The Elixirr Digital team began with a complete review of the retailer’s ad spend, which amounted to £650k. During this review, the team identified key issues and opportunities for improvement. The work included…
Updating the strategy – Our client had previously been applying Facebook’s DABA (Dynamic Ads Broad Audience) approach to their ads. This can be beneficial as it leaves the optimization up to the network’s AI, however, using this approach also impacts data visibility and takes control away from the brand. To counteract this, our team balanced the existing ad set ups with lookalike audiences of purchasers, testing different levels of similarity. Our 5% lookalike audience is now the best-performing audience for the retailer, exceeding the results of the original DABA approach.
Improving the ads – Our client’s existing ads were not built in line with Facebook’s algorithm. As the retailer ran offers on a regular basis, they often had to update the adverts. This meant resetting the ad’s ‘learning phase’, which stopped Facebook from optimizing the ads. To overcome this, we restructured the account and separated out different campaigns. Moreover, we added a dynamic feed to the prospecting ads, meaning we didn’t need to update the actual advert when offers changed. This allowed Facebook to complete the learning phase on the ads and optimize each one effectively.
Optimizing the audience – Our client generally saw low average basket values, coming in at around £35. To attract customers that were likely to spend more, our team changed the bidding strategy, moving from the lowest CPA (cost strategy) to a value optimization bid strategy, which targeted users that were likely to spend more. This strategy has been one of the best-performing campaigns week-on-week for our client.
