Is standardization needed before fitting logistic regression? My question is do we need to standardize the data set to make sure all variables have the same scale, between [0,1], before fitting logistic regression. The formula is:  
$$\frac{x_i-\min(x_i)}{\max(x_i)-\min(x_i)}$$
My data set has 2 variables, they describe the same thing for two channels, but the volume is different. Say it's the number of customer visits in two stores, y here is whether a customer purchases. Because a customer can visit both stores, or twice first store, once second store before he makes a purchase. but the total number of customer visits for 1st store is 10 times larger than the second store. When I fit this logistic regression, without standardization,  coef(store1)=37, coef(store2)=13; if I standardize the data, then coef(store1)=133, coef(store2)=11. Something like this. Which approach makes more sense?
What if I am fitting a decision tree model? I know tree structure models don't need standardization since the model itself will adjust it somehow. But checking with all of you. 
 A: Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. For example, if you use Newton-Raphson to maximize the likelihood, standardizing the features makes the convergence faster. Otherwise, you can run your logistic regression without any standardization treatment on the features.
A: If you use logistic regression with LASSO or ridge regression (as Weka Logistic class does) you should.  As Hastie,Tibshirani and Friedman points out (page 82 of the pdf or at page 63 of the book):  

The ridge solutions are not equivariant under scaling of the inputs, and
  so one normally standardizes the inputs before solving.

Also this thread does.
A: @Aymen is right, you don't need to normalize your data for logistic regression.  (For more general information, it may help to read through this CV thread: When should you center your data & when should you standardize?; you might also note that your transformation is more commonly called 'normalizing', see: How to verify a distribution is normalized?)  Let me address some other points in the question.  
It is worth noting here that in logistic regression your coefficients indicate the effect of a one-unit change in your predictor variable on the log odds of 'success'.  The effect of transforming a variable (such as by standardizing or normalizing) is to change what we are calling a 'unit' in the context of our model.  Your raw $x$ data varied across some number of units in the original metric.  After you normalized, your data ranged from $0$ to $1$.  That is, a change of one unit now means going from the lowest valued observation to the highest valued observation.  The amount of increase in the log odds of success has not changed.  From these facts, I suspect that your first variable (store1) spanned $133/37\approx 3.6$ original units, and your second variable (store2) spanned only $11/13\approx 0.85$ original units.  
