Multi dimension customer segmentation and what can you expect from each data type

What’s better for clustering your audience/users, using Behavioral or Demographic variables?  This is a hot topic since the first websites came along. Since I remember companies use to segment their clients and potencial clients mostly using a Demographic criteria, which means grouping people into groups based on age, sex, income, occupation, religion, race, etc. Historically those variables are classified into Hard, Soft and moment referred ones. 1- Hard Variables: The Hard Variables are the ones that normally never change like the birthdate or name. 2- Soft Variables: The Soft Variables are those that can change from time to time, like level of education or income. 3-

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FLOWCARD INFORMATION ON THE GO

Since I´m working in Analytics I´m trying to get an analytics model that allows me to understand and analyze each particular system in a properly way…as something alive. The current Analytics models are great to analyze pictures of the system, or “movies” but about each part of the system, with an holistic and not systemic vision of Analytics. The holistic vision says that if you optimize each part of your system you have an optimized system, while the systemic vision says that the key is not on every part of the system but on their interactions. You can have a beautiful landing page but it is important is

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THE FOUR LINES MAGIC CHART

I do agree with you if this post title sounds a little funny to you but I’ll show you that it makes sense.I’ve post several times about the importance of integrating information, it provides us the total picture of our situation avoiding inferring part of it, which means building better scenarios for most efficient decision making.Lets use an example the ecommerce site from a computer company. What information this company normally use to understand what is happening with their businesses? Traffic and sales. Both are behavioral metrics which means that tell us what are people doing but not why.What happen

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