There are more than a dozen shopping comparison engines that a merchant can use to list products for sale. Listing products on CSEs that operate on Cost-Per-Click model is a bit risky if conversion rate is not monitored, as improperly generated feed files could result in wasting a lot of money on advertising products that do not convert. Traffic and conversion analysis are the key in optimizing data feed file for comparison shopping engines. While researching for data feed optimization, I came across an interesting report from Channel Advisor on "average" conversion rates for each CSE by industry. If your CSE result falls below the average shown below, your feed file needs optimization.
The average conversion rate report by Channel Advisor provides a merchant with a clue on what to expect from various comparison shopping engines. The merchants should use this data to optimize their data feed to achieve higher than average conversion rate obtained by other merchants in the similar category.
It would be interesting to see this conversion rate broken down into a couple of periods. For example, the conversion rate during the Christmas Season may be different than the regular season for many industry retailers. Also, for Home & Garden (i.e. pools, boats and outdoor) products, the conversion rate may be differ during the summer and winter months. This number may be used to analyze if the seasonal rate hike (i.e. 25% increase) by many CSEs is justified and by how much.
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