuse, Microstrategy, Tableau software

 

To go further about the LinkedIn case, you can usefully follow the below 50’ video presentation, entitled "Data Science @ LinkedIn: Insight & Innovation at Scale", by Manu Sharma, Principal Research Scientist and Group Manager, Product Analytics at LinkedIn: http://www.youtube.com/watch?v=W7ZcUJEHAOk

Published by bruley
commenter cet article
21 janvier 2014 2 21 /01 /janvier /2014 10:05

Analyzes of texts put lights in two main types of information “facts and opinions”. Most current treatment methods of textual information aim to extract and use factual information, this is the case for example of research we do on the web. Analysis of opinions is concerned about feelings and emotions expressed in the texts, it has grown much today because of the space taken from the web in our society, and the very large volume of daily comments expressed by consumers with the advent of the Web 2.0 world.


What is opinion analysis? It identifies the orientation of an opinion expressed in a piece of text (blog, forum, comments, website, document sharing site, etc.). In other words, it determines whether a sentence or a document expresses a feeling positive, negative or neutral regarding a defined object. For example: "The movie was fabulous" is an expression of opinion while saying "the main actor of the film is Jean Dujardin" is the formulation of a factual matter. Opinion analysis may occur at different levels. At the word level: the film is entertaining and motivating; on the sentence level: the police (subject) hunt (verb) smuggling (object), or finally at the document level, that is to say a set of sentences: his early films were very good, but this one is worthless.

 

In fact an opinion can be characterized by a formula of five components, a quintuple: Oj, Fjk, Hi Tj, SQijkl, where Oj is a target object; Fjk a characteristic of the target object; Hi a bearer of opinion; til the time the opinion is expressed, and SOjkl the opinion orientation, of the bearer Hi, about the Fjk characteristic of the OJ object at Tl time. Using this formula we can structure an entire unstructured web document, highlighting all quintuples included in the text. Quintuples represent structured data that can be analyzed qualitatively or quantitatively, and visually represented with traditional tools of decision systems. All kinds of analyzes are possible. Analysis of opinions is not only to characterize the opinion of one person by words and phrases, but also for example to compare the opinions of different people or groups.


The first step is to delete sentences that contain only facts, keeping only those who express and define the polarity (positive, negative or neutral). Specifically you have adjectives (red, metallic) that indicate facts or positive feelings (honest, important, mature, big, patient), negative (harmful, hypocritical, ineffective) or subjective being neither positive nor negative (curious, strange, odd, perhaps, likely). It is the same for verbs, positive (praise, love), negative (blaming, criticizing), subjective (predict), or for the names:  positive (pleasure, enjoyment), negative (pain, criticism) and subjective (prediction, impression).


Be careful with sequence words meaning, a sentence can be complicated and punctuation that is of great importance, can play tricks. “Not good at all” is different with “Not all good”. The sentence might express sentiment not in any word: “convinced my watch had stopped” or “got up and walk out”. We must consider that words or phrases can mean different things in different contexts and domains, or the subtlety of the expression of feelings when someone makes ironic statement.
 
Ultimately, however, the analysis of opinions and feelings is able to provide much information about the populations studied and warned marketers already know how take advantage. This is true of many Teradata Aster customers, like Barnes & Noble for example. If you want to go further on this subject you can usefully consult the following site:

http://www.asterdata.com/solutions/data-science.php

Published by bruley
commenter cet article
3 décembre 2013 2 03 /12 /décembre /2013 10:32

 

Companies are looking increasingly to take advantage of Big Data, especially textual information, those generated via user tools by web or desktop applications. The analysts specialized in this subject believe that 70% of information of interest to business are nestled in word documents, excel, email, etc. These data are not predefined in a model and cannot be perfectly stored in relational tables. They occur most often in the very free form, but contain dates, numbers, key words, facts that can be exploited.

A new challenge for companies in data analysis is to significantly advance the operation of this type of unstructured data. In terms of customer knowledge for example, it is possible to better use of the archives of business proposals and contracts, or listen to web conversations or take advantage of dialogues via email. Master relationships, including discussions about the company with its community of customers and stakeholders in its ecosystem, are very important for marketing today which has to change, push in that way by customers who largely use new technologies (mobile, social media).


The amount of this type of digital information usable is constantly growing, and as "manual extraction" of information is extremely difficult or even impossible on a large scale, so the use of specific computer tools for data processing unstructured text is required. Thus recently appears text mining tools, which automate the processing of large volumes of textual information, to identify statistically different topics raised and extract key information.

Text analytics techniques apply to the documents linguistic processing, including morphological, syntactic, semantic, and various other techniques of data analysis, statistical classification, etc. A major objective is to synthesize texts (classify, organize, summarize) by analyzing relationships, structures and rules of association among textual units (words, groups, phrases, documents). In the end it automates the production and the management of documents (including abstracts) and information (extraction, research, dissemination).


Text analytics has many applications for example in the field of customer relations, it allows in particular: exploring the contents of documents (e.g. open-ended questions in a survey, comments and complaints from customers, analysis of warranty claims) ; assign documents to predefined topics (redirect, mail filtering, organizing documents into categories, ranking contacts for call centers) ; compose text summaries (abstraction and condensation) ; examines texts by concepts, keywords, subjects, phrases to get results sorted by relevance like Google ; and finally increase the performance of predictive models by combining text and structured data.

To conclude, text analytics are a collection of technologies that detect the elements, or building blocks, within language, turning them into a type of data that can be manipulated and computed. To go further you can click on the link below to discover why it is necessary to use advanced analytical tools such as specialized Teradata Aster, to fully exploit unstructured data. Retail or Internet companies like Barnes & Noble or LinkedIn are already using these text analytics solutions in order to get a competitive advantage.

http://www.asterdata.com/product/faq.php

 

Published by bruley
commenter cet article
7 novembre 2013 4 07 /11 /novembre /2013 10:12

Companies are looking increasingly to take advantage of Big Data, especially textual information, those generated via user tools by web or desktop applications. The analysts specialized in this subject believe that 70% of information of interest to business are nestled in word documents, excel, email, etc. These data are not predefined in a model and cannot be perfectly stored in relational tables. They occur most often in the very free form, but contain dates, numbers, key words, facts that can be exploited.

A new challenge for companies in data analysis is to significantly advance the operation of this type of unstructured data. In terms of customer knowledge for example, it is possible to better use of the archives of business proposals and contracts, or listen to web conversations or take advantage of dialogues via email. Master relationships, including discussions about the company with its community of customers and stakeholders in its ecosystem, are very important for marketing today which has to change, push in that way by customers who largely use new technologies (mobile, social media).


The amount of this type of digital information usable is constantly growing, and as "manual extraction" of information is extremely difficult or even impossible on a large scale, so the use of specific computer tools for data processing unstructured text is required. Thus recently appears text mining tools, which automate the processing of large volumes of textual information, to identify statistically different topics raised and extract key information.

Text analytics techniques apply to the documents linguistic processing, including morphological, syntactic, semantic, and various other techniques of data analysis, statistical classification, etc. A major objective is to synthesize texts (classify, organize, summarize) by analyzing relationships, structures and rules of association among textual units (words, groups, phrases, documents). In the end it automates the production and the management of documents (including abstracts) and information (extraction, research, dissemination).


Text analytics has many applications for example in the field of customer relations, it allows in particular: exploring the contents of documents (e.g. open-ended questions in a survey, comments and complaints from customers, analysis of warranty claims) ; assign documents to predefined topics (redirect, mail filtering, organizing documents into categories, ranking contacts for call centers) ; compose text summaries (abstraction and condensation) ; examines texts by concepts, keywords, subjects, phrases to get results sorted by relevance like Google ; and finally increase the performance of predictive models by combining text and structured data.

To conclude, text analytics are a collection of technologies that detect the elements, or building blocks, within language, turning them into a type of data that can be manipulated and computed. To go further you can click on the link below to discover why it is necessary to use advanced analytical tools such as specialized Teradata Aster, to fully exploit unstructured data. Retail or Internet companies like Barnes & Noble or LinkedIn are already using these text analytics solutions in order to get a competitive advantage.

http://www.asterdata.com/product/faq.php

Published by bruley
commenter cet article
30 octobre 2013 3 30 /10 /octobre /2013 15:20

If you attract thousands of new customers this is worthless if an equal number leaves. Minimizing customer churn is surely a smart objective. But how can I predict when my customers will churn and did Big Data could help?

 

Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.

 

http://www.decideo.fr/bruley/docs/8___churn_com___v0.ppt

 

 

 

Published by bruley
commenter cet article
23 septembre 2013 1 23 /09 /septembre /2013 10:20

Today’s organizations are challenged to gain insight into most productive marketing and sales actions across multiple channels they use. Doing this requires multi-channel marketing attribution approach.

 

Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights:  http://www.decideo.fr/bruley/docs/6___mkg_attribution_v0.ppt

 

To go further with the Teradata Aster Marketing attribution solution look at the Infohub pages: http://sharepoint.teradata.com/infohub/aster_attribution/default.aspx

Published by bruley
commenter cet article
22 août 2013 4 22 /08 /août /2013 08:57

 

Social CRM is a business strategy, supported by a technology platform, business rules, workflow, processes and social characteristics, designed to engage the customer in a collaborative conversation in order to provide mutually beneficial value in a trusted and transparent business environment. It's the company's response to the customer ownership of the conversation.

 

Facing this topic I have made a personal research, and realize a synthesis, which has helped me to clarify some ideas. The attached presentation does not intend to be exhaustive on the subject, but could perhaps bring you some useful insights.

 

Big Data series #7 – Social CRM   http://www.decideo.fr/bruley/docs/7___scrm_v00.ppt

 

 

Published by bruley
commenter cet article

Présentation

  • : Bruley's Blog
  • Bruley's Blog
  • : This blog is dedicated to Data Warehousing & Big Data. It proposes articles, presentations on key topics that will interest enterprises which want to do more with their data.
  • Contact

Recherche

Liens