It's a bit untypical question I guess, but I hope you can help me. I know a little bit of statistics. I'm not a specialist, but I find it really interesting, so I learn in my free time. I need your opinion on the following conflict.
I need to prepare sales forecasts for an e-commerce website. Imagine it as Amazon (it's not Amazon).
I took past sales data. First I analysed seasonality and trend.
I also analysed the main factors influencing the sales
I tried out several forecasting methods (ARIMA, Holt-Winters, neural networks), but in the end a regression model based on the factors identified as main determinants was the most convincing one. (I didn't have enough data for neural networks). The forecast resulted quite good.
My coworker's approach:
- We should take the data on "sessions" (i.e. how many times the website is accessed by customers daily), let's say it's 2k
- We should then take the data on the "conversion rate" (how many sessions on average result in a sale, e.g. 20%)
- This will allow us to get our sales forecasts: by multiplying the number of sessions by the conversion rate. In this example, it's 2k*20%=400.
I find his idea problematic for several reasons:
- First, we can only get data on the conversion rate and sessions for NOW. Not for the future. So if we need this data for future, we need to forecast it, adding complexity to the model
- Data on sessions and the conversion rate is strongly volatile - it's just volatile as the sales data - adding complexity to the model. Basically, we would have 2 volatile variables instead of one.
- The determinants of the conversion rate and sessions are not necessarily the same as the determinants of sales
Is there any possibility that his way of approaching it - i.e. making forecasts on the basis of the data on sessions and the conversion rate - is better than mine?
I want to be 100% sure my approach is correct. I'm keeping an open mind and if his idea is better, I want to know that.