Artificial Intelligence (AI) has many applications that we have previously explored in other blog articles and posts on our social networks.
Today we'll tell you how it can leverage the vast amount of data generated online to analyze and predict user behavior on digital products (websites, mobile apps and social networks), which can help companies deliver personalized experiences to improve customer satisfaction.
Why implement it?
- Make informed decisions: you will be able to make decisions based on objective, tangible and accurate data, stop doing it with simple intuition or without prior knowledge.
- Personalize your customers' experience: you will be able to offer them specific products, content or promotions according to their preferences.
- Increase revenues and profits: with data analysis you will be able to identify the products or services that generate a higher profit margin, so you will know where to focus your budget.
What kind of data should be analyzed?
To be able to predict the behavior of our users, we must first rely on AI to analyze the data we can obtain from them. Here are some examples:
Those that include the age, gender and geographic location of customers.
Past purchases made by the user can be useful in predicting what products or services will interest them in the future.
This may include the searches the user has performed online, the pages the user has visited and the time spent on them, and can therefore provide information on the user's interests and preferences.
Social Networking Activity
In this case, the pages that users follow, the posts they like and the comments they post on publications indicate the user's preferences and opinions.
Location is essential so that the information on users' interests and preferences can be more specific to their location.
How is the data analyzed?
Once you have all the data, the next step is to analyze it, for this there are different techniques that we will tell you about below.
It uses historical data to find relationships between variables in order to predict future behavior. It uses a dependent variable, which is the one you want to predict, and one or more independent variables, i.e., it can be simple or multiple depending on the variables used.
That is, you can predict the sales of a product based on price, advertising spending and other factors you want to involve.
Companies like Uber often use this type of analysis to predict the demand for rides that their drivers will have. How do you do it? It analyzes the historical data it has on trips, such as time, day, week, weather, and events in a given area, to adjust the supply of drivers.
These are models in which users can be classified according to certain criteria, for example, they can be groups that determine whether or not the user will buy the product.
This is achieved by training algorithms with historical data to predict them.
One company that uses these models is Amazon, have you noticed that they conveniently show you just the right suggestions of what you need to buy? Well, it is thanks to this methodology, a machine learning model analyzes your purchase history to offer you products similar to what you have bought in the past.
Time series analysis
This technique is used to analyze data that can change over time, so it is useful to identify patterns and trends, in order to make accurate predictions about future behavior.
Of course, this analysis is very useful for companies that are constantly launching products. Did you know that for Netflix this analysis is crucial, thanks to it they can predict which series or movies will be more popular in specific regions. How does it do it? By using historical viewing data of its users, i.e. demographics, history and preferences.
It is used to discover hidden patterns in a large amount of data, hence its name, you can use a lot of methods such as:
- Clustering: it consists of grouping a set of similar data according to their characteristics in order to identify patterns in the data and make decisions. The classification can be based on colors, content, texture, shape, etc.
- Association analysis: used to find relationships between data, it can be useful to find buying patterns among customers and predict which items will be purchased together.
- Text analysis: algorithms can recognize patterns in language to analyze user sentiment in the opinions they leave about products or places on websites.
Surely you are thinking that this is something complex and that few platforms use it, but it is not, are you registered on LinkedIn? Well, let me tell you that you have been a victim of data mining.
How do they use it? In a simpler way than it may seem, because in this social network we share a lot of information of our personal life, it is easy to apply the segmentation of users according to items such as: work experience, work field and skills to recommend jobs and people with whom to establish connections.
This technique is mostly used to train artificial intelligence models and make them capable of recognizing patterns in the data to make predictions about users' future behavior.
For this to work, algorithms are used that are capable of adjusting to the data being provided.
Neural networks: used to recognize complex patterns in data, can be applied for voice recognition (speech patterns, tone, language and accent in virtual assistants such as Alexa and Siri), can also be used for visual recognition (gives the ability to recognize information from images and videos) and can also process natural language (processes text written by humans, applied in chatbots and virtual agents).
Surely this technique is the one you have heard most about and probably the most used today. One of the companies that apply machine learning is Google, it does it to improve their search results and provide personalized recommendations to you as a user in the case of paid results or ads.
Applications of user behavior prediction
As you can see, data analysis can bring multiple benefits to your business, if the biggest and most successful companies apply it, why not you? Here are a few suggestions on how you can apply it.
Improve customer segmentation
With techniques such as clustering algorithms you will be able to segment your customers into precise groups according to their behaviors, preferences and other characteristics. With this you will be able to offer promotions, information, content, products or services specific to each group of customers.
As we explained in an example above, regression analysis is very useful for price optimization. That is, you will be able to analyze the history of purchases of a certain product or service and predict how to adjust prices to maximize the profit you will get from the purchases.
You can use time series analysis to analyze the demand for certain products at different times of the day or week and thus have enough inventory to meet your customers' needs.
For this you can make use of data mining, as it will help you to visualize suspicious patterns in transactions, so you can act before it happens.
These are some applications of AI in data analysis, have you already implemented it in your business? And if not, what are you waiting for?
If you want to know more about how to give a good experience to your users, we can advise you, contact us at firstname.lastname@example.org and be sure to follow our social networks.