Analytics broadly means “analysis” and is extremely important for accelerating business. However, many people may not understand the importance of analytics. Analytics can provide useful data when used appropriately, so you should consider implementing it as a method or tool right away.
This article provides an overview of analytics, why it is important, and the personnel required for analysis. We will also explain how to analyze analytics and what analytics are useful on the web, so please use this as a reference.
What is analytics?
First, let’s take a look at an overview of analytics. Analytics refers to techniques and tools such as statistics, machine learning, and predictive modeling. Analytics is used by businesses to measure data from a comprehensive perspective, discover patterns for future improvements, and identify bottlenecks.
Analytics has become extremely important in accelerating business these days. This is because most companies have stepped into the IT/web field, and there is a need to visualize and improve all results as numbers. Companies in their industry that boast the strongest ability to attract customers always use analytics to analyze numbers. By using analytics, you can gain a detailed understanding of your company’s improvement points and current situation, allowing you to implement higher quality measures than your competitors and attract more customers.
Types of analytics
When it comes to analytics, it can be broadly divided into three types.
- Descriptive statistics
- Predictive analytics
- Directive analytics
Let’s look at each type in turn.
Descriptive statistics
Descriptive statistics is the world’s oldest form of analytics. Descriptive statistics are summary statistics that quantitatively summarize data and help you analyze why events occur. For example, a familiar example is measuring the number of clicks on a web page. Descriptive statistics are also useful when comparing sales and production numbers.
Descriptive statistics were first used by Sweden in 1749. At the time, Sweden used descriptive statistics to create demographic statistics.
Predictive analytics
Predictive analysis is a type of analytics that has been frequently used in recent years. Predictive analytics, as the term suggests, helps predict a user’s next action. It is possible to quickly analyze thousands of pieces of data at once, and from that analyzed data, it is possible to identify the actions that users are most likely to take.
More and more companies are incorporating predictive analytics into their business processes, allowing them to determine whether the measures they are implementing are appropriate.
Directive analytics
Directive analytics is the newest technology in analytics. In recent years, attention has been rapidly increasing. Analytics can provide recommendations for decision-making, which is useful when determining management decisions and business direction.
However, prescriptive analytics requires big data. In order to use big data, your company must either have a customer list of several million people or have a budget of tens of millions of yen or more per year, so it is basically analytics for large companies.
Why analytics is important
Analytics is important because it helps us make decisions that don’t rely on intuition. If the decision-maker or project manager makes a decision based on intuition, it will not be reproducible even if it is successful because each person’s values and analysis of users are different.
In addition, competitors are increasingly using analytics to accurately analyze users and take optimal measures. Therefore, if your company does not utilize analytics, you will never close the gap with your competitors.
However, by using analytics, you can make decisions based on data rather than relying on intuition, and business success becomes more repeatable. The use of analytics is extremely important, especially in today’s age where IT and the web are developing, as it allows us to determine things like “why something happened” and “what will happen next”.
Personnel required for analytics analysis
Even though we use analytics, various people are required for analysis. Business growth can be further accelerated by assigning the person in charge of the analysis in detail. If your company handles big data, be sure to have people in charge of the following roles.
- project manager
- data scientist
- Analyst
I will explain each in turn.
project manager
A project manager is a person who has decision-making authority for the entire business. Based on the information received from the person in charge of analyzing analytics, we consider and steer the next measures. Since these personnel have a large impact on the success rate of a project, in most cases people who have been involved in the industry for many years or external consultants are assigned.
data scientist
A data scientist is someone who performs
segmentation
using highly complex descriptive analysis. Simply put, they are data analysis professionals who can perform advanced analyses. Many companies hire external data scientists for consulting. These people are essential when leveraging big data or using prescriptive analytics.
Analyst
Analysts are people who analyze detailed numbers and data on a daily basis. In addition, we also report on daily numbers and provide project managers with measures to implement. We also collaborate with data scientists to perform more useful data analysis.
For small and medium-sized companies that do not handle big data, we recommend that you always assign two people: an analyst and a project manager.
How to analyze analytics
From here, I will explain how to analyze analytics from the standpoint of the analyst mentioned above. The steps required are:
- See the big picture
- Decide which metrics to analyze
- identify bottlenecks
- Develop an improvement plan
- Start improvement by specifying a period
- Analyze the data again
Let’s look at each in turn.
See the big picture
First, get a complete picture of what Analytics can analyze. This is to make the task of determining the next indicator to be analyzed more efficient. When it comes to individual metrics, if you don’t see the big picture, you can’t determine how much each individual metric contributes to the business as a whole. Therefore, first of all, let’s understand the big picture and check the situation in each case.
Decide which metrics to analyze
Next, decide which metrics to analyze. Examples include the click rate for each button on your website and the detailed age of users. When deciding on metrics, it is important to focus on metrics that have the most impact on your company and overall business objectives.
For example, if your company’s goal is to “increase awareness” of your product, it would be extremely important to analyze the age of your users. This is because awareness often spreads from users who are old enough to find that their products can solve their problems or find them useful.
As in the example above, determine the metrics to analyze by working backwards from your company’s objectives.
identify bottlenecks
Once you have decided on the metrics to analyze, identify bottlenecks in the metrics. In most cases, when you first begin your analysis, there are many situations that can be improved. Let’s find out why the numbers are bad and what parts are causing the numbers to be bad.
Develop an improvement plan
We will develop improvement plans for the bottlenecks mentioned above. To give a simple example, if the click rate of a button is poor, a suggestion for improvement would be to change the button design or wording. It is also important to create a plan that includes the reasons why the improvement is useful.
Start improvement by specifying a period
Next, specify a time period and start improving. The purpose of specifying a period is to make comparisons easier when analyzing the data again. Basically, improvements are often made by specifying a period of about 3 days to 1 month, although this varies greatly depending on the amount of data.
Analyze the data again
Finally, let’s analyze the data after the improvements. Again, in most cases there is room for improvement, so we start again by identifying bottlenecks. By repeating the same flow, the overall business numbers will gradually improve.
Google Analytics is useful on the web
In this article, we have explained about analytics, but Google Analytics is extremely useful in the web domain. Google Analytics is an access analysis tool provided by Google that allows you to analyze data after users visit your website.
By issuing parameters, you can analyze the click rate for each button, as well as the bounce rate and exit rate for each article. If you have not implemented Google Analytics on your current
homepage
or
owned media
, please consider implementing it now.
summary
In this article, we have explained about analytics. By using analytics, it is possible to uncover correlations and patterns across your business and data. This will make it possible to improve the weakness of business situations that rely on intuition.
Also, if you are developing a web business, Google Analytics is extremely useful. Why not start by implementing analytics that is appropriate for your company’s situation?