Customer Science: Improving the Experience with AI

In the second installment of our blog series, we explore a more profound comprehension of customer science, including the tools that can enhance expertise in this field.

Understanding Customer Science

Customer Science deals with the study and analysis of data obtained by technological tools on the interactions, behaviors and preferences of consumers with the aim of improving their experience and, consequently, increasing business profitability. This discipline is composed of three phases: data collection, study of human behavior, and data analysis and management. 

Data Collection Tools

Data collection tools play a fundamental role in the process of knowing the customer. They allow the collection of valuable information about customer preferences, behaviors, needs and opinions, which in turn helps companies to better understand their different target audiences and make decisions to support their business strategies.

Within each organization, there are different types of tools to collect data, from online survey systems, CRMs, EPRs, for sales and transaction data analysis, tools for social media data analysis such as Hootsuite, Sprout Social, HubSpot or Buffer that allow monitoring and analyzing customer interactions, tools for visit frequency analysis such as appointment management systems and queue or shift management systems that also allow to effectively manage the flow of customers at the points of care; chatbots that can be used to interact with customers in real time and collect information through automated questions and answers. 

These are just a few examples, however, it is important to choose the right tools according to the objectives and nature of your business, as well as to consider privacy and data protection aspects when collecting customer information.
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Tools for the Study of Human Behavior

Different tools help the study of human behavior such as tracking and recording technologies like eye tracking, which records eye movements to analyze attention and visual perception. There are also motion tracking devices, physical activity monitors or online electronic behavioral records such as Hotjar. 

On the other hand, audio recording systems can accurately record and store face-to-face interactions with customers. This AI-driven voice analysis evaluates various aspects of service, allowing your organization to fine-tune and improve service processes to maximize your customers' experience. 

Online surveys are often the most popular method of assessing the quality of service your customers receive and gathering their feedback in real time. Thanks to the information obtained, you can predict the behavior of your business users, propose process improvements, help identify problems or gaps in the customer experience such as long waiting times, and take corrective action.

Tools for Data Analysis and Management

Given the large amount of data that organizations have at their disposal, it has become essential to have data storage and processing platforms as well as visualization and Artificial Intelligence tools for the analysis of such data. In this case, the result of operations based on data mining and machine learning algorithms is the ability to locate correlations and identify reusable patterns and trends.

There are different types of tools with different purposes such as:

  1. Big data storage and processing platforms: Tools such as Apache Hadoop, Apache Spark and MongoDB enable efficient storage and processing of large volumes of data. These distributed platforms facilitate parallel processing and large-scale data analysis.
  2. Database management systems: To manage large volumes of data, scalable, high-performance databases are essential. Examples of popular tools include MySQL, PostgreSQL, Oracle and Microsoft SQL Server. There are also NoSQL databases, such as Cassandra and MongoDB, which are particularly well suited for storing and quickly retrieving large unstructured data sets.
  3. AI and machine learning platforms: For data analysis and implementation of artificial intelligence models, there are tools such as TensorFlow, PyTorch, and scikit-learn. These frameworks provide capabilities to train machine learning models, perform predictive analytics, and classify data based on patterns and features.
  4. Data visualization tools: Data visualization is critical to understanding and communicating the information extracted from big data analytics. Tools such as Tableau, Power BI, and D3.js allow you to create interactive and engaging visualizations that make it easy to understand patterns and trends in the data.
  5. Cloud data analytics platforms: Cloud service providers, such as Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, offer a variety of services and tools for big data analysis and management. These platforms provide scalable resources and data analytics services such as Amazon Redshift, Google BigQuery, and Azure Data Lake Analytics.

One of the functionalities of the AI-based system is precisely to study all data such as flow movements, active retail channels, branch behavior, and at all touchpoints, to provide results on trends, seasonality, and profitability of each service, to best identify and implement specific market strategies.

Benefits of Customer Science

Each discipline and area of research may require different tools and approaches, and the choice will depend on the specific objectives of the study and the characteristics of the behavior to be analyzed.

In summary, data collection and analysis tools play an essential role in the process of getting to know the customer. They provide accurate information, identify patterns and trends, enable personalization, assess satisfaction and help identify opportunities for improvement. This data is valuable for companies to make strategic decisions and improve the customer experience, which in turn can have a positive impact on customer loyalty and business success:

  • Ensure an even more personalized customer-centric approach that does not exclude the human relationship.
  • Preserve investments thanks to immediate integration with omnichannel technology.
  • Benefit from a digital transformation that involves all players in the organization, from management to the customer.
  • Generate more and more trust in a brand that renews itself by focusing on a smart approach, while keeping it simple.

At ACF Technologies, we offer a suite of tools that empower customer science and improve the user experience of your business. Adopting these solutions can result in increased customer satisfaction, data-driven decision making and the creation of competitive advantages in the marketplace.

Would you like to learn more about how to adapt Customer Science in your organization? Contact our team of business development experts to learn more.