Archive for the ‘Email List Management’ Category

Increase the Efficiency of Your Overall Cross Selling Efforts

Posted by Bill Leming on February 24th, 2014

temptationblog width=We all know that temptation is a powerful force in our personal lives. It’s also a powerful force in our professional marketing lives, particularly when one begins to look at distributions of the number of services per household, the number of individual sales per customer, or the number of sales dollars per customer. In Financial Services and many retail services sectors, the number of single service households is far greater than the number of two service households which, in turn, is far greater than the number of three service households, etc.  And that’s where temptation rears its insidious head.

As marketers and as managers we focus on that great big, juicy opportunity of selling a second service to all those single service households. And why wouldn’t we? The number and percentage are typically much larger than any other segment so the opportunity is a huge, ripe plum just waiting to be eaten. By definition they are customers who somehow chose to do business with you, so while they might not be advocates, they’re still customers who must have additional service needs that marketers just haven’t yet satisfied.  And then the “numbers” temptation…if we could just get ⅓ or even ¼ of those single service households to use a second service, look at the positive impact on our customer retention rates, our retail asset base, and our bottom line.

The problem is that the cost to sell these single service customers a second service is generally pretty steep (you can quantify exactly how steep it is in any number of ways). What we all know to be true is that the cost to do so is comparatively steep especially when compared to the cost of selling an eight-service household a ninth service.

And that’s exactly where we should begin the up-selling effort; namely where it is most cost effective — and that’s not at the single-service level but rather at the eight-service household or the highest level within your organization. Almost no one has all the services or products you offer, so begin from the top down.  Eventually if you follow this process, you’ll get to the single service household, which is what everyone wanted at the outset.

By avoiding the temptation to begin with that juicy single service plum, you’ll have done so with not only an eye toward efficiency but also with the knowledge that we can get to them largely because your cost per service sold was well below what you were willing to pay at the single service household level. You’ll have spent your marketing dollars where the cost per new service sold is lowest first, followed by the next lowest and so on until your cumulative cost per new service/product sold is where you want it to be. In effect you’ll be able to go deeper into the customer file, ultimately down to the single-service customer level because you were so successful at the higher services per customer tiers, because the cost per new service sold at the highest number of services per customer was so low.

But temptation is what it is…tempting.




Are Monsters Lurking In Your List? (part 2 of 2)

Posted by admin on October 31st, 2012

Last week we went over the different types of monsters that may be lying low in your lists. Specifically, we’re talking about Spam Traps and unknown addresses. At this point we should already understand the difference between the two and the threat they pose.

In part two of this article, I’m going to cover how to eliminate those monsters from your database and keep them out. Oh, and sorry to disappoint, but this method will not consist of Bill Murray and a proton pack.

Once again, if you’re following best practices for growing your audience, then you shouldn’t be at risk of adding any new traps to your list. Typically an existing trap only makes its way into your database if you’re gathering addresses in a way which you shouldn’t be. If you are purchasing lists or using affiliate lists then you can help yourself avoid these nightmares by simply not using them.

For the senders who are using proper opt-in methods, there’s still the issue of recipients going inactive and becoming dead weight or worse. Allow me to elaborate with a story…stay awhile and listen.

Old Winthorpe Wolski may have been a great customer three years ago—he engaged with about a third of your emails and purchased regularly, a real stand-up guy. Unfortunately, our dear Winthorpe was unable to fend off a hoard of hungry zombies a couple of years back. The walking dead don’t check their emails, but Winthorpe’s email address has stayed in the system for this entire time even though his email account was closed. The bounces that his ISP was sending back were not indicative of a hard bounce and soon his old email address,, was turned into a Spam Trap. That “walker” of an email address is now lying low, waiting to be mailed to.

The scenario with Winthorpe is common and that’s why it’s important that we find a way to spot these inactive addresses and remove them. They simply aren’t worth the chance. The first thing you should have in place is a process that finds inactive recipients — my personal preference is to base this on the frequency at which you contact your subscribers. As an example, you want to make sure your ESP has a way for you to see the activity of all records over the past year. Look at these reports and find anyone who has been sent X amount of messages and has never opened or clicked. Now delete them, they’re dead weight. If you only send these people emails four times a year, then you may need to go back a little further, or better yet, set up a reactivation campaign to all of these inactive people. Let this group know that you haven’t heard from them and ask if they’re still interested in hearing from you.

So you did your first list cleaning as instructed above, great! Over the next few months you’ll likely notice higher click-through rates, more opens, and lower unsubscribe rates. Just make sure you don’t forget about ole Winthorpe. Making the first move of cleaning your list is great, but to make it really work, you need to do this yearly. ISPs aren’t going to stop recycling addresses into traps any time soon. To make sure you’re keeping the monsters out of your lists – keep them clean!


Are Monsters Lurking In Your List? (part 1 of 2)

Posted by admin on October 24th, 2012

No matter how relevant your emails are or how upstanding your opt-in process is, monsters can still be lurking in your lists. More often than you might think, ISPs are recycling old email addresses into Spam Traps. Spam Traps can bring a mailing campaign to its knees, especially for a sender who hasn’t sent enough messages previously to build up a solid reputation. Additionally, old inactive addresses can also stir the pot by adding to your bounce count with an ISP.  While no one wants to see these addresses in their database, they play an important role in reducing the amount of unwanted messages that negatively impacts the entire email industry. Let’s cover a couple of different list monsters:

Unknown Addresses - Unknown addresses are mostly acquired through people entering bogus addresses in sign-up forms or are simply addresses that have expired.  When you deploy a large mailing that has many unknown addresses to an ISP, you appear to be a spammer. No matter how clean you think that list is, those ugly monsters in your pack are making you look bad. Sending to too many unknown addresses will cause ISPs to defer your mailings, and when mixed with enough complaints can lead to blocks.

Spam Traps – These addresses exist for one reason, to catch senders with poor sending habits. As mentioned above, a Spam Trap can either be created specifically to track spam or can be an old recycled address. When an ISP has an old address, they may start returning ‘unknown address’ bounces for several months. After several months of informing senders that this address is no longer valid, it’s turned into a “trap” — identifying senders who continue to send to the unknown address that should have been wiped from their list already. Where a spam complaint can be thought of as a slap on the wrist, a Spam Trap is more along the lines of “off with your hands.”  If your mailings are tripping traps like these, then you’re doing something wrong.

Next week I’ll be following up with the second part of this post, explaining not just how to clean your list but how to keep it clean.


Are you going “all in” with your email testing strategies?

Posted by Rob Ropars on August 26th, 2011

Going All InWe’ve all heard that if you’re in marketing, in particular email marketing, you should constantly be testing to maximize results.  The most common test mentioned is the ubiquitous “A/B” split test, meaning a 50/50 list split to test one variable against another (graphics, copy, offer, layout, list, time of day, day of week, etc.).

But is an A/B test all you can or should do?  If you have only a few thousand or fewer emails to work with, an A/B test may be all you can do to ensure statistically reliable results.  However, if your list is too small, an A/B test might not make any sense.  For example, if you only have a few hundred email addresses, splitting and conducting one test will literally tell you nothing (statistically) other than directionally relevant information.  Instead you may need to try to replicate the test over time, to aggregate the results and to analyze your collective data over a longer period.

The first consideration is to quantify how many email addresses you need to test to ensure you have a representative sample and more importantly, to ensure the results are reliable.  There is a lot of math and science behind this topic, and fortunately a lot of math/science/statistics sites have free online tools such as this one.

You must set up the test(s) correctly (with sufficient sample sizes and assumed response rates) on the front end to ensure that results on the back end are reliable, meaning with a confidence level that you’re comfortable with (we recommend a 95% confidence level if it’s possible).  Again, there are resources online to assist such as this one.  The key is to avoid the common mistake of merely looking at results and assuming winners/losers based on seemingly different response rates.

Before testing, you have to identify the goal or the question you’re trying to answer. We recommend that you actually write these down and then, as briefly and concisely as possible, describe the various yardsticks you will use to determine your winner. As form follows function, the goals/objectives of the test coupled with the means to measure results should help drive copy, graphics, and/or layout to ensure the messages are properly structured and focused on whatever question you’re trying to answer..

Let’s say your goal is a higher click rate and after an A/B test you find “A” has a 2.7% CTR and “B” has 2.85%.  It is a common mistake to use subtraction and declare that “B” was the winner or that “B” was only 0.15% higher and that could lead you down the path of thinking it wasn’t a significant result (i.e. a virtual “tie”).  Or maybe you routinely just pick the higher percentage as the winner and run with that.  Using proper percent increase/decrease calculations, we find that this is actually a  5.56% increase from “A” to “B.”

That however may or may not be statistically significant, but as you can see it’s a much larger increase than originally assumed.  In order to determine if the results are statistically significant, use one of the calculators, plug in each version’s list size and the click percentage (or open percentage, or conversion rate, etc. depending on the key metric you’re analyzing) and it will instantly tell you whether this difference is enough to be reliable (with a 95% confidence level).

In this example, let’s pretend I sent “A” and “B” to a random 2,000 people each.  The calculations indicate that this would not be enough of a difference to be statistically reliable.  In fact, the “B” cell’s click rate would have to have been at least 3.81% in order for the difference to be reliably significant.  However, if you didn’t analyze the results properly you wouldn’t know this.

The other way to ensure you’re maximizing your results is to avoid doing a full scale A/B test. If your database for an email marketing campaign is large enough (again calculate minimum sample size), you can do a different kind of split test. First, split your list 10%/90% (ensuring it’s random). Then split the 10% group in half so you have two small splits and the remaining 90%.

Deploy your test to the 10% splits, give as much time as possible for activity to occur (twenty-four hours if possible), analyze the results and then deploy the winner to the remaining 90%. That way you’ve done your best to maximize the campaign’s results without going “all in” on a typical full file A/B split.

As with gambling, learn the rules, do the math, analyze the data and place your bets.  Do it right, and the odds will swing in your favor.


5 List Filtering Techniques

Posted by Dave McCue on June 29th, 2011

While there are a myriad of advanced email marketing techniques available, the fact remains that for some marketers it’s enough of a struggle simply getting messages out the door in time without adding any more layers of complexity to the process. If you find yourself in this camp, remember that there are some basic strategic techniques that can help you move “beyond the basics” without putting your deadlines at risk. One tried-and-true technique is the application of filters to your mailing list(s).

Here are 5 effective ways to use list filters:

  • Engagement
    Filtering based on recipients who rendered and/or clicked on a message(s) can be very useful. This demonstrated interest in a topic/product/offer provides valuable insight into what these recipients are looking for. Use this knowledge to deliver messages with related content, complementary products or similar offers. Or, use this as a suppression filter for an “inactive re-send” campaign that sends the same message to recipients who did not engage with the original.
  • Non-activity
    This filter is useful not only for the “inactive re-send” approach mentioned above, but to identify recipients who have not engaged with messages over a period of time (12 months, for example). Some email marketers actively reach out to this group of recipients to learn if there is something specific they would like to see on future emails. Others simply use this filter to exclude unengaged recipients, as continuing to include them on mailings only serves to drive down response rates and incur message volume that could be put to better use.
  • Clicked a specific link
    More definitive than a filter based on general click activity, this filtering technique is valuable when conducting follow-up to a message with several different calls-to-action.
  • ZIP Code radius
    If you’re collecting ZIP Code as part of your subscriber opt-in process, a radius filter is a great way to target your messages. If your organization has multiple locations, promote the location nearest your subscribers. For special offers, you can factor in travel distance and sweeten your offer accordingly (e.g., 15% off for recipients within 10 miles, 30% off within 20 miles, etc.)
  • Date of opt-in
    The date that a subscriber signed up to receive your emails can be an effective filter when setting multi-touch Welcome campaigns (e.g., 30/60/90 days out). You may also want to exclude recent subscribers from the sort of “inactive” segmentation mentioned above, or from only receiving the tail-end of an ongoing series of messages (e.g., “Message 9 of 9″).

The filtering techniques mentioned here are only a sampling of the way list filters can be used to make your email messages more relevant and more productive, and since filters can be used on any number of deployments, they only require an initial setup before becoming part of your normal processes.