Tuesday, May 23, 2017

5 Customer Activation Emails to Add to Your Funnel

If you’re familiar with the world of digital marketing – you’ve probably come across the term “marketing funnels.” And no, they have nothing to do with plastic instruments you would use in the kitchen or the smoking chimneys found on steamboats.

Instead, a marketing funnel describes the journey a person takes from initially visiting your website to becoming a paying customer. With a clearly defined funnel, you can optimize each lead’s journey to maximize the chances that they’ll eventually become paying customers.

Check out this marketing funnel diagram, courtesy of TrackMaven:

Image Source

In order to execute an effective marketing funnel, you have to increase the number of cold leads entering your funnel and decrease the number of leads who exit your funnel without converting.

Since leads can exit your funnel at any stage, it’s important that you keep them engaged at all times and constantly moving towards the end of the funnel. One of the best ways to do this is with customer activation emails.

Depending on which stage of the funnel your lead is in, I recommend sending activation emails with one of five purposes:

  • Initial Indoctrination – Help leads to get familiar with your brand.
  • Engagement – Encourage leads to take small actions and build familiarity.
  • Conversion – Encourage leads to make a purchase and become customers.
  • Reengagement – Reacquaint dormant customers with your brand.
  • Advocacy – Encourage customers to refer their friends to your company.

Here’s how to put these different activation emails into practice:

1. Initial Indoctrination

This type of email needs to educate new leads about your brand. Allow them to get familiar with the ethos of your brand and try to convey the value you consistently provide to your customers with your products and services.

Since between 30-50% of leads that enter a marketing funnel are qualified but not ready to buy, this is not the time to discuss specific product offerings or try to get a sale. Simply treat this phase as an opportunity to build trust and establish a relationship with each lead.

Marketer and entrepreneur Melyssa Griffin sends out an excellent initial indoctrination email as soon as you subscribe to her mailing list.

She conveys elements of her personality, such as her penchant for green smoothies and dance parties, and also makes it clear what types of people will benefit from her newsletter. Her informal tone of writing is incredibly endearing and allows new leads to warm to her immediately.

She also states that she can’t help people who: “Aren’t willing to put in a little work to make their dreams a reality. My strategies don’t work unless you do.”

In other words, she makes it clear that she isn’t selling a magic pill – and her advice requires real action in order to be successful. This immediately builds integrity, compared with marketers who promise the moon and then fail to deliver.

2. Engagement

Once a lead is somewhat familiar with your brand and your messages stand out from the multitude of emails in their inbox, you can move to the engagement phase. This entails strengthening the relationship with your lead by sending them contextually relevant, high-value content.

You aren’t going for the sale just yet, but you’re warming the lead to your products and services. Many marketers offer case studies, free webinars and other pieces of informative content during this phase in order to build rapport.

Entrepreneur and social media expert, Kim Garst, demonstrates exactly how to promote a free webinar via email:

The title of the webinar clearly conveys what information will be imparted, social proof is leveraged by mentioning the number of people already signed up, and urgent language encourages leads to sign up immediately instead of procrastinate.

3. Conversion

Once a lead has engaged with your brand (perhaps by reading a few blog posts or attending a free online webinar) and has learned about the value of your products and services, you can begin encouraging these leads to make a purchase.

At this stage, start discussing the ways in which your products and services can benefit the lead. Buying is an emotional – rather than a rational process – which is why it helps to discuss the benefits of your products rather than its objective features.

If you can get your leads to start visualizing how their life will be enhanced after purchasing from you, this is an excellent starting point.

In my experience, promoting discounts via email is an excellent way to create scarcity and encourage leads to purchase immediately.

Try including compelling imagery in your conversion emails. In the following image by Lucky Vitamin, notice how “50% Off” and “Shop Now” really stand out from the rest of the image, due to a careful choice of colors, placement and font size.

Immediately beneath this image, Lucky Vitamin provides contextually valuable content for its readers with an article about hair washing. Adjacent to it is an article about hair coloring, which simultaneously promotes another product.

By delivering informative content mixed with product promotions, you can encourage sales while also growing trust for your brand.

4. Re-Engagement

Think of the re-engagement phase as a second engagement phase. The principle of providing value through useful content still applies, except now, you’re targeting customers instead of leads.

Since you already have a purchase history for each customer, segmenting your re-engagement emails according to each customer’s interests is a great way to make your emails feel more personal.

In a 2013 study by Experian, personalized promotional emails had 29% higher unique open rates and 41% more unique click-through rates.

If a customer has purchased a particular type of product, send them re-engagement messages pertaining to that.

Sports betting sites, such as BetVictor, are notorious for sending personalized bonuses to players who haven’t wagered in a while. For instance, players who primarily bet on mixed martial arts fights receive different email bonuses than those who bet on soccer.

By sending personalized offers to segmented audiences, you dramatically improve your chances of re-engaging previous customers.

In a study by Campaign Monitor, marketers noted a 760% increase in revenue from segmented campaigns.

Pinkberry, a restaurant franchise that specializes in frozen desserts, sends its customers free gifts if they’ve been absent for a while. Because the offers have time restrictions (in this case, 7 days), a sense of urgency is created that leads to higher purchases.

5. Advocacy

Finally, when a person makes multiple purchases and develops an emotional connection with a brand, they have the potential to transform from a customer to a brand advocate.

It’s essential to single out these people, based on their customer history, and send them personalized emails encouraging them to tell their friends about your brand.

Because you’re specifically targeting people who have a history of positive interactions with your brand, they’re far more likely to be receptive.

Judging by this advocacy email by tanning salon franchise, Tanning Shop, the brand has a very clear buyer profile (young, predominantly female, takes pride in their physical appearance, enjoys travel, wants to look good on the beach).

People like this usually have friends who match the same buyer profile, making incentivized referral emails a no-brainer in this situation.

How to Build Your Email Funnel

Now that you know the types of customer activation emails you should be sending, you can move onto building out your funnel.

If you’ve never done any sophisticated email marketing before, this phase can sound like a technical nightmare, but it’s much easier than you would expect. Most email marketing platforms are simple to use, even if you have no prior experience.

First, it’s important to choose the right platform based on your specific requirements.

With Kissmetrics Campaigns, it’s easy to create behavior based, automated emails for every step of your funnel. Campaigns is also tightly integrated with Analyze, which is extremely helpful for measuring the impact of your campaigns and analyzing user behavior.

Next, you’ll need a killer lead magnet and an email opt-in form conveniently located on your site. Try placing your form in a feature box or directly beneath each blog post for excellent visibility (split test your form’s placement for best results).

Once you’re ready to begin marketing, input the content for each automated email in your sequence and determine when each email will be sent. I recommend sending the indoctrination email straightaway, and your engagement email the day after.

With the Kissmetrics Funnel Report, you can see where exactly people are dropping out of your funnel. If people are clicking through to your landing page but aren’t converting, you might want to set this as a trigger that sends a personalized email the next day reminding them of what they’re missing out on.

Once your form and email funnel sequence are properly integrated, you’re ready to start collecting leads.

With an automated email funnel, scaling your marketing (and business) becomes much easier.

Can you think of any other email strategies for keeping people engaged with your marketing funnel? Please let me know in the comments below.

About the Author: Aaron Agius, CEO of worldwide digital agency Louder Online is, according to Forbes, among the world’s leading digital marketers. Working with clients such as Salesforce, Coca-Cola, IBM, Intel, and scores of stellar brands, Aaron is a Growth Marketer – a fusion between search, content, social, and PR. Find him on Twitter, LinkedIn, or on the Louder Online blog.

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This Red Goes to #FG0000: Wide-Gamut Color with ≪Picture≫ and Cloudinary

Recently, I had the pleasure of reading Craig Hockenberry’s short, sweet, and informative Making Sense of Color Management. In the book, Craig points out that while new, Hi-DPI screens present us with more resolution than our eyes can actually see, they’re nowhere close to being able to produce all of the colors that we’re capable of perceiving.

Excitingly, that’s beginning to change. Various long-stagnant pieces of our graphics stacks are lurching forward into a more-vibrant future. The next frontier in making things look amazing on screens is wide-gamut color.

Wide-gamut Screens

For the last 20 years, the display industry has settled on a limited, standard range of colors, called the sRGB gamut.

If the lopsided, chopped-off-rainbow-disc below (technical name: the “1931 CIE Chromaticity Diagram”) represents all of the colors that human eyes can see, the sRGB gamut is circumscribed by the little triangle in the middle.

The latest-generation of displays can produce colors outside of sRGB’s limited range. These screens expand the triangle of possibilities out, to what’s called the P3 gamut:

The P3 gamut, compared to sRGB. It’s bigger!

Triangles are one thing and actual photographs are another—for a sense of what this wider range actually looks like in practice, beg, borrow, or steal a wide-gamut display and go check out Craig’s gorgeous example images, or look at this quick example, from yours truly. Basically, if your subject is really saturated (for example: an incandescent sunrise, or bright-green grass) P3 will let your images pop in new and notable ways. But if your image’s palette is more subdued, sRGB already has you covered and you’ll see no visible difference.

So – devices that can display new, more vibrant colors are shipping en masse. How can we take advantage of them, without screwing things up for everybody else?

Wide-gamut Resources

First, we have to produce files that contain these new colors. Generally, you can do this by making sure that: 1) your camera is capturing– 2) your software is editing– and, crucially, 3) that you’re exporting– in a wide gamut (like P3, Adobe RGB, or Adobe ProPhoto). And don’t forget to embed a color profile.

For a detailed tutorial on how to do this with Photoshop, buy Craig’s book!

If you’ve got a shiny new iPhone, though, you don’t have to worry about minding your Ps and Qs in obscure preference panes. The iPhone 7 captures, processes, and saves photos in the P3 gamut out-of-the-box.

Wide-gamut on the Web

Ok, so let’s say you’ve captured a beautiful sunrise and produced a wide-gamut Jpeg with an embedded profile. Great! How are you going to share that image with the world?

Let’s start by marking it up using a single-src <img>:

<img src=”sunrise-p3.jpg” alt=”Oranges and blues” />

In browsers that implement “color management” – browsers that know how to use the image’s embedded profile to map the image’s wide-gamut colors to a device’s particular screen – the sunrise will always look as good as the hardware will allow. It will look good on sRGB-ish screens, and great on wide-gamut displays. But many browsers are not color managed. And in color-unmanaged browsers, the raw color values in our image are painted directly to the screen, without consideration for how the file’s wide profile relates to the display’s limited gamut. This results in a dull image – much worse than if we’d just left well enough alone and exported our image in sRGB:

Comparison between a vibrant, sRGB sunrise and a a dull, wide-gamut sunrise on a color-unmanaged display

In color-dumb browsers, wide-gamut images look worse than their narrow-gamut, sRGB counterparts.

What can we do about this sad state of affairs? Unfortunately, there’s no easy way to feature test whether or not a browser is color-managed. But we can ask the browser if a screen’s profile is more sRGB-like or P3-esque, using the brand-new color-gamut media query. If we use this query within a <picture> element, we can make sure that we only send wide-gamut images to wide-gamut screens – and send sRGB images to everybody else:

<source media="(color-gamut: p3)" srcset="sunrise-p3.jpg" />
<img src="sunrise-sRGB.jpg" alt="Oranges and blues" />

That, my friends, is a color-adaptive responsive image. Neat!

Responsive Color with Cloudinary

Make no bones about it, creating responsive image assets is tedious. Whether you’re rendering multiple resolutions, crops, formats, or, now, color gamuts – the task of generating alternate versions of your assets is ripe for automation.

Enter Cloudinary.

Cloudinary’s color-smarts are still evolving, but today, the service has two key features:

  1. If an uploaded image has a color profile, Cloudinary preserves it.
  2. Cloudinary can convert any image to sRGB using the cs_srgb transformation.

So, if we generate a wide-gamut original and upload it to Cloudinary, we can deliver it color-adaptively, like this:

media="(color-gamut: p3)"
srcset="http://ift.tt/2rw8lkL" />
alt="Oranges and blues" />

This pattern allows us to generate a single, wide-gamut resource, and deliver it in a way that looks great for some, and good for everybody. ✨☺️🌈😎

Ready for Anything

As screens begin to evolve along this new axis – becoming more colorful – it’s gratifying to see the techniques and toolchains built to cope with the Retina-revolution so ready to tackle a new challenge. On the ever-evolving, always-diversifying web, adapting bitmap images to varied browsing contexts is a general problem, and responsive image markup patterns – paired with a centralized, automated image-processing back-end like Cloudinary – are here to solve it, no matter the particulars.

So – armed with P3, <picture>, and Cloudinary’s cs_srgbvoyage forth bravely into a wider world of color.


[– This is a sponsored post on behalf of Cloudinary –]


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Monday, May 22, 2017

How to Run a Cohort Analysis in Google Analytics

Traffic and page views are nice.

But they’re limited. In a few ways.

Site wide traffic looks nice on a blog post or meeting with your HiPPOs. But it’s not actionable. And it doesn’t tell you what’s going on beneath the surface.

For example, you have no idea if those users are returning. If they’re subscribin’ or buyin’. Or how they compare to peeps from a year ago.

In order to find out that detailed info that ultimately moves the needle, you need to dig a little deeper. And you need to be able to view these basic metrics through a more detailed lens that includes segment information.

Google Analytics cohort analysis tool can help. Here’s what it is, why it’s important, and how you can run your first cohort analysis report today.

What is a Cohort Analysis?


A cohort is “an ancient Roman military unit, comprising six centuries, equal to one tenth of a legion.”

Wait. That’s not right. Is it?

Ohhhh. It’s the second one.


My bad. A cohort is simply a grouping; a subset of people brought together because of a similarity or shared value.

Think of a retail store. You have a cohort of customers who bought in the last week. And another that bought this same exact week, but last year.

A cohort analysis, then, is the number crunching. It’s the sleuthing to determine if the customers from this week are worth more or less than the ones from a year ago.

Things change over time. Maybe the products are different. Maybe you switched manufacturer’s and the quality is different. Or maybe you’re using a new layout in your retail store that affects how people ‘flow’ through it.


Those changes, while seemingly small, can have a big impact on the bottom line. There’s a ton of psychology behind where the eggs are in grocery store (and where they’re hiding the booze).

So analyzing trends and patterns from customers based on when they shopped (i.e. acquisition date) can provide a lot more meaningful feedback on what changes resulted in different results (and why).

Here’s why that’s important (beyond just finding out where the booze is).

Why Cohort Analyses are Better than Standard Metrics

Google Analytics provides a wealth of data.

It’s perfect for finding certain things at a glance. Like aggregate, surface level data. That’s not a knock; it’s one of the best tools to see simple site wide metrics like top visits from certain sources, or dive a little deeper on how individual pages or pieces of content are performing.


But as with the retail store example earlier, websites change. A LOT.

Each time you redesign it, come out with a new product, update your service offering, and a host of other random reasons.

When those changes happen, it’s important to put these metrics in context. Comparing traffic or Time on Site of a particular blog post from now vs. a year ago might not be super relevant if it’s undergone a tremendous visual change in the meantime.

Cohorts can help. It’s like layering on a filter to add context to data you’re looking at. Viewing those details, by segments, now should produce more accurate findings. (And not just a vanity sepia filter to hide your bald spots. Just me?)

For example, let’s take a look at how tablet and mobile traffic compares to our site’s average over the course of a day.


Pretty interesting right?

Check out that massive Time on Site difference!


This information is interesting… but not sure helpful or actionable by itself.

So let’s add a cohort. Let’s look at the number of first time visitors who’ve left our site today, and see how many of those come back the next day.


Now we can dive deeper into how many of those people are coming back to our site (within X number of days of their first visit).

This brings us closer to Activation, Retention, and all those other Pirate Metrics to obsess over.

Zooming out, you can see these changes both numerically and visually.

How about the plain English version?

First, the graph depicts the percent of returning visits over a (default) seven day range.


The colorful, blue comparative table below the graph is where things start to heat up. (Literally.)

The table shows you what percentage of people came back to your site within seven days of their initial visit.

The second column from the left, Day 0, reflects the day on the left-hand column under all users:


The next column, Day 1, represents the first day after this group of people visited your website on May 9th.

That means 2.86% of people who visited your website for the first time ever on May 9th returned the next day. Day 2 would be what percent of those visited again on Day 2, etc.

Each date under All Users starts a brand new cohort. So May 9th is one. May 10th another. And so on. And each has their own pattern of returning users.

According to the tiny sample size in this example, the oldest cohort, May 9th, has seen a majority of first-time visitors come back to the site.

Make sense? Kinda, sorta?

Well if that wasn’t nerdy enough for you, it’s about to get a whole lot more geeky.

How to Use Google Analytics Cohort Analysis Tool

Let’s do a step-by-step walkthrough to see how you can start using Google Analytics’ cohort analysis tool.

Pull up Google Analytics, click the Audience drop down in the left-hand sidebar, and look for Cohort Analysis:


Here’s how the Google Analytics cohort analysis report will look like at a glance:


  • Report settings and metrics are all the way at the top
  • In the middle is a giant graph (that’s kinda useful, but more for the visual peeps out there)
  • While the final table at the bottom shows the results by cohort and date.

Here’s what that graph in the middle is showing:


We selected Acquisition Date for our specific cohort type, so that’s how the information is sorted in this graph. Day 0 is your acquisition date. While Day 1 is one day after, Day 3 is three days after, etc.

You can adjust these different cohort factors up at the top:


Here are the main factors you can analyze:

  • Cohort Type: Restricted to Acquisition Date
  • Cohort Size:  Sort by day, week or month
  • Metrics by category:
    • Per user:
      • Goal completions per user
      • Pageviews per user
      • Revenue per user
      • Session duration per user
      • Sessions per user
      • Transactions per user
    • Retention:
      • User retention
    • Total
      • Goal completions
      • Pageviews
      • Revenue
      • Session duration
      • Sessions
      • Transactions
      • Users

You can access all of these in the cohort analysis drop down menus:


Here you can select to run an analysis of a group of users sorted by day, week, or month (or whatever other variable you want).

For example, if you want to know how many pageviews each user had (metric), sorted in groups by day (cohort size) for the last 7 days (date range), you simply enter the following into the drop down menu:


Then, I am presented with the following graph:

So, what we see here is:

  • The May 9th cohort of users had 1.5 pageviews per user
  • That same May 9th cohort also had an average of 0.03 pageviews per user the next day (Day 1).

Now, let’s jump back to our original chart, showing the following data.


You may be asking: “How the heck do I use this information?”

“What do I do (ha – you almost said doodoo) with the fact that only a tiny percent of first time visitors are returning the next day (or the one after that)?”


“Why did 2.86% of the cohort visit again the next day with the may 9th sample, but then a big drop off for the May 10th cohort?”

Let’s find out.

Fortunately, Google Analytics allows you to break down these reports even further. So you’re not stuck in the proverbial analytics dark.

Notice at the top, we can add different segments to break down our report further:


Now let’s go back to analyzing the Mobile and Tablet segment:

Select it, and you can now see a comparison from your original data set (all cohort users segment) vs. the Mobile and Tablet traffic:

So, this data is showing us the cohorts of people sorted by date, who visited our site the next day after visiting for the first time, sorted by mobile and tablet. (Or, the very definition of a boring example.)

But check out that leap in return visits from the May 11th mobile cohort!

Obviously our conclusions in this case are limited because it’s a tiny sample of a too-limited date range. However, hopefully you can see the potential here.

If that’s not enough, you can also sort by just mobile, or even traffic sources like Organic Search, Direct, and more. (If you’re masochist.)

For example, here’s what Organic Search visitors look like:


Hmmm. Interesting. Organic Search visitors from the May 11th cohort are returning more frequently than average.

Was there a new blog post that day that’s bringing them back?

Dunno. But you get the idea.


Cohort analyses allow you to view data by segments of people.

Businesses of all shapes and sizes and flavors can use them to determine what changes (if any) resulted in better overall performance.

Google Analytics cohort analysis tool can help you put otherwise generic, aggregate website data under the microscope.

In all of about five minutes, you can quickly compare how different cohorts compare with others. And then cross reference that information with your own actions or marketing decisions may have played a role.

They allow you to zero-in not only on who is your most profitable customers, but why (or what) influenced them to become your most profitable customers.

And how you can do more (or less) of the same to scale results accordingly.

About the Author: Brad Smith is the founder of Codeless, a B2B content creation company. Frequent contributor to Kissmetrics, Unbounce, WordStream, AdEspresso, Search Engine Journal, Autopilot, and more.

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