Case Study: Major Home Goods Retailer
OVERCOMING A SHOPPING PROGRAM PLATEAU
A mix of PLA technology and a unique methodology yields 146% more revenue for major retailer
About The Client
Our client is a large multichannel retailer that sells household products. Because Product Ads can be difficult to manage at scale and product feeds require real time optimizations, the retailer wanted a partner who could manage this piece of their business. While working with a top tier search agency to manage their PLA business, their performance for the retailer hit a plateau. Knowing that PLAs are a core driver of their digital revenue, they began looking for a new agency who could take their program to the next level. As the first Certified Google Shopping Partner and the first to introduce PLA management technology, they were confident Adlucent was the right partner for the job.
Building a Predictive Program
Adlucent began by implementing a PLA-specific machine-learning algorithm within its proprietary technology, Deep SearchTM, that’s aligned with the retailer’s unique products, customers and goals. This algorithm incorporates a variety of data sources including historical, merchandising, seasonality, geographic, promotions, and competitive data in order to predict future performance.
Expanding the Feed of Eligible Products
Adlucent quickly noticed that the retailer wasn’t showing ads for thousands of products in their catalog. Because Deep Search automates the process of mapping categories based on product types, the team was able to quickly expand the feed of eligible products. Adlucent also implemented its 62-step feed optimization process to ensure maximum product reach, improve click through rates and achieve greater ad relevance.
Mapping Products Based on Intent
To drive more efficiency from the program, Adlucent was able to accurately map consumers to products based on their level of intent. Whereas Google matched the retailer’s products based on bids and the content of their feed, Adlucent first used historical product and device data, as well as cross-channel analytics, to understand how their products are likely to perform. Adlucent then prioritized key terms and segmented traffic based on queries with high purchase intent. This unique approach allowed us to more accurately match products based on shopper intent, driving more conversions at a lower total cost.
Building a Mobile Strategy
With the majority of searches now taking place on mobile devices, Adlucent knew it needed to expand the retailer’s mobile investment to capture additional traffic and drive local shoppers to nearby storefronts. Adlucent increased spend on mobile PLAs to capture top funnel queries and invested in Local Inventory Ads (LIAs) to drive traffic with local intent to the closest storefronts.
Within the first month, Adlucent had already broken through the performance plateau created by the retailer’s previous agency, with revenue up 48% while improving ROI by 17%. Performance improvements didn’t stop there. Within one year of taking over the account, revenue is up 146% while ad cost is down 2%.
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