I've ordered a bunch of seeds over the past weeks.
Last year I ran into a lot of stockouts and delays which set me back for much of the growing season. 2021 is shaping up to be a repeat so I got all of my orders in early.
This past weekend my tomato and pepper seeds were delivered. That same day, I got an email from the seed company:
Subject: Speed your tomato, pepper, and eggplant germination!
Along with some content about using heat and a few options for buying heat mats.
This is what I mean when I write about creating a relevant New Customer Welcome campaign.
I've been a subscriber for awhile, but now I've shown a strong interest in tomatoes and peppers by ordering a few. I'd be a ripe candidate for products that'll help grow them (heat mats).
This technique is more advanced than I usually recommend (sending an email based on the product types ordered) but it shows the power once you get to that level. You could easily get started with a basic campaign that linked out to your various product categories (e.g. link to tomato growing, link to lettuce, etc).
You could even start with some seasonal campaigns that you rotate through based on which season a customer orders (e.g. spring vs summer growing, fall fashion).
You can always add more behavior and complexity as your campaigns turn a profit.
If you were going to create a product-specific campaign, the First Product Analysis in Repeat Customer Insights should be the first place to look. It'll show you which products are in a customer's first order and how valuable those customers end up being.
Build campaigns around your top 5 products first and you'll have a better chance of overall success.
Eric Davis
Segment your customers to find the diamonds in the rough
Not all customers are equal but it is difficult to dig through all of your data to find the best customers.
Repeat Customer Insights will automatically analyze your Shopify customers to find the best ones. With over 150 segments applied automatically, it gives your store the analytics power of the big stores but without requiring a data scientist on staff.