How to improve accuracy of random forest multiclass classification model?
How is your training acc? I assume that your acc is your validation. If your training acc is way to high, som normal overfitting might be the case. Random forest normally handles overfitting very well.
What you could try is PCA of your data, and then try classify on that. This gives you the features which accounts for most variation in the data, thus can be a good idea to try, if you cannot classify on the original data (and also it reduces your features).
A side note: remember, that the fitting of SVM is quadratic in the number of points, thus reducing your data to around 10-20000 for tuning parameters and then fit a SVM on the full dataset with the optimal parameter for the subset, might also speed up the process.
Also remember to consider trying different kernels for the SVM.
I am working on a multi class classification for segmenting customers into 3 different classes based on their purchasing behavior and demographics. I cannot disclose the data set completely but in general it contains around 300 features and  50000 rows. I have tried the following methods but I am unable to achieve accuracy above 50% :

Tuning the hyperparameters ( I am using tuned hyperparameters after doing GridSearchCV)
Normalizing the dataset and then running my models
Tried different classification methods : OneVsRestClassifier, RandomForestClassification, SVM, KNN and LDA
I have also removed irrelevant features and tried running my models
My classes were imbalanced, so I have also tried using class_weight = balanced, oversampling using SMOTE, downsampling and resampling.

Is there something else I can try to improve my accuracy ( and by accuracy I mean f-score, precision and recall ).
Any help will be appreciated.