How Big Data in eCommerce Guides Decisions For The Future

Chapter 1:

What Is Data-Driven Decision Making?

Introducing Data Driven Decision Making

Data-driven decision making involves businesses taking advantage of the statistics, metrics and data that they have available to direct their business decisions. Utilizing business intelligence metrics based on company and customer data allows businesses of all kinds to make smarter decisions internally and externally.

Chapter 2:

The Importance of Data Driven Decision Making

Why You Should Care

Technology has become more advanced in the eCommerce space, and merchants have the ability to collect data on their customers, audiences and orders in more detail than ever before. According to a 2020 LinkedIn article, 53% of top-performing salespeople have a higher confidence level in their CRM data than their counterparts. However, one of the greatest things about using data to make decisions is that it is not limited to a specific role or industry. Sales teams, analysts, marketers, HR managers and professionals in a breadth of other roles have the ability to base their decisions off of data.

Data driven decision making has a variety of benefits, too. For one, leveraging data enables merchants to make smarter decisions with more confidence. Whether you’re focused on targeting your marketing campaign to a unique segment of your audience, launching a new product line or offering a new service, the data that you collect prior will help you reach a decision with a high level of confidence.

Further, data is objective in nature meaning that it is unbiased, logical and shows proof of how a company’s efforts in certain areas impact the business. With objective data, individuals and teams are better able to look at what’s currently happening within a company, regardless of their opinions and viewpoints, and work to resolve the issue at hand. Though data driven decision making is helpful in finding resolutions the majority of the time, it is important to note that not all decisions based on data will be perfect or be guaranteed to resolve an issue that a company faces.

How Starbucks Uses Data For Store Locations

Before we dive deeper into the data driven decision making process, let’s quickly review how a larger corporation, that people from around the world have come to know and love, utilizes data to grow their business.

Did you know that Starbucks works with Esri, a geographic information software company, to help map store locations? Since Starbucks strives to be in locations where its target market primarily frequents, the corporation needs to leverage important data such as the area’s population, average income of those living in that area and traffic patterns in the area. This enables the corporation to make data driven decisions when it comes to choosing the location of a future store, designing the layout of the store and more.

Chapter 3:

Steps To Making Data Driven Decisions

Putting One Foot In Front Of The Other

1. Identify The Questions You Want Answered

The first step to data driven decision making is to uncover the problems that your company or organization is facing. What are you going to collect data on if you don’t know what you’re trying to solve? Identifying and understanding the questions or problems that you want answers or solutions to lays the foundation for making data driven decisions.

2. Know Your Sources & Gather The Data

Next, it’s important to identify each source from which you’ll gather the data from. Are you collecting 10 

data from your CRM? Website forms? Social media? Google Analytics? Regardless of the channel that you’re pulling the data from, coordinating these sources is fairly straightforward when going into each individual platform. However, an analytics platform, such as Glew.io, that connects multiple sources of data streamlines the collection and analysis process. Tools like this save time from logging into multiple accounts and manually calculating metrics.

3. Clean and Analyze The Data

After gathering the relevant data, it’s then time to “clean” it and organize what you’re looking at. According to IBM, data analysts spend 80% of their time cleaning and organizing data and only 20% of their time performing the actual data analysis. This is called the “80-20 rule,” and it describes the importance of putting all data in order before interpreting and analyzing its meaning. Trust us, it’s unpleasant enough looking at an organized spreadsheet with hundreds of thousands of data points - let alone one that is unorganized and messy.

When you’re finished cleaning the data, your next step is to analyze the picture that it paints. A business intelligence platform or all-in-one analytics tool is useful at this stage. Tools like these provide valuable visualizations and timely comparisons in addition to static metrics.

If you don’t know what metrics you’re looking for when it comes to data analysis, refer back to step 1 above where you identified the questions that you wanted answers to. Metrics will vary based on the issue at hand. For example, the metrics you’re tracking and analyzing when you want to increase average order value are going to be much different from the metrics related 

4. Draw Conclusions To Guide Strategic Decisions

Finally, it’s time to draw conclusions based on your data analysis! This is the most exciting step as it guides data driven decision making. Remember how we said that data is objective in nature? Well, objective data can be used to judge subjective thoughts and assumptions. For example, your marketing team may have thought all along that your customers really wanted to see XYZ. After viewing and analyzing the data over time, it’s shown that your customers really were not interested at all in XYZ. Now, your marketing team can throw those subjective assumptions out the window and focus on the data at hand to make their next decision.

Data driven decision making really helps merchants and organizations find new ways to achieve their goals. No matter whether your goal is to increase the number of new customers to your store or generate more online sales - among the variety of eCommerce goals out there - be sure to regularly look at the metrics that are important to your goal.

Chapter 4:

eCommerce Metrics To Account For

Big Data KPIs

PSA: The metrics that you track should be dependent on your company’s goals. The list below is not exhaustive as there are hundreds of other metrics that merchants and organizations can account for when analyzing data.

The metrics outlined below are relevant to evaluating eCommerce growth, and we hope that eCommerce merchants and organizations measure these metrics (if they’re not already!)

Total Sales

Calculating total sales is one way to see how much revenue your company or organization is bringing in. This metric helps measure the direction of a business from a high-level perspective. Determine how many products or services you’re selling to see where there is an opportunity for growth.

Year Over Year Growth

Want to see the long-term effect that your marketing or sales strategies have on your company? Then look for year over year (YoY) growth. YoY growth refers to measuring a specific metric that is important to your business from one year to the next.

Average Order Value

Average order value (AOV) is quite literally the average amount of money that a customer spends on an order from your eCommerce store. You’re probably wondering why this metric is important. Well, it helps merchants better understand customer lifetime value. Are customers spending small or large amounts per order? Is the AOV higher or lower than you anticipated? Knowing the value of this metric is an important component in driving more revenue and actually increasing AOV in the long term.

Customer Lifetime Value

As one of the most important metrics in the eCommerce industry, customer lifetime value (LTV) illustrates the amount of revenue that each customer may potentially contribute to a business throughout their time as a customer of that business. It not only evaluates a customer’s singular transaction, but it also evaluates a customer’s potential contribution to a company’s revenue in the long term.

Chapter 5:

Our Favorite Data Analysis Tool For eCommerce Decision Making

Big Data Analytics Tools

Glew.io is a business intelligence tool that businesses of all sizes use to gain valuable insights into their audience and brand data - empowering their data driven decision making and, ultimately, helping brands grow. Suitable for small brands to enterprise-level businesses, Glew.io provides the features and capabilities that merchants need to make smarter business decisions and increase their omnichannel sales.

Glew. io Alerts & Automation

One reason the Groove Commerce team loves Glew.io is for its daily snapshots into the previous 17 

day’s KPIs. This makes it easy for teams to see how metrics change daily and weekly. It also lets merchants schedule automated reports using any of the data that lives in Glew, such as daily KPIs, monthly financial reports, marketing channel reports and more.

Glew. io Performance Analytics

Glew.io provides all the analytics that eCommerce merchants need to thrive and grow. The tool’s inventory analytics show critical metrics such as sell-through rate, depleting days, holding costs and more. And it doesn’t stop with inventory management. Glew.io shows product, marketing, customer and subscription analytics.

Product analytics enable merchants to better understand what is selling on the individual SKU level as well as the category level. Marketing analytics allow merchants to see all advertising and marketing channels in one place. Customer analytics let brands automatically sync customer segments with email platforms to streamline targeting. Finally, subscription analytics show the metrics that matter most in its own dashboard to allow merchants to filter and segment based on subscription level.

Glew.io Enterprise Analytics

If your business is enterprise level, Glew.io is the software you need to get your most complicated analytics questions answered. Glew’s custom integrations and data connectors - in addition to aggregated reporting across all channels, domains and locations - make it easy to connect data sources and analyze that data in one place. 

Chapter 6:

Conclusion

Closing Thoughts on eCommerce Big Data

Data driven decision making is a key piece in making smarter decisions, reaching business goals and driving eCommerce sales. If you have any questions about integrating a business intelligence solution with your eCommerce store, don’t hesitate to contact us for help. We’re happy to have our team hop on a call with yours to learn more about your eCommerce needs and help you build, design and grow your website.