The first 60 days decide everything

When I was the CRM and lifecycle manager on the account, I studied every first-time buyer from February 2024 to January 2026. I grouped them by the month of their first order, which gave me 24 monthly cohorts and about 1.5 million people. I wanted to see when people came back after that first order, and what each return was worth.

What I found shaped how I designed the tiers and the flows, so it seemed like the right place to start this newsletter.

What the numbers showed

The average first order was $81. About 64% of first-time buyers never placed a second order.

Over 24 months, the average first-time buyer was worth $116. The first order alone was $80.66 of that, which is 70% of the two-year value. The following 12 months added $21.47. The 12 after that added about $14.

Then I looked at timing. For each cohort, I tracked the share of the original group that placed an order in each month after their first purchase. In month 0, the month of the first order, it was 4.18% of the cohort. The steepest drop came between month 0 and month 2. From month 6 to month 11 it held at about 1.3% of the cohort per month. There was one small bump at month 12, to 1.67% of the cohort, which looked like people coming back for the same season a year later.

Put those together and the picture is plain. The first order is most of what you'll get from the average customer. The best chance of adding to it is in the two months right after. Past that point the line is close to flat.

One caution. These are averages across cohorts. The 64% who never return pull them down, and the people who do come back sit well above these curves. The averages show you when to act. They don't describe your best customers.

What I built around it

The migration came first. We moved 9 million profiles from Cordial to Klaviyo during peak Q4, and once we were live I migrated the core flows (welcome, browse and cart abandonment, post-purchase, winback and loyalty) within the first 30 days. That got the program running. The cohort data told me where to put the weight.

I designed five tiers based on order count: no orders, one order, two to four orders, five or more with under $840 in lifetime value, and five or more above $840. Every flow splits by tier, so the same trigger gets a different message depending on where the person is. In this data the tier that matters most is the one-order group, because that's where 64% of first-time buyers stop.

For a new customer, the post-purchase series runs 14 to 21 days. The confirmation suggests pieces that go with what they bought. The delivery message asks them to share a photo. Five days later there's a review request, with 10% off the next order for leaving one. At day 14, an email recommends new arrivals based on their order. Separately, a cross-sell email goes out seven days after delivery with matching items and 10% off a different category.

The abandonment flows treat that group differently too. When someone who has never bought leaves a cart, the 15% off first-order offer comes at 72 hours. When a one-time customer leaves a cart, 10% off the cart comes at 24 hours. They've bought once already, and the next few weeks are when a second order is most likely, so the offer comes sooner. Customers with two or more orders get free shipping or perks in that flow instead of a discount.

I also recommended custom RFM settings in Klaviyo. The default splits customers into equal thirds, and with 64% of the base on one order, that would have put a lot of one-time buyers in the middle frequency group. In the settings I proposed, the lowest frequency score covers the bottom 70% of customers, which is the one-order group, and the top score goes to the top 10% of customers, people with four or more orders.

Three things to check in your own account

1. Your reorder curve. Group first-time buyers by the month of their first order. For each group, work out the share that ordered again in month 0, month 1, month 2 and onward. Find where the drop is steepest. At the retailer it was inside the first two months. Yours will depend on what you sell and how often people need it.

2. How much of two-year value is the first order. Take cohorts that are at least two years old and divide the average first order by the average two-year value per customer. At the retailer the first order was 70% of the two-year value. If yours is anywhere near that, the first order is most of what the average customer will spend with you, and every dollar beyond it comes from someone who placed a second order.

3. What a one-time buyer gets in days 0 to 60. List every flow and campaign a first-time buyer receives in that window. Mark which messages were written for someone who has bought once and which are the campaigns everyone gets. Note when the post-purchase series ends. If it stops at day 14 and the next message written for them is a winback months later, the period when they're most likely to buy again is covered only by your general calendar.

Do this Monday

At the retailer, December brought in two to three times as many first-time buyers as other months, many of them gift buyers and holiday shoppers who need a different post-purchase path. This year's holiday buyers will start arriving within a month.

On Monday, export last December's first-time buyers and count how many placed a second order within 60 days of their first. Then do the same for an ordinary month from earlier in the year. If December's share is lower, rework the post-purchase series for holiday buyers now, before this year's group arrives.

The first order is most of a customer's value and the next 60 days are your best chance at the rest, so build the lifecycle program around that window.