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Growth strategy

How long does it take a customer to make their first purchase?

The short answer

Longer than your marketing assumes, and longer the more you charge. My rough rule is about two weeks for every $100 of order value, and the curve steepens as the price climbs. Measure your own number with a sign-up-to-purchase cohort, then fund campaigns months before you need the revenue.

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Why does time to purchase matter so much?

Because your funnel is a timeline as well as an audience. We usually draw it as circles, from everyone who’s heard of you down to the people who buy. The reason it makes sense as a funnel is that it’s actually time.

People move from the emotional reason to buy through to the rational details, like how they’ll pay for it. For a cheap product, someone can go from never having heard of you to buying in a single day. For an expensive one, that journey can take months.

Time to purchase is a massive metric. Plan your campaigns around it.

How does price change the buying cycle?

The higher the price of the product, the wider those circles go. A product around $100 might convert in about two weeks from first touch. A purchase of $10,000 to $25,000 typically takes something like 60 to 180 days.

Leads that look like they closed fast have often been seeing the brand for months. When you trace a big purchase back to its first touch, it’s often an educational article or a “how much does it cost” search from hundreds of days earlier. The offer at the end just prompted them to put their hand up.

Consumables have their own lag. People often need to finish what they’ve got before they’ll switch to you. We doubled the length of one campaign for exactly that reason.

How do you measure your own time to first purchase?

Run a cohort analysis. For an online store you can do it in Klaviyo. If you sell bigger-ticket items through a sales process, do it in your CRM.

  1. Take everyone who signed up, through your pop-up or a lead form, and later bought.
  2. Work out the number of days between sign-up and first purchase for each of them.
  3. Look at both the median and the mean.
  4. Then look at the long tail: the people who sat on your list for hundreds of days before buying, and what first brought them in.

I asked for exactly this on one store recently, because the gut feel was that customers buy within days of signing up. I suspect the real window is a lot longer. It usually is.

Make the average time to purchase, and those long-tail buyers, a standing item in your monthly marketing meeting.

What should you do with the number?

First, use it to set how long your nurture runs. If customers typically take a couple of months to buy, a welcome flow that stops after a week goes quiet right when they’re deciding.

Then use it to time your spend. What you run today determines your sales three to six months out, and for big-ticket products it can be further. If your buying cycle runs from three months to a year, the revenue target for any season is really decided by the campaigns you run six to twelve months before it. Even when the goal is a pipeline number for a particular month, really what we want to be doing is going six months before that, 12 months before that.

That’s when to spend heavily on list building. Push for sales when the season arrives, but fill the list well before it.

What if you need sales this month?

Be honest about which levers move short-term revenue. In a long sales cycle, any new funnel work you start today can take around six months before it actually drives sales. It won’t rescue this month.

The quick options are blunter. Email without an offer won’t do much in a hurry. Direct sales outreach will, and so will lead-form ads with a real offer that go straight to your sales team. The lead quality is usually not as good, but it does get you people. It’s a numbers game.

Then start the long-cycle work anyway, so you’re not in the same spot six months from now.

Nathan Frater

Managing director of Backbone, an e-commerce marketing agency in Auckland. Most weeks I'm on calls with Shopify brands working out what to fix next, and I write up the patterns here.

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