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Problem
Every day we create 2 500 000 000 000 000 000 (2.5 quintillion) bytes of data.
Every day we create 2 500 000 000 000 000 000 (2.5 quintillion) bytes of data. This will cover 10 million blu-ray disc, which stacked
on one another would measure the height of 4 Eiffel Towers.
Big part of it is
transactional data.
Big part of it is
transactional data.
How to effectively analyse it?
Solution
It is a set of techniques for automated discovery of statistical dependencies, patterns, similarities or trends in very large databases.
Data mining
Data mining
In retail data mining is used to perform market basket analysis and explore purchasing trends.
Market basket analysis
Involves finding a relationship between bought products by imaging and discovering links using simple rules, known as the rules of association.
Most common use of associacion analysis is finding products which are complement to each other and are therefore the most commonly consumed together. It positively affects sales - knowing that the client buys product A, you can show him the products B, C and D, which are usually purchased together with A.
A B
Construction of association rules
[body] [head]THENIF
Construction of association rules
[frozen pizza] kechup
EXAMPLE
[frozen pizza] kechup
EXAMPLE
If a client bought a frozen pizza there is a substantial likelihood that he will buy a ketchup.
[frozen pizza] kechup
EXAMPLE
It is not necessarily true 100% of the time
Measures of rules quality:
● Support● Confidence● Lift
This measures outline the quality of the rules - that is how much they are true and valuable from the point of the business view.
Measures of rules quality:
● Support● Confidence● Lift
Interesting are rules, where both the support and confidence are greater than some minimal values fixed by an expert from the field – we say then, that the rule of association is strong.
Support
It is the percentage of transactions containing the rule in all transactions in a retail outlet. In our example support determines the probability of buying both, frozen pizza and ketchup by a randomly selected customer in a particular store.
ConfidenceIt is the probability that the B product will appear when the A product also will be present. In our case it is likely that the customer will buy ketchup, once he bought frozen pizza.
Lift
It informs what is the impact of the sale of the product A on the sale of the product B: ● if the value of lift is 1, then we say that the
products do not affect each other; ● if it is less than 1, we deal with products
antagonistic; ● if it value is greater than 1 both products
being complementary.
Let’s calculate it!
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
200 of them bought loaves of bread...
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
200 of them bought loaves of bread...
and from those who bought loaves of bread, 50 bought a butter.
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
200 of them bought loaves of bread...
and from those who bought loaves of bread, 50 bought a butter.
The rule is: if someone buys bread, also buys butter.
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
200 of them bought loaves of bread...
and from those who bought loaves of bread, 50 bought a butter.
50/200 = 25%
The rule is: if someone buys bread, also buys butter.
Confidence:
Let’s calculate it!
Suppose that last Thursday in the supermarket were 1 000 clients...
200 of them bought loaves of bread...
and from those who bought loaves of bread, 50 bought a butter.
50/1000 = 5%
50/200 = 25%
The rule is: if someone buys bread, also buys butter.
Confidence:
Support:
Special types of associacion rules
1. Negative association rules
2. Cyclic association rules
3. Inter-transactional rules
4. Ratio association rules
Negative association rules
Discover links between the products present in the basket and those which are not. You can discover that when someone buys a Coca-Cola, he does not buy Pepsi, or if he buys a box of juice, he will not buy bottled water.
Cyclic association rules
Do not look for dependencies in a single transaction, but take into account the repeatability/cyclical nature of purchases over time. Client gets the advertising message while his activity increases, it is shown in the time, when he usually buys specific products or services. These rules are discovered by data analysis.
Inter-transactional rules
Do not look for dependencies in a single transaction but examine the relationship between purchases made in a certain period of time. These rules take into account the context of shopping (eg. time, place, customers). They aim to find sequences of events.
Ratio association rules
Besides information about purchased products they also contain information about the amount of money spent for various goods and services. As a result, it is possible to predict the value of the transaction and the purchase of specific items from the e-store's offer.
To consider
!Without having the right data which is appropriatly analysed, you will not be able to find important and high quality association rules.
Benefits
Market basket analysis allows you to:
optimize marketing campaigns by identifying the probability of purchase of individual products;+
Market basket analysis allows you to:
optimize marketing campaigns by identifying the probability of purchase of individual products;+develop effective methods for cross - selling (selling a product or a service to the customer associated with the purchase of another);+
Market basket analysis allows you to:
optimize marketing campaigns by identifying the probability of purchase of individual products;+develop effective methods for cross - selling (selling a product or service to the customer associated with the purchase of another);+develop effective methods for up-selling (selling more expensive products versions).+
Market basket analysis allows you to:
Synerise solution
With Synerise you can collect data from various points of contact and gather them in
one place. Important in market basket analysis transactional data, you will gain from both: online stores and stationary,
thanks to integration with the POS.
After finding high quality rules you can easily reach a customer with a suitable offer with a dynamic content
available on the site (in the form of banners and pop - ups) and e-mail.
You can also profile the message based on client’s segments.
If, despite a personalized offer, your customers will add products to the market basket, but do not decide to buy them, you can send them an e-mail with products from abandoned
basket, and a discount to encourage them to complete the transaction – it will take
place in an automated way.
