4 Types of Data Analysis Used in eCommerce to Get Key Business Insights
Have you ever found yourself stuck with no knowledge on how to further develop your business? When you can’t figure out the next step to move forward. Moreover, the digital world is bringing new trends regularly, with which you have to keep up with. You are not alone, staying competitive is challenging for all web store owners.
So, how can you stay relevant? Data analytics in eCommerce can help you to provide a working strategy for your business. Today you will learn about the 4 types of data analysis that give a better solution when it comes to developing an online business.
What is data analytics?
Data analytics is the process of extracting and processing metrics. It is particularly beneficial in the realm of online consumer behavior.
The information helps eCommerce companies stay competitive in their niche markets. With these important insights, businesses can identify bottlenecks in their selling processes which provides an opportunity to refine strategies.
5 reasons to pay attention to data analytics in your eCommerce business
- When shopping patterns emerge, you can incorporate better business strategies. Data analytics reveals how customers interact with your website, what their preferences are and their favorite brands. Because data analytics are so involved behind the scenes, they can even tell you when spikes in demand take place so you can better plan for sales.
- Data analytics can reduce your costs. Because the metrics give you a greater awareness of what’s profitable and what’s not, you will not waste your budget on dead-end endeavors.
- New doors open when data is analyzed Consumers’ needs and wishes are hidden behind numbers, so you have tangible proof of what they really want. When you are equipped with the facts, transformation occurs in the form of new product launches and building a brand around market demands.
- Trends that emerge through data analytics help you manage your inventory better. Before the technology existed, it was difficult to predict how much of each product would be needed at a specific time – such as the holiday season. Measured data also reveals a clear supply and demand formula so you can price items right.
- Loyal clients are often the result of a well-thought-out data analytics strategy. Because data sets help you get to know your customers better, you will be able to cater to their needs more efficiently. You can also get a feel for why carts are abandoned and work to resolve those issues.
Process of data analytics in eCommerce
Data analysis in an eCommerce project 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 eCommerce allows you to predict your customer’s behavior.
3. Data Processing
Modern data analytics in eCommerce 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 eCommerce field. Without precision processing of data analysis for an eCommerce 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.
Four types of data analysis in eСommerce projects
There 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.
eCommerce applications of descriptive analysis:
- 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. Diagnostic 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 eCommerce, 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.
eCommerce 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.
Difference between the key terms: KPIs, Analytics, Metrics and Reporting
Reporting is for successful analytics, you need to understand the four key terms. They may seem deceptively similar, but in their differences lies the crucial point to comprehensive analysis. Let’s analyze each one of them closer.
Key Performance Indicator is a measure of the success of your product or service. With this point you can evaluate the effectiveness of your business strategy and growth. It’s used for various systems, such as marketing or finance.
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Analytics stands for the predicting of decisions and actions. This metric can be used to improve all the processes or strategy. It’s important to note that the analytics also include advanced planning and not just the reviewing of the current situation.
Metrics allow you to accurately measure the success of the processes, for example: ads. It’s not about numbers, it’s about the value of each point in the large mechanism. By using them you can predict various scenarios for your business.
Reports stand for the results, or in other words, KPIs. Counting all the data allows you to understand what was done and how effectively. It will further be used to improve the performance.
Is it possible to run an eCommerce business without data analysis?
Yes, you can, but it’s the same as trying to find your way around an unknown city without navigation.
Without this knowledge, building the right strategy is almost impossible. Why? Because you can’t predict where you will arrive if you don’t have the concept of the start position.
Here is an example. You want to increase the sales of a product. If you don’t use the starting data, such as number of sales and value for the customer, you could waste your money used for ads.
In the analysis, you examine both the marketing campaign and advertising and other factors that affect the sales. As a result, the next sales will be aimed at the target audience.
Data analysis is not a one-time service. The interaction between the user and your web store constantly changes. You should always know:
- what the customers like about your store;
- what they are up for on a Friday evening;
- what they will definitely buy as a present for their loved ones.
Regular data updates is the key to successful eCommerce business growth. Learn more about how to increase your income with PowerSync’s 20+ years of experience in eCommerce.
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What is data analysis in eCommerce?
It’a a process of collecting, processing and analyzing incoming information. The main benefit of data analysis is to predict the future steps of your online commerce business growth.
Why are data analytics for eCommerce so vital?
With the collected information, you can analyze all your future steps and main points. For example: the effectiveness of strategies, growth of business goals and the increase of your income ✅
How long does it take to provide data analytics in eCommerce?
Processing of data analytics can take up to several weeks. It depends on the amount of data, the platform you are using and the level of preparation for the analysis.