Like many companies, a higher performance storage company was seeking to optimize their marketing campaigns. They spend significant resources and budget on events, webinars, list purchases, lead generations tools and an inside sales team. They also have a significant list of current customers and partners. Each week they spend time and risk sending thousands of emails. Their return on this investment was good but not up to industry standards and did not seem to reach the potential of the the focus and funds invested. To this large pool of subscribers they were looking for ways in which to quickly and easily deliver personalized and relevant content to increase the engagement and ultimately conversion.
The company was sending out more than 60K emails a week to their nearly 300K subscribers. At that time, they were sending the content to segmented lists of recipients based on categories assigned by lead general tools or on assumed common interest derived from event participation. Results were not meeting expectations as both the open rates and the click through rates were substantially below their industry benchmarks. The challenge was how to go about increasing engagement. Refinement of categorization or segmentation based on manually re-assigning was laborious and daunting. A/B testing sometimes revealed insights but required extra work and at times did not seem to reveal consistent trends. They felt as though they had the right lists of prospects and customers but were not able to get the right message to the right people to optimize their return. As such, the company was interested in finding a way to improve engagement by optimizing segmentation, subject lines and text within the emails to help drive improvements in open and click through rates.
Synaptiq’s Machine Learning solution was able to identify a few areas for improvement after reviewing historical email campaigns. The first challenge was creating content that is more relevant to each of their subscribers. Synaptiq reviewed the company’s customer list and its Machine Learning models were able to identify 4 distinct segments. By segmenting their subscriber list, the company will be able to create content and emails that are more relevant to their prospects and customers in an easy to deploy, automated way.
After segmenting the customer list, the next topic was optimizing the content of the subject lines in order in increase open rates. Synaptiq’s Machine Learning model inspected millions of historical email campaigns and the data associated with them. Based on that data, Synaptiq scored the effectiveness of each and every word in the subject line. This analysis allowed the company to make changes to their subject lines based on historical data showing which words, phrases, and content led to an increase in their open rates. This was especially useful as they discovered more detail on how their audience engaged with their content. For example, it is commonly believed that using questions in the subject line is effective, and Synaptiq’s Machine Learning models have shown that questions are opened less often. As the company implements these solutions, we expect to see improved results in both open rates and click through rates as they develop targeted content by audience as well as optimize their subject lines based on the Machine Learning models from Synaptiq