The last few days I've taken some breaks from work to plan out next years garden.
Each plant has times where it grows the best. Peas like a cold start in spring, lettuce needs a bit warmer weather but not summer-warm, tomatoes like it warm, etc.
I created a single-page monthly calendar for all of the plants we're going to grow. It describes when they should be started (e.g. mid-May) and roughly how long each one goes for (e.g. September).
It's not only a 2022 calendar though, it applies to each and every year we're here. As I try new plants and collect new data, I can update the calendar so it improves year-by-year.
Your Shopify store could benefit from a growth calendar too.
Instead of plants though, it would list your various marketing campaigns and initiatives. e.g. you'd start your New Years campaign at the end of December and into January, maybe your anniversary sale is in April, launch year's new product lines in August/September, etc.
A birds-eye view of your Shopify store like this would make planning easier and you could discover gaps in your plans (nothing planned for June or July).
Every year you can improve the plan. Extend or shorten some campaigns, get rid of weak ones, test new ones, launch something early. The overall plan would iterative and evolve over time.
I'd recommend you leave the specifics of each campaign like content, number of contacts, and customer segments to be decided on each time.
In Repeat Customer Insights there is a bunch of advice around campaign planning and who should get what kinds of campaigns. Combining its customer segmenting with your year's worth of plans has the potential to increase your store's growth.
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.