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Email frequency management is important to ensure your customers don’t get spammed by emails from too many campaigns running at the same time. Most email marketing platforms offer a way to set up email capping as a generic frequency policy set for the entire database. At Exponea, email capping is a subset of a broader, more customizable frequency management policy that dynamically adapts to customer behavior. Our Frequency Management works intelligently to take into account the level of interaction and engagement of a particular customer. This use case shows how you can set up these custom frequency policies for yourself.
John is the CRM Director at a company that has several outreach programs managed by different team members. The team manages multiple automated lifecycle campaigns, promotional emails, seasonal newsletters, and triggered emails. Each channel and campaign have audience lists that are maintained separately. The team needs an overarching frequency policy to limit outbound communication.
Sending too many emails to some customers can pester them, make them unsubscribe, ruin your domain reputation and ultimately erode their trust in your brand. But sending too few emails to loyal customers means missing out on revenue. Exponea’s frequency management can intelligently limit the number of emails a customer receives based on interactions with your website and campaigns. So customers are informed – but not overwhelmed.
The policy can also be extended to other channels like SMS, notifications, and webhooks.
Improve retention and website conversion by keeping your most loyal customers informed and engaged.
Achieve better open rates and click-through rates by giving the customer only as much information as they want.
Avoid spam complaints and decrease unsubscribes by keeping the customer informed, but not overwhelmed.
Actual numbers realized by a real-world implementation of these features by Exponea clients:
+26% open rate
-12.8% unsubscribe rate
+30.9% revenue per email
Frequency Management finds and automates the best possible campaign frequency for each customer before targeting them. So, instead of setting a generic rule for the whole project that says ‘no more than 2 emails in 24 hours’, the customer base can be divided into segments, each with a different frequency setting. Marketers can do this easily and have full control over how segments are defined and therefore decide how many emails a particular customer should receive.
Consider a smart newsletter frequency policy that’s based on a customer’s engagement level. First, define different segments using customer or event attribute filters like consent received, last web activity, and last email opened time. For example, a segment of highly engaged customers can be defined as people who have opted in more than a month ago and have opened or clicked an email and have an open rate of more than 75%.
Once all segments are defined, set the maximum messages per customer per period for every segment. A segment of customers with a high email open rate may allow multiple emails per week, but one with low open rates can have communication limited to just one email a week. You can set up multiple frequency policies and have different policies applied to different types of campaigns.
Here’s a look at how it works on a sample customer base.
Setting up something like that manually sounds daunting — so we made it easy! With the help of our email deliverability expert and data scientists, we have created an out-of-the-box e-mail sending policy which can be used for newsletters. It is designed to limit the number of emails sent to inactive and lapsing segments, but distinguish those who recently visited the website. Also, it identifies the segment of subscribers that opens a majority of the emails they receive, who can receive an extra automated email per day.
An electronics e-commerce company in the UK was running multiple email campaigns that sent out messages to several emailing bases. In spite of all the outreach efforts, there didn’t seem to be an increase in profitability from emails and the company was having to deal with frequent returns.
The team was sending out multiple campaigns, each to a separate email list. There wasn’t much analysis being done on the number of emails each customer was receiving or which type of customer was converting.
As a first step, Exponea prepared detailed analytics and segmented customers by attributed emailing revenue. This clearly showed some staggering disparities—for one type of a campaign, only 1% of the entire audience generated email-attributable revenue. Having noticed this, they started with more detailed analytics across their marketing outreach activities. They implemented a new frequency policy which included sending very few emails to customers who hadn’t opened any emails in a long time. Further, customers who were frequent returners were excluded from promotional campaigns.
Not incentivizing frequent returners to place more orders led to an increase in profitability
By creating segments and only sending emails to customers who were engaging with them, the company also saw a considerable improvement in deliverability.