The Sarah Raven website was not achieving its full revenue potential, largely due to a lack of traffic. We identified opportunities for boosting both category and product pages, but we were challenged to work with a restrictive budget.
There was also an added, immediate complication of there being a warehouse full of dahlia flower bulbs that the company needed to sell quickly.
We decided to compare a range of pages to identify those that were statistically most likely to convert. With a huge amount of data available, limited time and restrictive budget, we used machine learning analysis to help us yield some quick gains.
We used four different algorithms to develop a machine learning model that could predict, with a 70% or greater degree of accuracy, the likelihood that a page would convert. The calculation was drawn from a correlation of large numbers of conversion indicative metrics. The analysis identified that the dahlias category page (rather than individual product pages) was the one to promote.
We improved the page meta data to better match buyer intent and improve the call to action. This was designed to encourage a higher click-through rate.
We quickly achieved significant increases in traffic, organic transactions, and revenue – a trend that continued for months afterwards. At the time of writing, click-through rates are 76% higher than previously, and transactions are up by 194%.
Without machine learning, it would not have been possible to achieve such impressive results in such a short space of time on a limited budget. We are happy to say Sarah Raven has expanded its relationship with Vertical Leap, from SEO into PPC.
We appointed Vertical Leap in February 2017 and they immediately had a big impact on our business. They’re very data focused which meant the decisions they made for us generated a good return right from the offset We very quickly saw significant increases in traffic levels, organic transactions and organic revenue, and this trend looks set to continue. I would have absolutely no hesitation in recommending them to others.