it depends on the data you have: do you have purchases identified by buyer, corresponding prices, other information about the buyer? information about the products?
Most of today's econometric consumer choice models (aids, quaids, easi) use a form close to Holbrook Working's form, something like:
$w_n^i = \alpha^i + \beta^i \log(y_n) + \sum_{k=1}^J \gamma^i_k \log(price_k) + \sum_{l=1}^L \delta^i_l socio^l_n + \epsilon_n $
where $w^i_n$ is the share of the expenditure of consumer $n$ on good $i$ in her total expenditure, $y_n$ is the total expenditure of consumer $n$, $socio^l_n$ is the $l$th of $L$ socio-demographic control variables for consumer $n$, and $\epsilon$ is the error term.
This form fits remarkably well on many macro and micro consumer expenditure datasets, so it is worth a try. It is straightforward to fit (if you don't need to conform to microeconomic theory of consumer choice), and easily gives you the estimated effect of a change in total expenditure or price on the quantities purchased. That would be the economic way of evaluating the importance of products. However usually it is used on a small number of broad categories of goods, and not on hundreds of products as in a webshop.
Alternatively, you can use machine learning methods for regression (predict the probability of buying a given object?) or unsupervized learning (to see which kinds of consumers are "close" to given products). And I think there is also a whole literature about recommender systems, but I don't know anything about that.
