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Continente Siete :: General Activity

Ningún departamento de marketing desea malgastar el tiempo atendiendo y desarrollando individuos que no tienen intenciones genuinas de comprar, pero es una tarea difícil y agotadora determinar rápidamente cuáles son realmente potenciales compradores, y cuáles no. La complejidad aumenta considerablemente cuando el número de aparentes oportunidades de negocio escala a valores imposibles de analizar humanamente.  

Cuando un negocio tiene aceitado el proceso de captación de oportunidades de negocio (leads), el siguiente paso es el manejo de los mismos para optimizar el uso de recursos y así desarrollar cada cliente según sus necesidades particulares. 

Lead Management

Lead Scoring es una técnica que permite calificar el nivel de interés y calidad de un potencial cliente (lead), y es una forma de valoración de las oportunidades de negocio. Esta calificación se lleva a cabo cuantificando las diferentes acciones directas e indirectas que realiza el lead durante su ciclo de vida.

En la práctica, el lead scoring viene a ayudar a responder preguntas como:

¿Cuáles son datos de calidad, y cuáles pueden ser ignorados?

¿Debo seguir trabajando sobre este lead, o debo descartarlo?

¿Qué clientes necesitan un seguimiento personalizado, y a cuáles puedo hacerles un seguimiento automático?

Con una correcta implementación de un sistema de Lead Scoring, se pueden responder estas preguntas en forma efectiva, dándole diferente peso a características y eventos que el potencial cliente realiza durante su ciclo de vida. Por ejemplo, podríamos pensar que para algunos productos que tienen entrega restringida a cierta área geográfica, el código de área del número telefónico será muy importante para determinar si un lead entrante debe ser desarrollado o directamente descartado. También podríamos deducir que una persona que hace click en un newsletter o que asiste a una conferencia o que hace un llamado para preguntar sobre algún producto, tiene un nivel de interés importante.

Si juntamos la información declarada por el usuario (ubicación, edad, interés declarado en un producto/servicio, etc.) y la combinamos con la información deducida de sus comportamientos (clicks, llenado de formularios, visitas a página web, etc.), tenemos una base de datos útil para encontrar los leads idóneos.

Para traducir esta información, que es no solo numérica sino también en gran medida categórica, en un “score“, debemos dar un puntaje a cada una de las variables que podamos identificar y desarrollar un modelo que permita, por ejemplo, pronosticar la probabilidad de compra de cada cliente potencial. Existen modelos simples (en general aditivos) y otros más complejos que capturan relaciones no lineales entre el comportamiento los usuarios y su compromiso con la compra. En definitiva, lo que buscan las técnicas de Lead Scoring es maximizar la rentabilidad aumentando las ventas y optimizando el uso de los recursos limitados a través de un análisis automatizado y sistemático del comportamiento de los leads.

En el desarrollo de un nuevo sistema de Lead Scoring, hay dos grandes desafíos. El primero es la generación del algoritmo, que sólo se logra estudiando finamente al cliente de cada negocio para identificar las conductas que revelan sus intenciones de compra. El segundo desafío es la implementación del sistema, que busca unificar todo el proceso de gestión de clientes del departamento de Marketing. Continente Siete desarrolla sistemas de Lead Scoring para sus clientes teniendo en cuenta tanto la generación del algoritmo como la implementación del mismo, y de esta manera los ayuda a crecer basándose en la inteligencia de la gestión de sus.oportunidades de negocio.

Al realizar un desarrollo ad-hoc para el cliente se genera una gran ventaja respecto de sistemas o algoritmos enlatados, dado que no es el cliente el que tiene que adaptarse en sus procesos e interpretaciones de los datos, sino que todo se integra con sus procesos actuales y con métricas que tienen sentido a la hora de la toma de decisiones.

Nicolás Oteiza

Continente Siete

We are all different, right? Each person has its educational, contextual background, history, personal characteristics, hence a particular way to see the world and then, to act in our lives. You can decide to study mathematics, your friend could choose to go on holidays to Paris, and your neighbor could enjoy staying home the whole weekend watching TV. We are different.

However, have you noticed that many of your problems are the same as your friends’ ones?

  • Have you ever been on traffic jam on a highway and asked yourself: why are we stopped as if there were a traffic light when actually there is no one?
  • If you ever had the chance to see the market penetration ratio for a new product, didn´t you wonder why you get always the same penetrations curve no matter who deploys the marketing strategy?
  • Why all the countries have inflation? Why all the big cities have traffic problem?
  • Are you dealing with problems originated by a solution implemented some time ago? A solution that brought more problems instead of real solutions?
Here you have some cases of common problems; they happen all the time, everywhere and generally they are quite similar. But then, why do they still happen and we are unable to solve them definitely?
 
It turns out to be extremely hard to understand the systems in which we are involved in. And actually, you are part of the traffic system of your city, a member of the market that decides whether a new product will succeed or not, part of the economic system in your country, and part of a hundred of other systems. “System Dynamics” is a discipline focused on understanding the inter-relations among the different components of the system, identifying loops, letting you understand the originally unexpected results: political interventions that do not more than increasing poverty or business strategies aimed to gain new clients that lead to losing the old ones and eventually reducing the company’s opportunities to keep on growing.
 
“System Dynamics” focuses its attention on STRUCTURES. Structures end up conditioning the individual behaviors and the inter-relations among them, then defining the global results and behaviors. That is the reason why, no matter the people involved in the system, the results always are qualitatively equal.  And by structures I refer to the laws, rules of the system, the available information, the control mechanisms, the carrying capacities, the way of connecting the parts, etc. Structures lead the way you understand reality, and then, the way you act and react.
 
Life is somehow like a large and a little bit more complex game. In a game, you have clear rules that say what you can do or do not, the cases that you will be rewarded and the cases you will be punished, the information you will have available at the time of making a decision, and also, the time when you will be able to act. The game’s creator cannot anticipate which gamer will win, but he definitely knows the number of resulting winners, the ending conditions of the other participants, and also an idea of the possible evolutions and alternatives that could arise from the game. And why? Because he understands the structures behing it.
 
But don’t panic, you can still consider yourself a free person! “System Dynamics” do not look at individual behaviors and his particular way of acting. It studies the masses’ behavior, the global results. You are free and you will decide differently from others, but at the end the global results will be quite predictable by understanding the structures of the system you are part of.
 
In the book “Foundation” by Isaac Asimov, threre is an interesting dialogue between Hari Seldom and Gaal Dornick:
 
HS: – “What of psychohistory?”
GD: -“I haven’t thought of applying it to the problem.”
HS:- “Before you are done with me, young man, you will learn to apply psychohistory to all problems as a matter of course”
 
Without making comparison, and without going so extreme, I would say these last words to you but regarding “System Dynamics“. Once you start digging in the world of System Dynamics, you will see how useful it will be to understand a great part of the problems we face today. And remember that understanding the problem, you have 75% of the path to the solution completed!
 
“You will learn to apply System Dynamics to all problems as a matter of course”
 
Suggested bibliography: 
- “Business Dynamics” – by John D. Sterman.
- “The Fifth Discipline” – by Peter Senge.
- See other posts in our blog, related with structures: 
“Predator-Prey – Migration Adapted” & “Keep calm and win again”
  • The Show

During “La Semana de la Matemática” (“Math Week”), an event organized by the Buenos Aires University this past April 23 to 25, two members of C7 had the opportunity to make a presentation about Noise, “Procedural Generation of nth Dimensional Noise” specifically.

Math Week aims at delivering 4th and 5th year high-school students the Big Picture about studying and following a career in Mathematics.

With this in mind, we, Julia Picabea and Agustin Ramos Anzorena prepared a small talk on “Noise”, oriented towards the procedural generation of textures and maps used in 3D Animation, film and videoGame industry. This approach, we believe, softens the hard-algorithm impact that might scare the students away, by providing a familiar frame.

We started out by explaining how noise is, showed an example of a procedure using Voronoi Diagrams, and finally did a realTime demonstration inside a 3d Modelling Application, of how to create a realistic stone, from a simple cube, using nothing but procedural noises. This is what we learned in the process.

 

Also, take a look at the Poster we presented, where you can find some of the stuff explained here, plus q walkthrough of how to create a 3D Stone, in a 3D modelling application, starting from just a simple cube, using nothing but noises. It’s quite interesting.

Look at the Poster

 

  • What’s up with the noise

Noise can be thought of as an error imposed over a signal or measurement of data.

In Computer Graphics (CG), noise can help simulate naturally occurring phenomena that would be very difficult to generate otherwise.

Usually, random noise is no good, as Einstein said “God does not play dice with the universe”. Otherwise, we would have tropical trees scattered around polar glaciers.

This is why researchers seek ways to produce noises, that generate coherent values with parameters that make them highly controllable, while remaining “random” to the human eye. They fall into a category called “Procedural Generation”.

One of the most famous noises is the Perlin Noise, created by Ken Perlin, to produce organic textures for the movie “Tron”, in 1982. The film received an Academy Award for Technical Achievement (14 years later).

Nowadays, noises are widely used to create all sorts of special effects: clouds, fire, organic textures, terrain generation, object scattering and real-Time mesh wreckage in game physics engines.

This is simple Perlin Noise

Perlin Noise

This is simple Perlin Noise

 

This is a fractal Perlin Noise, where many scaled versions are applied on top of each other:

Perlin Noise Fractal

Perlin Noise Fractal

 

  • Voronoi Diagram and Worley Noise

Used in optimization, the Voronoi diagram can also be used to produce textures, and it’s also widely used in CG.

Let’s take a look at the algorithm implemented in a 2D plane in a screen:

  1. Generate random points across the area. These are the “feature points”.
  2. For each of the remaining points/pixels.
    2.1 – Measure the distance to each feature points.
    2.2 - Recognize which feature point is closer that is find the minimum distance.
    2.3 - Inherit/transfer properties of the nearest feature point to the point/pixel

That’s it!

This means that, if the pixel inherits the color of the feature point, we will end up with a vitraux-like plane, where each colored area represents all the pixels nearest to a certain feature point of the same color.

Voronoi Diagram

Voronoi Diagram


Voronoi Diagram (Area Edges)

Voronoi Diagram (Area Edges)

 

But it does not end here: we can tweak some parts of the algorithm to come up with totally different patterns.

For example:
What if we change the way we measure the distance in step 2.1?

The first image measures distances using the euclidean distance.
If A=(xa,ya) and B=(xb, yb) are 2-dimensional points, the euclidean distance between them is

d(A,B)=sqrt((xa-xb)2+(ya-yb)2)

But in CG you could use other distances, for example: Manhattan, Chebyshev, and Minkowski distances.
Let’s take a look at just one of them: Manhattan distance between the points A and B would be

d(A,B)= abs(xa-xb) + abs(ya-yb)

This distance is also called TaxiCab distance, since it would be the shortest distance that one could travel in a city, where it is not possible to move diagonally.

Voronoi Diagram (Manhattan Distance)

Voronoi Diagram (Manhattan Distance)

 

Usually, in CG, if we want to create a texture to control something other than the color of an object, we would create a grayscale image, because it represents an easy-to-handle 1-dimensional gradient. This means working with 1 range/scale of values at a time.

Different types of grayscale images can be generated by playing around with the Voronoi algorithm.

So, instead of inheriting the color, the pixel becomes a grayscale value, which is linearly interpolated between 0 and a specified maximum range.

Worley Noise (Distance: Linear Interpolation to closest Feature Point)

Worley Noise (Distance: Linear Interpolation to closest Feature Point)

Welcome to the Worley family of noises, where what matters is the type of Interpolation and the Feature Selection.

What does this mean?
You can interpolate the distance from the pixel to the feature in whatever way you like, for example:

Linear: sqrt(a2+b2)
Linear Squared: a2 + b2
Quadratic: (a2 + a*b + b2)

Or any other operation…

And what does Feature Selection mean?

All these distances are calculated from the pixel position towards the closest Feature Point. There are variations, called “F values”, which take into account the distance to the second closest Feature Point, or the third, or the nth.

Worley Noise (Distance: Linear Interpolation to second closest Feature Point)

Worley Noise (Distance: Linear Interpolation to second closest Feature Point)

Other variations include operations between F values

The following is F2 minus F1:

Worley Noise (Distance: Linear Interpolation to the difference between the second and the first closest Feature Point)

Worley Noise (Distance: Linear Interpolation to the difference between the second and the first closest Feature Point)

 

As you can see, a lot of these noises can be use to create textures of all sorts, such as leather, animal skin or scales, some plastics, or clouds, and a lot more with some creativity.

 

We hope you enjoyed this post!!

 

Music!

February 19, 2013 — Deja un comentario
At our office we spend many hours a day. Some of us more, some less, but it is common to have people around all day long. So, the problem since its beginings is how to maintain (us) entertained for such a long time.
The simplest answer? Music.
Music is known worldwide as a means to exchange tastes, and share with other people. It can even be considered a means of communication and identification.
The problem is, how do you share music in an office, without disturbing the people who work there? And, having fix this, how can you agree among so many people?
In our case, we have multiple music options, as explained below.

Jango

It is an easily accessible page which performs a first search according to a favorite artist, and then generates a list of tracks from similar artists, which can be rated as good or bad, according to the preferences of the user. Thus, selected artists lists are generated, and some unknown musics are discovered.

Grooveshark

In case of not wanting to explore new music, Grooveshark may be one of the best options. On this page you can find a variety of artists, songs and choose those you like best, assembling personal lists to your liking. In general, if you do not have a good internet connection is not recommended. However, being without video songs, is a good choice to put background music, without the risk of ending up listening to an artist who is not of your taste.

Youtube

It may happen that the connection to YouTube (since it is a page more developed than the previous ones) works better loading videos. In that case, you can create a list of your own, or use the suggested lists from the page.

Streema

If you are not convinced to listen to a particular artist, an interesting option is to connect to any online radio. Here, our favorite page is Streema, a platform that includes numerous radios from around the world, easy to use and very effective.

Server music

If we are not convinced with all of the above, or if the connection is working very slow, we have music on our servers, which are connected to the speakers of the office and we can access from our own computers. Thus, anyone in the office can change the music if it is not of its liking, or just prefer a change.
All previous cases follow a very particular dynamic that is generated and mutating over time. Sometimes the music is directly censored, and sometimes it is celebrated by most of the office. Also, there are times when it generates some discussion about what music to listen, while in other cases there are fights between two or more people entering the servers to simultaneously choose music, being favored in that case the fastest one with the cursor.
But, of course, the office always has music that entertains our work!