Email Marketing: Going Beyond a Unique Open and Click

Is 20% a good open rate? What about a click-through rate of 5%? Well that depends on the industry you are in and what type of communications you're sending. There’s one thing I can tell you without knowing anything about you, you want these rates to be higher.

Each week you send out a newsletter and each week you might get a 20% open rate. Is it the same 20% opening each time? There might be 20% engaging with your email, but it won’t be the same people consistently opening that’s for sure! Now I’m not going to say that you shouldn’t be focusing on opens or clicks, because you should. They are important. What I am saying is that you need to look beyond these metrics, look beyond the open rate and find out how your database is performing.

Within email marketing, we would be naive to think that 100% of our database is clicking each individual email every time we send. This is where database segmentation comes into play and more importantly database engagement segments.

What Are Engagement Segments?

Simply put, engagement segments are groups of customers placed into various buckets dictated by the last time that they interacted with an email.

Why Are They Important?

They allow us to understand why our 20% unique open rate is declining—and more importantly, they allow us to implement marketing tactics to engage our database.

What Are These Segments?

They can vary, however the methodology is always the same. They are based on a timeline of interactions.

  • New to Database = Subscribers with a join date of 0 – 30 days
  • Highly Active = All other subscribers who have engaged within 0 – 30 days
  • Active = Subscribers who have engaged within 31 – 90 days
  • Lapsed = Subscribers who have engaged within 91 – 180 days
  • Inactive = Subscribers who have engaged within 181 – 270 days
  • Dormant = Subscribers who have not engaged for more than 271 days
  • No Interactions = Subscribers who have never engaged (there are a lot of them!)

Engagement is defined as clicks and opens (a true form of engagement), and you can also individually look at clicks or opens. Engagement segments can be customized; if you have an impulse product or a short purchase cycle, then the above might suit you best. If you have a highly considered purchase, for instance a 30 strong fleet of airplanes, then that segmentation might be extended to define highly active and active as one active segment lasting 90 or 180 days.

To learn more about using Engagement & Intent to boost the power of your email communications watch our webinar.

What Can I Do With This?

Well, I'm glad you asked.

Within both the Responsys and Eloqua marketing platforms you can run programmatic communications. This means that we can trigger communications to be sent based on an event. An event might be a welcome sent email to a customer who is new to the database, or listening for the moment; or it could be a conversion email sent to a dormant customer (hasn’t engaged in the last 271+ days) who suddenly becomes highly active. Finally (but not limited to these examples), you can utilize the segmentation tools to run re-engagement programs and awaken that 40-50% of your database that is dis-engaged (hasn’t engaged in over 181 days). Trust me, you might be surprised at how disengaged your database is when you start introducing engagement segments.

On the positive side, if you start to shift your thinking beyond a click and an open, and start to think about database engagement, you can start to unlock the power of your email communications and craft communications that are focused on driving database engagement, a step beyond email engagement.

To move beyond a click and an open, download our Email Deliverability Guide for Modern Marketers to help you identify your engagement segments and focus on driving database engagement.

Email Deliverability Guide

Oracle Blogs | Oracle Marketing Cloud

Keyword Research Beyond SEO

Keyword Research Beyond SEO written by John Jantsch read more at Duct Tape Marketing

Marketing Podcast with Brian DeanBrian_Dean_Headshot.png

Keyword research has long been the primary tool for search engine optimization plans. The idea was that you found out what people in your industry were searching for and you optimized your site and pages to try to show up on page one – pretty simple.

Today, I believe that content is the primary tool for optimizing  your effectiveness is just about every channel your clients use – it’s the key to SEO, social media sharing, referrals, email marketing and even online and offline advertising.

Without great content, you’re limited in your marketing in many, many ways.

My guest for this week’s episode of the Duct Tape Marketing Podcast is Brian Dean, internationally renowned marketing and SEO expert and founder of Backlinko. We discuss the relationship between marketing and SEO, keyword research, and the impact of backlinks on your keyword rankings.

Questions I ask Brian:

  • How can you use keyword research beyond SEO?
  • How do you reach out to publishers for backlinks without being ignored?
  • How do you earn someone’s email?

What you’ll learn if you give a listen:

  • How to hack the Google keyword planner to find keywords your competitors can’t find
  • Tips and tricks for picking the correct keyword on which to bid
  • How to leverage a content upgrade to improve your conversion rates.

This episode of the Duct Tape Marketing Podcast is brought to you by FreshBooks, small business accounting software for non-accountants. Freshbooks is offering a free month of unrestricted access just for Duct Tape Marketing podcast listeners. You don’t even need a credit card to register. To get your free month, go to and enter DuctTape in the “How Did You Hear About Us?” section.

Blog – Duct Tape Marketing

Beyond Digital Analytics Metrics

Beyond Digital Analytics Metrics

We have all heard many times that reporting data has nothing to do with being a data driven organisation, yet most digital analysts complain that all they basically do is report data.

One of the reasons why digital analytics is often seen as merely reporting is that it often does not speak the language of the business. If we make an effort to incorporate business metrics and concepts in our analysis, we will have a greater chance to influence decision making.

In this post, I will discuss COGS (Cost Of Goods Sold) and LTV (Lifetime Value), two fundamental business metrics that can help web analysts be more meaningful and influential with their analysis.

COGS – Cost Of Goods Sold

COGS is the cost of the goods we sell. If we understand cost, we can make a distinction between revenue (total sales) and gross margin (total sales – cost of products sold). These metrics are essential not only to have a more accurate idea of net income, but to understand how well a business can grow and scale.

The following screenshot shows a typical Google Adwords performance report.

AdWords Performance reports

The report measures business performance based on revenue. We are told that for every Adwords click, we have a revenue of 4€ on average. Since an Adwords click is costing us around 2€ on average, that looks like a pretty good revenue. The report uses ROAS as a measure of how “profitable” the campaign is. (Tip: you can add COGS to Google Analytics using product data import).

But is ROAS a good measure of business performance? Well, it depends. Depending on cost, we could be actually losing money with the Adwords campaign, no matter how good ROAS looks. Hence, this report needs to take into account COGS if the analysts wants to fully understand whether the campaign is good for the business or not.

COGS is also very useful for e-commerce analysis. Take a look at the following report:

Google Analytics Ecommerce Analysis

Usually, product performance is judged on the basis of conversion rate, session value and / or average order value. For instance, according to the report, conversion rate for the “Women” product category in France is so good that we’re tempted to throw lots of money into campaigns to promote it. That could be a good decision, or a bad decision. It depends on the margin that we have for that category in that particular country. Perhaps conversion rate for “Men” is much lower, but gross margin per sale is way higher, and hence we can spend much more on advertising to get to the same amount of sales.

In summary, if you want your analysis to be influential, revenue is not enough. First, because you do not know if you’re actually optimising for what makes sense for the business (revenue? net income? gross margin? scale?) Second, because you’re missing part of the language that senior managers use. They might not know anything about CPC, RPC or ROAS, but they like to discuss revenue, net income, gross margins and scale, and for good reasons. Use that to your advantage.

LTV – Customer Lifetime Value

Customer Lifetime Value (LTV) is a metric that predicts the profit of an entire relationship with a client. Instead of focusing on the profit for the first sale, we try to estimate the profit for all sales we will make to that customer. Hence, we focus the company on long term relationships rather than short term profits, in other words, we incentivise innovation, better products and better customer service. Not bad for a single metric 🙂

In the previous example, I suggested that a positive ROAS doesn’t necessarily mean a profitable campaign. But even a campaign that is not profitable for the first sale could be very profitable in the long run. Many businesses depend on repeat purchases / renewals to be profitable. Hence, if we just optimise our campaigns based on first sale profit, we’re optimising for the wrong metric, with potentially disastrous strategic consequences.

This is also very important to take into account when we deal with attribution problems. In my opinion, just as important as accounting for multi-click / multi-device / multi-channel effects, a good attribution strategy should take into account LTV. You can be very good at building a sophisticated attribution model, but if you attribute for first sale, and your business only makes money after a repeat purchase, your models are useless.

It’s surprising how often this is overlooked, and we’re fascinated by statistically impressive models that are modeling (and hence optimising) for the wrong business behaviour.

Is Digital Analytics useless?

In this article, I have explained a couple of business metrics that can enrich your digital analysis and take it to another level. Am I saying that digital analytics is useless? Not at all, but like any other analysis, if you miss important variables to model the problem, your analysis will be probably useless.

Many of the digital analysts I meet rarely go out of their digital analytics packages to look for data. This is a dangerous thing to do, because it produces analysis that are not very useful, and in the long run devaluate the business value of digital analytics. Instead, digital analysts should be sitting with senior managers to understand the business KPIs that drive the company forward, and then adapt their tools and analysis accordingly.

There are many articles discussing how to use a free tool like Google Analytics to collect all sorts of data, including CRM and financial data. You can also use tools like Tableau, or even Excel, to analyse different data sources at the same time. I am not saying that this is an easy thing to do, but after all it’s a technical problem, and we have the tools to solve it.

What’s your experience as a digital analyst? Do you include concepts such as cost and lifetime value in your analysis? Should the digital analyst be doing this kind of analysis? What other business data should we use if we want our analysis to be more meaningful?

AdWords Performance reports
Google Analytics Ecommerce Analysis

Online Behavior – Marketing Measurement & Optimization