Big data analysis can provide
much more information than stock levels and the popularity of different products.
It can identify the most profitable products (which is not necessarily the same
as the most expensive or the most popular), allowing you to focus your
marketing efforts on these and reduce the stock of less profitable items or
abandon them entirely.
Analytics can also determine how
quickly the promoted products move out of the aisle and predict when they will
need to be replenished, reducing the number of empty shelves and dissatisfied
customers. This analysis can even be done during sales: by analyzing the first
hours of sales, it is possible to get a better idea of ??the movement of these
items, which allows you to predict more accurately the inventory levels
It is particularly important to
accurately predict inventory levels for perishable goods. Although there are
some charities that take unsellable inventory from supermarkets, the stores are
left with a huge amount of waste. Globally, 1.3 billion tons of food are wasted
each year and grocery stores are able to reduce that number. By using big data
analysis to closely monitor stock levels, it is possible to significantly
reduce overstocking without leading to bare shelves.
Loyalty programs are a good way
to collect data and use it effectively. They can provide a substantial overview
of customer preferences and buying behavior, both aggregate and individual.
This data can then be used to provide more targeted marketing and promotions to
each customer, making it more efficient.
Identifying which products, a
customer buys is not the only way to use this data, and indeed it is best not
to use this information in isolation. If a customer buys a durable product such
as peanut butter or a bathroom cleaner, for example, sending a promotion for
this item next week will be useless (and frustrating for the consumer who wants
to have this deal last week). Google Analytics allows you to determine how
often a customer buys a particular product and then offer them a deal when they
want to buy it back.