# Case Study: The Retailer Whose Cheapest Clicks Were Its Most Expensive Sales | Crank Source: https://wearecrank.com/case-studies/beauty-home-fragrance-measurement-2016 An analytics view with no filters applied was hiding duplicate and blank-value transactions, and the channel that looked cheapest per click was costing four times as much per sale. --- ![Beauty and home fragrance case study: The Retailer Whose Cheapest Clicks Were Its Most Expensive Sales](/images/case-studies/home-fragrance-retail.png?dpl=dpl_2uJjggmfov9ibhArygujfRBb3pax) Beauty & Home Fragrance, 2016 Audit # The Retailer Whose Cheapest Clicks Were Its Most Expensive Sales An analytics view with no filters applied was hiding duplicate and blank-value transactions, and the channel that looked cheapest per click was costing four times as much per sale. Challenge The analytics view had no filters applied, duplicate transactions and blank-value transactions sat inside the reported revenue, and there was no way to see whether people were failing to add to basket or failing at checkout, because neither event existed. Judged on cost per click, shopping looked competitive; judged on cost per sale it was four times as dear, so budget was moving steadily toward the most expensive way to buy a customer, and no one could tell a merchandising problem from a checkout one. Approach Filter the reporting so the revenue figure can be trusted, add the missing basket and checkout events, and move the account onto cost per sale by channel and by device. Outcome Shopping and brand search could be funded on their true unit cost rather than click price, and the mobile gap became identifiable as an experience problem rather than a media one, with visitors who'd been prompted to return giving a defensible, evidenced reason to keep funding demand creation rather than cut it. 01 ## A number nobody had checked The analytics view had no filters applied. Duplicate transactions and transactions with blank or zero amounts were present in the reported revenue, uncorrected. Beyond that, no add-to-basket or checkout-progress events existed at all, only a couple of store-location actions were being recorded, alongside product views and back-in-stock requests. Without those events there was no way to tell a merchandising problem from a checkout problem: both looked identical from the outside, because neither had a signal of its own. 02 ## What the platform's own view was hiding Shopping campaigns were costing $21 per sale against $5 for brand search, and a ROAS (return on ad spend) of 2.95 vs 14.25, yet the account was being assessed on cost per click, where shopping looked competitive. Judged on the number that actually mattered, shopping was four times as expensive per customer. Mobile told a similar story from a different angle: paid search on mobile cost $12 per sale against $5 on desktop, with basket completion at 35 per cent against 61 per cent. The gap wasn't a media problem, it was a mobile experience problem showing up in a media metric. 03 ## The audience already working, unrecognised Visitors who arrived without any prompt converted at 2.3 per cent. Visitors who'd been prompted converted at 7.2 per cent and added to basket almost three times as often, a difference large enough to matter to budget, and invisible to anyone looking at cost per click alone. Cutting the activity that creates demand because the activity that harvests it looks cheaper is the standard mistake this kind of gap produces. Seeing the conversion rate rather than the click cost is what prevents it. 04 ## What replacing the metric let the account do With duplicate and blank-value transactions filtered out and the basket and checkout events in place, shopping and brand search could be funded on their true cost per sale rather than click price, and the mobile gap stopped looking like a media problem and started looking like the experience problem it actually was. The evidence for demand-creation activity, previously invisible to anyone reading cost per click, was now sitting in the reporting rather than requiring a separate argument to defend it. 05 ## What this means for you Cost per click tells you what traffic costs. It cannot tell you what a customer costs. The two regularly rank your channels in opposite orders. ## What replacing the wrong metric found, and let the account do 4x Cost per sale, shopping vs. brand search 2.4x Cost per sale, mobile vs. desktop 26pp Basket completion gap, desktop vs. mobile 7.2%vs2.3% Conversion rate, prompted vs. unprompted visitors, funded with confidence once seen Drawn from Crank's account audit conducted in 2016 for this beauty and home fragrance retailer, before the account was taken on. Client not named. ## Want to know what this looks like in your own account? These findings came from asking straightforward questions before any budget moved. We're happy to do the same for yours. 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