5 Technologies That Enable Customer Data Analysis
Data analysis is the systematic application of statistical and logical techniques to evaluate data to derive meaningful conclusions. In customer data analysis, information on customer behaviour is used to make business decisions that identify, attract, and retain the most profitable customers.
When companies better understand a customer’s buying habits and lifestyle preferences, they can better predict their behaviours and improve the customer journey.
Customer analytics technologies are used to segment buyers into groupings based on behaviour, determine general trends, and tailor marketing campaigns to those specific segments. More than just analyzing data, these technologies are used to put the data to fair use by building better campaigns, making more informed decisions, and predicting customer behaviour.
These technologies, which several companies are using to enhance their processes and performance are listed below.
#1 Predictive Analytics
The most commonly used technique, predictive analytics, utilize models to forecast what could happen in specific scenarios. After gathering customer data, predictive analytics helps turn it into actionable goals by predicting future events or behaviour based on historical data. The model is then applied to the current data to predict what would happen next by finding critical patterns and trends.
Predictive modelling techniques help guide business decisions and generate clear, actionable outcomes to support achieving a given goal. Such goals include less material waste, less stocked inventory, and manufactured products that meet specifications.
Predictive models help different businesses to expand their customer base by engaging and appealing to the most valuable customers. When correctly done using structured and unstructured data inputs, predictive analysis can be a confidence booster for customer experience investments. The models also help in determining buyer’s responses and in promoting cross-sell opportunities.
#2 In-memory Databases
In-memory database technology (IMDB) uses a technique to provide more immediate access to data stored on its main memory. Also known as a main memory database system or memory resident database, IMDB is a database management system that depends on the main memory for computer data storage.
The IMDB technology allows a specific dataset to be stored in RAM in its entirety so that it. If a data analytics solution can process data stored in memory, it can perform faster than data stored on a hard drive. IMDBs differs from those database management systems that employ a disk storage mechanism in that IMDBs are faster than disk-optimized databases. This speed is because the internal optimization algorithms are more straightforward and therefore execute fewer CPU instructions.
This reality means that the processing data creates a scenario in which real-time information processing is a possibility. With these databases’ availability, most parties interested in in-memory databases and their application will have a much easier job of pulling this off. This technology helps distribute large quantities of data across system resources such as Dynamic Random-Access Memory (DRAM), Flash Storage, or Solid-State Storage Drives (SSD) of a computer system. Software vendors that offer in-memory database technology include Oracle, Microsoft, and IBM.
#3 Artificial Intelligence
Having been nearly around since the inception of computers, Artificial Intelligence (AI) has become more prevalent in recent years. Divided into two subsets: machine learning and deep learning, Artificial Intelligence is the complex software that performs tasks similar to human brains. These tasks include learning to solve problems, recognizing the nuances of speech, or exhibiting some form of human-like creativity. The software does this by sensing and responding to a feature of their environment.
Artificial Intelligence is useful and very relevant in the field of customer data analysis. It can provide data analysts with new insights, and the average accuracy rate on the majority of AI predictions also increases. The main interest of AI technology is its ability to make decisions that can provide a reasonable likelihood of achieving a definite goal.
#4 Machine Learning
The concept of machine learning is enormous in the customer data analysis industry. Machine learning is a broad technology of its own, though often seen as a part of Artificial Intelligence. In machine learning, a model based on a given data performs accurately on new tasks based on experience garnered.
Machine learning techniques can automatically identify new patterns, thereby shining a spotlight on new areas of interest as they occur in real-time. It allows systems to look at historical data, recognize patterns, build models, and predict outcomes. As a result, it is closely associated with predictive analytics.
#5 NoSQL (Not Only Structured Query Language) Database
The NoSQL incorporates a wide range of different database technologies that are developing to design modern applications. It stores unstructured data and delivers faster performance. It also proffers flexibility while dealing with varieties of data types on a colossal scale. To better work with NoSQL, it is beneficial for one to know SQL.
NoSQL covers the integrity of design and uses data structures different from those accounted by default in relational databases. Examples of NoSQL databases include MongoDB and Cassandra. MongoDB offers a natural alternative to the strict scheme of relational databases. It is also able to handle wide varieties of data types in large volumes. Cassandra is an open-source NoSQL that manages massive volumes of data over numerous commodity servers. Though free, this database requires some extra effort in troubleshooting and maintenance.
Conclusion
Customer data analysis is the systematic examination of a company’s customer information and customer behaviour. It is a process that involves examining data to understand customer’s actions and purchase decisions better. The analysis is an essential component of a company’s business plan since the results can guide product manufacturing and improve marketing decisions. You would want to meet customer needs better and keep engagement levels and sales high.
Without the knowledge of how to translate data into actionable insights, customer data becomes useless. However, without large amounts of accurate data, any sense from the analysis could be wildly inaccurate. By using specific technologies to measure and analyze extensive data, organizations can create successful customer interactions.
- 5 Technologies That Enable Customer Data Analysis - March 16, 2021
