Accounting for promotional factors
If you know that promotional factors influence unit sales, then you would take them into account in the design of your analysis. I might build a simple predictive model (such as a multiple regression model) with unit sales as the target variable and promotional features (ad frequency, price reduction, ad size, media circulation, gross rating points, etc.) as predictor variables. Once you build a model having a good fit, you can "remove the influence" of the predictor variables by computing the expected demand with certain promotional variables equal to zero. This would place all products on equal footing, and you might then continue to cluster products.
Another method is to begin with a clustering exercise as discussed above. I mentioned up there you might study which products had similar sales patterns and speculate on reasons why. Merchandising and marketing people might notice that products appearing in the same clusters were all advertised in a fall circular. You'd smack your forehead and say of course their similarity in sales patterns is the result of having similar promotional characteristics. This might or might not be important to you, depending on what your company is doing strategically. For example, you might notice that certain products DO NOT appear in the cluster marked by heavy promotion, even though they SHOULD. In this case, you have discovered products for whom promotions aren't working.
On the other hand, if your strategic goal was to find out more about the consumer's habits - to understand how some products are purchased periodically or in certain seasons - then you might wish to "control" the promotional factors. One way to do this without resorting to statistical regression analysis is to group products into those that are promoted versus those that are not. Or those that receive promotional strategy A versus those that receive strategy B versus C. You might then cluster products within these groups, and you could be sure that the products that show up in the same clusters are not there due to promotional factors (something THE COMPANY does) but rather due to demand factors (something THE BUYER does).
Notice that the act of grouping products into categories based on their promotional features is itself an act of clustering. If you have good promotional data (which most firms do not), then you could get fancy and cluster the products in 2 stages, first by promotional patterns using promotional data as attributes, then by sales patterns within promotional clusters using unit demand distribution as the attributes.
My opinions anyway.