A data-driven email marketing strategy will help your business build and maintain a mutually beneficial relationship with customers and lead to more sales. Setting up your email marketing optimization strategy is one of the most important steps after launching an email marketing campaign.
In the age of digital transformation, many businesses opt for email marketing as a high-ROI technique to connect with customers. Unfortunately, not all emails are read through, or even clicked on. In fact, most people have an attention span of only 8 seconds, which is an incredibly short amount of time to convey an engaging message to customers, provided that they do click on the email.
Why you should care about email marketing
Email marketing remains to be one of the most effective tools to generate leads and drive engagement, with 70% of customers preferring to communicate over email. However, you are not the only one reaching customers via email. There are expected to be 361 billion emails sent out every day by 2024 and 4.3 billion email users. So how do you stand out?
Email campaign optimization means taking the necessary steps to make sure your message is reaching the target audience in the right time and with the right content. The data-driven email optimization also looks for any flaws to fix in your current email marketing campaigns.
Optimizing your email marketing strategy will help improve engagement, connect with customers through precisely targeted personalization, and bring more sales.
Using Data Science To Optimize Email Campaigns
The data behind your email marketing campaigns can make or break your customer relationships. Machine learning and even the simplest data science methodologies can help you achieve striking email marketing results.
At Amplify Analytix, we use your available data to analyze your marketing campaigns and deliver practical data-driven insights. Through a data-driven email optimization, we help our clients understand the cause of their current engagement rates and optimize for higher ROI.
A predictive machine learning model coupled with the efforts of bright data analysts (like our own) can calculate the likelihood of customer engagement increasing or decreasing during an email campaign, find the cause of the engagement fluctuations, and suggest the improvements to ensure better campaign performance.
Our customer’s case
A takeaway from our recent project: customers are more likely to unsubscribe if they get overlapping emails on different topics.
As you can see below, our customer, a global B2C retailer, has been sending their customers emails regarding the Black Friday campaign, Christmas campaign, regular weekly communication, special deals – all to the same customers at the same time. Due to the overwhelming number of emails from different business functions, customers were largely unsubscribing from all communication.
We analyzed the historical customer journeys from the 1st email received, the next action (open/click/ignore) taken, to the next email received, follow up actions, etc. We looked for mistakes in email sending business rules, customer drop-off points, and any inconsistencies in the technical setup of the email campaign flows.
Yellow and Red arrows indicate one of the two undesirable results of campaign performance, making these areas easy to identify.
Yellow – When a customer unsubscribes.
Red – When the flow was not running as agreed (i.e., a failure in the technical setup).
Through the customer journey analysis, our customer was able to see the exact breaking points and technical inconsistencies. They improved the customer satisfaction score along with the email engagement rates through shortening the email flows, breaking up the recipient groups into more personalized subgroups, and so on.
To learn more about cases similar to this, download our white paper on ‘Email marketing optimization’.