What is Online Sales Analytics? Definition And Key Metrics

With the rise of digital transformation, big data, and the COVID-19 chaos, it’s no surprise that eCommerce retail has grown massively all over the globe, which means the competition has become more intense than ever. To accurately measure and assess your business performance, online sales analytics is a solid foundation for developing the right strategies to improve your business and even scale up.

If you seek to enhance your sales outcomes, we’ll break down the basics of online sales analytics, which metrics to track, and some tips for making the most of sales data.
 

• What is Sales Analytics?
• What Online Sales Metrics Should You Track?
• Traffic Sources & Conversion Rates
• Average Order Value (AOV)
• Customer Acquisition Cost (CAC)
• Customer Lifetime Value (CLV)
• Challenges of Doing Sales Analytics
• Tips to Enhance Online Sales Analytics
• Wrapping Up

 
Online sales analytics is the process of collecting, analyzing, and interpreting data related to the sales activities of an online business. Online sales analytics aims to help online merchants like you make data-driven decisions to improve sales performance and overall profitability.

It’s no exaggeration to say that sales analytics is your right-hand man in building and developing your empire.

To get a clear picture of how your e-store works and, importantly, to prevent your brainchild from falling deeply into debt or bankruptcy, you must be familiar with and keep an eye on the following metrics.
 

 
Thanks to advanced technology and social media, you can sell your products through various channels. However, equally investing your resources into all channels is not brilliant. Instead, identify which one brings you the most visitors. Is it organic search, social media, or paid advertising? Meanwhile, remember to double-check the conversion rate of those channels. The conversion rate here is calculated by simply taking the number of conversions and dividing that by the total ad interactions that can be tracked to a conversion during the same period.

A conversion can mean different things for varying types of organizations. To eCommerce brands, the conversion would be a completed sale transaction.

An example of calculating organic search conversion rate in the last three months with the inputs and calculation:

• Website sessions: 29,112
• Total conversions: 27,659.
• 27,659 conversions/29,112 visits = 95% conversion rate.

By tracking these data, you will know which channel needs optimizing and which should be invested more. For instance, if the number of visitors from organic social accounts for only 15% of total traffic, but its conversion rate is twice that of other sources, why not adjust your plan to conduct more activities on your social pages?
 

 
AOV is one of the most critical metrics all digital stores must know. As its name suggests, it measures the average total of every order completed with you over a specified time. AOV is calculated using sales per order rather than sales per consumer. Although a customer may return to make multiple purchases, each order is factored into AOV separately.

So why should you track this number regularly? AOV neither reflects your gross profit nor profit margins but provides information about establishing those figures. Let’s take an example of an e-commerce store that sells three handcrafted bookmarks at $10, $21, and $39, with an AOV of $17. This implies two consumer behavioral patterns.

• Customers do not purchase numerous goods.
• The majority of sales are for low-cost bookmarks.

By improving AOV, online businesses boost the ROI of all marketing initiatives. The higher your AOV, the more you get out of each consumer – and, as a result, out of every dollar invested to attract those customers.
 

 
In a nutshell, CAC is how much you spend to acquire a new customer. Some would say it’s the total amount of money spent on marketing and sales efforts. Well, it is true, but not enough. This estimate also includes the product, labor, shipping, and other expenses contributing to attracting and retaining customers.

If you are just starting, you could look at your industry’s benchmark to see whether you have achieved a healthy CAC.

Customer acquisition cost by industries (Source: Startup Talky)

As you can see, CAC varies by initiatives and the marketing and sales tactics you use. Each business is different, so comparing your CAC to customer lifetime value (CLV) is a helpful approach to assess it, and that’s what we’ll mention in the next part.
 

 
Customer lifetime value is the overall revenue you gain from a customer over time. It takes all of their orders into account. A high CLV indicates that the product-market fit, brand loyalty, and recurring revenue from existing customers are all present. You should evaluate and optimize customer lifetime value if you want to grow steadily. Although it varies significantly across categories, the average CLV for eCommerce brands is $168.

(Source: Dinarys)

Calculating CLV can be challenging, but it is time well spent. It allows you to allocate resources more effectively while assisting with budget forecasts for marketing, advertising, and customer acquisition. Back to the comparison between CLV and CAC, a good ratio is 3:1.
 

 
Effective sales analytics demands substantial assets, experience, and technological infrastructure. You’d better be fully aware of the following challenges and then be well-prepared when running an online business.

• Data quality: One of the biggest challenges of sales analytics is ensuring the data’s quality and accuracy. Poor data quality can lead to inaccurate insights, wrong conclusions, and bad decision-making.
• Data integration: Sales data can come from various sources, such as CRM systems, eCommerce platforms, and marketing tools, making it challenging to integrate the data and create a single source of truth.
• Data privacy and security: Sales data may contain sensitive customer information, which needs to be protected to comply with regulations and maintain customer trust.
• Limited resources: Sales analytics requires specialized skills, expertise, and technology infrastructure, which may not be available to all organizations. Limited resources can make hiring skilled personnel and investing in the necessary technology challenging.
• Lack of standardization: Sales data can come in different formats, structures, and levels of granularity, which can make it challenging to standardize and compare data across other sources.
• Complex analysis: Sales analytics involves complex data analysis techniques, such as regression analysis, forecasting, and segmentation, which require a deep understanding of statistical concepts and software tools.

 

 
If you’re struggling with these metrics, and feel they bring a mild headache, here are some handy tips to keep everything clear.

1. Identify key performance indicators (KPIs): Determine the most important KPIs that align with your business goals and focus on those when analyzing sales data. This will help you identify trends and insights most relevant to your business.
2. Use visualization tools: Visualizing data with charts, graphs, and dashboards can help you quickly identify patterns and trends in your data. Visualization tools like Tableau or Power BI can be used to create interactive dashboards.
3. Implement predictive analytics: Predictive analytics can help you forecast future sales, identify customer behavior patterns, and optimize pricing and product offerings. Tools like regression analysis or machine learning algorithms can analyze historical data and make predictions.
4. Integrate your data sources: Integrating data from various sales channels such as CRM, ERP, and eCommerce platforms can provide a comprehensive view of sales performance. This will help you identify patterns and insights that would not be visible if you only analyzed data from a single source.
5. Focus on customer behavior: Analyze customer behavior, such as purchasing patterns, search behavior, and engagement with your storefront. This can help you identify opportunities to optimize customer experiences, increase lifetime value, and reduce cart abandonment.
6. Automate data collection: Automating data collection can save time and improve data quality. Tools like Google Analytics, Adobe Analytics, or Mixpanel can automatically collect data from online sales channels.

 

 
Performing online sales analysis for the sake of doing so is a pointless exercise. As a store owner, you must keep the goals of your sales analysis and the sales metrics in the forefront of your mind. Sales analytics may assist businesses like yours in activating their performance by serving as a guiding light, demonstrating exactly where targets are being reached or falling short.