Determine which factor is responsible for a change in a top-line business metric?

Are there any techniques for determining which factor(s) is (are) responsible for a change in a top-line business metric?

E.g., revenue drops - but was it because of a drop in global visitors, or perhaps a drop in conversion rate at the London store, or maybe there were heavy discounts on the weekend, etc.

So far I've explored Value Driver Analysis, Sensitivity Analysis, Root Cause Analysis, Factor Analysis, but I'm not sure if they're useful.

Example

I have $n$ retail stores, each contributes to the company's total revenue.

Revenue is a product of visitors, conversion rate, and average order value.

            store-revenue
          /              \
         /                \
     purchases     average-order-value
    /         \
   /           \
visitors   conv-rate

$$\mathrm{Total\,Revenue} = (N_1 \cdot \mathrm{CR}_1 \cdot \mathrm{AOV}_1)\,+\,(N_2 \cdot \mathrm{CR}_2 \cdot \mathrm{AOV}_2)\,+\,\ldots\,+\,(N_n \cdot \mathrm{CR}_n \cdot \mathrm{AOV}_n)$$

Where $N$ is visitors, $\mathrm{CR}$ is conversion rate, and $\mathrm{AOV}$ is average order value. Subscripts $1 \ldots n$ refer to the store.

If there is a drop in total revenue, I'd like to attribute it to an underlying reason. It could be due to:

• a global drop in a metric over all stores (e.g., Christmas Day might draw visitors away from high street shops)
• a local drop in a metric in one store - e.g.,
  • a drop in visitors (e.g., a road closure prevents people from getting to a particular store)
  • a drop in conversion rate (e.g., many employees self-isolating at a particular store means there are fewer floor staff to close sales)
  • a drop in average order value (e.g., a promotional discount distributed outside a particular store)

So how do I determine whether it was $N_1$ that caused the drop in revenue, or perhaps $\mathrm{CR}_2$, or total $AOV$, etc.?