It’s no secret that Google Shopping Ads are a favorite among marketers. In fact, they represent more than half of the total paid search investment among Adlucent clients. With Shopping ads generating an average of 76% revenue growth YoY1 , it’s no surprise that retailers are shifting their investment. With stagnant (or often shrinking) advertising budgets, it’s no surprise that this effective form of digital advertising is popular among marketers who are trying to maximize their spend.
So how do you make the most of the money you invest in your PLAs and create the optimal shopping program? We recommend that you ask yourself the four questions below. And since it’s not in our nature to leave you hanging, we have some ideas to help you answer these questions and drive more efficient growth.
Q: Is my PLA showing the right product?
A: Most retailers organize their Google Shopping account based on taxonomic structure such as grouping products within the same category or the same brand. This makes sense from how a retailer sees their business, but it doesn’t make sense in terms of optimizing performance. To ensure you’re showing the right product, you can implement a query-level product architecture to funnel traffic to the product that has the highest likelihood of netting the sale. At Adlucent, we funnel traffic to the right product by looking at the most popular products sold. We do this by evaluating query data from PLAs, text ads and onsite search combined. By managing at a query level, you’ll be able to invest more in the most profitable queries and stop wasting money on the least relevant, least profitable queries. This can be an extremely complex process for the average marketer so consider leveraging feed management services and technology (like Adlucent’s Deep SearchTM ) to help you with this.
Q: Is my PLA at the right price?
A: Since nearly half (45%) of people we surveyed said they’d choose an ad that advertised the lowest price over all other extensions, it’s important to incorporate your competitor’s pricing data into your bid management algorithm. At Adlucent, we use Deep Search to help clients evaluate real-time competitor pricing data. This allows us to ensure we’re showing an ad when we’re in the best position to win the sale, and pull ads when we’re not—thus, optimizing budget. A simpler approach would be to take a look at your Product Suggestions Report to learn how to price products that you might advertise by providing five benchmark prices for each product.
Q: Is my PLA showing at the best possible time?
A: We recommend you use data (such as promotion, inventory and CRM data) to predict future performance so you don’t waste budget on ads that are unlikely to convert, such as when you’re not in the best position to win the sale. By leveraging predictive bid management, you’re more likely to show an ad in the moments that matter most for your shoppers.
Q: Is my PLA reaching the right person?
A: Methods for ensuring your PLA is showing to the right person are becoming more available. By leveraging solutions like Google’s Customer Match, you can target segments of individuals that look like your current customers, increasing your likelihood for a sale. By partnering with third party data providers, you can gain insights into aspects such as demographic and behavioral data to get a more complete picture of a retailer’s customers. At Adlucent, we integrate third party data into our machine learning algorithm to infer the presence of potential customers everywhere. This allows us to reach them with highly relevant search ads. In doing so, we can ensure we’re allocating spend to the shoppers who are likely to be the best fit for our client’s business.
For more strategies to create the optimal Shopping program, check out our latest white paper, DNA of The PLA: Essential and Advanced Strategies For An Exceptional Shopping Program. Featuring both beginner and advanced strategies, our new white paper is packed with ideas for marketers at every level. And as always, let us know how these tips work for you in the comments below!
1 Adlucent client data: Q4 2016-Q4 2017