Going by an eMarketer industry report, global eCommerce retail sales are projected to cross $4 trillion by the year 2020. That represents a healthy 14.6% of the overall sales in the retail industry.
The underlying fact is that more shoppers are now ready to go for online retail shopping for a range of products or services. Even a large retail brand like Walmart has reported a 29% increase in its U.S eCommerce sales.
“Know Your Customer” is currently the success mantra for the eCommerce sector as they adopt innovative ways of tracking the customer’s online journey and how to serve them more efficiently. As a result, analytical technologies like Cloud data analytics for eCommerce are being deployed to get a deeper understanding of customer behavior.
In recent years, consumer behavior analysis in the eCommerce industry has emerged as an effective analytical tool for knowing how any online shopper interacts with the eCommerce website.
In the next few sections, we shall understand how consumer behavior analysis works in the eCommerce sector along with some of its key metrics.
What is Consumer Behaviour Analysis?
For any online retailer, data about the online behavior of its consumers is probably the most important business asset. Through behavioral data analysis, eCommerce stores can extract deeper consumer information and present a more personalized website experience to these customers.
In simple language, consumer behavior analysis (also known as customer behavior analysis) is a data-powered observation of online consumers and how they interact with your company. Behavioral analysis in eCommerce can be used to categorize consumers on the 3 following behaviors, namely:
Online browsing behavior of any consumer is used to track their online activity on the eCommerce store. This includes factors such as:
- How the online shopper is attracted to a particular eCommerce store?
- What products do they search for on the eCommerce store?
- How does a personalized landing page design influence their conversions?
Purchase behavior provides detailed insights into consumer needs and interests and is a more accurate indicator of consumer behavior. Data related to purchasing behavior can be deployed to determine:
- Buying patterns (example, seasonal purchases or a preference for a particular product category)
- How shoppers respond to promotional activities like product discounting or special offers.
In the age of instant messaging, email marketing is still the most effective mode of eCommerce marketing that drives sales and revenues. Email behavior is a measure of how consumers are responding to your email marketing message. This can be used to analyze consumer behavior based on:
- The number of business emails that were opened by consumers
- The number of users who opened (or clicked) the emails
- The number of emails that prompted a website visit or an online purchase
In the next section, we shall evaluate how consumer behavior analysis has business benefits in the eCommerce sector. We shall consider this with the help of an industry case study based on cloud data analytics solutions for eCommerce implemented by Countants.
How Consumer Behaviour Analysis Benefits eCommerce Business
eCommerce retailers can derive multiple benefits from the insights taken from consumer behavior analysis tools. These valuable insights can lead to a more personalized approach to customer needs that can increase their lifetime value to the business. Besides that, behavioral analysis in eCommerce can reduce customer acquisition costs, improve brand recommendations, and improve the lead generation process.
Powered by a cloud-based data analytics platform for eCommerce customers, here is an eCommerce industry case study that was facilitated by Countants:
A notable eCommerce company needed the right technology to provide a comprehensive view of its customer behavior across different cloud-powered platforms and marketing activities.
Their current infrastructure lacked sufficient capability in achieving the following business objectives:
- Managing and scaling the growing volume of business data from multiple sources.
- Leveraging the complete benefits of online consumer behavior data.
- Responding efficiently (with shorter lead times) to business-related queries from their internal processes.
With its expertise in cloud-based solutions, Countants was successful in implementing a customized solution using Google Cloud that could satisfy its growing customer needs. This included implementing consumer behavior analysis using ETL (short for Extract, Transform, and Load system) that caters to integrating data from varied data sources into a destination system. These varied data sources included eCommerce-related data (such as user experience, sales, margins, product inventory, and demand) from online marketing vendors including Google AdWords, Bing, Yahoo, and eBay.
Countants implemented this customized client solution using backend technologies such as:
- Google BigQuery (for data warehousing)
- Google Cloud Storage (for data storage)
- Google BigQuery and App Engine (for data processing)
- Google Compute Engine (for job handling).
Thanks to this scalable cloud solution implemented by Countants, the eCommerce client could leverage their capability towards:
- Efficiently migrating data using ETL.
- Effective business reporting using rich and interactive data visualization tools
- Better insights from data analytics through business reporting
- Better management of historical data
- Improved in incoming data quality
Finally, which are the key metrics or KPIs that can be used to measure consumer behavior? Let’s see that in the next section.
Want to understand the factors that are influencing your consumer behavior? Then monitoring these 4 key metrics can help you understand how your consumers act:
Average Session Time
The average session time is a good indicator of how long consumers spend on your website. Longer session times indicate a higher likelihood that the session will end with an online purchase.
On the positive note, online shoppers interested in your store tend to spend more time browsing through products, reading product reviews, and interacting with your customer support executives.
Pages per visit
This is an effective metric for analyzing customer behavior for measuring the number of pages (or content) being viewed by shoppers in every visit. Based on this metric, you can identify the most (or least) viewed website pages and work on their respective strengths and weaknesses.
For the least viewed pages, a page-per-visit measure of less than 2 would be insufficient for executing a conversion. Similarly, a high page-per-visit measure works great for boosting CTR and overall conversions.
The online traffic flow is an efficient metric in monitoring how consumers move (or navigate) through your store pages. Traffic flow can indicate the online store pages that are most attractive to shoppers.
Through this data, you can design the best navigation path for shoppers to reach your most popular products or product categories. Similarly, traffic-related metrics can help simplify the checkout process and deliver the right marketing message to the target audience.
Whether it is through free shipping or freebies, customer loyalty is an excellent yardstick for observing customer behavior. Customer loyalty metrics can help you track the buying habits of each shopper and understand the merchandise preferred by each demographic group.
Apart from tailoring the shopping experience of loyal customers, customer loyalty programs can be used to monitor the purchase behavior after the sale is made. For example, if the customer has made an additional purchase (complementing the previous purchase) or has made a product return.
The role and impact of e-commerce on customer behavior is driving the industry-wide adoption of customer behavior analysis for attracting more shoppers and improving their shopping experience.
With its extensive expertise in customizing cloud-based data analytics for eCommerce customers, Countants is the right solution provider for implementing projects in cloud analytics and cloud visualization.