Entendiendo cómo el Big Data está ayudando a construir mejores productos

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En los últimos años, encontramos algunos ejemplos muy llamativos en el uso y análisis de Data y Big Data que, de alguna manera, sirven tanto para crear nuevos productos, como para predecir comportamientos y tendencias, optimizar acciones de marketing, etc.

Ejemplos de su uso

Macy’s y sus precios en tiempo real

Macy’s es uno de los comercios minoristas más importantes de los Estados Unidos, que destaca por su e-commerce. Utilizando la tecnología de SAS Institute ha conseguido mejorar sus ingresos y la experiencia del usuario. Gracias a la velocidad de análisis y los informes obtenidos con esta nueva tecnología, han reducido en 500.000 dólares el gasto anual de analítica. Macy’s sabe hoy perfectamente el impacto de sus newsletters y notificaciones y conoce mejor a los clientes más satisfechos, lo que les gusta y lo que no… Hoy, el uso de estos datos, les permite segmentar al máximo sus envíos, de manera que envían menos emails, pero con mucho más impacto y han conseguido reducir las de suscripciones hasta en un 20%. Gracias a la utilización de un algoritmo y al control de la demanda y el inventario, pueden lanzar ofertas cruzadas, ajustar precios y hacer rebajas casi en tiempo real para sus 73 millones de artículos a la venta.

Juegos de pelota y millones de datos

Casi todo el mundo ha oído hablar de la película Moneyball: Rompiendo las reglas (2011), si no es por Brad Pitt, al menos sí como ejemplo del uso de Data. Ocurrió en la pretemporada de 2002 en Oakland Athletics de las Grandes Ligas de Béisbol de los Estados Unidos. El gerente deportivo Billy Beane, revolucionó la historia del club y posiblemente del deporte en general tras fichar a un joven economista, Peter Brand, que traía nuevas ideas. Juntos contrataron jugadores infravalorados, pero económicamente rentables, con un criterio de selección muy diferente. La intuición y sapiencia de los ojeadores es sustituída por las conclusiones de los análisis de estadísticas y números acumulados a la hora de establecer las necesidades del equipo y los jugadores que mejor se adaptan a éstas.

En la actualidad contamos con muchos más casos en los que se usa Big Data en el deporte. Los equipos de la NBA ya tienen implantado el uso de datos a la hora de preparar la estrategia en partido, mientras que la NFL tiene una plataforma que ayuda con sus aplicaciones a los 32 equipos a tomar las mejores decisiones en base a la analítica de datos: desde el estado de la superficie del césped a las condiciones climatológicas, pasando por datos de la etapa universitaria de cada jugador…todo está registrado y todo puede servir para sacar conclusiones diversas, como la de prevenir lesiones en jugadores. Además, analiza las preferencias de los aficionados gracias a su aplicación NFL Now, que ofrece la posibilidad de que éstos creen su propio canal con contenido variado de la NFL: vídeos divertidos, cheerleaders preferidas, información por equipos, por jugadores, etc. También utilizan NetApp para almacenar todos estos datos. Con esto consiguen establecer las demandas de los fans y facilita las cosas a la hora de establecer acciones de marketing, expandir el mercado, encontrar los partners más apropiados, etc.

 

La reelección de Obama

Tras su primer mandato, el presidente de los EEUU, Barack Obama, decidió utilizar Big Data para su reelección en 2012. Un centenar de personas trabajaron en el departamento de analítica de la campaña. 50 estaban fijos en las oficinas centrales, otros 30 se movilizaron a lo largo y ancho de las distintas sedes del país, y 20 estaban única y exclusivamente centrados en la interpretación de los datos recibidos. Tras un primer análisis, los esfuerzos de la campaña se enfocaron en tres aspectos: registro (recoger datos de los votantes convencidos), persuasión (dirigirse a los dudosos de una forma eficaz) y voto del electorado (asegurarse de que los partidarios fueran a ejercer el voto sí o sí). Y, por primera vez, los tres equipos más importantes de las campañas electorales: el de campo, el digital y el de comunicación, trabajaron con una estrategia unificada con los respectivos datos de cada uno. El motor de todo, la plataforma inteligente utilizada fue HP Vertica. Entre las acciones más efectivas que permitía esta plataforma estaban: recoger datos a pie de campo y realizar un feedback muy rápido via notificaciones email por parte del equipo online (se mejoraba en tiempo y eficiencia); o detectar los nichos en los que funcionaría mejor la publicidad en TV cruzando datos de los votantes con otros demográficos, audiencias, precios de publicidad, programas… (se mejoró en impacto y segmentación). Con su analítica, el equipo de Obama optimizó la comunicación y mejoró la respuesta del electorado afín, permitiendo no malgastar recursos, tiempo y dinero en los votantes que no eran partidarios de su partido.

BBVA: Mobile World Congress y Turismo Madrid

BBVA también ha realizado diversas pruebas de Big Data, en las que además, se ha dado importancia a su visualización para permitir que sean más comprensibles a ojos de un espectador neófito. En Barcelona en 2012, se hizo la medición del impacto económico del Mobile World Congress. Para ello, se extrajeron datos de las transacciones realizadas con tarjetas de crédito, tanto la semana antes como la semana en que tuvo lugar el evento. Los resultados sirven para concluír los lugares, días y horarios donde más “movimiento” hubo, algo que, por ejemplo, puede servir a los comercios para reforzar sus acciones de marketing y venta de cara a eventos similares o a las propias ciudades para hacer lo propio con sus promociones turísticas.

Otro ejemplo de estudio sería el realizado por BBVA en el que se analiza el uso de las tarjetas de crédito en España durante la Semana Santa de 2011 en cuatro sectores: mercados y alimentos, bares y restaurantes, moda y gasolineras.

Un último ejemplo de la entidad fue el llevado a cabo junto con el Ayuntamiento de Madrid que, bajo el título Dinámicas del Turismo en la Ciudad de Madrid, analiza el comportamiento de los turistas en cuanto a su actividad comercial durante 2012. Entre los muchos resultados, el estudio sirvió para cuantificar el impacto económico del Orgullo Gay en diversas zonas de la ciudad. El gasto comercial aumentó un 24% respecto a la misma semana del mes anterior. Además, se conocen otros datos interesantes como los turistas que más gastan, en qué gastan, por dónde se mueven, etc.

El Big Data están cambiando muchas cosas, ya no sólo a la hora de tomar decisiones comerciales, deportivas, políticas o de otra índole sino también a la hora de crear productos, de innovar, almacenar datos, desarrollar, visualizar las cosas… Es una tendencia que se ha generalizado y que parece que va a quedarse entre nosotros mucho tiempo.

Big Data en las apps: 7 herramientas pensadas para el usuario final

Lo más habitual cuando pensamos en Big Data es que nos venga a la imaginación grandes ordenadores procesando mucha y variada información para obtener algún resultado relevante para una empresa. Pero no siempre es así, también hay herramientas de Big Data pensadas para usuarios finales, para nuestros clientes.

Las siguientes siete herramientas están precisamente pensadas para nuestros clientes, para los usuarios finales. Son todas conocidas, pero a lo mejor nunca hemos pensado cuál es la tecnología que hay detrás.

Swiftkey, un teclado predictivo

Una de las tareas más tediosas de los móviles y tablets es escribir. Y lo es porque una pantalla táctil no es precisamente el mejor instrumento en el que incrustar un teclado. Pero hay empresas como Swiftkey que han logrado hacer esta experiencia mucho más agradable, y gracias al Big Data.

Lo increíble de Swiftkey es su capacidad predictiva. Es capaz de predecir no solo la palabra que estás escribiendo (con una o dos pulsaciones) sino la siguiente palabra que vas a escribir. ¿Cómo lo hace? Primero con una gigantesca base de datos de las expresiones más comunes. Pero también aprendiendo de cada usuario cómo suele escribir.

La experiencia de Swiftkey es increíble y ahora que se puede usar también en los iPhone / iPad nadie tiene la excusa para no usarlo. Una vez que lo usas no quieres volver al complicado teclado de serie.

Pandora, la música que te gusta

Pandora es un servicio de música que desgraciadamente tuvo que cerrar en Europa. Su modelo era muy simple. Le dices una canción que te gusta y te crea una radio personalizada con canciones de estilo similar, a las que puedes decir que te gustan o no.

Lo increíble del servicio era la capacidad de adivinar tus gustos que teníaPandora. Mucha gente sigue con pasión ahora a grupos de música que conoció a través de Pandora. Su capacidad venía, como no, del análisis del Big Data, analizar montones de información sobre gustos de sus usuarios.

Google Now, la información que necesitas sin ni siquiera saberlo

Google tiene un servicio muy integrado con sus móviles Android (aunque también está para iPhone y iPad) que básicamente te da información que necesitas sin que se la pidas. Por ejemplo, por las mañanas te dice el tiempo que vas a tardar en llegar al trabajo por el tráfico que hay. Te recomienda lecturas basándose en lo que buscas y haces click en el buscador. Te dice el tiempo que hace en la ciudad en la que estás pero también en la ciudad de destino si vas a viajar (mira en Gmail si tienes billetes de avión comprados).

Google Now es increíble. Cada vez hay más funcionalidades y todo ello gracias al Big Data. Usa de forma automática todo lo que sabe sobre ti para facilitarte un poco más la vida. Una vez que lo usas no quieres volver atrás.

Youtube, recomendaciones personalizadas

El gran suministrador de vídeos de Youtube también tira de Big Data, cómo no. Y lo hace sugiriendo vídeos que te pueden interesar, basándose en los vídeos que ves y los que has marcado como interesantes.

Esto permite dos cosas muy interesantes tanto para el usuario como para el propio Youtube: para el usuario, le muestra constantemente cosas interesantes para ver incluso si entras a la web sin nada concreto que ver; para Youtube, permite que sus usuarios pasen más tiempo en su web y así aumentan los ingresos. ¿Win-win, verdad?

Amazon, compras sugeridas

El gigante minorista Amazon también tira de Big Data. En esta ocasión para recomendar productos a sus clientes, basándose en lo que buscan, miran y compran, así como información de otros usuarios similares. Esto lo hacen tanto cuando navegas por su web como con emails periódicos personalizados.

Es claramente una estrategia ganadora, es como el típico catálogo de las tiendas pero cada usuario de Amazon tiene el suyo propio, adaptado a sus gustos. Sería interesante saber cuál es el ratio de conversión de estas recomendaciones personalizadas, pero aunque sea bajo, dado el volumen de usuarios de Amazon seguro que el sistema de Big Data se paga solo.

Twitter, recomendaciones para seguir

Cuando entras a Twitter siempre ves una pequeña caja que te recomienda gente a la que seguir. Y estas recomendaciones están basadas en Big Data: gente similar a la que sigues, perfiles similares al tuyo, etc.

Estas recomendaciones son siempre interesantes y esto es por la capacidad de adaptación a tus gustos. Una pequeña caja en una web puede llevar mucho trabajo por detrás, al final lo importante es que sea valioso para el usuario, el Big Data no tiene que ser espectacular en su presentación.

Sistema detección temprana de terremotos y tsunamis de Japón

En 2011 hubo un devastador terremoto en Japón que vino acompañado de un brutal tsunami. Lo increíble del asunto es que no hubiera más muertos debido a la brutalidad del suceso. Pero tiene una explicación: desde 2007 Japón tiene un sistema de detección temprana de terremotos y tsunamis.

Para ello cuenta con una red de sismógrafos distribuidos por el país, un sistema centralizado que recoge la información, la analiza en tiempo real y predice, según las ondas P, previas al terremoto real, cuándo y dónde se producirá el terremoto. De forma automática se informa a los japoneses de esta información por televisión, radio y teléfono móvil.

Lo increíble del asunto es que en el terremoto de 2011 pudieron avisar de la existencia del terremoto unos 30 segundos antes de que se produjera, y de los tsunamis con horas de antelación. Desde luego un sistema muy caro que analiza muchos datos en tiempo real pero que sirvió para salvar muchas vidas.

Infografía: Las claves de Big Data

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