Getting the right message to the right person at the right time - this is the fundamental question anyone needs to think over and over when developing an email marketing strategy. But what does it really mean? How does it work in practice? The answer is data segmentation, breaking the information in your database into relevant data groups. Focussend, the nation's leading email marketing service provider, offers four tips on data segmentation to help you achieve effective email marketing.
Know your database
First, before you categorize contacts and create custom emails, you should do an in-depth analysis of the data to figure out what data you've collected, the amount, quality, type of data, and whether it's current or outdated. Only when you understand the value of a database can you determine the best way to subdivide data.
Second, you must define your marketing strategy and customer profile. Data analysis can help you determine what information you need, whether the data you currently have is sufficient, or whether it is necessary to acquire new data.
After you've identified your goals and analyzed your database information, you're ready to segment your contacts.
Personal data
The simplest way to group contacts is by gender, age, address and other personal data. Such data is easily accessible and is often filled in by users during the registration process.
If you want to set up a data collection form with a lot of fields to fill in, remember to make the user's Email address the only required field. This gives the user more freedom to decide what personal information to share with you, as users are often reluctant to share too much personal information during this phase of the interaction.
Therefore, make sure that the information you ask for is necessary for your marketing strategy. Asking for too much may reduce the conversion rate.
Behavioral data
The breakdown based on behavioral data is mainly related to user behavior, such as how users react to your email or website. This type of breakdown will help you understand where the user is in the transformation process.
If we consider how the recipient of the email might react, we can divide it into four categories based on the following four behaviors:
u The subscriber opens the message
u The subscriber does not open the message
u Subscriber opens and clicks on message
u Subscriber opens but does not click the message
If you want to know the engagement level of your contacts, you can calculate the loyalty index using a simple formula: number of messages opened/total number of messages sent. The data is then evaluated according to the following categories:
u Loyal users: Index >75%. Loyal users should be rewarded in special ways, such as discounts or free services.
u Uncertain users: 25%≤ index ≤75%. This group is potentially loyal, and can be converted by optimizing email content, subject lines, sending times, and telling people they will be rewarded if they stay loyal.
u Disloyal users: Index <25%. The conversion rate for such users is very low. They rarely open email, and when they do, it's because they're bored. They were more likely to have negative feelings about the messages they received, which further affected their judgment of the value of the messages. In addition, promotions and special offers can be a double-edged sword, bringing in customers who may only be loyal to the low price rather than to the company.
Transactional data
This kind of data is related to online and offline purchase behavior, including product types selected, purchase frequency, order quantity, total order amount, brand preference, color, and so on.
It's not easy to go through this data and use it effectively. Our advice is to analyze the data for the characteristics of your database and then break it down. Here are some examples:
Special offers: For customers who repeatedly click or buy a particular brand of product.
Shopping cart: For customers who have dragged products into their cart but have not yet placed an order.
Cross-marketing: If a user has bought a bag or dress in a certain color, send an email recommending a related product in that color (whether it's on sale or not).
Marketing up: Recommending a more expensive product mix to a customer based on their spending level.
Pre-sales marketing: Email customers who have placed orders in recent months to announce seasonal promotions.
RFM analysis
RFM analysis is a popular method in traditional marketing and email marketing: A breakdown of data based on predictive statistics of user behavior. You can score each variable based on the user's specific situation based on the following three variables:
u Time: The most recent purchase date.
u Frequency: Purchase frequency.
u Amount: The average amount consumed during a fixed period of time.
Here are the three principles of RFM analysis:
Customers who have made recent purchases are more responsive to current promotions than those who have made purchases long ago.
Regular customers are more responsive than occasional customers.
Customers with higher average consumption are more responsive than customers with lower consumption.
A good customer is, of course, a customer with a high RFM score. We also suggest setting a minimum score, not sending marketing emails to customers below this score, and preferably adopting a new strategy to deal with it.
About Focussend
Focussend, founded in 2008, is currently the best licensed email marketing service provider in China, and the first research and development company in China to develop an email service platform that conforms to the Chinese user experience based on the advanced core engine developed in the United States. Since its establishment, Focussend has been adhering to the core philosophy of "Simple, Reliable and Affordable", providing comprehensive email lifecycle marketing planning and execution services for tens of thousands of clients. Focussend currently provides professional and effective online marketing services to companies in various fields such as e-commerce, education and training, travel, aviation, hospitality, media and FMCG.
