About
Michelle Familier, co-founder and CMO of Sunday Citizen, needed a clearer view of what actually drives lifetime value. Shopify reporting and a mix of tools could show orders, but not the cohort behavior and product combinations that determine what to promote and how to allocate budget.
SourceMedium helped the team move from “directional” reporting to repeatable decisions: what to push in marketing, how to merchandise the store, and how to tie spend to downstream value.
Sunday Citizen is an online bedding, bath, and loungewear brand founded in 2019.
The challenge
- Shopify reporting couldn’t answer retention and cohort questions at the depth the team needed
- Promotion and budget decisions depended on knowing what drives downstream value, not just orders
- A patchwork of tools made it harder to keep definitions consistent as questions evolved
Sunday Citizen had Shopify reporting and a mix of tools, but not the depth needed to answer the questions that drive LTV.
To increase customer lifetime value (LTV), they needed a clearer view of:
- Who customers are and how cohorts behave over time
- Which products are frequently purchased together (and where that shows up in retention)
- How paid spend maps to downstream value, not just order volume
The solution
- Cohort and purchase-pattern views to understand how customers buy over time
- A shared baseline the team could use without a dedicated analyst for every question
- A practical loop: decide what to promote, then validate it against cohort performance and spend
Sunday Citizen chose SourceMedium because it made the data usable across the team, not just for one analyst. With a clearer, more granular view of customer behavior and product combinations, the team could move from “directional” reporting to repeatable decisions.
They used purchase-pattern insights to decide what to promote in direct marketing, and they used the same patterns to influence on-site merchandising so the store reflected how customers actually buy. On the paid side, they compared cohort performance against ad spend to optimize toward ROI rather than just purchase quantity.
What changed
- Decision enabled: The team shifted direct marketing and merchandising based on real product-pairing behavior and cohort signals.
- How they validated it: Cohort and purchase-pattern views made it possible to compare performance consistently and tie outcomes back to spend.
- What got faster: Fewer manual lookups and quicker iteration on campaigns and store layout changes.
The results
-
Higher-confidence LTV decisions
The team could prioritize actions tied to lifetime value instead of relying on “directional” snapshots. -
Cross-sell and merchandising backed by purchase patterns
Commonly purchased combinations informed what they promoted in email and what they surfaced on-site. -
Paid optimization tied to ROI, not just volume
Cohort performance and ad spend could be compared to steer budget toward profitable growth.