How to Use Customer Data for Machine Learning

Machine learning (ML) is becoming increasingly important, and those who quickly grasp the functionality of ML will gain an advantage over their competitors. Many companies of varying sizes use purchased technologies to develop marketing strategies, but not all market players accurately assess the suitability of their databases for ML.

Progress does not stand still: companies will be able to derive real benefits from machine learning technology if they learn to collect high-quality source data. The quality of input information directly affects the results of ML.

How to unlock the full potential of machine learning

To ensure that machine learning models function effectively, it is crucial to load them with a large amount of data. The more comprehensive the information you provide, the more productive the results from machine learning will be. Incorrect or contradictory data can lead to inaccurate predictions by the models.

The accuracy of predictions is important for making informed managerial decisions. "Good data" reduces the risk of errors.

Machine learning algorithms rely on the information they are provided to continue learning and improving. Information is also necessary for adapting the model to new conditions and predicting events in current realities.

Terms and definitions in machine learning

Machine learning algorithms: These are code elements designed to explore and analyze incoming data. Algorithms can be appropriately referred to as the "brain" of machine learning.

Machine learning model: This is a file that is trained to recognize correlations and patterns. Models are trained based on sets of data using a specified algorithm.

The learning process in machine learning: This is an ongoing process of adjusting the model based on incoming information. Training concludes when the model creator is confident in the accuracy of the predictions.

Facial recognition is a widely used function of machine learning. Models are trained on thousands of photographs for an extended period of time.

Training on big data: This is a branch of machine learning that specializes in processing large volumes of information. Without Big Data, achieving reliable results is challenging.

Stages of machine learning

Let's suppose ML scenarios may seem standard, but the application of this technology improves the customer experience and enhances personalization. Additionally, it improves audience segmentation, enables customer churn prediction, and generates quality analytics. The more data you have, the more accurate the forecasts. Therefore, companies need to choose a reliable customer data management platform to accumulate sufficient information. We recommend CDP Altcraft Platform.

The first stage of machine learning is the processing of incoming information. It concludes with obtaining a refined dataset. In this initial stage, it is important to identify relevant sources of information. Then, tools are applied to quickly process, validate, and cleanse large volumes of data.

Otherwise, this stage can be called 'data cleaning,' which takes up a significant amount of time and effort. If the input information is in the wrong format or lacks proper context, the training will be incomplete, and the model will not produce accurate results.

The second stage is the processing of test datasets. It is at this step that the time and resources invested in the first stage begin to pay off. Machine learning algorithms come into play, and the information from the previous stage becomes the 'test set.' The sets are constantly changing as the process is iterative. Data analysts carefully monitor the model's response to new tests throughout the process and adjust the accuracy of predictions.

The third stage essentially involves testing the machine learning model in real-time. It begins when the model demonstrates reliability in the previous phase.

The route of data

The data journey involves a chain of data transformations, including the collection and processing of input information using machine learning algorithms to enable the model to make predictions and decisions.

Step 1: Data collection

Information is collected from various sources such as databases, pixels, platforms, social networks, etc. It is important to have relevant and reliable information about the problem that the ML model is intended to solve.

Preparing customer data for machine learning is not an easy task. This question becomes particularly acute when multiple sources of information are involved, both external and internal.

For companies, input information includes user activity on the web, data on purchases made, interactions with customer service, and monitoring of customer activity in mobile applications.

The process is further complicated by new regulatory acts (such as GDPR) that require companies to obtain prior consent for the use of customers' personal data. Without the users' consent to use their information for machine learning, it cannot be used.

Step 2: Data standardization

Collected data is useless without standardization: it is converted into a format that can be processed by machine learning algorithms, typically CSV or JSON. It is important for the data to be consistent. Duplicate or outdated information is removed, and missing values are compensated for. Data analysts spend a significant amount of time cleaning and harmonizing "dirty" data.

Event specification involves real-time quality checks on incoming data sets. When a new event occurs, the verification is performed. This ensures the cleanliness and alignment of the information for machine learning purposes.

Why do we need data infrastructure?

Reliable information is crucial not only for machine learning operations but primarily as a foundation for making informed, data-driven management decisions. Decisions that are not based on data have a high probability of resulting in financial losses.

Customer information is collected and analyzed to improve products according to consumer needs and expectations. In addition to the product itself, channels of promotion and communication with customers are adjusted based on the gathered insights.

A strong information foundation leads businesses to growth and enables the automation of routine tasks. The freed-up time allows employees to focus on developing conceptual solutions. The result is increased workforce efficiency, savings in the marketing budget, and growing company profits.

Importantly, without proper data collection, organization, and management, companies will not be able to comply with GDPR and CCPA requirements.

Handle your data intelligently with the help of CDP Altcraft Platform. The platform also includes an ML module called Optimal Sending Time. It allows you to determine the best time to send emails, facilitating better customer engagement. This can significantly enhance user involvement with your messages.