Case study: Big Stock
The optimization team at BigStock, an online stock photo provider, noticed their customers were having problems with their searches. If they misspelled a word in their search, visitors were less likely to see the results they were looking for. The team came up with a solution: adjust the search algorithm to create a “fuzzy autosuggest” when running searches. As a result of this algorithm, they estimated that they would see an uptick in people selecting results from the auto suggest.
With this hypothesis established, they ran their experiment. When the results came in a week later, Bigstock found that visitors selected results from the fuzzy auto suggest 9.6% more often. They used their analytics, coupled with their results, to find a 6.52% increase in the number of images added to visitors’ carts and a 3.2% increase in image downloads.
By centering their hypothesis around a problem they found their customers were having based on analysis of their data, BigStock was able to solve a real issue with their search algorithm. Their results came quickly and had a direct impact on their conversions.