1- Declaratives
Declarative data is derived from the information that a user lebanon mobile phone number list declares when they provide us with their data. This data can be sociodemographic (age, sex, population, language, marital status, etc.) or based on their interests (telling us what their tastes and preferences are). Below we can see the subscription form for the Madia Markt newsletter, a good example that allows us to understand where the declarative data comes from.
2- Behavioral
Behavioural data, as its name suggests, takes into account the behaviour of users in relation to their activity on the website (what they search for, where they browse, how much time they spend on a page), their purchasing behaviour (where they buy, how often), their activity when they receive an email (whether they open it or not, whether they click on a link or not, whether they unsubscribe) or the frequency with which they visit physical stores.
3- Transactional
Transactional data shows the transactions made by the user with the brand (amounts of each transaction, payment methods, whether they have purchased online or offline, products purchased, etc.).
4- Origins of the record
This data shows the origin of where a particular user's registration comes from . For example, if we have a user who registers after having done a search on Google, we will identify that registration as coming from an ad on Adwards or from an organic search. But we will also have other users who will come from acquisition actions, such as email marketing actions on third-party databases, or by filling out an application in a physical store.
5- Identifier of the stage it occupies in the life cycle
It is very important to know what phase of the user life cycle all subscribers are in (acquisition and activation, conversion, growth, retention or reactivation, and therefore, whether we are talking about prospects, customers, recurring customers, VIP customers or inactive subscribers. Depending on their level of involvement, the email campaigns that will be sent will contain one type of message or another.
6- Segment identifier
Let's imagine that we have a sports goods store and that, among the users we have identified in the growth phase, we have detected three clusters that are invariably repeated: users who buy products from the “outdoor”, “water” and “indoor” categories. It will be interesting to have each of the members of our database tagged with an identifier of the cluster to which they belong, so that we will take this reality into account in each of the communications we make.
7- RFM cells
Recency , Frequency and Monetary Value segmentation, which is typical of traditional direct marketing, is very useful for predicting user behavior regarding a given promotion. The idea is that users will be more inclined to respond to a message the more recent their last interaction with the brand was (both online and offline), the more frequent their interaction with the brand has been in the past, and the higher their spending to date. Of the three variables, recency has proven to be the indicator that best predicts a user's future behavior.
