“81% of online shoppers who receive emails based on previous shopping habits were at least somewhat likely to make a purchase as a result of targeted email.” – e-marketer
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 Email Behavior is acknowledged as one of the most important customer behavior to keep track of. After all, email – more straightforwardly targeted than other channels, 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. Other behaviors such as Purchase, Browsing or Cart Abandonment give insights into how customers behaved in the store. Although, only email behavior reveals how they respond to the campaigns sent based on other behaviors.
Unlike any other, 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 recently did the store connect via emails?
How many emails were opened?
How many emails were clicked for visiting the store?
How many emails resulted in a sale compared to the sent emails?
How much did the sales match up against the expected revenue from email campaigns?
Answers to these i.e. insights help strategize better email campaigns with precision targeting. Here, RFM plays a vital role in yielding all this information for retailers. RFM methodology helps to find out the Recency, Frequency and Monetary stats and insights for emails sent to each customer. This makes it convenient for an e-commerce business to understand current engagement via emails. Further, it guides on how to enact on improving the ROI of email marketing.
With the help of an analytical tool, retailers can segment customers by tracking varying email behaviors across each level. The aim of doing this is to motivate customers to advance further to the next level.
For example, customers visiting the store recently via email campaigns promoting a premium range of sportswear are all grouped together. Since their interest is known, personalized crafted campaigns with offers on premium sportswear are sent to this particular segment. As a result, many of these customers grabbed the offer and made a purchase. Also, it’s a successful transition of customers with only email click behavior into the customers with active email purchase behavior.
Such a process of optimizing email campaigns work in a cycle of measuring campaign success and then action for improvements. But if 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.
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.
Enalito – an analytics and marketing automation app evaluates the store’s relationship with customers to create useful segments. By putting data to work, it enables marketers and retailers to use the in-built campaigning tool for creating campaigns that can really sell.