Read about how we helped a Chief Marketing Officer at a growing B2B technology company to increase outbound email open rates and prospect engagement. We used machine learning to evaluate past marketing campaigns, which allowed the marketing team to optimize new email campaigns, improve the team’s efficiency, and deliver a better return on investment for outbound marketing.
A high performance storage company wanted better return on investment in marketing, increased engagement, and higher conversion rates. The marketing team sent out more than 60,000 emails per week to nearly 300,000 subscribers. They sent content to segmented lists based on categories assigned by lead generation tools or on derived common interest based on engagement. Even though prospect and customer lists were good, open rates and click-through rates were too low. A/B testing sometimes revealed insights but required extra work and did not reveal consistent trends.
The company had already dedicated a substantial amount of resources and budget on events, webinars, list purchases, lead generations tools, and inside sales. Each week marketing spent more time than it could afford tailoring campaigns and sending thousands of emails. Results were positive but not up to industry standards, particularly given the investment.
Manually refining, categorizing, and segmenting was not an option given the size of the marketing database. 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. It wanted a way to optimize marketing campaigns to quickly and easily deliver personalized and relevant content, and increase engagement and velocity through the sales funnel.
The head of marketing engaged Synaptiq, which identified several areas for improvement by using Machine Learning to review historical email campaigns: list segmentation, subject line improvement, and content alignment. Based on findings, Synaptiq reviewed the company’s customer list and used models to identify four distinct segments.
Next, Synaptiq used a Machine Learning model to inspect millions of historical email campaigns and associated data. Synaptiq scored the effectiveness of each and every word in the subject line. This was especially useful as they discovered more detail on how their audience engaged with their content. Finally, Synaptiq applied list segmentation and subject line analysis to recommendations on appropriate content for segmented recipients.
How Machine Learning Helped
Synaptiq’s analysis allowed the company to optimize subject lines using words, phrases, and content that showed better open rates. For example, Synaptiq’s Machine Learning models debunked the common held belief that using questions in the subject line is effective. As the company implements these solutions, it expects to see improved results in both open rates and click through rates as it develops targeted content by audience as well as optimize its subject lines based on the Machine Learning models from Synaptiq.