Even a fairly simple customer database can already give you enough information to create meaningful segments. <\/p>\n\n\n\n
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What does your data actually tell you? <\/h2>\n\n\n\n
Your database might contain fields such as country, signup date, order count, last purchase date, or recent clicks.<\/p>\n\n\n\n
For segmentation, the important part is what those details can tell you about the person behind them<\/strong>. A recent purchase can show where someone is in the customer journey, while repeat orders may reveal something about the strength of the relationship. <\/p>\n\n\n\n The links they click can point to what they are interested in<\/strong>, and where they signed up can give you context about what brought them to your list in the first place<\/strong>. <\/p>\n\n\n\n Clicks, purchase dates, or the signup source do not necessarily need to become a separate segment. It can also be a condition you use to define the right audience for a particular campaign.<\/strong><\/p>\n\n\n\n Customers versus non-customers is a good starting point, but purchase history will help you go deeper<\/strong>. For instance, look at how recently someone purchased, how often they do it, what they purchased, and how much money they spend.<\/p>\n\n\n\n These ideas are often grouped into an RFM framework: recency, frequency, and monetary value<\/strong>. Recency<\/strong> tells you how long it has been since someone last purchased, frequency<\/strong> shows how often they buy, and monetary value<\/strong> looks at how much they tend to spend.<\/p>\n\n\n\n For example, a customer who bought from you twice in the last month<\/strong> could be treated as an active repeat customer, whereas someone who used to order regularly but has not purchased for a year<\/strong> may be a better fit for a win-back campaign. A higher-spending customer<\/strong> could be separated from occasional low-value buyers when promoting a premium offer. <\/p>\n\n\n\n You can then add what they purchased<\/strong> to make the segment even more relevant. If someone bought running shoes, for example, they may be a better audience for offers about running accessories than someone who has only bought casual clothing.<\/p>\n\n\n\n Instead of creating one broad \u201cengaged\u201d segment, define what different activity levels mean for your business<\/strong>. <\/p>\n\n\n\n You might separate subscribers who clicked several emails in the last 30 days from those who have not clicked in three months. Frequent clicking on product, pricing, or booking pages<\/strong> can also indicate stronger intent.<\/p>\n\n\n\n That gives you groups you can treat differently: people showing concrete interest, subscribers who need more nurturing, and those who may need re-engagement<\/strong>.<\/p>\n\n\n\n Before a subscriber builds purchase or engagement history, you may already know how they entered your list<\/strong>. <\/p>\n\n\n\n The person that downloaded a guide comes from a different place than the one subscribing at checkout, signing up for an event, or using the regular newsletter form.<\/p>\n\n\n\n This information will remain relevant even after the welcome email as people that signed up for a webinar might continue to receive related content, especially if their later clicks show the same interest.<\/p>\n\n\n\n Basic customer information can become valuable when it changes what is relevant to someone<\/strong>. Location, for example, can help you target an in-person event, a local offer, or a service that is only available in certain areas.<\/p>\n\n\n\n The same applies to customer type. If you serve both B2B and B2C customers, those groups may need different examples, offers, or even different explanations<\/strong> of the same product<\/strong>.<\/p>\n\n\n\n The important part is to connect the data to a difference in communication. If knowing someone\u2019s location, customer type, or language would not change what you send, there is probably no reason to build a segment around it.<\/p>\n\n\n\n Be careful with copying standard 30-day, 60-day, or 90-day rules without considering your normal customer cycle.<\/p>\n\n\n\n A customer who has not bought anything for three months is considered inactive for a coffee shop, but not for a furniture business, where purchases happen less often. <\/p>\n\n\n\n The same applies to engagement. Use your own buying frequency, sales cycle, and email schedule<\/strong> to decide what recent, active, or inactive<\/strong> actually means.<\/p>\n\n\n\n Once you start using the information you already have, you will see what you genuinely do not know. That\u2019s when you should collect more information.<\/p>\n\n\n\n If customer interests could be beneficial for your campaigns and you can\u2019t make any guesses based on their behavior, ask about their preferences<\/strong>.<\/p>\n\n\n\n Collect information because you know how you will use it, not because it might become useful someday.<\/strong><\/p>\n\n\n\n You can already do a lot with the data you have<\/strong>. Purchase history, engagement, signup source, location, and customer information can all help you build more relevant segments.<\/p>\n\n\n\n Start by using those signals well, then collect additional information when you know it will help you understand your audience better.<\/p>\n\n\n\n When you start thinking about email segmentation, collecting more data can seem like the obvious place to start. That could involve adding more fields to your signup forms, sending a survey, or asking subscribers about their interests. All of that can help, but before collecting more, it is worth understanding how much you can already […]<\/p>\n","protected":false},"author":3,"featured_media":3504,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[18],"tags":[13],"class_list":["post-3500","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-email-marketing","tag-ready"],"_links":{"self":[{"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/posts\/3500","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/comments?post=3500"}],"version-history":[{"count":3,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/posts\/3500\/revisions"}],"predecessor-version":[{"id":3506,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/posts\/3500\/revisions\/3506"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/media\/3504"}],"wp:attachment":[{"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/media?parent=3500"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/categories?post=3500"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/profiletter.iconopixel.eu\/en\/wp-json\/wp\/v2\/tags?post=3500"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n\n\n\nUse purchase history to understand customer behavior <\/h2>\n\n\n\n
\n\n\n\nDefine what engagement means for your business<\/h2>\n\n\n\n
\n\n\n\nUse signup source to understand intent <\/h2>\n\n\n\n
\n\n\n\nSome basic details can make a difference <\/h2>\n\n\n\n
\n\n\n\nChoose thresholds that fit your business<\/h2>\n\n\n\n
\n\n\n\nLet the gaps tell you what to collect next <\/h2>\n\n\n\n
\n\n\n\nFinal thoughts<\/h2>\n\n\n\n
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<\/figure>\n","protected":false},"excerpt":{"rendered":"