Platform and technology agnostic
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.
Dynamic and real-time updates
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.
You send a newsletter campaign out Monday, but the subscriber opens it Friday.
If your product modules/recommendations aren’t dynamic and updated in real-time, all the user’s behavior is not registered and thereby updated in the email.
If the user has shown interest in a specific pair of sneakers from Nike on Tuesday, this product won’t be shown in the email when the subscriber opens it Friday. Furthermore, if the price has changed Tuesday it should also be the updated price in the email when it is opened on Friday.
Avoid the manual process of choosing products
Instead of guessing which products could be relevant for your subscribers, you can instead let your subscribers’ actions determine which products should be shown in your emails.
No matter the email subject, the modules will make sure that there always will be relevant products tailored to the individual in the emails you send.
Data from multiple data sources
To deliver the best recommendations, you should integrate customer data from multiple data sources. The more data you can use as a determiner of which products should be displayed to each user, the more precise recommendations you can display to the individual subscriber.
If your company both has a commerce site and a retail store, you can connect the user’s customer- and transaction data.
Both B2C and B2B can use this feature.
If you are a B2C e-commerce business an easy way to do this is by having a “loyalty club”, so whenever a user purchases anything in your retail store and use their membership card, the data will be saved and you can use this in your online recommendations, both through your website, emails and all your other channels.
If you can identify your users offline (loyalty club, e-mail, or with third-party integrations like StoreBox, etc.), then we can combine online and offline data.
If you are a B2B with online commerce, your customers often have an account for their specific company. Likewise, they often have an account card/identifier, which they use to make purchases in your retail store, which means that their offline transaction data can be connected with their online data, which then can be used to make better recommendations online.
Know your subscribers before they become one
With our tracking script, we know subscribers before they decide to become a subscriber.
Before a user becomes a subscriber, they might have already purchased a product and visited your site multiple times. Even though a user has just become a subscriber, you can use these previous behavioral patterns, to make accurate recommendations in your emails.
Why is that an important feature?
If you do not start collecting data on your subscribers before they become one, it will take time to gather enough data to be as accurate as you would like in your recommendations. The more data you have, the better and more precise recommendations, you can present to your subscribers.