Getting in the Feedback Loop

I wanted to write about the recently introduced Hotmail unsubscribe button but realized it’s too early to analyze its use and potential for several reasons. The basic system has been covered by ClickZ executive editor Rebecca Lieb, the program still has a number of problems, and, if my sources are right, exactly how it will work is still being determined.

Problems include sending the unsubscribe request to the wrong address (the return path rather than the list unsubscribe destination) and automatically adding the sender to the sender’s blocked users list. I’m also hearing contradictory reports on when the button is displayed. Some mailers have found mail must be from Sender Score Certified IPs, others believe the sender must be in the recipients’ safe list. All this leads me to believe Microsoft is still fine-tuning this functionality, and I should wait until it’s finalized before passing judgment.

Thinking about the unsubscribe button brought me to already common feedback loops (FBLs). It seems marketers are quite au fait with whitelisting, as I regularly get asked whether we’re whitelisted at the major ISPs. Yet I’m rarely asked about FBLs, even though they’re at least as important. FBLs not only help with list hygiene and deliverability but are an invaluable source of behavioral data.

A feedback loop is a mechanism whereby an ISP will let you know when one of its subscribers complains about your e-mail. All the Big Four operate such loops (except Yahoo, who is still developing its), as do a number of other ISPs, including Road Runner, United Online, Excite, and Outblaze.

FBLs invariably require sender registration and may have other requirements. To scale more effectively, for example, AOL is moving toward requiring SPF (define) records for its whitelist and FBL management. Once registered, when a recipient complains about your e-mail (by clicking the “this is spam” button or contacting the ISP’s abuse desk), an e-mail is sent to the address you registered. The e-mail is typically in the MAAWG (Messaging Anti-Abuse Work Group) endorsed automated response format (ARF).

It’s important to understand that though your list may be 100 percent opt-in, it may still receive a substantial number of FBL complaints. For years, end users have been told not to trust e-mail unsubscribe links, so many users hit the spam button as a way of unsubscribing. The result is complaint rates through FBLs can be much higher than a list’s unsubscribe rate. To further complicate matters, most ISPs have complaint rate thresholds above which your messages may be filtered or blocked. Unfortunately, most don’t publish these thresholds, which vary by ISP. AOL states mailers should aim for a complaint rate of 0.1 percent (1 complaint per 1,000 deliveries). That’s a useful baseline.

Smart marketers use FBLs in two main ways. The first is for list hygiene: removing the subscriber from future mailings. Although some ISPs make responses anonymous (the recipient line is often erased), it’s generally still possible to determine the original recipients and unsubscribe them. Some may call this list-washing, but it’s just common sense. Even if someone previously opted in to receive messages, if he complains, the first thing you should do is cease mailing to him.

The second use of FBLs is to analyze the complaint rate. Too many marketers dismiss complainants as troublemakers and malcontents. The reality is there’s a wealth of data in who complains and what they complain about. Regardless of whether you believe the complaints are unfounded, if they complained they were dissatisfied. Smart marketers aim to avoid dissatisfied customers (or prospective customers).

If a particular mailing, list, or list segment produces too many complaints, it bears further investigation. In my experience, the majority of complaints are caused by a failure to meet expectations. A common case is high complaint rates among new subscribers. This can be caused by subscribers not realizing what they signed up for, subscribers not getting what they thought they signed up for, or a long delay between sign-up and the first mailing.

By analyzing FBL data, such problems can be identified and solutions developed and tested. The net result is happier recipients, happier ISPs, improved deliverability, and better results. Make sure you’re on the FBLs of all ISPs that offer them and that you’re using the wealth of data those FBLs provide.

Until next time,


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