Data Analytics – As we already know, data is an important asset owned by companies. The data includes records of financial transactions, customer databases, customer satisfaction surveys, or recorded communications via email and messages.
Unfortunately, most businesses do not understand that those data can be valuable if they know how to process it.
Through data analytics, companies are able to process and manage those data as well as generate reports that will help them increase revenue and optimize business processes.
Get to Know Data Analytics
Simply put, data analytics is a process to analyze raw data in order to gain a conclusion from those informations and actionable insights. Then, the data will be converted to a clear picture that can help companies get more precise and accurate outcomes.
Data analytics is used to predict what the future may hold and to look at current market trends, customer preferences, and other information needed to make better business decisions.
In addition, data analytics consists of several processes, such as :
- Evaluating the case
- Data identification
- Data filtering
- Data extraction
- Validating and cleaning data
- Data aggregation and representation
- Data analysis
- Data visualization
- Utilization of data analytics
4 Types of Data Analytics
There are four types of data analytics that are used across the industries. The four types are descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Those types are interconnected with one another.
Descriptive analytics is also known as the easiest and the most common data analytics type used in business today.
It uses a wide variety of data in order to provide an overview of “what has happened” in the company and how it differs from other periods in the same timeframe.
Using descriptive analytics, one can, for example, find out “who is the largest supplier of these products?” or “which activities consume the company’s most budget?”.
In this type, only simple information is available such as the number of people visiting at one time, the most popular websites, yearly price changes, total customers, and monthly sales growth.
In most cases, this information is displayed in the form of tables or graphs depending on the type of data and the desired outcome.
Through descriptive analytics, a company can identify its strengths and weaknesses. Later, this information will be used for the next management strategy.
After knowing “ what has happened within a business” through descriptive analytics, the next type is diagnostic analytics which is responsible for explaining “why did it happen”.
For example, “why did the company lose money after implementing strategy A”, or “why did strategy B fail”.
Diagnostic analytics is commonly conducted by using several methods such as data discovery and data correlation.
As part of this process, the data analyst will determine whether there are any events affecting the changes or if they are related to those losses.
Predictive analytics is used to predict future events. The analysis relies on statistical modeling, which requires additional technological resources like machine learning and also competent resources to make a foremost.
Please, keep in mind that
The prediction is only an estimation, and its accuracy depends on the quality and how detailed the data is.
In this step, the data analyst will provide actionable insights and data-driven information. The two data will be used for reference to make the next business steps.
Predictive analytics involves regression analysis, foremost, multivariate statistics, pattern analysis, and predictability analysis.
Companies tend to struggle with this kind of analysis since it requires large amounts of high-quality data. In addition, this technique also requires a deep understanding of statistics and programming languages such as R and Python.
Business applications of predictive analytics include:
- Risk assessment
- Sales prediction
- You can use customer segmentation to find leads with high conversion rates
- Predictive analytics in the customer success team
A prescriptive analysis answers questions about how a company should respond to the upcoming changes and what steps should be taken next.
Prescriptive analytics itself uses modern technology and data practices. For instance, Artificial Intelligence (AI).
A large amount of data is required for AI systems. Those data will be used for decision-making. A well-established AI system is able to communicate those decisions and even implement them.
By utilizing AI technology, business processes can be run and optimized every day. Today, most big companies such as Google, Facebook, and Apple use prescriptive analytics and AI to improve their decision-making.
The Importance of Data Analytics for Business
Businesses today will need data analytics for their business. By using it, companies are able to improve their decision-making whether it is for macro decisions, micro, real-time, strategic, tactical, or operational.
In addition, data analytics allow businesses to explore and find an effective and efficient way to run a business which leads to reduced costs.
Companies also can utilize data analytics for better decision making and identify as well as analyze market trends and customers satisfactions.
The results can be used to improve products or services or create new products to meet market needs.
The Benefit of Data Analytics
Data analytics has many benefits for business, such as:
- Simplify statistical calculations
Statistics data can be calculated and analyzed more easily using data analytics. Therefore, employees do not need to analyze statistics manually.
- Improves efficiency in calculations
The use of data analytics itself will shorten the processing time of statistical data. Therefore, employees don’t have to spend too much time doing calculations; instead, some other tasks can be performed simultaneously.
- Provide accurate and reliable outcomes
Analyses using data analytics can generate accurate and reliable data, which reduces human error in the statistical analysis process.
- Assist companies in market research
Every company needs market research since the company will be able to know market needs and capabilities in responding to their products.
The use of data analytics will simplify this market analysis, as it provides a better understanding of what has occurred as well as what is expected to happen in the future.
- Provide assistance in predicting future sales and marketing behavior.
- Evaluate marketing campaign effectiveness.
- Identify opportunities to innovate and create new revenue streams.
Optimize marketing efforts with LOKASI Intelligence
Aside from the benefit mentioned above, data analytics can also be used to optimize marketing efforts by making accurate targets and personalization.
Today, most customers want companies to treat them as an individual, and marketing personalizations can do that.
The benefit of doing this personalization is increasing customer engagement and revenue, and customer retention.
In order to do so, businesses can use LOKASI Intelligence. It is a platform that combines geographic information systems, and big data spatial by using machine learning.
Furthermore, LOKASI intelligence has telco data that are able to provide information about customer behavior, customer origin, stores they often visit, and what products they purchase.
The information will allow companies to create messages, content, or promotions based on the preferences of each customer.
Learn more about LOKASI Intelligence and our Telco data by contacting firstname.lastname@example.org or WhatsApp di 087779077750.