One of the contentions that surround email marketing at the moment is the issue of when you retire an email address. Leading up to Christmas, when the heat is on, ambitious sales targets tempt even cautious marketers to push out the boat and send to everyone. If an email list is causing deliverability issues, it is quite common for a bit of a clean up to be suggested. It’s not a “stab in the dark” strategy, because when used correctly it can lead to a net increase in response and revenue.
However, you cannot ignore, when retired email addresses are mailed, they often produce some revenue. This almost flies in the face of the no response/retirement strategy, but in reality, some fine tuning is in order to squeeze all the value from your list.
To deal with this issue properly, you will certainly need response (sales) data for your customers, and need to know which email addresses the data relates too. In most instances the full picture of your list can only be achieved through wider knowledge of the customer.
All too often, the most responsive customers are the ones who have been opening and clicking your emails recently. But it’s also important to segment those who are no longer interested, from those that have disengaged from your emails due to a higher contact frequency than their needs require.
The first stage of the solution should be test the differing frequency of those people who haven’t opened or clicked for a while. Although a 6 month open/click window might be fine for some businesses, it might not suit those businesses with a longer sales cycle or a wider range of buying frequency. In these instances, sending mailings for twelve months or even longer might be better, but proper testing should help you decide when a customer is signalling defection.
If you have transactional data, you can use the principles of RFM (Recency, Frequency and Monetary value) to build up a model which predicts your most responsive customers. In an ideal world you could marry up the purchase RFM data alongside the online engagement data, to see the point where Recency for online engagement (opens/clicks/visits) signals a lapsed customer.
Using email response data, we create two segments, those that are recently engaged, and those that are not (don’t throw any away yet!). The engaged segment can carry on receiving the main campaign emails at the normal frequency. The less engaged segment now gets a rest (for about three to four times the normal frequency of you campaign emails). So if you generally send weekly, rest this segment for a month.
What we are trying to do is identify a segment within the email database that has stopped responding to emails due to a mailing frequency that is too high for them. By responding to the users behaviour, you are able to make changes to the email frequency of this group.
If people from this lower frequency segment, respond, it is important that they don’t go straight back into the main campaign mailing frequency, but give them more of a rest between mailings.
What we are trying to do is to start down the road of mailing people at a frequency that suits them, keeping them engaged and encouraging them to buy more. Managing frequency is the easiest way to respond to behaviour (or lack of it) but if you have more resource, you could try content too. One of the other top reasons why people stop opening emails, is that the emails are no longer relevant to them. The difficultly with content relevance, is that it relies on a deeper customer knowledge, or web behaviour data.
Unfortunately there will be those email addresses in the list that despite your best efforts will never be responsive again. So, at some point you will have to bite the bullet and let these addressees go, or put them into a pot of mailings once a month or once every two months. It is important to accept that the damage that is done to the whole email programme (in the shape of poor inbox deliverability and reduction in response) will outweigh any extra revenue gained by mailing these inactive email addresses.
(Best Practice Advice from the DMA EMC)
