The Resultsįast forward a few years and we took Manta Sleep from $10k to $100k in monthly revenue on Google Ads after less than a year of working with them. We didn’t stop there though - by the end of March 2023 we took the account to over $723K in monthly revenue (and hit over $1m in December 2022)!īeyond that, across the period we’ve worked with them we achieved a ROAS of 467%, meaning for every dollar spent we’re getting back $4.67 in revenue. When initially scaling Manta Sleep from $10k to $100k, Google Shopping campaigns formed a large part of the activity we ran but as we moved into 2022 we wanted to ensure we were using all tools available to us. This is where Performance Max came in, Google’s powerful, semi-automated product that’s perfect for running E-Commerce campaigns at scale while supplementing our existing Shopping activity. As we gradually devised a formula over the following months for PMax, we saw them become so efficient that they eventually replaced our Smart Shopping activity entirely. In order to set up our PMax campaigns for success, we used a tiered campaign structure. ![]() This meant having one campaign (our highest spending) for only the four or five highest performing products at any given time, meaning they had plenty of budget available to them for maximum delivery. Aside from that, we segmented campaigns by country and product type (Sleep Kits, New Products etc.) meaning not only were all assets hyper-relevant to the products they were associated with but also we were able to adjust budgets depending on the performance we were seeing from a particular market or product category. ![]() These smaller, more precise campaigns were perfect for a partner like Manta Sleep which has a lower number of SKUs but each of which drive significant sales volume. Something which proved valuable in the early stages of setting up these PMax campaigns was making sure we provided Google with Audience Signals of what we expected the highest performing audience looking like, rather than suffering a period of low performance while Google figured the audience out for itself. ![]() This included obvious signals like users who’d searched for terms around ‘sleep’ recently, as well as targeting which we’d seen perform well for our older Shopping activity (including Dating and Fitness Affinity audiences).Įqually important as we set up these campaigns was an acute awareness of ROAS targets and when these should be adjusted over time.
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