Customer behavior is one of the most reliable, accurate and useful aspects to know the customers and predict their future buying possibilities. Studying customer behavior helps marketers to understand what influences and motivates customers to buy. Through a better understanding of customer’s decision making, a business can identify which products are needed and which are not.
In online retail, it is necessary to understand customer’s purchase habits, known as Purchase behavior, however, customer behavior isn’t limited to this only. An ideal segmentation is a combination of behaviors that can give more accurate and actionable insights. The significant behaviors for an eCommerce store that can be grouped together for better insights include:
- Browsing behavior
- Purchase behavior
- Cart abandoned behavior
- Email behavior
Let’s get ahead and know what each of these behaviors means and how they help an online retail business to peek into customers’ interests to increase their engagement and sales.
Browsing behavior is the behavior that demonstrates the browsing activities of customers in the online store. From landing on a page to the products they show interest in, searching a product or the products they look out again and again; all of these falls in the category of browsing activities and form up the browsing behavior of customers.
The objective of analyzing On-site or Browsing behavior of customers can be understood by these three words:
“Know Thy Customers”
Analyzing the browsing behavior of customers is the most important aspect of modern retail e-commerce. It focuses upon tracking and understanding the finest details of the customer’s on-site activity.
The more information an online store has about the customer, the better it’ll be able to serve them. When every behavioral aspect of the customers is wholly understood, the site can offer personalized on-site experience and targeted campaigning to them.
The process of knowing a customer’s behavioral action starts when a user interacts with the website. The interaction begins when a customer is attracted to visit the platform. Most often, a landing page is the first interaction of the visitor. Where a visitor goes from there informs about more of the interests and needs.
Once a visitor lands upon a page, the browsing or on-site behavior is the first information that is recorded and from there on the each activity is tracked and used to serve buyable suggestions and campaigns. Based on the searches and interest shown in specific products, more similar products are suggested.
Also note that all the products a visitor or customer looked at, there are other visitors or customers too who reflect the same interest and browsing habits. Figuring out such similarly behaving visitors and customers help to form effective customer segments based on browsing behavior.
Purchase Behavior of customers is the strongest source of customer preference data. It provides information related to purchases made by a customer. One browses and looks at so many products but buys only what is most important. This is why, purchase behavior gives an upper hand in addition to the analysis of browsing behavior.
Relying upon the purchase history of customers, many more products of similar nature can be offered to them. The purchase history can also be accessed for Predictive Analysis to figure out other similar products in which the customers might be interested in.
The analysis of purchase behavior helps to understand many other aspects that affect the future purchases of a customer. For example: Purchase behavior helps to understand Product Affinity (a way to recognize the liking of a customer based upon his previous purchase), which can be used for personalization and effective campaigning. Consideration of Product Affinity enables an organization to track purchase patterns, customer behavior and offer cross-sell opportunities to increase revenue.
Furthermore, purchase behavior assists in making customer segments by the next level analysis of buying patterns like Seasonality and Discount Sensitivity of customers.
For example: If a customer buys much more in a specific month or season of the year, then it is reasonable to approach with special offers at that time.
Similarly, Discounts and Special offers make up an integral part of success for most of the modern-day retailers. We know that different customers have different sensitivity to discounts. However, an analysis of purchase history can help to identify the discount sensitivity of each customer.
For example: when revenue generation is the primary concern, products with higher margins can be promoted with bigger discounts to those customers who tend to buy more with higher discounts.
Customers with relatable browsing and purchase behavior patterns can be grouped together to form meaningful customer segments. Similarly, purchase aspects like seasonal purchase behavior and discount sensitivity can add more value for forming customer segments.
Cart Abandoned Behavior
Cart abandoned behavior is an activity by customers after adding a product to the cart. A customer either completes a purchase after adding a product or just abandons it. Having insights into the details of cart abandonment is really important for an online business.
The aim of an online store must be to stop the customers from doing so and instead motivate them to buy the product added to the cart. Such motivation can be catalyzed by offering customers with offers that can lure them to complete the purchase, but for doing it; useful insights about cart abandonment are required.
If a business knows which customers reached the cart but abandoned it later, then the business can pitch them with a discount or an offer that can bring them back to buy the same product.
On the contrary, if a business just doesn’t pay concern over the customers who abandoned the cart then it will not only lose the purchase they abandoned but possibly all the sales that could’ve come from those customers in the future.
This is why, cart abandoned behavior is a crucial element to retain the customers a business might lose. Also, this behavior contributes a lot to the revenue from the customers who could just go away if not approached back with an exciting offer.
Email being the oldest of any of the present digital marketing tactics is still the most impressive one. Around one-fourth of retail revenue comes from email marketing. Also, researches have shown that email alone drives as much revenue as the other digital marketing solutions drive altogether.
This is why the fourth most important behavior of the customer to acknowledge and keep track of is Email Behavior.
After all, email (more straightforwardly targeted than other channels) is the channel that becomes the medium for targeting campaigns, and the response to it determines how close a business is getting to drive more sales.
It is extremely necessary to analyze email behavior as it reflects how well the campaigns are working. Imagine, there’s no analysis of email behavior and therefore there won’t be any idea of how many customers even opened it. The rest of the behaviors give insights into what customers have behaved like in the store, but email behavior shows how they respond to the campaigns sent based on other behaviors.
Unlike any other behavior, email behavior is first to denote how well has the analysis worked and how well it is turning out. An effective analysis of email behavior data is executed by observing and optimizing:
- How many emails were sent to a customer?
- How many emails were opened?
- How many emails were clicked for visiting the store?
- How many emails resulted in a sale?
However, it’s obvious now which behaviors are necessary for a business to analyze in order to increase customer engagement and sales. But it still remains out of human potential to keep track of all these behaviors without an analytical tool.
Suppose there’s no analytical tool involved, then it would be impossible to know which customer made a visit to the store, which customer abandoned the cart and especially which customer opened or clicked an email. It is possible to have all these insights using a little technical help, yet it’s not possible to analyze and update customer segments with ever-changing data with respect to time.
It is definitely important to have an automated solution to analyze the behavioral data to make effective customer segments and market accordingly to uplift the business. After the behavior of customers is tracked and analyzed, rule-based segments are meant to be created in order to campaign the similarly behaving customers.
Such rule-based segmentation becomes a hectic and ineffective process for retailers who don’t go for automation. Marketing automation works hand in hand with a business to manage customer segments in a way that can handle everything from collecting data, its analysis, creation of customer segments to creating campaigns that can really sell.
Let’s find out the role of marketing automation in ideally incorporating rule-based segmentation.