BOSTON, June 6, 2017 – Search advertising campaigns report dramatic lift in performance using AI technology automation
E-commerce search marketers who use long-tail keywords to capture more of the intent of their shoppers see over 30% improvement in conversion rate, according to a new Vioby study, “Do Long-Tail Keywords in Search Ad Campaigns Really Improve Conversion?” An analysis of performance data for apparel items for retailer campaigns encompassing 80,000 clicks by shoppers found that keywords that included size or color performed significantly better than similar keywords that did not.
Search advertising (SEM) marketers create their advertising campaigns with a high number of keywords to match the large variety of search queries of their shoppers. Long-tail keywords are longer phrases that are more specific to what the retailer is selling and can capture more of the intent of the shopper’s search query.
“By including long-tail keywords in the campaign, the matching keyword better captures the shopper’s query,” said Alec Belfer, CEO of Vioby. “When the shopper then sees what she just searched for, the experience is better, she is several clicks closer to ‘add to bag’ and the conversion rate increases.”
Vioby analyzed Google AdWords search advertising campaign performance data for keywords such as “evening dress” and “polo shirt” compared to long-tail keywords containing size and with color such as “evening dress size 8” and “blue polo shirt”. For each keyword, the destinations on the e-commerce website were selected so the shoppers always landed on the most relevant content. The data shows that the long-tail keywords converted at a 32% higher rate.
The campaigns analyzed comprise about 50,000 keywords spanning several categories of apparel. Details of the study can be found on the Vioby blog article, Do Long-Tail Keywords in Search Ad Campaigns Really Improve Conversion?
Vioby has developed the first product for search marketers that automates the problem of always getting the shopper to the most appropriate, relevant content on the website. Using natural language processing and machine learning to capture and understand shoppers’ intent, understand the products and the website, the webpage that best matches the shopper’s search is determined. By reducing the number of clicks the online shopper needs to get to “Add to Bag”, more shoppers purchase and the user experience is greatly improved.