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Posts tagged ‘api’

07/06/2012

tdrlogoblack94

Presentamos Tuitele, medidor de la audiencia social de la televisión en España

Hoy en The Data Republic estamos felices de abrir la beta de Tuitele, un nuevo producto sobre la televisión social en España. Un producto que estamos convencidos va a ser una herramienta de gran utilidad para la industria de la televisión y todo lo que se mueve a su alrededor.

Y es que en pleno siglo XXI, en pleno auge de las redes sociales, creemos que una industria como la de la televisión, que mueve cientos de millones en publicidad, contratos o sueldos, no puede basarse únicamente en los algo más de 4.000 audímetros que hay en 4.000 hogares españoles, debe tener en cuenta también las audiencias sociales. Los datos lo corroboran, según un estudio de The Cocktail Analysis, un 34% de los usuarios de Internet en España comentan en redes sociales sobre lo que se están emitiendo en televisión.

 

En The Data Republic, después de más de un año trabajando en proyectos de análisis de datos 2.0, hemos creído necesario ofrecer un servicio como éste, donde proporcionamos un conjunto de datos e indicadores en abierto y un amplio abanico de servicios a medida para las empresas. Hasta ahora muchos de nuestros clientes (y potenciales clientes que al final no lo han sido) han probado decenas de herramientas de monitorización de social media, sin embargo nunca han conseguido la plena satisfacción, ya que están diseñados para ser útiles en el mayor número de industrias y no centrados en una sola como es este caso la televisión.

En Tuitele monitorizamos, a partir de la API de Twitter, 24 horas al día y 7 días a la semana en tiempo real los comentarios sobre los programas de televisión que se emiten en España y de esta forma creamos una versión complementaria de las audiencias diarias, son las audiencias sociales. Y no solo eso, el mundo de la web 2.0, las redes sociales, dan para mucho más, con un sistema como Tuitele podemos saber mucho más que el porcentaje de audiencias, podemos saber cosas sobre los espectadores que ven los programas: de qué hablan, qué opinan, o qué gustos tiene.

En el blog de Tuitele iremos mostrando con ejemplos todas esas oportunidades que brinda el análisis de las audiencias sociales de la televisión para empresas como productoras, cadenas de televisión, marcas anunciantes o agencias de publicidad.

El pasado 3 de mayo pusimos la máquina en marcha, monitorizando en tiempo real todos esos datos, y en un mes ya hemos analizado más de un 1.500.000 comentarios de más de 2.000 emisiones de programas, llegando a analizar en un solo día más de 130.000 tuits, como por ejemplo el pasado lunes 28 de mayo coincidiendo con la final de Gran Hermano 12+1.

Como hemos dicho al principio, esto es una beta, tenemos muchas ideas en la cabeza y estamos seguros de que recibiremos muchas ideas (nos las podéis enviar a este blog, por Twitter en @tuiteletv o @thedatarepublic o por mail aquí o aquí), así que seguiremos en beta durante un tiempo.

Para todo el equipo de The Data Republic este proyecto es un gran reto tecnológico y de negocio, que nos permite profundizar en nuestro objetivo de transformar los datos 2.0 en valor para las empresas. Esperamos que guste y sobretodo que sea muy útil.

Tuitele.tv

junio 7, 2012  

29/03/2012

tdrlogoblack94
1 Comment

Streaming Twitter API, Big Data and AC Milan vs FC Barcelona

These days we are working on a project based on monitoring of tweets through the Twitter Streaming API. This API allows you to open a connection to Twitter and start receiving tweets that meet the search criteria, in our case containing certain keywords. Using this API we can get all the tweets published on Twitter. The standard API  search does not offer all tweets, it is rate limited and it is not in true real time.

At this stage of project development, we need to perform several tests, mainly to assess whether the system we’re designing is capable of processing large amounts of data (tweets) per second.

On the occasion of the Champions League match AC Milan vs FC Barcelona, we thought that this might be a good opportunity to monitor different keywords associated with the game. During the match, 30 minutes before and 30 minutes after, we opened a streaming connection to the Twitter API to read all the tweets with these keywords:

milanbarça, milanbarcelona, forçabarça, forzamilan, milan-barça, milan-barcelona, milan-barca, milan-fcb, milan vs barça, milan vs barcelona, milan vs barca, messi, ibrahimovic

To perform this monitoring, we developed a small console application written in C # and based on this code written by Shannon Whitley (@swhitley). We stored the tweets in a Microsoft SQL Server database. For this test we decided not to use MSMQ queues.

The result was great and we stored in our database 83,582 tweets during the 172 minutes that the connection to the Twitter API was open, which means an average of 8.09 tweets per second.
Read more

marzo 29, 2012   1 Comment

12/12/2011

tdrlogoblack94

Data 2.0 as an indicator to launch new tourism products

Cities, regions and travel-related businesses such as hotels or travel agencies are constantly looking for a way to differentiate from their competitors in order to attract new tourists, within an industry where the price factor is many times key for consumers to decide for a destination, a hotel or a travel package.

In addition to this, regions usually depend too much on certain destinations, lacking of diversification and therefore experiencing concentration of tourists in a few cities or even on a single one. That is the case of Catalonia, where Barcelona represents more than 50% of all the tourist pernoctations in Catalonia. Moreover, most of the tourists that visit some other places in Catalonia, usually visit Barcelona as well.

Therefore, we find that tourism  institutions need to put into value new destinations, in order to spread the profits and the impact of tourism all around their territory. Similarly, tourism businesses need to increase their competitiveness, by launching new products to diversify their portfolio and attract new customers.

In this context, data 2.0 appears as a new way to better understand tourist preferences. In our new project, Flickr stands out as an indicator that can show us what places within a region tourists like to visit the most, depending on the season or their nationality for example. Through this analysis, any business can offer new and demand-adapted travel packages to their customers.  Additionally, institutions can access to data about tourists travelling around their regions, something they usually lack, because many times these visits are simple getaways of one day, wihout hotel pernoctations. Again, data 2.0 happens to be an effective way to complement available data 1.0 or even to analyze preferences and habits where data 1.0 fails to do it.

With Barcelona’s tourists travelling around Catalonia, we have wondered where tourists visiting Barcelona like to go somewhere else in Catalonia, depending on the time of the year. This information aims to help all tourism business and institutions to better understand their current and potential customers.

Take a look at this project.

 

diciembre 12, 2011  

14/10/2011

tdrlogoblack94

#TodayImWearing

The @hm twitter channel sometimes invites its followers to share their outfits with them using the #TodayImWearing tag. This morning we are monitoring the fashionistas who are sharing their outfits using this tag.

Here are the results of what they are wearing today:

Last update: 3:30pm (Barcelona timezone)

 

Related posts:

Analyzing Twitter followers: Let us tell you a little bit about your customers

Mango’s clothes on the streets

octubre 14, 2011  

05/10/2011

tdrlogoblack94

Analyzing Twitter followers: Let us tell you a little bit about your customers

Companies have decided that they should be in Twitter, but in most cases they look for a quantitative approach of their performance: “We have 3,000 on Twitter“.

But is that enough? What do these numbers tell us? Well, we think that not really much. Some companies, the eagest ones, want to look further and try to gather some personal details about their followers: Where are they? What music do they like? What are their sources of information? What other brands do they consume? Through this analysis, companies are able to profile their followers as they might be potential customers. Therefore they will be able to know some of their preferences, habits and behavior patterns.

That is great, that is something we have already done at TDR. But now, we want to go a little bit further: what about those Twitter accounts followed by my Twitter followers?

With this new project we want to offer companies and organizations that are in Twitter an easy and simple way to find out what other accounts are followed by their followers. Take a fashion brand such as H&M. In Spain, the Twitter account @hmespana has more than 18,000 followers. If we know what other accounts are most followed by these 18,000 followers, we would be able to detect some common patterns, preferences and dislikes by our potential customers. If we know what celebrities they are following the most, maybe we can make the right decision when looking for someone to promote our products. Moreover, if we track this data in a long-term, we will able to understand changes in preferences and, for example, change the TV stations or other media channels we are using to broadcast our adverts.

So, we are currently working to offer brands and companies an analytic and qualitative tool to better understand their customers. We are using data 2.0 to help businesses to to achieve their strategic and commercial objectives.

Take a look at our Twitter analytics project.

octubre 5, 2011  

10/06/2011

tdrlogoblack94
2 Comments

Tourists’ behavior patterns in Barcelona on the air: but still more to come

It took us a little bit more than we expected (bit busy around here), but today we are glad to announce that our project about tourists’ behavior patterns in Barcelona is on the air. Well, at least the first part of the final work we would like to complete, because we keep on working in a second part, which will be published very soon.

With this first part we have built two maps that gather more than 42,000 geolocated pics taken in Barcelona during a period of a year. One of the maps (built with Geocommons) provides with information about the major areas of tourists concentration all over the city. At the same time, we built 30 polygons for the Top 30 tourists attraction areas in Barcelona, so we can take a look at which of them are most visited. The other map (built with Google Fusion Tables) allows you to play with several filters in order to identify how behavior patterns changes according to variables such as the day of the week, the hour, the country of origin or the weather.

You can play with our maps by yourself but as a sign of how we understand data research at The Data Republic, we have published a report to bring you some of the major conclusions of the analysis, together with some graphs.

 

But we thought that this was not enough because we have many more questions still to answer. Now, we would like to analyze the impact of tourism over the occupation of public space all over the city. We want to compare the number of tourists in certain areas with variables such as population or number of businesses, so we can visualize which neighborhoods are above or below the average and, therefore, which have a greater or lesser degree of saturation.

To be continued…

Read the complete project and play with the maps

 

junio 10, 2011   2 Comments

02/06/2011

tdrlogoblack94

My Twitter followers: from quantitative to qualitative

Now that companies have decided that they should be in Twitter, now is the time to go further. Currently, the vast majority of companies find impossible to calculate the ROI of their activity in social networks so they simply respond with quantitative data:

“We have 3,000 on Twitter and 2,800 Facebook fans“.

But is that enough? What do these numbers tell us? Well, we think that not really much.

Our aim at The Data Republic is to go deeper and get the most of these 3,000 followers on Twitter. Where are they? What music do they like? What are their sources of information? What other brands do they consume? Etc.

My Twitter followers: from quantitative to qualitative

Extracting the information of a brand followers through the Twitter API and applying analytical work to the results, you may get some interesting answers to these questions.

We are currently helping a local clothing chain to get to know their customers through this analysis methodology, because they found out that they had a very poor knowledge of them. They do not have a Twitter account, but some of its competitors do, so they want to profile those followers as they might be potential customers. Through our analysis they will be able to know some of their preferences, habits and behavior patterns: where they go out on weekends or where they like to go shopping, what other beer or electronics brands they love, what their favorite bands are or what media sources they use to be informed. If they know their common places of leisure or shopping, they will be more effective when choosing a new store location. If they know the music they listen to, they will play that music at their stores. If they know what other brands they love, they will be able to set up partnerships with them and look for synergies. If they know their favorite media sources, they will put ads there.

All these possibilities do not deny the work of the community manager, whose job, to build relationships face to face with customers, is still necessary. But that’s another story.

It is not simply about your customers, it is about your future customers, it’s about increasing your sales.

junio 2, 2011  

05/05/2011

tdrlogoblack94

What are the most frequented places by tourists on a rainy day?

There are dozens of businesses related to tourism, like travel agencies, hotels and restaurants or just small businesses in the retail sector.

Photo by herman van hulzen

For some local businesses such as souvenir and gifts stores, a restaurant, a cab company or a bicycle rental business, it is important to know where to find the tourists (their potential customers) in a city. We all know the great icons of each one: Times Square in NYC, the Eiffel Tower in Paris, the Prado Museum in Madrid and the Sagrada Família in Barcelona. But, are you sure you know all the city corners where you can find them? And moreover, do you know which ways and streets do they use to reach those spots?

Furthermore, there are different variables that may determine the behavior of tourists such as the day of the week (weekdays vs weekends), the weather (rain or sunshine) or their nationality and habits associated. Do you know what things do tourists do or what places do they go on a rainy day?, or on heatwave day at 30 º C? or on a Tuesday? and, do U.S. tourists visit the same places than the Italians?

We want to analyze the behavior patterns of tourists from public information online, and discover where they move, considering seasonal and climatic variables. Our first test will be based in Barcelona, ​​choosing a period of 365 days, between March 1st 2010 and March 1st 2011.

Let’s see what happens!

mayo 5, 2011