Product Feed Optimization, Shopping Ad Technology, and Rich Expertise, All in One Place
Maintaining and optimizing your product feed is critical to Shopping ad success, as the keywords in your feed are used to properly match products to search queries. Getting the right product to show for right queries is both art and science. At Adlucent, we have fully integrated our proprietary technology, Deep Search™, with the Google Merchant Center. This gives our team of PLA experts the ability to optimize feeds using our 62-step process to improve relevant matching and maximize product reach in real time. Within our PPC department, we have a dedicated team focused solely on shopping ads and feeds for each client. There's no one-size-fits-all campaign structure in our book; we tailor each one to our clients' specific strengths and goals.
While several products may match to a particular query, retailers have little control over whether the right product – the one most likely to sell – is showing. Adlucent leverages its advanced analytics and query-level architecture to funnel traffic to the right products, and away from unprofitable queries, to help you grow PLA revenue efficiently. So, we analyze the KPIs that matter to retailers, down to the individual SKU and query level.
Adlucent leverages real-time demand data to proactively predict how a product will sell versus reactively adjust. Deep Search incorporates real-time signals and data on factors such as competitor pricing, promotions, and seasonality to make rapid changes in bids, saving you time and maximizing revenue. Because the marketplace, Google auction, and even your competition are always changing the game, we continuously measure results and try new solutions to ensure the highest return on ad spend at every turn.
Essential and Advanced Strategies For An Exceptional Shopping Program
Read Our White PaperImplementing Customized Ads to Drive Up Impressions and Clicks During the Holiday Season
Read Our Case StudySolving the Challenge of Marketplace Feed Management with a Cost Threshold Rule
Read Our Case Study