In our last blog post we‘ve described 5 advantages of recommendations in transactional emails. In this one we give you 5 concrete examples of types of transactional emails where recommendations make sense.
Type 1: Order Confirmation Emails
Trigger
This email is sent after you‘ve placed an order in an online shop. Also downloads of apps and closing a subscription can be trigger for an order confirmation email.
Which Recommendations?
Display personalized products fitting the customer’s taste. Then also his last order is contained in this calculation, but not only. This is important, as returning customers are also sometimes ordering for relatives or friends. If you would only display products fitting to the last purchase, then this could result in totally irrelevant recommendations.
Additionally you would display similar products and therefore assume, that the customer has bought the wrong article or you assume that he has forgotten something. This could result in a higer return rate.
Type 2: Shipment Confirmation Emails
Trigger
When a shipment is created and the package is shipped to the customer.
Which Recommendations?
In shipment emails you can display complementary products for the ordered ones. Also cross-sells make lots of sense, as the customer gets recommendations for fitting products he maybe didn‘t think of before.
Type 3: Registration Confirmation Emails
Trigger
This email is sent, when a new customer confirmed the registration. Normally by clicking on a confirmation link in the registration email (double opt-in).
Which Recommendations?
As the new customer normally didn’t buy from you before, his personalization profile is also quite new. But he’s already visited some of your articles and therefore a personalization profile is already created. Personalized bestsellers make lots of sense here to mix up the first insights in his personal preferences mixed with your bestsellers.
Type 4: Cart Abandonment Emails
Trigger
Other than most types of transactional emails, cart abandonment emails presume some kind of negative customer experience. These emails are sent some time after the potential customer has created a cart, but didn’t buy. This is normally in a time frame between 1 and 24 hours after the customer left the shop.
Which Recommendations?
Display personalized recommendations in the cart abandonment emails. The customer has already made his product choice and therefore his personal profile has enough information to display matching products according to his taste. Alternatively you can also display complementary products for the current cart or accessories for the cart content. Mostly these are cheap products to use the cashpoint effect of a “little take away product”.
You could also display the last viewed products of the customer. The browsing history in cart abandonment emails makes lots of sense, because it remembers the customer, what he was interested in before, and maybe animates him to add one of these products to the cart, because of the feeling he forgot something.
Type 5: Customer Review Emails
Trigger
A customer receives a review email after his purchase is completed. This is normally the case when he has received his shipment. Some online shops even wait a longer timeframe because of possible returns.
Which Recommendations?
When the customer receives a review email, then normally his purchase was completely successful and he should be satisfied. The shop owner expects a positive review. This is the perfect opportunity to offer more products to this customer for the next purchase. Choose personalized recommendations for this email, as the customer left a user profile full of information with his purchase already. The personal recommendations can animate him to come back to your online shop and fill up the next cart.
There are Many More
The above mentioned transactional emails are only the most common ones. There are many more types of transactional emails where recommendations make lots of sense. Go through the list of your transactional emails and place the recommendations in most of them. Afterwards you can measure the performance and fine tune the logic behind the recommendations in the different kinds of emails.
