Your average subscriber receive a lot of emails, from many different companies every day and a lot of them are probably your competitors.
How do you make sure that they buy from you?
Personalized emails is the key to stand out, once you have convinced them to open your email.
Your subscribers has become used to personalized emails, and expect that they are relevant and based on their interest and behavior.
Most email platforms offer some sort of “personalization” option in their suite. This function can be used to customize your email content to different segments of your choice.
Segments, however, are not the ideal way to personalize your emails, since they are based on group behavior and not the individual.
There is nothing wrong with segmenting subscribers, we encourage you to do so and nurture them through a well-planned email flow.
But, when it comes to the personalization options offered by the email platform, they are often not that advanced.
For some, a personalized email is one that starts with “Hi [first name]”. We are not here to tell you that you should not use dynamic tagging and call people by their name. But that’s the simple and basic stuff, that any given email platform can do.
If you want to make an impact on your email performance, you should start by getting to know every last person on your email list including their interests and preferences.
Raptor tracks the subscriber’s behavioral pattern on your website, which then can be used to personalize the products and content of the subscriber’s email through a module.
Whenever the subscriber opens an email, a signal is sent to Raptor’s database to check if there is any previous behavioral patterns from that induvidual subscriber to display in the email, depending on which module is chosen for the specific email campaign.
If there is a behavioral pattern, the products that will be recommended are based on previous behavioral data.
If the user is unknown, or if we do not have any previous behavioral data on a product, we use backfilling to look into the category structure.
Through relevant and related categories, we can recommend relevant products and content without vast amounts of user data.
If a subscriber has no previous behavioral data from your website our fall back logics will still be able to show relevant products to the subscriber.
Usually, a module consisting of either bestselling or trending products is the best fit for these subscribers.
A module is a collection of algorithms containing customer data, that serve a specific purpose such as generating personalized recommendations.
Raptor modules are the recommendation blocks you can build and implement in your emails. Each module has an underlying API method and algorithm. Modules return data. They can return JSON, XML, CSV, HTML or even images.
Generates targeted personalized recommendations, displayed as product images, to feature in your newsletters campaigns and automations.
Generates a list of products within a given Brand or Category. The recommendations will be personalized if the subscribers are known, if not our backfilling will choose the most relevant products.
Generates a list of all products, that are currently trending on the website, to feature in newsletters to users. You can determine the period. If you have a short period, the module will be very dynamic and vice versa if the period is long, it will be a static module.
Platform and technology agnostic means that the business needs and demands of each specific client, with different systems and platforms, enhances our ability to support a wide variety of different organizations.
At Raptor we have a variety of technology integrations, that enables any client to merge their data into our recommendation-engine such as POS data, which can enrich the recommendations.
Likewise, we have a lot of platform integrations and our open API makes it possible to make integration into any email platform.
Delivering updates in real-time means that customer- and subscriber data is processed within milliseconds, to always show the most up to date and relevant products in your emails.
As a user’s behavioral patterns change, so should the products in your emails and it has happen instantaneously to match the user’s expectations.
If you are not updating your product selection in real-time, but in batch, you are going to end up with a problem. If users have additional interactions and behavioral patterns in the time between an email is sent, and the time they decide to open the email, these will not be taken into consideration. This is because the point in time where the batch was updated could be a long time ago. If you update in real-time, this won’t be a problem and you don’t have to worry about not having the most updated data in your emails.
This is also very important in terms of price/stock changes. If a specific product during the time of being placed in the basket suddenly changes to be on sale, the discount could have expired, or the product could go out of stock before the user has had time to finish the purchase.