1. A method for optimizing transactions for merchants co-located with charging stations, the
5 method comprising:
detecting a transaction on a payment terminal at a charging station;
determining a footfall at each of one or more merchant stores in vicinity of the charging
station based on the transaction on the payment terminal, user information, time of the transaction,
historic demand trends at the charging station, and historic demand trends at each of the one or
10 more merchant stores using a footfall prediction model;
determining a dynamic discount for products at the one or more merchant stores based on
at least one of the footfall, average purchase product price, discount offered, and cumulative
revenue generated at each of the one or more merchant stores during a current time period; and
transmitting the determined dynamic discount for the products at the one or more merchant
15 stores to a device transacting at the payment terminal.
2. The method as claimed in claim 1, wherein determining the dynamic discount for the
products at the one or more merchant stores comprising:
determining the cumulative revenue generated at each of the one or more merchant stores
20 during the current time period;
determining whether the cumulative revenue at each of the one or more merchant stores is
greater than maximum threshold revenue; and
determining a first dynamic discount for the products at the one or more merchant stores
based on at least one of the footfall, the average purchase product price, and the discount offered
25 when the cumulative revenue at the one or more merchant stores is not greater than maximum
threshold revenue.
3. The method as claimed in claim 2, further comprising:
determining a second dynamic discount for the products at the one or more merchant stores
30 based on at least one of the footfall, the average purchase product price, and the discount offered
when the cumulative revenue at the one or more merchant stores is greater than maximum
threshold revenue.
4. The method as claimed in claim 1, wherein the method further comprises building the
35 footfall prediction model, and steps of building comprising:
28
receiving the historic demand trends at the charging station, and the historic demand trends
at the one or more merchant stores from a database;
determining a footfall matrix for a time slot of day and associated discount offered at each
of the one or more merchant stores based on at least one of the historic demand trends at the
5 charging station, and the historic demand trends at the one or more merchant stores; and
learning relationship between the time slot of day and the associated discount offered at
each of the one or more merchant stores using the footfall matrix to build the footfall prediction
model.
10 5. The method as claimed in claim 1, wherein the dynamic discount is offered to a user using
the device for transacting at the payment terminal during charging or refuelling of a vehicle.
6. The method as claimed in claim 1, wherein the charging station is one of an Electric
Vehicle (EV) charging station or a fuel station.
15
7. The method as claimed in claim 1, wherein the user information comprises at least one of
user transaction frequency at the charging station, user product preferences, and user shopping
frequency at the one or more merchant stores.
20 8. The method as claimed in claim 1, wherein the historic demand trends at the charging
station comprises at least one of a station transaction time, a station transaction amount, and
charging duration.
9. The method as claimed in claim 1, wherein the historic demand trends at the one or more
25 merchant stores comprises at least one of a store transaction time, a store discount, a store discount
range, time slot of day, a merchant identifier, and a store location.
10. The method as claimed in claim 1, wherein transmitting the dynamic discount for the
products at the one or more merchant stores to the device is via a short messaging service or a
30 multimedia messaging service.