Process of data analytics in E-Commerce
ata analysis in an E-Commerce projects is not one-dimensional, it unfolds in many steps.
1. Data Requirements Specification
During this stage, data is grouped. Once your audience visits your website, they may be divided by age, education, income, relationship status, etc. These details help you to know your customers inside and out.
Consumer behavior is vital for conversation and income: the better you know the reasons why your customer buys something, the better chance you get on repeating the sale.
2. Data Collection
At this stage, you are ready to dive into further analysis of the user’s data. It’s up to your company to decide what information to collect.
Browser cookies, web databases and ad interactions are some of the most common ways further details are gathered. Data analysis for E-Commerce allows you to predict your customer’s behavior.
3. Data Processing
Modern data analytics in E-Commerce software organizes information through an automated process. On the back end, information is organized into rows and columns that become structured into graphs and charts. Therefore, it will be easy for your team to analyze the information, choose what is best needed, structure and process it.
4. Data Cleaning
This follow-up audit eliminates duplications and corrects errors before the data is ready to be analyzed. This step is especially crucial when working with financial data in the E-Commerce field. Without precision processing of data analysis for an E-Commerce project there could be losses and other risks for the business.
5. Data Analysis
This is the step where clean data is presented and ready to be analyzed. Looking at the data sets can help you draw conclusions that will help you make more informed business decisions.
At this stage, you need AI systems or manpower to help you transmit the information. As a result, you will get the full data of your current business situation and ways to improve it.
4 Types of data analysis in E-Commerce projects
here are four ways to make sense of data once it has been formatted for reporting.
1. Descriptive Analysis
It is the foundation of data analysis that serves as the backbone of dashboards and business intelligence tools. It also takes a close look at the times that something happened and when and where it happened. The importance of this type of analysis is that it allows you to see all the features of the object.
For example, an analysis of the users’ experience can discover that they stick on the banner. So, eCommerce data analysis can help you to increase the conversion.
- Key Performance Indicator (KPI) dashboards. It’s the biggest use that describes how a business is performing based on chosen benchmarks.
- Monthly revenue reports. It’s needed to analyze streaming income and forecasting.
- Overview of sales leads. It is provided in order not to lose the lead and to bring it to sale.
2. Daignostic Analysis
This type provides a deeper understanding of business processes. It helps companies to create clear connections between data and behavior patterns. By using it, your team can create a better strategy, which will be based on the experience of the previous actions.
For example, you can determine how to increase the efficiency of your team; compare how much time they spend on different tasks. You will realize that the routine job can be automated and it will improve the team’s working hours.
eCommerce applications of diagnostic analysis:
- Investigating the dip of revenue. For example, if your website showed significantly fewer revenue last month, you may implement a drill-down exercise. It will help to remind you about a server failure or more days off than usual due to holidays.
- Determining which marketing activities increased purchase activity. You will use this point in the future whilst planning other marketing strategies for a product or service.
3. Predictive Analysis
This type of analysis looks at cause-effect relationships, interdependencies and trends. The data analysis in eCommerce business tells the story of your customer’s experience. With this information, logical predictions can be made.
For example, you discover some experimental product or service customers didn’t like. This can be seen in low sales, add engagement and other indicators. You determine the reasons and find a flaw which affected it. Now in the future you will be able to avoid financial and reputational losses.
eCommerce applications of predictive analysis:
- Risk evaluation. Save money with a risk assessment.
- Sales prognostics. This is necessary to plan the budget for the next period.
- Determining which leads have the best chance of converting
4. Prescriptive Analysis
It’s required when AI and big data join forces to help predict outcomes in complicated circumstances. This method involves special software. It’s vital for planning and anticipating the next stage without risks for the business.
For example, you create a strategy for email marketing. Based on one of the types of data analysis in E-Commerce, you predict how many people will open the letter, click on the link, etc. The next marketing letters will be more targeted and will bring a higher number of potential buyers.
E-Commerce applications of prescriptive analysis:
- Scheduling. Planning will not allow you to miss important points
- Optimization of the customer experience. Customer loyalty boosts revenue.
- Production lines optimization. You will know which products are more profitable and which can be removed.
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