John Anderson is a Web Developer, Creative Content Director, Social Media Specialist, and Commissioned Artist. He is particular in watching web and social media changes and uses. He is also a commissioned artist and cartoonist. He is interested with various internet trends.
Last year marked the highest U.S. ecommerce sales, with a whopping $7.9 billion in revenue; and that was only during Cyber Monday.
Because of this trend, more and more people are eventually getting in the ecommerce bandwagon—while consumers are presented with more shopping choices, ecommerce businesses will feel the tension as they have more competitors in tow.
Ecommerce business owners usually rely on dashboards to make business decisions. The problem is, that’s only the tip of the iceberg. We may be reluctant to dig deep in these numbers, but the “popular items bought” shouldn’t be the only metric you need to look at. There’s no problem in collecting the data—we have a ton of analytics tools we can use but getting insights or translating the data is the major struggle we all face.
What makes an ecommerce store different from a physical store is the personalized experience that it can cater. Physical stores cannot accurately target their chosen demographic, but with ecommerce, you gain insights on your customer’s buying behavior, their preferences, and more.
A few months ago, Propelrr discussed the potential of ecommerce business data and how to use it to leverage online sales at Seamless Philippines 2018. Here’s their six-step approach on e-commerce data analysis.
1. Set Your Business’ Objectives
Why do you need to set your objectives in the first place? Acknowledging the problem makes it easier for you to know what you need to do with your data. Have you noticed that your Customer Lifetime Value is lower? Have you seen some changes in your consumer’s buying patterns? Or is your strategy simply not working anymore?
Setting objectives can help you decide what data you would need to determine what needs to be fixed in your strategy.
2. Determine the Type of Data You Need
Once you identify your objectives, it’s time to create a data model suited for your needs. The data that you have now has to be classified according to your objectives. Once you determine the type of information you need, it’s time to correlate these data sets on how it affects each of your objectives. With this, you can now identify your KPI, and even anticipate trends that will predict the performance of your store.
3. Choose an Analytics Tool
Perhaps this is the easiest step in the guide but take this seriously. A great analytics tool can help you visualize your data easier. For starters, you can use Google Analytics as your analytics tool, or you can also rely on your own ecommerce analytics dashboard if it displays the information you need. CS-Cart has a built-in analytics tool that can integrate with Google Analytics. You can also export your customer’s data from CS-Cart to supplement your analytics reports.
If you’re at a loss on what to use, here’s a handy list of the best ecommerce analytics tools:
4. Select an Analytical Model
An analytical model, as explained by Wayne Eckerson, “estimates or classifies data values by essentially drawing a line through data points.”
Selecting an analytical model is one step further into the world of predictive analytics. An analytical model can predict outcomes based on the data presented. To understand this further, Auritas’ 10 Data Analytical Models may help you understand how these models work, and what kind of information it can provide.
5. Examine and Interpret Data Insights
Now it’s time to sit down and crunch these data. Does it meet your objectives? Does it help you realize the problem with your current strategy? Does it make you understand your customers’ sales journey? This is the eureka moment for your business. Certainly, there will be areas to improve upon with these newfound insights to your business.
6. Apply Insights, Re-evaluate the Data
Now that you understand how your business works and what data is valuable to keep it running, apply these insights and regularly evaluate the data you have. Rinse and repeat! In the future, you may need to rework on your data and analytical models to meet new objectives.
Ecommerce analytics requires hard work, but the insights on understanding your customers’ actions can help you better serve them, eventually leading to increased sales.