So, there's some sort of function mapping from (cities,widgets)->sales, plus randomness. In general, I would say use some standard machine learning technique, but if you know the function is linear you can do it directly.

So:

sales=constant x cityvalue x widgetvalue + noise

d sales/d cityvalue = constant x widgetvalue

d sales/d widgetvalue = constant x cityvalue

(all vectors)

So then you pick random starting values of cityvalue , widgetvalue, calculate the error and do gradient decent.

Or just plug

Error = sum((constant x cityvalue x widgetvalue - sales)^2)

Into an optimisation function, which will be slower but quicker to code.