Recommendation engines are thus proving to be one of spain mobile phone number list the best ways to increase user-brand interaction rates. They are tools that “filter” irrelevant content for the user, thus providing a more personalized experience. Recommendation engines are based on the analysis of different data. For example:
Sociodemographic Information
Age
Place of residence
Gender
Frequency Size Category
Product Preferences
Transactional Information (RFM)
First purchase
Last purchase
Total amount spent
No. of purchases
Last product purchased
Behavioral
Product visited for the longest time in a session
Product abandoned in the shopping cart
No. of openings/clicks in the last 30 days
Recommendation in Email Marketing
This data needs to be imported into the recommendation engine. Once there, the algorithm, which has already been programmed to detect the relationships we consider relevant, will analyze this information in search of those relationships. There are different types of “relationship” that explain how an algorithm works. For example:
Product hierarchy : If you bought a suit, you will need a tie.
Relationships based on product attributes : If you have purchased “organic” meat and eggs, you will be interested in the offer of “organic” fruits and vegetables.
Content-based filtering : This involves finding products that can be classified under the same category. For example, products classified as “men's” will tend to be presented together. Thus, if a user has purchased a pair of men's trousers in the past, we will recommend other items in that same category.
Collaborative filtering : users who bought the same laptop as you also bought a certain peripheral, then we recommend that same peripheral to you.
Typically, recommendation engines combine different algorithms (collaborative filtering, content filtering, product hierarchy, etc.) in a hybrid model.
These types of recommendation-based emails are part of efforts to make email marketing more personalized and relevant to the user. Adapting the content to the user's preferences, expectations, and reality yields great results. For example, according to Salesforce-ExactTarget, personalized emails sent automatically generate between a 15% and 25% increase in conversions . On the other hand, Experian indicates that emails whose content is adapted to the user's reality obtain 29% more openings, a 51% higher click rate, and transactions increase by an average of 6 points .
