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Case #6 · The Overlooked Metric

Mobile Converts Worse — Cut It?

Mobile's conversion rate was half of desktop's, and the spreadsheet said the answer was obvious: stop spending on it. The conversion path said the opposite — and nearly nobody looked at it.

The scenario

Cedar & Bloom is a fictional online plant nursery — a small team selling potted plants and care kits to people who want their apartments to feel alive. Most of their growth came from a steady drip of paid search spend, split roughly half-and-half between phones and laptops. The product photographs beautifully, the price points are impulse-friendly, and the business had reached the point where every marketing dollar was being watched.

Sofia, who ran growth, was asked to find waste. She opened the report that breaks revenue down by device category and found what looked like an easy win. Desktop visitors converted at a healthy clip. Mobile visitors — who made up nearly half the traffic and half the ad budget — converted at roughly half the rate. On the surface, mobile was paying full price for half the result.

The confident wrong conclusion

The conclusion drew itself. Mobile is the leak. Same spend, half the return — so cut it. Sofia built the case in an afternoon: pause mobile bidding, shift the freed-up budget to desktop where the conversion rate was double, and watch overall efficiency climb. The math was clean. The story was tidy. Half the spend was clearly underperforming the other half, and the obvious move was to stop feeding the weak side.

The proposal was to cut mobile ad spend entirely and reallocate it. On a single column of numbers, it was hard to argue with.

Mobile wasn't the channel that closed the sale. It was the channel where the sale began — and the spreadsheet only counted the closing.

The overlooked metric

Before anyone paused a campaign, one number reframed the whole thing. The conversion-rate comparison Sofia had been reading was built on last-click credit — it asks only which device was open at the moment of purchase, and ignores everything that led there. When she instead pulled assisted conversions, the picture inverted. Mobile was involved in a large share of the journeys that ended in a desktop sale.

The reason became obvious once she looked at the conversion path reports. A typical buyer discovered Cedar & Bloom on their phone — a lunchtime search, a tap on an ad, ten minutes of scrolling through fiddle-leaf figs. They didn't buy then. They came back days later on a laptop, often by typing the name in directly, and completed the order with a card they'd rather not punch into a phone keyboard. Mobile was consistently the opener. Desktop was consistently the closer.

Last-click handed all the credit to the closer. The conversion rate Sofia nearly acted on wasn't measuring whether mobile worked — it was measuring how often mobile happened to be the final touch, which for this audience was rarely. The work mobile did was real; it just landed in a different column.

The corrected interpretation

Read through the conversion path, mobile wasn't a failing channel. It was doing essential top-of-funnel work — introducing the brand, holding attention, and setting up purchases that desktop quietly finished. Its low last-click conversion rate wasn't a sign of weakness; it was a sign of where it sat in the journey.

Cutting mobile would not have moved that spend to desktop. It would have starved desktop. The desktop conversions Sofia wanted to protect were, in large part, downstream of mobile discovery. Remove the opener and many of those tidy desktop sales would never have started. The "efficient" reallocation would have quietly knocked out a chunk of the very revenue it was meant to grow.

The assisted-conversion view, paired with path analysis, was what revealed mobile's true contribution — a contribution that the headline conversion rate had been hiding in plain sight.

What to do next

If a channel looks like dead weight on a conversion-rate report, check where it sits in the journey before you cut it.

Mobile wasn't worse. It was earlier. The spreadsheet wasn't wrong about the conversion rate — it was just answering a narrower question than the one Sofia actually needed answered.

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Cedar & Bloom and Sofia are illustrative — a composite created to demonstrate a real and common pattern.

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