How Adlucent Uses Ratings and Reviews in PPC Campaigns

By — 05.12.11

In an earlier post, Integrating Ratings and Reviews in Paid Search, I discussed that product ratings and reviews can dramatically impact conversion rates. But how does Adlucent leverage that knowledge?

Predicting Conversion Rates

When building campaigns for new products that have no historic CVR data, Adlucent uses the review quantity and average rating as two of several elements of a scoring mechanism to predict CVR. (Another element, for instance, is the historic performance of comparable products.)

Four StarsThe particular values we use vary by retailer, category, product type and several other factors—every retailer is different. Holding other predictive variables constant, the highest-rated, most reviewed products are prioritized over the lowest-rated, least-reviewed products. Once Adlucent begins accumulating actual performance data, we minimize reliance on predictive proxies.

Improving Business Performance

Adlucent partners with retailers to improve their overall business performance, not just to drive profitable revenue growth through SEM. Ratings and reviews can play a valuable role here, in two areas.

First, we match customer demand data derived from the search engines with ratings and review data to find areas of opportunity. We research the ratings and reviews of brands, categories of products, and specific products that our clients do not carry. Products with favorable reviews tend to sell better, so we frequently recommend products for our clients to consider adding to their catalogs.

Second, products with unfavorable reviews tend to have higher than normal return rates. By merging paid search performance data with ratings and reviews data, we can identify and resolve issues before they lead to margin erosion or customer losses.  In many cases, the retailer can rectify issues by improving the product description, resolving delivery issues, or fixing incorrect specifications. In other cases, we can approach the manufacturer to share with them actionable customer feedback and search performance data so they can make the quality control changes necessary to improve customer satisfaction.


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