# Market Basket Analysis [Association Analysis]

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Market basket analysis (also called *association analysis*) is one of the most
important methods used to uncover relationships between items. It looks for combinations
of items that frequently occur together in transactions. In other words, it enables
retailers to identify relationships between the items that customers buy.

## What does association analysis do?

Let's say you have set up your own online clothing store. Your goal now is to achieve the highest possible turnover with this store.

In order to achieve the highest possible sales, you naturally want every customer to buy as much as possible. One way to motivate the customer to buy more products is to suggest more products. The big question now is: Which product do I best suggest to the customer? This is where market basket analysis or association analysis comes into play.

The market basket analysis gives an answer to the question: How likely is it that a
customer buys *product A* if he already has *product B* in his market basket.

The market basket analysis tells you which products or goods are often bought together.
So if a customer already has *jeans* and shoes in the shopping cart, how likely is
it that this customer will also buy a *shirt*, *socks* or a *t-shirt*?

## Market Basket Analysis Example

To calculate a shopping cart analysis, you need a list of past purchases, where you can see which products were bought together in one purchase.

So you have the respective products listed and each row is a transaction. Let's say that
is your example data, you have the products *jeans*, *shirt*,
*jacket* and *shoes*.

Each row is a transaction or a purchase. "1" means bought, "0" means not bought. So the
first person bought *jeans*, *shirt* and *shoes*.

Now, so that we have results that we can interpret, let's first calculate a market basket analysis using DATAtab for this data. To do this, go to the market basket analysis calculator on DATAtab and copy your data into the table.

Now we can specify a minimum support and a minimum confidence. For this data DATAtab issued us these association rules:

The association rules are in the form: if the products under Lhs (Left hand side) are present in a transaction, then the products under Rhs (Right hand side) are also present with some probability.

## Market Basket Analysis results interpretation

We look at the results of the basket analysis using the first set of association rules.

### Frequency

The frequency in the results table tells us how often the products under Lhs and Rhs
occur in a transaction, so in our case, how often does *shirt* and
*shoes* occur in a transaction.

So let's just count through how many transactions both occur in, which is 8 transactions.

### Support

The support tells us what percentage of all transactions correspond to this one, or in
other words, how likely it is that *shirt* and *shoes* will occur in a
transaction. So we just divide the frequency by the number of all transactions.

19 transactions we have in total, so we get 8/19, which is equal to 0.42. The
probability of *shirt* and *shoes* occurring in a transaction is hence 42
percent.

### Confidence

The confidence tells us, if the products under Lhs are in an order, how likely it is that the products under Rhs are then also in the shopping cart.

In our example this means: ¿ How likely is it that if *shirt* occurs in the cart,
then *shoes* are also in the cart? We can calculate this by dividing the frequency
of *shirt* and *shoes* by the frequency of *shirt*.

### Lift

And finally, the lift. The lift indicates the factor by which the probability of buying
the products under Rhs increases if the products under Lhs have already been bought. So,
in our example, if the product *shirt* is in the shopping cart, it is 1.27 times
more likely that *shoes* will be purchased than if the product *shirt* is not
in the shopping cart.

## Market basket analysis and data mining

Shopping cart analysis is a method from the field of data mining. Depending on how much data is available, the analysis can be very computationally intensive.

However, with the Apriori algorithm, there are very effective methods to efficiently determine the association rules.

## Critical note on the market basket analysis

Let's say your market basket analysis shows that if a person buys *jeans* and
*shoes*, there is a high probability that they will also buy a *shirt*. Now
you suggest a *shirt* to all customers who buy *jeans* and *shoes*. This
increases the probability that a *shirt* will be bought under this condition and
another future market basket analysis will be biased.

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