In this paper, we propose a new framework to estimate the occupancy dynamics of the shoppers over a whole retail store, based on the received power measurements of wireless links that are installed in only a small number of aisles, and without relying on people to carry any device. More specifically, we utilize the received power measurements collected by a small number of wireless links installed in only a few aisles of a retail store and show that we can estimate the rate of arrival of people in all the aisles of the retail store. We first show how a pair of wireless links in an aisle can estimate the rate of arrival of people into that aisle for the general case where people can have a bi-directional flow. We then propose a new framework to estimate the rate of arrival of people into all the aisles of the retail store, using the received power measurements of a number of wireless links that are installed in only a few aisles. Our proposed approach utilizes the sparsity in the spatial and temporal gradient of the occupancy dynamics and poses an optimization problem to estimate the arrival rates over the whole store based only on a very small number of wireless measurements. We thoroughly validate our framework with several experiments in three different retail stores - Kmart and two anonymous retail stores (Store-2 and Store-3), using the RSSI measurements of Bluetooth Low Energy (BLE) Chips. Our results confirm that our framework can accurately estimate the rate of arrival of people into different aisles of a retail store with minimal wireless sensing. More specifically, we show that our approach can estimate the rate of arrival of people in different aisles of a store, with an average root mean square of 0.03 people/minute, when averaged over all the aisles and all the time, and while reducing the number of required wireless links by 57%.