With the fierce competition in today’s online marketplace, data analytics has become an essential part of the eCommerce movement. And with more business owners catching onto its power, the sales growth of any company depends on it.
While the human brain is capable of manually analyzing large data sets, computers use mechanical processes and algorithms that pick up on trends instantaneously.
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.
Data analysis process
Data analysis is not one-dimensional, and it unfolds in many steps.
- Data Requirements Specification
During this stage, data is grouped. Once they visit your site, your audience may be separated by age, race, education, income, gender, relationship status, etc. These details help you to know your customers inside and out.
- Data Collection
Once you know who your audience is, you are ready to dive into further analysis of their buying behaviors. Browser cookies, web databases and ad interactions are some of the most common ways further details are gathered.
- Data Processing
Modern data analytics software organizes information through an automated process. On the back end, information is organized into rows and columns that become structured graphs and charts.
- 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.
- 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 artificial intelligence (AI) systems or manpower to help you sift through the data. Today, data analysis is a common field of study, so there are professionals who can help sift through it.
4 types of data analysis
There are four ways to make sense of data once it has been formatted for reporting.
- Descriptive Analysis is the foundation of data analysis. It serves as the backbone of dashboards and business intelligence tools. It answers the question, “What exactly happened?” It also takes a close look at how many times it happened, when it happened and where it happened.
eCommerce applications of descriptive analysis include:
- Key Performance Indicator (KPI) dashboards (the biggest use that describes how a business is performing based on chosen benchmarks);
- monthly revenue reports;
- overview of sales leads.
- Diagnostic Analysis provides a deeper understanding of business processes and answers the question, “Why did it happen?” This type of analytics helps companies create clear connections between data and behavior patterns.
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 that will help remind you about a server failure or more days off than usual due to holidays, which helps explain the dip);
- determining which marketing activities increased purchase activity.
- Predictive Analysis looks at cause-effect relationships, interdependencies and trends. This step answers the question, “What is likely to happen?” The data tells the story of your customer’s experience. With this information, logical predictions can be made.
eCommerce applications of predictive analysis:
- risk evaluation;
- sales prognostics;
- determining which leads have the best chance of converting.
- Prescriptive Analysis is when artificial intelligence and big data join forces to help predict outcomes in complicated circumstances. This method requires special software. It considers, “What is the best action in this case?” It may also ask, "Will we have a more positive outcome if we try something this way?" This type of analysis suggests which decision to make given the circumstances.
eCommerce applications of prescriptive analysis:
- scheduling (delivering the right products at the right time)
- optimization of the customer experience
- production lines optimization
Is it possible to run an eCommerce business without data analysis?
Data analysis is critical for running a prosperous business in the modern eCommerce sector. Many business owners have much to learn about data analytics, but there is a wealth of online information that can help.
When you discover how users are interacting with your site and products, you can serve your customers more effectively and move toward higher profits.