One of my clients is focused heavily on e-mailing his customers.  However, it is only part of the equation.  Consumers are increasingly online 24 hours a day, 7 days a week.  Yes, they are still checking e-mails but they are also on Facebook, Twitter and blogs. 

It used to be that the question was direct mail or e-mail?  Now the question is not what channel to use but rather which channels to leverage.  The direct marketing strategy needs to consider traditional direct channels, such as e-mail and direct mail, as well as social networking sites.  The need for integration of branding and messaging has become even more important as consumers have a multitude of ways to learn about your company and its products and services. 

The other challenge with the plethora of channels that have evolved is that consumers are bombarded with information.  Some are abandoning their e-mail accounts because they are overwhelmed by their inboxes.  Others ignore their inboxes in favor of communication channels they control.  I don’t bother sending my sister e-mails anymore because they disappear into the black hole that is her inbox.  However, she will respond instantaneously to a text message and will e-mail me on occasion, when it is the best channel for her to communicate with me.   

As marketers, we need to go where our customers are and offer them relevant and honest information.

Thursday’s Wall Street Journal had a quote from Carol Bartz, the CEO of Yahoo, “Are we leading up to “I’m both too old and too stupid to know what the Internet is’?”  Her remark was in response to a question about her experience but it made me think about a potential generational gap in Internet usage. 

At a book club meeting almost a year ago, several members asked for a description of Twitter, as if it was a foreign country or new-fangled religion they had heard about.  One member provided an excellent summary based on his usage of the site but several were left trying to get their heads around why anyone in their right mind would use Twitter.   Once again Twitter came up during a recent book club meeting.  No one new had tried out the service in the 10 months since our last discussion.  It made me wonder if the technological gap is not just rich versus poor but also young versus old.  As the remark by Carol Bartz indicates, there is a general perception that the Internet is a young person’s game.  High profile anecdotes have reinforced that assumption.  According to a July 2008 Frank Rich Op Ed piece in the New York Times, John McCain doesn’t know how to use a computer.

For marketers, this represents a challenge and an opportunity.  To me, it is further proof that we need to develop integrated campaigns with both online and offline channels for outbound communication and inbound response.  You cannot assume that everyone will be on the Internet 24/7.  Broadcast media, print ads, direct mail, etc. can play an important role in reaching an older audience that may not be on the Internet as frequently and they reinforce your message to those who are active on the Internet.  A recent report found that displaying a URL within a  Yellow Pages print ad drove an increase in online leads.  Of course, you should measure the interaction of online and offline behavior to see what drives the most responses and to optimize future campaigns.

Direct mail marketers face several challenges. First and foremost, they get no respect. Almost everyone refers to direct mail as junk mail and many think it is bad for the environment. Second, their business has been negatively affected by the current economic conditions. The credit card companies who were responsible for mountains of solicitations have fallen on hard times and reduced their mail volume. Third, e-mail is replacing some direct mail as it is cheaper and offers the same measurability as direct mail. And now there is news of Postal Service carriers who did not deliver the mail entrusted to them.

According to a recent Associated Press article, one Postal Service carrier stored third class mail in his garage for six years. He was placed on probation and fined $3,000 but an e-mail marketing firm, MailChimp, paid the penalty. AP reported that Ben Chestnut of MailChimp said, “We’re doing everything we can to stop junk mail.”

A client asked me how her campaigns compared to industry standards. It is a common question and there are many resources available. The DMA compiles statistics and reports on response rates. Most recently they published the DMA 2007 Response Rate Trends Report. MarketingSherpa produces reports such as the 2009 E-Mail Marketing Benchmark Guide. Then, of course, there are websites offering benchmark rates for e-mails such as www.bronto.com.

Just because there are resources available does not necessarily mean that you should use them. Finding the right benchmark rate requires finding comparable campaigns. That means looking for rates based on campaigns having the same:
1. target (i.e., business or consumer)
2. channel (e.g., direct mail, print ad, web banner, e-mail)
3. industry (e.g., retail, financial services)
4. message/offer (e.g., sales)

Even if you find rates for campaigns that meet all of those conditions, how can you be sure that the campaigns are truly comparable? As noted in a recent Molecular blog post, you do not always know the context of the numbers.

The question of industry standards can also obscure another great resource, your own past campaigns. Again, you need to consider the factors above, but these will also provide a benchmark against which you can measure the success of current campaigns.

I was catching up on Tangyslice’s blog and enjoying his 5 meaningless marketing metrics post, when I thought of another meaningless metric.  Last week I was reading a presentation which described the response rate of one group as slightly greater than the control group.  What does slightly greater mean in this context?  Well, it turns out the difference was statistically significant once I did the math.  What I find meaningless is when analysts do not look for statistical significance when comparing two groups.  This is known as A/B Testing.

Conceptually A/B testing is very simple.  You are comparing Group A to Group B.  A might be a control group and B the test group.  Alternatively, A and B might be two different offers, landing pages, e-mails, direct mail lists, or landing pages.  As the name suggests, this is a test which is why A/B testing is also known as split testing.  Ultimately, you want to know if A and B differ in a way that is statistically significant.

Here’s an example to make it concrete. Let’s say that you marketed to 50,000 customers encouraging them to purchase product A and 5,000 of them responded. That is a 10% response rate. In addition, there were 5,000 customers that you could have marketed to but that you did not.  Instead, you assigned them to the control group.  They look and act just like the 50,000 customers that you mailed. The reason for the control group is that some customers might buy product A regardless of whether you market to them or not.  In this example, 450 of them or 9% purchased the product. Is the difference between 10% and 9% statistically significant?  Was the campaign successful?

In this case, we perform the two-proportion z-test for equal variances using the following formula:

and

where…

p1=10% (response rate for Group A)

p2=9% (response rate for Group B)

x1=5,000 (number of responders in Group A)

X2=450 (number of responders in Group B)

n1=50,000 (quantity mailed in Group A)

n2=5,000 (quantity mailed in Group B)

If the value of z is greater than 1.96 then the difference is significant at 95% confidence.  In this case, the z value is 2.26 so the difference is statistically significant.

In order for the test to be valid a few assumptions must be met:
1. Your control group needs to contain customers or prospects that look and behave like the treatment group
2. You need to have sufficient numbers of direct mail recipients and responders such that n₁ p₁ > 5 AND n₁(1 − p₁) > 5 and n₂ p₂ > 5 and n₂(1 − p₂) > 5 and n2>29 and the groups contain independent observations

The math might look scary but really the hard part is making sure that the test is done properly.  It is vital that the control contains a random selection of customers who are similar to the treatment group.  If not, you could end up with very strange results

Limeduck sent me this excellent comic strip about direct mail and targeted marketing.  Hope you enjoy it as much as I did.

This is a blog about marketing analytics.  I am a direct marketing professional who loves marketing, strategy and analysis and I welcome your thoughts and feedback on these topics as well.

First, let me tell you a little more about me.  At a dinner several years ago, a client announced to my colleagues that he knew how to make me smile.  He simply had to say that he had data.  It is very true; data makes me happy.  I love analyzing data because it can answer business questions and provide insight into marketing challenges.  At the end of the day, your recommendations are grounded by data. 

That is one of the reasons that I work in direct marketing.  There are lots of data elements that can be analyzed and almost endless possibilities.  For example, you can create models for targeting, conduct A/B testing, analyze response rates and calculate ROI (return on investment) by customer segments. 

This blog will discuss current trends in marketing analytics, various techniques and the field more generally.