Information is generated in the network every minute. For the benefit of companies, the same huge amounts of data open up enormous opportunities to optimize their department store. However, in order to be able to recognize decisions and relationships, certain processes are inevitable that make Big Data tangible. the statistical data analysis structures, groups and correlates information that can then be processed visually.
In this fee, you will be familiar with which methods of statistical data analysis are available and which processes are used in the marketing probably have to do with it.
Statistical data analysis helps to make strategic decisions
The statistical data analysis takes place in companies in different departments make data-driven decisions. Especially in online marketing, enormous amounts of data are generated that contain valuable information and can now help to get to know your own target group better, to recognize optimization potential in processes and to derive measures.
In the context of that data analysis, it's about using statistical methods to provide information from information to advantage and to visualize it accordingly. At the point of the individual raw data, information is logically provided that z. Hd. These companies are of validity. Reports or dashboards visualize the information and bring together easier access. The data analysis now helps to make strategic decisions on the basis of facts.
Data analysis is often carried out using business intelligence software. The information is collected, structured and correlated in order to answer company-relevant questions. With the help of Business analytics methods on this foundation, forecasts of trends and likely outcomes can then be made.
In the controversy over business intelligence, business analytics deals with the continuation of future-oriented solutions. With regard to the information obtained, future-relevant relationships are logically used in order to make forecasts and assess possibilities and risks.
the Methods that data analysis can be summarized in the following approaches:
exploratory data analysis,
descriptive data analysis,
diagnostic data analysis,
prescriptive data analysis and
predictive data analysis.
Which is Exploratory Data Analysis?
In the context of that exploratory data analysis, information is analyzed and evaluated, to which it is there are no or little known connections. It thus forms a merging approach to investigate completely new facts and to identify previously hidden structures or abnormalities.
In the context of that method, hypothetical claims are made in order to be able to draw conclusions about certain causes. This is a common use case of exploratory data analysis Data mining, in the context of making huge amounts of data accessible and examining possible product samples and relationships.
Which is descriptive data analysis?
Descriptive or descriptive data analysis (secondary: descriptive analytics) uses information and information from that past with the aim of answering the question “Which happened?”. The findings from that descriptive data analysis only help now, Answer decision-making questions and provide absolute information.
For example, in the Marketing Report analyze whether your social media subscribers have increased or your website is generating regular organic traffic. Descriptive analytics, on the other hand, does not answer causal relationships. A combination with other data analyzes can therefore be beneficial in order to additionally find out why certain events have occurred.
What is the diagnostic data analysis?
The diagnostic data analysis (secondary: Diagnostic Analytics) tries, as the name suggests, to diagnose e.g. Hd. To ask certain facts. In other words it is to be found out why a particular event occurred. For this purpose, information from that past is comprehensively analyzed and brought into context.
The same cause research and pattern recognition should clarify the question of why something happened. For example, you could find out why your number of social media followers is stagnating. Possible initiators are, for example, irregular postings or content that is not tailored to the target group.
What is predictive data analysis?
The predictive data analysis (secondary: predictive analytics) deals with Predictions about facts in that future. For this purpose, the predictive assessment uses the results of those exploratory, descriptive and diagnostic calculations. Secondary algorithmic procedures on the foundation of artificial intelligence are used umpteen times.
The connections give away clues for this purpose, to predict future forecasts and trends. For example, it can be analyzed how customers will react to a new product. For this purpose, information from previous product launches is evaluated and compared with one another. How precise the forecast of predictive analytics is always depends on how much information is available from previous, comparable situations.
What is prescriptive data analysis?
The prescriptive or prescriptive data analysis (secondary: prescriptive analytics) uses internal and external information. Past and current information is also included in the calculation. It is precisely this form of data analysis that is used to determine future measures and processes. With the help of prescriptive analytics you can, for example, examine which optimizations e.g. Would ensure an increase in your website traffic.
Above all, AI-supported systems and simulations are used for this purpose. Thus, prescriptive data analysis is e.g. For companies, this is a very time-consuming method and should only be used in the context of appropriate resources.
Marketing procedures: multivariate data analysis, cohort analysis and churn assessment
In marketing, data analyzes are increasing with that digitalization become essential. With the help of individual data, valuable links can be established and conclusions can be drawn about the performance of campaigns, your own website or, secondarily, your social media strategy. In addition to the classic methods of data analysis, further procedures are developed that specially tailored to this marketing are. Examples for this purpose are:
Multivariate data analysis:
The multivariate data analysis is comparable to A / B testing. In contrast to A / B tests, however, this method is used when at least three different variables are considered. The aim is to recognize connections in the information and to plan optimizations.
In marketing, multivariate data analysis is mainly used e.g. Hd. Usability analyzes of websites utilized. The entirety of information that a website visitor leaves behind while surfing can be e.g. The calculation should be considered - this includes, for example, the length of stay that Bounce rate and this scrolling behavior.
The cohort analysis refers to a calculation of different customer groups. Here, people who carry out similar actions at similar times are grouped into so-called cohorts. This design of that customer analysis can, for example, now help the Understand the customer lifecycle and derive optimization measures from this. For example, it can be investigated at what intervals a merging cohort makes new purchases.
Especially in SaaS companies based on long-term orders, the churn assessment is an important method to determine the Evaluate the churn rate of those customers. Factors are analyzed that induce customers to leave a subscription, for example. Reasons can be, for example, defects in products, poor quality of customer service or image-damaging content.
Lost customers naturally have an enormous impact on the expected turnover, which is why companies use this churn assessment to try to keep the churn rate as meager as possible.
In the context of that ABC assessment, customers will be according to their Value z. Hd. This company is divided. This classification is rarely useful in order to implement measures in a way that conserves resources and is customer-oriented.
The ABC assessment is now based on this Pareto principle. As a result, 20 out of a hundred of those customers are z. Hd. 80 out of a hundred responsible for sales. Finding these 20 out of a hundred and tailoring the corresponding solutions exactly to them is the aim of that ABC assessment.
Conclusion: valuable insights through data analysis
With this statistical data analysis, past and future developments and relationships can be understood and anticipated. The information entails direct power on business decisions and the definition of measures. Line in Online marketing The various methods now help to constantly question this business model and uncover optimization potential.
Cover picture: twomeows / iStock / Getty Images Plus
Originally published on the 1st fourth month of the year 2021, updated on the fourth month of the year 01 2021[ad_2]
Original source Hubspot