What I changed, why I changed it, and what happened next.

Where we started

A year earlier, there was no retention system in place. By January 2026, around 30 automated journeys were live, and I was running retention strategy and execution.

With fewer new customers entering the business, I focused on where the existing customer base could contribute more.

What I worked on

• Recovery journeys across browse, cart and checkout abandonment.
• Product-specific post-purchase education and replenishment journeys.
• Winback segmented by purchase frequency as well as time since purchase.
• Campaign audiences and contact rules based on purchase history.
• Weekly and monthly reporting to track performance and messaging costs.

What the data changed

A broad mid-month sale campaign in April barely covered its messaging cost. In June, the sale was split across ten product-based segments. It used roughly one-sixth of the messages and generated more than four times the revenue.

Winback showed another useful difference: lapsed customers with at least two previous purchases returned 18–25x ROI. One-time buyers inactive for six months or longer returned closer to 2x.

That gave me a clearer basis for deciding where to spend.

The result

Returning-customer revenue increased by 4.4% year over year, and automated journeys consistently returned 10–14x ROI.

The brand also had strong organic demand, so CRM wasn’t the only contributor. The retention programme helped make returning revenue more dependable during a period of slower acquisition.

Where we started

The account generated revenue around sales, but had limited post-purchase automation, little cross-sell and no consistent reporting system.

Before building more, I needed to establish what was actually working.

What I changed

• Corrected reporting. The CRM platform understated store revenue, making CRM’s contribution appear more than twice as high as it was. I rebuilt reporting against Shopify.
• Built post-purchase journeys. Loyalty and cross-sell became part of the programme, with recommendations informed by purchase history.
• Analysed buying patterns. I mapped roughly 7,000 product combinations to understand which products customers bought together.
• Changed campaign targeting. Analysis of 61 campaigns showed purchase conversion was roughly 7x higher during sales. Between sales, I prioritised smaller audiences with a clearer reason to buy.

What the data changed

The account needed a way to generate revenue after someone became a customer and between major sale periods.

Purchase patterns helped make recommendations more relevant, while clearer reporting showed which journeys justified further investment.

The result

Typical monthly CRM contribution grew from around 5.5% to approximately 10%, reaching 14% at peak.

Post-purchase journeys grew from zero to roughly 25% of automated journey revenue.

What I changed

I introduced a structured monthly calendar and tightened the audience for each campaign.

The work included:

• More selective segmentation.
• Reviewing and improving lifecycle flows.
• Regular founder-led plain-text emails.
• Separate analysis of campaign and flow performance.

What the data showed

Average audience size per campaign fell by roughly 36%, from around 31,200 to 20,100 recipients.

Campaign revenue increased by 24%, while revenue per recipient rose by 25%. Flow conversion rate also improved by 28%.

These results helped assess the programme beyond the size of each send and showed where more selective targeting could support stronger performance.

What changed

The list was intentionally reduced from roughly 12,000 to 7,000 profiles, and B2B contacts were separated from DTC reporting and automations.

Nine lifecycle flows were put in place across welcome, abandonment, post-purchase, cross-sell, winback and re-engagement. Server-side tracking was also implemented to improve the data feeding the programme.

What the data showed

Revenue per subscriber increased from $0.18 to $0.22. Campaign click-through rate improved month over month as total sends decreased.

Across the wider store, orders increased by 53% and revenue grew by 12.7% from June to August. These provide business context alongside the CRM results.