In brief
You’ll have a loyalty program you can run week to week, with earn and burn rules that fit your margins, customer segments, and tech stack, so repeat purchase moves without training customers to wait for discounts.
You reached out because paid acquisition is costing more for the same revenue and you need returning customers to carry the next season. I’ll pin down earn rates, reward costs, and the rules that stop gaming. If you’d like to proceed, I’ll send a short intake and get started on your numbers.
I start with your current retention and cohort behaviour, then I work forward into the few levers that matter: who you should reward, what it should cost, and when customers should see it. Every rule goes into a single rulebook so your margins, flows, and app settings all match the same logic.
A bit about me
I’m Kate McCaffrey, a loyalty consultant. I got into loyalty on the unglamorous side of it: points liabilities, reward costs, and why “more points” often means “less profit”. I’ve spent most of my consulting time translating retention goals into rules a store can actually run inside Shopify, email, subscriptions, and a loyalty app.
People hire me when they want the numbers nailed down before anything ships. I’ll tell you where I have to assume, what I can verify from your data, and what I won’t recommend because it looks good on paper but leaks margin in the wild. You’ll get a rulebook and an implementation path that your store can follow without weeks of tag wrangling.
Recent work
Here are a few loyalty program strategy engagements that show how I handle margin, segments, and platform constraints.
Portfolio
Tiered points for skincare. Rebuilt earn rates and tier thresholds around contribution margin instead of revenue. Added rules to limit reward stacking and removed low-value redemptions. The store kept points attractive while reducing the average cost per redemption.
VIP program for apparel. Mapped lifecycle triggers to reorder behaviour and returns patterns, then rebuilt tiers so perks followed second and third purchases, not first-order discounts. Added a “cool-off” rule for high-return behaviour to protect margin.
Subscriptions and loyalty alignment. Aligned Recharge subscriptions with loyalty so subscribers earned benefits without double-rewarding discounted renewals. Tightened earn rules on subscription offers and set burn options that cleared points without pressuring cash margin.
“The rules finally made sense, and the margins stopped feeling like a guess.”
E-commerce owner, a growing DTC store
How this runs
I keep this work tight and numbers-led. You’ll see the assumptions as I make them, and you’ll see what I check against your data before any rule becomes “the program”.
1. Data read Days 1-3 I review your current retention numbers: repeat purchase rate, time to second order, cohort curves, and discount and returns behaviour. I’ll come back with a short list of what the data supports and what it does not. I’ll also call out any tracking gaps that will affect launch measurement. 2. Rulebook draft Days 4-8 I draft the earn and burn rules, tier logic, and the guardrails that stop obvious gaming. This is where I answer the hard questions in writing, like how to treat high-return customers, discount hunters, and store credit. You’ll review a draft before anything is locked. 3. Rewards and lifecycle Days 9-12 I build the rewards catalogue with margin caps and redemption rules, then map lifecycle triggers to the moments that actually move behaviour. You’ll see when customers earn, when they get nudged to redeem, and where loyalty should sit alongside your existing email and SMS flows. 4. Stack and handover Days 13-14 I scope the platform setup around what you already run, then produce an implementation checklist that ties rules to settings, tags, events, and flows. You’ll leave with a clear build path, plus the key numbers to watch in the first month so you can tell if repeat purchase is moving.
Pricing
Pick the tier based on how much of the build you want me to take to “ready to implement” in the same 14-day window.
Priced items
Get started
If you want me to hold time for this, the next step is to sign and book it in.
1. Sign the proposal using the signature box on this page. 2. Pay the 25% booking invoice to lock in the start date. 3. Send back the intake so I can start on your cohorts and margins.
Signature
Fee summary
Payment
To book the work, I invoice 25% when you sign. I invoice the remaining 75% when the engagement is complete. Each invoice is payable within 14 days of its date.
Schedule and access. The 14-day timeline starts once I have access to the data and tools we agree on. If access lands late, the dates move by the same number of days.
What I need from you. I’ll send a short access list on day one (store analytics, email/SMS reporting, returns, and product margins). You’ll get the best outcome if those are in place within 24 hours.
Changes to scope. If you want to add work that is not in the agreed loyalty program strategy steps, I’ll describe the extra time and fee in writing before I start. Nothing changes quietly.
Assumptions and unknowns
Any model I build is only as good as the inputs. Where data is missing, I’ll state the assumption and the impact, and I’ll mark what I want to verify before you launch.
Ownership. Once the final invoice is paid, you own the rulebook, models, and specs I produce for your store. I can reuse general learnings, but not your numbers or customer data.
Confidentiality. I’ll keep your store data, margins, and customer lists confidential, and I won’t share screenshots or results publicly without your written approval. You can ask for an NDA if you use one.
Platform and app work. I scope the stack and integrations, but I do not install apps or build flows inside your accounts unless we agree that separately in writing. You stay in control of your logins and billing.
