Cluster analysis is a method of analysis in which objects are divided into groups according to important criteria. A simple example: products are arranged in rows in a supermarket, and each is signed — “vegetables”, “meat”, “cereals” and beef will never get to rice.
Such division of objects into groups is called clustering.
The groups (or segments) obtained after clustering are studied. Let’s say the analysis algorithm has identified several groups of clients. One of them includes people who buy a product twenty times a year, the other — those who buy it once a year.
A marketer can study this cluster and understand how to make people buy from it more often.
Cluster analysis is useful wherever you need to allocate groups of clients and objects. For example, banks use analysis to determine a credit rating, and insurance companies use it to detect fraudulent transactions.
How cluster analysis is used in marketing
Collecting and storing a lot of data about your customers is useful for business. But when you decide to analyze this data, you will realize that it is impossible to study information about each client separately.
Our brain is not able to process such a large amount of information, and it is also impractical.
It is impossible to study all the information at once, since the data varies greatly from client to client. You need to find a middle ground between analyzing everything at once and studying each client individually. Therefore, it is necessary to divide all clients into several groups.
This way it will be possible to understand what different types of customers need.
This is what cluster analysis is for — customers are segmented according to one or more criteria. If there is a lot of customer data, machine learning algorithms are used for cluster analysis.
Here are some examples of how clustering is used in marketing.
Description of the client’s behavior. Users can be clustered based on different data:
- – how often and how deeply do they browse the site;
- – how often do they buy and for what amount;
- – what products are bought;
- – how they behave offline.
Description of the purchase process. You can cluster clients according to different criteria. For example:
- – when you bought a product or service;
- – who bought the product — the customer or someone for him;
- – in which store you bought the product.
SEO. Cluster analysis can be used to analyze keywords — divide them into groups depending on the rating, relevance, complexity and other parameters.
You can also use clusters in your work:
- – to set up retargeting and remarketing;
- – adjust advertising and marketing messages;
- – personalize the user interface;
- – personalize the product to meet the needs of customers.
Why use cluster analysis when there are other methods
The main task of cluster analysis is segmentation. You can also divide objects into groups manually, but cluster analysis allows you to work with a large amount of data.
Services like Google Analytics has the function of manual segmentation. There you can select the traffic and user segments of interest and analyze them.
But these tools have limitations. It is convenient to work with them if there is not enough user data. And when there are a lot of them, it becomes difficult to process all the information — for example, to keep data in your head at the same time about a lot of segments made up of hundreds of parameters.
Then cluster analysis comes to the rescue. Automated systems for working with data can conduct it themselves, you will only have to evaluate the segments. Such systems free up resources and can use more parameters for analysis than a human.
Pros and cons of cluster analysis
Cluster analysis is not an ideal solution for everyone. Here are the pros and cons to keep in mind.
- – the data is easy to visualize and interpret;
- – the analysis is easy to scale to millions of records;
- – the system is dynamic — if you change the data, the clusters will also change.
- – different algorithm executions can give different results;
- – when using the k-means algorithm, the marketer must determine in advance how many clusters there should be;
- – before applying cluster analysis, you need to prepare the data.
The main thing about cluster analysis is that
- Clustering is suitable for activities where it is important to divide data into groups.
- If there is not enough data, cluster analysis is not needed — you can use Google Analytics or other simple analytical tools. Cluster analysis is suitable when there is a lot of data.
- Clustering of data occurs using algorithms. They divide all objects into groups based on the distance between the “points”.
- It is important to prepare detailed data and collect them in one place for clustering.
- Cluster analysis is good because it can be used to easily analyze a large amount of data and visualize them.