Forecasting monthly demand for automobiles given historical daily data

I am trying to predict demand of Automobiles at dealer and Variant level. I have 2 year, 2 months daily sales data. I need to predict demand at monthly level based on daily data for Enquiry.

Just to make it more clear, if I am on 5th of March 2017 so based on enquiries so far, earlier sales data etc. I need to predict total sales of March. Again, if I am on 15th of March so based on total enquiries till 15th, sales so far in month (in last 30 days etc.), I need to predict total sales for the month of March.

I have tried regression using lagged variables like total sales in last 30 days, total enquiries in last 30 days etc, but results are very poor. I have tried auto.arima with exogenous variable but results are not very good.

Sales are high on weekends and on March and October (Festival season in India).
I have few questions:

1. What should be my approach?
2. What are the other techniques I can use?
3. I had built ARIMA at day of month level but it gives me only 26 data points. (12*2+) 31 models in total and it does not take care of weekend effect. Can this be improved?
4. Can some state space model be used here?