This smart customer targeting strategy helped Ruffwear rescue lost sales

Ruffwear makes durable outdoor gear for dogs, so it’s no surprise the brand began on a mountain biking trip. Watching a friend struggle to give her dog a drink from a plastic bag, founder Patrick Kruse had the idea for Ruffwear’s first product: a collapsible cloth dog bowl. 

Since then, the brand has expanded into other SKUs, from harnesses to dog boots, all while prioritizing sustainability.

The Ruffwear team wanted to increase customer lifetime value. But they had already implemented every retention best practice in the book. Their next LTV growth lever would need to be pretty sophisticated.

Specifically, they wanted fresh insight into their disengaged customers, who were all receiving the same standard re-engagement flows at the same cadence.

But which customers were most valuable? Most likely to return? If the team knew, they could focus their retention efforts (and discounts!) on them.

Re-engage at the right time, every time 

Ruffwear decided to use Klaviyo CDP’s powerful RFM segmentation capabilities to shine light on their disengaged customers. One way they did this was sending RFM-triggered re-engagement flows.

If a customer enters the “Needs attention” or “At risk” RFM segments—for high- and low-LTV customers who haven’t bought recently, respectively—it triggers a retention flow. 

The flow content is personalized based on purchase history and recommends bestsellers the customer hasn’t yet bought.

“Compared to our other post-purchase flows, these ones are doing really well,” says Natalie DeRatt, Ruffwear’s ecommerce communications coordinator. “We’ve done all of the out-of-the-can approaches, and now we’re taking a more personalized approach to retention. It feels like we’re getting an extra edge with Klaviyo.”

Get similar results 

Your business can also target disengaged users and create custom experiences that bring them back to your brand. Here’s how:

• Read the full Ruffwear x Klaviyo case study 
• Learn how to use RFM analysis to improve customer retention