9 Conversion Rate Optimization Misconceptions (and How They're Costing You Money)

There are few marketing practices more misunderstood than conversion rate optimization.

Some businesses see it as an unnecessary expense that doesn't really move their business goals.

Most others see it as a silver bullet for all their marketing woes. From growing traffic to converting more leads, CROs are often expected to do it all.

Some of these misconceptions are harmless. Others, however, can cost you money, time and valuable resources.

So in this post, I'll clear some of the common misconceptions about conversion rate optimization that are hurting your website and your business.

1. CRO is a single skill

One of the biggest misconceptions among businesses is an entrenched belief that CRO is a single skill set.

They might even call upon their “CRO guy” when they want to get more conversions from their sites.

In truth, CRO is a broad practice that encompasses a wide range of skills. To be effective at conversion rate optimization, you need at least three different skill sets:

• Copywriting: Whether or not you can write persuasive copy will have a big impact on your final conversion rates.


• Design: From your site's UI/UX to its choice in graphics, every design element on your site impacts conversion rates. You'll need a skilled UI/UX designer with knowledge of conversion rate optimization.


• Analytics: You'll need someone with the necessary skills to run tests and analyze their results.

Depending on how you run your tests, you might also need a developer to code up page variants.

Keep this in mind whenever you approach the idea of using CRO on your site.

2. Conversion rate optimization (CRO) is all about following “best practices”

I'm sure you must have stumbled upon best tips and tactics blog posts to boost your conversion rate. You may have also gone ahead and tried to implement those so called best practices but did they really work?

And if they did, do you really know why they worked?

The truth is that there are no one-size-fits-all “best practices” you can apply to your industry that will be guaranteed to work.

Your focus must be on how you can remove true barriers that are hindering with the flow to improve conversion rather than making little changes that worked for someone else.

For example, a common piece of advice for creating CTAs is to use a color like green (traditionally associated with nature or “go”).

Yet, when this best practice is put into practice, it doesn't always hold up.

In one study, changing from green to red colored buttons actually increased conversion rates by 21%.

Understand that conversion rate optimization is not a checklist that you can run down.

Instead, see it as a holistic practice where you aim to create a compelling user experience without sacrificing revenue. Your goal is to figure out your specific customers and what will work on your specific website.

To do this, you need to:

• Conduct your own audience research to understand what your users really want.


• Conduct an audit to see where users are dropping off from your website.


• Based on above information, implement changes and A/B test them.

Keep in mind that average A/B testing takes 4 weeks to run, so there is no point trying every possible “101 best practices”.

Here's an example:

Plenty of studies show video is better for conversions than static images or text.

Based on this, Brookdale Living, a community living service for seniors, tested two variants of its homepage – one with a static image:

And one with a video.

If you had to go by “best practices”, you'd assume the video page to win.

Yet, results showed that the static image variant had more engagement and drove more revenues (over $100,000 more).

Here's another example:

WedBuddy, a SaaS tool that helps couples create wedding websites, had a landing page that emphasized its free trial:

On paper, this makes perfect sense. After all, the word “free” is associated with higher click-throughs and sign-ups, especially when offered as a trial.

WedBuddy, however, realized that emphasizing the “free” nature of the service actually harmed conversions. People who signed-up thinking that the service would be free were harder to convert into paying customers.

To counter this, WedBuddy changed its landing page copy to focus on value instead of the “free trial”:

That's not the only change WedBuddy made.

It also went against convention and actually decreased the number of testimonials and list of benefits.

The final page was significantly shorter and had less social proof than the original variant:

The result?

139% increase in clicks and 73% increase in free trial sign-ups.

The lesson: don't blindly copy best practices. Instead, test them out and use them only if they actually help your target audience. For every case study proving a “best practice”, you'll also find 5 others that prove the contrary.

3. Conversion rate optimization is all about making small changes that lead to big rewards

You might have seen case studies like these:

Here, changing a single word (“my” instead of “free”) resulted in 90% more clicks.

Going by such case studies, you might be tempted to find that silver bullet where making a small change will reap big rewards.

In truth, case studies like these are incredibly misleading and give you only partial information.

They don't tell you:

• How long the test was done.


• Whether the traffic remained constant throughout the testing period.


• What other changes were made on the site.

If you really want to double your conversion rate, focus on following a framework that tests all elements of your site that get in the way of user experience and conversions.

Don't try to find these “holy grail” small elements. Take more risks by drastically redesigning your site with new a layout, color and images.

4. Conversion rate optimization is just about split testing

For most people conversion rate optimization is all about split testing site elements.

The truth is that CRO is all about identifying the actions of your users that lead to them buying from you. A CRO consultant's job isn't just to create tests; it is to identify and fix barriers to conversion on a site.

To do this, start off by asking yourself if your user's basic needs are met or not. Try to understand your user's psychology and what prevents them from buying your product.

You don't even need a lot of traffic for this. A basic usability test using the “think aloud protocol” may reveal plenty of user-experience roadblocks.

Here's a good model to work out what to focus on:

Based on this, your CRO practice should focus on the following (in decreasing order of priority):

• Functionality: Can the site perform basic functions? Does it work across devices? Are the buttons clickable?


• Accessibility: Is the site accessible to all users, regardless of their abilities, devices or locations?


• Usability: Is the site usable? Can people actually use it without guidance to find what they want?


• Intuitivity: Can users intuitively figure out the site's navigation and content?


• Persuasiveness: Is the site persuasive? Can it convince users to hand over their contact information or purchase a product?

You shouldn't completely ignore A/B testing altogether, but rather know when it is appropriate to implement.

5. No one reads long sales copy

“People don't read anymore”

“They have short attention spans”

“People are already too distracted”

These are all common refrains you've likely heard a thousand times.

This leads many businesses to believe that long sales copy can't sell.

In truth, people are now reading more than ever. Specially when someone becomes interested in any activity of actual commercial value, such as starting a business or getting a new job.

This popular post by Neil Patel on online marketing, for example, is over 30,000 words long, yet it has hundreds of shares and backlinks.

In fact, an analysis of over 1M search results by Backlinko found that longer content consistently outranks shorter content because people perceive longer content to have more value.

In his book Ogilvy on Advertising, David Ogilvy says:

“All my experience says that for a great many products, long copy sells more than short … advertisements with long copy convey the impression that you have something important to say, whether people read the copy or not.”

If your copy offers strong benefits that's relevant to the reader, they will read every word of it.

As the advertising adage goes, “The more you tell, the more you sell.”

For example, Moz tested a longer landing page which resulted in a 52% increase in sales:

Similarly, Crazyegg's longer homepage increased the site's conversion rate by 363% by making the homepage about 20 times longer.

6. Your user demographics and audience personas don't matter

Stop telling yourself that:

1. You don't need to survey or talk to your target audience.


2. You know your audience very well.


3. You know what's exactly going on in your audience's minds.


4. You make changes according to what you think is right and your audience must follow.

Wrong.

Regardless of the industry you're in, there is a very good chance your marketing is reaching to many different customer profiles in different demographics who would naturally have different motivations.

The wider your range of products, the more customer profiles and demographics you'll target.

For example, let's say you're selling beer online. A customer who just turned 18 would have different motivations to buy your product as compared to a 50 year old man who wants have a pint after a hard day's work.

Do you really think it is possible to think about all the market segments without doing any user research? There will be people with all kinds of personalities that will visit your website.

Some may want to have an in-depth description of how you brew your beer. Some would just buy looking at the reviews.

If you want your visitors to convert, get their feedback and use that information to craft changes accordingly.

Worse, lots of businesses believe that if they simply copy the design and copy of a successful website, they too can experience a massive increase in conversion rate, regardless of what their users are actually like.

Wrong. Again.

Successful websites convert better because they create an experience tailor made for their users. They research their audience and give them something they want.

Conversion case studies are there not for you to copy exactly, but for you to gain insights from.

7. If you focus on CRO alone, you can build a successful business

Because of all the love CRO has received of late, some businesses believe that if they win the CRO game their online business will start to shine.

CRO may help you increase your conversion rate from 1% to 2% which in turn will have a big impact on your sales. For it to work, however, you still need to get traffic, create a brand and keep attracting customers back to your site.

For instance, if you're selling a $100/month product with a 1% conversion rate and 10,000 visitors/month, there are multiple ways you can double your earnings:

• Improving conversion rate to 2%.


• Increase traffic to 20,000 visitors/month with the same conversion rate.


• Reduce customer churn.


• Increase conversion rate to 1.25% and traffic by 6,000 visitors.

Focusing on CRO is great since it gives you a solid, data-driven foundation for converting customers and generating leads. This doesn't mean that you should neglect other aspects of your business such as marketing, sales and the product itself.

8. You need to create a uniform experience for all your customers

For most businesses, creating a high converting page is a big deal.

But is it really okay to use that same landing page for all your visitors?

Your website is getting traffic from all sorts of channels – Facebook, Twitter, Youtube, Google, etc. Each of these customers come from a different context in mind and has different expectation from your website.

A user coming in from search expects to find answer to his query, while someone coming in after clicking a picture on Twitter expects something else.

To tackle this issue, if resources permit, try creating separate landing pages for users coming in from different channels.

For example, a few guest bloggers welcome users on a separate landing page instead of the home page when they click on “About The Author” section.

Similarly, Darren Rowse of ProBlogger links to his “About” page on ProBlogger's Twitter profile.

This works since the ProBlogger handle is tied to Darren. It makes sense to give readers coming from Twitter a better background on Darren, the person and the blogger.

Expedia's Twitter profile, on the other hand, links to the Expedia Viewfinder travel blog instead of the flight search page.

This is appropriate since people coming in from social media aren't necessarily looking for flights. Instead, Expedia wants to offer them a brand experience through its blog.

Try this on your own landing pages. Understand the expectations of your users and create an experience tailored for them.

9. Conversion is the only metric you should care about

What really is the end goal of CRO?

Conversion? Yes.

But who are you really trying to convert? For many visitors that come to your website (for the first time) conversion is a process. It may happen on their first visit, or it may happen on the tenth visit.

You can show them how they can buy your product and remove all the barriers standing in their way, but ultimately they will have to make the decision.

While conversion rate is important, it is only a part of the whole process. Instead, you should also pay attention to other meaningful metrics like – Visitor Recency (how long between visits) and Visitor Loyalty (how frequently people visit), etc.

Conclusion

CRO has the potential to make a major difference in your conversion rate, but only if you're willing to take a structured and systematic approach that relies on data and not myths and preconceived notions.

Before testing out anything you must understand your users and their problems. Stop all the guesswork and get down to do some user research. Workout the exact reasons why visitors aren't converting and implement the correct solution(s). Do this consistently enough and you'll never struggle with poor conversion rates again.

About the Author: Khalid Saleh is the co-founder and CEO of conversion optimization company Invesp, a leading provider of conversion rate optimization landing page optimization solutions.

How Machine Learning Will Force Marketing to Evolve (Whether We Like it or Not)

Promotion was easy in the 1960s.

Advertise on one of THREE networks. The one local newspaper. Maybe one of the few radio shows.

Things have changed a bit since then.

We're talking thousands of channels. Multiple devices. A never-ending Long Tail.

Not to mention, technological disruption that (a) erodes, (b) replaces, or (c) reverses most of the current best practices every 6-12 months.

That makes a marketer's role… complicated.

But you ain't seen nothing yet.

Cause the machines, they are a-comin'.

And sh!t ain't ever gonna be the same.

A Brief History of Machine Learning in Marketing

“RankBrain has become the third-most important signal contributing to the result of a search query”, according to senior research scientist at Google, Greg Corrado.

It's an artificial intelligence engine that uses pattern matching at scale to process millions of search queries daily. And with an 80% accuracy rating, it outperforms engineers by a wide margin.

There's no wonder that it's taking over the search giant then.

Part of that success comes from the ability to literally guess – based on millions of datapoints in fractions of a second – the searcher's intent behind a few random keystrokes.

In other words, it learns and adapts. Better and faster than any team of humans is able.

Cue the apocalypse.

Google's been working on this for half a decade though, fine-tuning and perfecting the approach. The savviest SEO's have even seen this coming since the beginning, tipped off by Google's patent filings a few years back.

However, Google's adventures in machine learning isn't an isolated event.

Facebook has also been using machine learning to enhance a user's newsfeed to help increase consumption and time on site. It's behind their 'facial recognition tool' that has a 98% accuracy when sensing who to tag in an image.

Machine learning also guides the way Netflix tailors content to you. And determining which email gets marked as spam or not (which is easy to remember with a mnemonic device like Spamalot).

But what is it exactly? And how does it work?

Don't ask me. Instead, Tommy Levi, the Director of Data Science at Unbounce (who just so happens to also possess a PhD in Theoretical Physics), had this to say in The Split (yes, I'm quoting Unbounce who's quoting Tommy Levi… how's that for some journalism?!):

“You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you're really just trying to either predict something or see patterns, and then you're just using the fact that a computer is really fast at calculating.”

This is one of the first big steps towards the practical application of artificial intelligence. But this time being used to help separate people from their pocketbooks. (Which is undoubtedly one of the great use-cases scientists had in mind for the promise of AI).

And sure. There are probably ethical concerns somewhere here. Like preying on the poor saps with drug, alcohol or gambling addiction (because machines haven't mastered political correctness just yet).

But let's not concern ourselves with that heavy stuff. (Honestly, read The Split for a more detailed – and responsible – overview of the drawbacks of this advancement.)

Instead, let's focus on where this is going.

Why does it matter for marketers? And how is it going to revolutionize our profession?

Why Machine Learning Matters

Teaching spam filters which stuff to let in your inbox is table stakes. No big deal.

Recognizing faces is fun. But playtime's over.

Providing more accurate search results? Useful. Although primarily for Google.

What about us? How will this impact marketers?

The epiphany you were looking for comes in the form of the Conversion Equation, introduced recently by Oli Gardner at this year's Unbounce Call to Action conference.

And it presents the most compelling example (that I'm aware of) in how machine learning will transform the role of marketers forever.

The ambitious goal: to create a standardized equation that predicts ways to improve conversions on your website.

For example:

• Moving your CTA down to this point in the page will result in a 4.5% conversion lift.


• The most interaction on this page happens around line 23, so place a video here.


• It will tell you, finally, what elements to A/B test (and which to ignore).

It delivers this through a combination of over 50-sub equations that blend (a) heuristic analysis, (b) rapid experiments, (c) conversion research and data, (d) video conversion and engagement data, (e) academic studies, and (f) tools and frameworks from the industry's best and brightest.

For example, the 'Page Clarity' sub-equation illustrates how machine learning is used to assess the following factors in real-time:

• Distraction: Are there too many conflicting CTAs on a page?


• Expectation: Does the information on this page match what the user's expecting to see?


• Readability: Is this page easy to understand, or riddled with jargon?


• Visual Identification: Does this page use good principles from data-driven design to help people subconsciously understand what to do on this page?


• Immediacy: Can the visitor quickly and easily understand what to do on this page?


• Specificity: Is the language explicit, or hard to comprehend?


• Hyperbole: Are you telling the visitor that you're good, or showing it?

The Clarity principle can help provide concrete, clear and specific instructions to improving the performance of a landing page. And it's just one tiny slice with 49 other options.

Here's what that means for our profession.

What Machine Learning Means for Marketers

Only about 1 out of 8 A/B tests deliver anything of note.

That means most are gonna fail. Which means you gotta do a bunch of them to see results.

Google alone ran more than 7000 tests in one year. And that was back in 2011! Ancient, primitive times compared to today.

They even tested 50 shades of blue for their CTAs (which sounds like a best-selling, geeky satire in the making).

Does your company have the bandwidth to match that volume? Do you, personally?

Most likely not. You probably have enough trouble getting one test off the ground each month.

54% of marketers say they don't work to improve conversions more because of a lack of resources (with lack of budget coming close behind at 35%), according to MarketingCharts.

The most difficult part of being a marketer today isn't the tactics. It's not figuring out what page elements to test. Here's 71 of them. It's not even how to do it. Here's how.

All that stuff is out there.

No, what's hard is knowing what to test on your website in the first place. And why.

That's where machine learning, and developments like the Conversion Equation come in. They provide predictable analysis, specific to your website based on rapid-fire pattern matching, that suggests what to test and why.

There ain't no maps for that. No 5,000 word Skyscrapers with 200 upvotes on Inbound.org.

Instead, it typically requires a reliance on creative analysis and years of experience. Human-based pattern matching that only comes with seeing multiple different use cases across again and again and again.

That means a few things.

On the downside, machine learning will probably mean greater unemployment (or the PC label: 'displacement') and/or consolidation as the industry matures. Competitive margins – currently measured by a marketer's ingenuity or experience – will diminish.

However on the plus side, machine learning will hopefully, mercifully, remove a lot of the guesswork (and political barriers) for the best data-driven marketers to do what they're good at: delivering action based on hypothesizing and iterating.

Stringing together small wins at each little step of the customer journey to deliver significant ROI increases every step of the way.

Conclusion

Machine learning is one of the biggest (and most promising) developments that will affect the way marketing evolves over the next few decades.

The initial, nascent steps are already influencing the platforms we use on a daily basis.

The near future offers unparalleled advancement in the way we deliver results for companies and clients.

And the combination of these developments will undoubtedly shake up the way marketers, well, market.

Machine learning may have a few drawbacks. There are very real ethical concerns, as well as the high probability of taking work away from lower-level marketers.

However it's inevitable.

It's going to remove a lot of the grunt work, manual labor and frustration we deal with on a daily basis. And it's got the potential to raise the influence of marketers worldwide.

That is, unless these machines don't kill us first.

About the Author: Brad Smith is a founding partner at Codeless Interactive, a digital agency specializing in creating personalized customer experiences. Brad's blog also features more marketing thoughts, opinions and the occasional insight.

How Machine Learning will Force Marketing to Evolve (Whether We Like it or Not)

Promotion was easy in the 1960s.

Advertise on one of THREE networks. The one local newspaper. Maybe one of the few radio shows.

Things have changed a bit since then.

We're talking thousands of channels. Multiple devices. A never-ending Long Tail.

Not to mention, technological disruption that (a) erodes, (b) replaces, or (c) reverses most of the current best practices every 6-12 months.

That makes a marketer's role… complicated.

But you ain't seen nothing yet.

Cause the machines, they are a-comin'.

And sh!t ain't ever gonna be the same.

A Brief History of Machine Learning in Marketing

“RankBrain has become the third-most important signal contributing to the result of a search query”, according to senior research scientist at Google, Greg Corrado.

It's an artificial intelligence engine that uses pattern matching at scale to process millions of search queries daily. And with an 80% accuracy rating, it outperforms engineers by a wide margin.

There's no wonder that it's taking over the search giant then.

Part of that success comes from the ability to literally guess – based on millions of datapoints in fractions of a second – the searcher's intent behind a few random keystrokes.

In other words, it learns and adapts. Better and faster than any team of humans is able.

Cue the apocalypse.

Google's been working on this for half a decade though, fine-tuning and perfecting the approach. The savviest SEO's have even seen this coming since the beginning, tipped off by Google's patent filings a few years back.

However, Google's adventures in machine learning isn't an isolated event.

Facebook has also been using machine learning to enhance a user's newsfeed to help increase consumption and time on site. It's behind their 'facial recognition tool' that has a 98% accuracy when sensing who to tag in an image.

Machine learning also guides the way Netflix tailors content to you. And determining which email gets marked as spam or not (which is easy to remember with a mnemonic device like Spamalot).

But what is it exactly? And how does it work?

Don't ask me. Instead, Tommy Levi, the Director of Data Science at Unbounce (who just so happens to also possess a PhD in Theoretical Physics), had this to say in The Split (yes, I'm quoting Unbounce who's quoting Tommy Levi… how's that for some journalism?!):

“You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you're really just trying to either predict something or see patterns, and then you're just using the fact that a computer is really fast at calculating.”

This is one of the first big steps towards the practical application of artificial intelligence. But this time being used to help separate people from their pocketbooks. (Which is undoubtedly one of the great use-cases scientists had in mind for the promise of AI).

And sure. There are probably ethical concerns somewhere here. Like preying on the poor saps with drug, alcohol or gambling addiction (because machines haven't mastered political correctness just yet).

But let's not concern ourselves with that heavy stuff. (Honestly, read The Split for a more detailed – and responsible – overview of the drawbacks of this advancement.)

Instead, let's focus on where this is going.

Why does it matter for marketers? And how is it going to revolutionize our profession?

Why Machine Learning Matters

Teaching spam filters which stuff to let in your inbox is table stakes. No big deal.

Recognizing faces is fun. But playtime's over.

Providing more accurate search results? Useful. Although primarily for Google.

What about us? How will this impact marketers?

The epiphany you were looking for comes in the form of the Conversion Equation, introduced recently by Oli Gardner at this year's Unbounce Call to Action conference.

And it presents the most compelling example (that I'm aware of) in how machine learning will transform the role of marketers forever.

The ambitious goal: to create a standardized equation that predicts ways to improve conversions on your website.

For example:

• Moving your CTA down to this point in the page will result in a 4.5% conversion lift.


• The most interaction on this page happens around line 23, so place a video here.


• It will tell you, finally, what elements to A/B test (and which to ignore).

It delivers this through a combination of over 50-sub equations that blend (a) heuristic analysis, (b) rapid experiments, (c) conversion research and data, (d) video conversion and engagement data, (e) academic studies, and (f) tools and frameworks from the industry's best and brightest.

For example, the 'Page Clarity' sub-equation illustrates how machine learning is used to assess the following factors in real-time:

• Distraction: Are there too many conflicting CTAs on a page?


• Expectation: Does the information on this page match what the user's expecting to see?


• Readability: Is this page easy to understand, or riddled with jargon?


• Visual Identification: Does this page use good principles from data-driven design to help people subconsciously understand what to do on this page?


• Immediacy: Can the visitor quickly and easily understand what to do on this page?


• Specificity: Is the language explicit, or hard to comprehend?


• Hyperbole: Are you telling the visitor that you're good, or showing it?

The Clarity principle can help provide concrete, clear and specific instructions to improving the performance of a landing page. And it's just one tiny slice with 49 other options.

Here's what that means for our profession.

What Machine Learning Means for Marketers

Only about 1 out of 8 A/B tests deliver anything of note.

That means most are gonna fail. Which means you gotta do a bunch of them to see results.

Google alone ran more than 7000 tests in one year. And that was back in 2011! Ancient, primitive times compared to today.

They even tested 50 shades of blue for their CTAs (which sounds like a best-selling, geeky satire in the making).

Does your company have the bandwidth to match that volume? Do you, personally?

Most likely not. You probably have enough trouble getting one test off the ground each month.

54% of marketers say they don't work to improve conversions more because of a lack of resources (with lack of budget coming close behind at 35%), according to MarketingCharts.

The most difficult part of being a marketer today isn't the tactics. It's not figuring out what page elements to test. Here's 71 of them. It's not even how to do it. Here's how.

All that stuff is out there.

No, what's hard is knowing what to test on your website in the first place. And why.

That's where machine learning, and developments like the Conversion Equation come in. They provide predictable analysis, specific to your website based on rapid-fire pattern matching, that suggests what to test and why.

There ain't no maps for that. No 5,000 word Skyscrapers with 200 upvotes on Inbound.org.

Instead, it typically requires a reliance on creative analysis and years of experience. Human-based pattern matching that only comes with seeing multiple different use cases across again and again and again.

That means a few things.

On the downside, machine learning will probably mean greater unemployment (or the PC label: 'displacement') and/or consolidation as the industry matures. Competitive margins – currently measured by a marketer's ingenuity or experience – will diminish.

However on the plus side, machine learning will hopefully, mercifully, remove a lot of the guesswork (and political barriers) for the best data-driven marketers to do what they're good at: delivering action based on hypothesizing and iterating.

Stringing together small wins at each little step of the customer journey to deliver significant ROI increases every step of the way.

Conclusion

Machine learning is one of the biggest (and most promising) developments that will affect the way marketing evolves over the next few decades.

The initial, nascent steps are already influencing the platforms we use on a daily basis.

The near future offers unparalleled advancement in the way we deliver results for companies and clients.

And the combination of these developments will undoubtedly shake up the way marketers, well, market.

Machine learning may have a few drawbacks. There are very real ethical concerns, as well as the high probability of taking work away from lower-level marketers.

However it's inevitable.

It's going to remove a lot of the grunt work, manual labor and frustration we deal with on a daily basis. And it's got the potential to raise the influence of marketers worldwide.

That is, unless these machines don't kill us first.

About the Author: Brad Smith is a founding partner at Codeless Interactive, a digital agency specializing in creating personalized customer experiences. Brad's blog also features more marketing thoughts, opinions and the occasional insight.

What's Old Is New Again: Outbound Marketing 2.0

It's hard for digital marketers to ignore headlines claiming, for example, “How Kraft Gets Four Times Better ROI From Content Than Ads.” That's certainly an impressive figure - four times better ROI! - but those who take it at face value risk missing the bigger picture.

More critical readers of that headline might ask: What mix of inbound and outbound marketing helped the Kraft brand, over its 100-year history, arrive where it is today? Would Kraft's ROI on content be as strong as it is, if not for their decades of advertising? Or, does this comparison even make sense in the first place?

The comparison does “make sense”, unfortunately. It's unfortunate that inbound and outbound marketing are so often framed as competitors racing for the higher ROI, but that comparison persists. And in one sense, given how their tactics differ, it's logical and convenient to view inbound and outbound tactics as two separate ballgames.

Examples of different outbound and inbound tactics. Here they're listed separately, but they're most effective when used together.

Marketing Isn't a Zero-Sum Game

Inbound and outbound have been discussed as inbound versus outbound ever since digital inbound marketing surged in popularity in the early 2000s. While it's true that many traditional outbound marketing tactics (e.g., cold calls, email blasts) have fallen from favor, these techniques can still be effective.

Today's savvy consumers still respond to outbound marketing efforts when they're used intelligently, aligned with inbound content and supported by quality data analysis.

The ideal marketing strategy recognizes the strengths and weaknesses inherent in both marketing “directions” and thoughtfully combines both into a cohesive message with a comprehensive reach. Inbound and outbound should be considered teammates-not competitors.

The difference between Outbound and Inbound marketing funnels (Image Source)

Highly segmented markets and fringe cases aside, most businesses get the best return when they present a singular, coherent marketing message. Mixed messages -even just messages that aren't obviously connected- can confuse consumers, leading them to seek competitors with more clearly positioned alternatives. Plus, for maximum efficacy, marketing initiatives should gain momentum over time, e'g', new marketing campaigns should build upon old marketing campaigns. The alternative is to continually start over from square one.

Outbound Marketing 2.0

Some marketers make the mistake of associating the outbound marketing tactics used today with those of a few years ago. This is unfair. Email marketing, as just one example, has advanced a great deal in recent years. We spoke with Steven Coufal, Senior Media Relations Specialist at Gartner, to learn more about the advancements in email marketing:

“It used to consist primarily of large scale, one-off email blasts, but now the technology has moved to the point where marketers can hyper-target small subsets and even individuals with very focused content tailored to their specific interests,” Coufal says.

Another professional marketer, Tim M. describes his old method in a review of his new marketing platform, “The other missing link was the ability to do email campaigns specifically designed for segmented target audiences.” With the newer email marketing platform, he continues, “we no longer have a one size fits all message that doesn't really work.”

Coufal gave us a rundown of how intelligent email marketing can be woven into the bigger marketing picture. “Say a prospect signs up for your email list and you begin to track them,” he explains. “Your marketing software scans their social media accounts to learn they are a female, in the 24-39 age range, a working-professional, living in London. It recognizes that she's in the target demographic for your company's new line of trench coats. She then gets opted into a specific email stream that promotes the styles and options known to be popular with her demographic.”

Not only can the message be tailored to the individual, it can be adapted to the individual's actions. Continuing with the above example, “Through your website tracking, you see your prospect visits a page, but winds up not buying a trench coat. Instead of simply reminding her about the coat in another email, a modern email marketing platform would let the follow-up email offer a 20% coupon, increasing the chances of a sale,” Coufal says. “It's this kind of microtargeting that can make outbound marketing so effective.”

Choose Your Platform Carefully

And these tools have never been more available, affordable or plentiful. Even single-purpose platforms such as RedCappi, Emma and other email marketing software tools are helping users implement precise segmentation and tracking of individual outbound efforts. Many reviewers praise more advanced platforms, such as Salesforce Marketing Cloud, because they centralize these (and many other advanced features) into a single, integrated inbound and outbound marketing platform.

While availability and affordability are both great, the very plentiful selection of options creates challenges. Ken M offers this wise advice, “Take the time to look at all the options. I almost made a decision early in the process because I just wanted to get the task done and move onto my normal duties. I am glad I took the time to be thorough.”

With the time you take, make sure to spend some looking at the range of options. Don't overlook analytics tools such as Kissmetrics. These can be integrated into a company's existing marketing platform, modernizing the strategy and execution of outbound efforts while providing the data needed to continually refine them.

The Sum is Greater Than Any Individual Approach

Calculating the ROI of inbound and outbound separately makes for compelling headlines, but it also suggests the two methods are in competition. But marketing isn't a zero-sum game where the winner takes all. Instead, marketing efforts should be made to cooperate. And with today's wide selection of advanced and affordable inbound/outbound marketing platforms, there's no reason both can't cross the finish line together.

About the Author: Craig Borowski is a Market Researcher at Software Advice, a Gartner company, providing analysis and recommendations for software buyers. A former Sr. Editor of TIME magazine, he now covers technology and changing trends in the CRM market, with a focus on customer service, marketing automation and the impact of technology on CRM strategy.

How the 80/20 Rule Affects Your Distribution Channels And Why It Matters to You

Have you ever seen one of your favorite products shut down? I have personally seen products that are beautiful and useful yet they have a hard time finding customers. What is happening here?

Developers and non-marketers like to believe that the best products will always win and that if “you build it, they will come” but life isn't like the Field of Dreams movie.

Peter Thiel, investor and co-founder of PayPal, even said this in Zero to One:

“The conventional thinking is that great products sell themselves; if you have a great product, it will inevitably reach consumers. But nothing is further from the truth.”

Once you build a great product, you need the right distribution for it. I'm using the word distribution as a “catchall term for everything it takes to sell a product”.

Lucky for you, there's hundreds of potential distribution channels that you could try. Blogging, Adwords, and Facebook Ads are just a few examples but what some people don't realize, is that there's 1 or 2 channels that will outperform all the other ones combined. This is an example of the classic 80/20 rule.

How Does the 80/20 Rule Affect Distribution Channels and Why Should I Care?

The 80/20 rule “states that, for many events, roughly 80% of the effects come from 20% of the causes.” In the distribution world, it means that 20% of all distribution channels will drive 80% of the results e.g. traffic, leads, new customers, etc.

This means that finding the “20%” of distribution channels for your business isn't just profitable but required for success. After all, we are all in a race against the clock which is usually when we run out of money or time.

To better understand the impact that one great channel can have, we need to look at the music industry. The most important question for music labels is this:

What distribution channels will ensure that songs become a hit? The answer to this question is radio.

Radio stations are crucial in creating awareness for upcoming hits and have been for years. Music hits depend on familiarity and radio provides this at scale. John Seabrook, who wrote The Song Machine: Inside the Hit Factory said this about the power of radio:

“Big Radio is still the best way – some would argue, the only way – to create hits. If the song seems to be playing everywhere at the same time, all at once, it is perceived to be a hit and becomes one.”

The effectiveness of radio stations has even led to criminal investigations against music labels who tried to purchase more playing time for their songs. The role of radio has changed over the last few years especially with new channels like Spotify, Pandora and YouTube, but this is an example where one channel outperformed all the other ones.

How PayPal Found Their Early Adopters By Experimenting with Different Customer Segments

Distribution channels are also affected by customer segments. A channel like Adwords might work on one segment but fail miserably on a different segment. A great example of this is the early history of PayPal, back in 1998. PayPal experiment with a few different customer segments including Palm Pilot users.

Yep, these were the early users of PayPal

These users were savvy and loved technology. Seems like a great fit for a startup that wants to send money through the internet right? As it turns out, these users were spread out all over the country making them hard to reach.

PayPal then decided to go after the Power Sellers in eBay. This segment had a high need for PayPal especially since the alternative at the time was to use checks. They happily embraced PayPal and were crucial in helping them “nearly double their user base every 10 days”.

Sometimes it can be hard to grasp how consistent exponential growth looks like so here is a chart that shows what your customer base would look like if you were able to achieve a 7% weekly growth.

You start with 100 customers and end up with just under 3500 in 12 months.

This type of growth is possible if you are able to find the most effective distribution channel and the best customer segment for your business.

The Mathematics Behind the Best Distribution Channels

Since you don't have the time and resources to try every possible distribution channel, we need a way to filter through the available options and narrow down to the ones that could be profitable.

You can view the entire list of distribution channels here.

While every channel is slightly different, we can use two metrics to find the best channels for us. These two metrics are:

1. Customer Lifetime Value (CLV)


2. Cost of Acquisition (CAC)

You can click through on each link to understand and calculate each of those metrics. Once you do that, come back to this article.

Imagine that your CLV is $400. This means you could spend up to $399 in CAC (this is a simplified example) and still make a profit. Your CLV provides an estimate of how much you could spend while also limiting which distribution channels you can use. Some channels have a low CAC e.g. digital advertising while other channels have a higher CAC e.g. inside sales team.

The graph below, from Zero to One, provides guidelines on typical CLV and corresponding distribution channels.

This graph gives you an idea of what typical CACs look like for common distribution channels.

Of course, it is possible that AdWords won't work for you (cost per click in your industry may be too high) and perhaps you can make a sales team work with a low CLV. The point of this exercise to is to filter down from 100 options into a handful of channels that are likely to work.

Systematically Testing Your Way to the Best Distribution Channels

With a handful of distribution channels, you can now run experiments and test different campaigns. There's a great article on what it takes to create a growth machine but the main point is that you need an established process that will make it easy to test different ideas.

Make sure to also explore different customer segments and see how each one affects your distributions channels. Testing customers segments is driven primarily by one question:

Who wants to buy my product and how can I best reach them?

Don't overestimate the impact that one channel can have on your business and how it can help you grow faster. Finding the best distribution channels might just be the thing that saves your company.

About the Author: Ruben Ugarte helps venture backed startups use analytics to make better decisions through his blog at Practico Analytics. You can also reach out to him on Twitter @ugarteruben.

Why Facebook Bots Have Amazing Potential (And Why You Should Still Ignore Them)

Chatbots are the latest in a long line of tech trends to sweep through the marketing world.

One use and it's delightfully obvious why.

Ranging from fun and whimsical to straight-up commercial, chatbots present interesting new potential for companies to reach and engage customers.

Facebook messenger bots specifically present one of the brightest areas for marketers, tapping into their huge network and built-in advertising features.

However despite the promising outlook, you might be better served by ignoring this trend (for now).

Here's why.

The Promising Future of Facebook Bots

Chatbots magically combine pattern matching (low-level artificial intelligence) to present options to users instantly based on a number of predefined rules.

Based on your real-time responses, a chatbot will work to get you closer to finding exactly what you're looking for. That intent-driven nature makes it especially promising for marketers, showing a glimmer of hope similar other intent-based (not to mention, high converting) platforms like search engines.

Chatbots on Facebook messenger have been begun popping up everywhere the past few months, with help from platforms like Motion.ai and Smooch.io that help bridge the technological gap for the masses.

Expanding into Facebook's existing advertising infrastructure is mouth-watering. For example, you're able to integrate existing SMS messaging campaigns to Facebook Messenger accounts with phone number tracking.

There's also the potential for these chatbots to replace 800-numbers and other awful customer service experiences in favor of a real-time, at-your-fingertips, information retrieval system.

That's not even taking into account the massive potential that is WhatsApp (and its 1 billion monthly users), which Facebook owns, but doesn't currently allow this technology.

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Some companies are already tapping into these benefits, as Andrew Tate highlights excellently on AdEspresso.

HealthTap allows you to speak directly to a doctor. Well, not exactly. It will start by showing you recommended answers from doctors to similar questions made in the past. Depending on your results, you can then send the question out to a real, live doctor to get an answer.

Spring gives you personalized shopping recommendations. List of questions for styles, price range, type of clothing and more to eventually whittle down its answers. This wonderful little chatbot does all the hard work for you, removing the need to browse for hours on end.

And one of the most cited examples 1-800 Flowers, where you can make a new order with a simple message. Enter the location for drop-off, and you can browse their options to make a purchase immediately. (although it's worth noting that you currently can't process credit card payments within Messenger).

Awesome stuff. No doubt.

But here's the problem.

Despite their awesome potential, chances are, you should NOT worry about chatbots right now.

Where do Buyers Come From?

In what seems like decades ago (ok, it was only about five years), Forrester Research analyzed over 77,000 consumer orders and released The Purchase Path of Online Buyers.

The goal was to determine which channels were responsible for the most buyers (not subscribers) to help marketers decide where to invest their precious resources.

The results were surprising exactly how you'd expect if you've been doing this for awhile.

Social accounted for less than a percent of sales (although in fairness, there's undoubtedly a host of attribution problems).

Otherwise, Search (both Paid and Organic) was the top driver of new sales. Email was at the top for repeat customers.

Fast forward a few years and McKinsey found that things had… well, not changed at all.

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The way people purchase today has become increasingly more complex, with multiple touch points and many different channels used along the way. Many marketers struggle with getting a handle on understanding their own customer journeys.

The point though, is that there are a few fundamental things that should be working flawlessly before spending time, money and attention chasing the latest trend (despite how promising this particular one is).

Here are a few examples.

Here's Why You Should Ignore Facebook Bots (For Now)

Why do people leave your site?

Somewhat surprisingly, it could be how sloooooooooow pages take to load. If an eCommerce site fails to load within three seconds than half of its traffic will bounce.

Then there's also poor design and navigation issues, too many competing or cluttered offers, and a mismatch between what got people there, and what's on the page. Along with a ton of other reasons.

What's the reason you should forget about Facebook messenger bots for the next few months?

Opportunity cost.

Exhibit A.

McKinsey also found that despite the majority of visitors opening your email on mobile devices, many of the landing pages people are being sent to still aren't responsive.

(Not to mention, just because a website is 'supposedly' or technically responsive, doesn't mean that the mobile experience doesn't suck.)

To make matters, 61% of those people with bad experiences won't return. While 40% will go straight to your competition.

That means not only are your landing pages costing you lost sales. But they're also serving as your competitor's best advertisements.

Fixing those landing pages should be important. Priority #1 in fact when you go in on Monday morning. It should at least place well ahead of dabbling in new social features which may, or may not, pan out.

Based on data and experience, you know – beyond a shadow of a doubt – that improving landing pages will result in greater conversion rates (and thus, more revenue).

You just haven't done it yet.

Because, to-do lists. Emails. Meetings. Etc.

Exhibit B.

Every good little marketer uses email. It's like PB&J at this point. But, it too is under some strain.

Competition is at an all-time high (and only getting worse), so getting your messages to stick out from the other junk in people's inboxes is a tall order.

Then there's deliverability issues, which email service providers getting ever-more sophisticated to noticing (and filtering out) your promotional emails.

A handy and helpful solution is marketing automation, that relies on timing, relevancy and personalization to cut through the crap.

On the plus side? It works. Delivering twice as many leads compared with your typical spray-and-pray approach.

The downside? Only 13% of marketers are using it. 13%!

Despite the fact that marketing automation can send you 451% more leads. Or increase average sales 34%.

Case-in-point: a whopping 85% of B2B marketers are not satisfied with their marketing automation efforts. Even despite marketers ranking it at the top of a latest survey from Smart Insights of digital activities with the greatest impact.

Wait. Why? Too busy Snapchatting or Instagramming to worry about increasing revenue?

But it gets better.

Exhibit C.

Let's see what happens if we combine the activities found in Exhibit A & Exhibit B.

Williams-Sonoma saw a 10x lift in response rates when they sent triggered emails based on specific things people were just looking at on the website.

(Pro tip: Williams-Sonoma is also a great place to score free coffee at the mall using their Nespresso display.)

Personalized campaigns “consistently and overwhelmingly beat” static ones after analyzing 650 multi-channel marketing campaigns.

HubSpot analyzed 93,000 calls-to-action (with “hundreds of millions of views over 12 months”) and found that ones targeting specific user actions resulted in a 42% improvement over standard ones showed to everyone.

This is the same approach remarketing takes, personalizing ad creative while capitalizing on impeccable timing to show people exactly what they were just looking at. At a 46% reduction in cost compared to normal ads to boot.

Want a trend to follow? There's your trend!!

Conclusion

Chatbots are undoubtedly one of the sexiest trends to watch develop.

Facebook messenger bots up the ante, initially promising a huge opportunity with their massive audience and built-in advertising features.

However…

What keeps getting pushed down your to-do list already? How many activities have you neglected (or ignored) that could (and more predictably) increase revenue over the next 30 days?

Poor mobile landing pages? Tired, boring email campaigns? Static website calls to action?

If you're working with Coca-Cola, go get you some Facebook bots!

If you're like the rest of the 99% of us and you're (a) already overwhelmed, (b) spread too thin, (c) understaffed, (d) on too tight of deadlines, with (e) not enough money to spend, maybe it's time to double down on the fundamentals.

Fix the underperforming stuff you're (most likely already) aware of. Increase sales.

Then go play with some Facebook bots.

About the Author: Brad Smith is a founding partner at Codeless Interactive, a digital agency specializing in creating personalized customer experiences. Brad's blog also features more marketing thoughts, opinions and the occasional insight.

Why You Should Dump Your “Analytics Person” (And Do THIS Instead…)

Whether you're a bootstrapped startup or a well-known company, you likely have one (or more) people on your team in charge of analytics.  These are the people who wrangle all that big data into meaningful insights that help propel the company forward.  They're the ones who make sense of all the input arriving from all the disparate tools and filter it into discussions, split testing ideas, sales hooks and other angles that help improve conversion rates.

So if they're so crucial, why am I suggesting you dump them?

Because if they are the all-encompassing heart of data at your business, they're doing more harm than good in the long run.

Now before you grab your digital torches and pitchforks, hear me out. I want to preface this article by saying that yes, analytics people are valuable – critical, even – to your business' success.  But no matter how varied their experiences or how unique their perspective, all of the insights you're moving on and data you're collecting are still being filtered by one or even a handful of people.

On the surface, there's no harm in that.

It's when that person leaves, or changes departments, or gets promoted that you start to run into problems.  Suddenly, this person who was the linchpin of the entire analytics action team has left everyone else scrambling to pull and/or make sense of the numbers and charts.

On the opposite spectrum, there's the “analytics fortress”. It's virtually impregnable and understandable only for a select few. There are whispers and rumors about the treasure trove of data it contains, but few have ever seen it first-hand. And while that may seem like rock-solid job security on the surface, it's a giant red flag for the organization doing the hiring. Remember, they want the system to be set up so that it's easy to understand, pull from and analyze. They won't hesitate to hire someone who can help them re-configure it to be more open, accessible and cross-functional.

Getting a better handle on your analytics starts with putting the information out there and letting your various teams, departments and decision makers brainstorm the possibilities while gradually guiding them on integrating more of the data into their everyday tasks.  One step at a time, and gradually, everyone becomes proficient.

But We're So Far Behind!

Did you know that analytics, as a field of study, is less than five years old? That means it's still at the proverbial not-eating-paste stage of its life.  Because there are so many advancements being made in the field and new tools being developed, it's easy to think that you're falling further and further behind. But the fact is, with the right dashboard and the right information at your fingertips, you can make course corrections in real-time for the betterment of the campaign or project as a whole.

What this kind of new-found power will do, however, is put a big dollop of transparency on everyone. Mistakes (and successes) will be visible. Scrutiny and the sizing up of campaign results will happen. But nobody's perfect and everyone's learning.  You can't afford to let your mistakes hold you back, but by the same token, you can't afford to rest on your laurels either. Always moving forward.  That's what it means to be data driven.

The New Data Wrangler

Every business wants someone with a brilliant analytical mind to swoop in, corral the data and turn it into a nice, neat and meaningful slideshow where everyone gets to bask in hefty sales figures and pretty charts. In a perfect world the numbers speak for themselves and the subsequent actions obvious.

Sadly, it doesn't work that way. And companies who forge ahead with this mindset are terribly misguided. But because transforming into a data-driven company is such a new process, it can be overwhelming to know where to start or even how to go about it.  You may already even have people in place, such as those who oversee proper compliance, those who fix broken or duplicate data, or even those who build monetization models. But data-driven isn't a person so much as a way of doing business.

That means you want your company to have a strategic, forward-thinking mindset. It's very much a cultural shift. People should easily be able to reference and use the information collected to make improvements and add value to the customer experience.  That means being able to make decisions that are practical for today as well as far-reaching for tomorrow.

Make It a Group Effort

The fact is, your campaigns will always improve over time whether you've got one person running it or 100. Why pin all your insights on a single person when you can make analytics a group effort instead?  Have a process in place that turns your company from reactive to proactive when it comes to data-driven insights. Oftentimes, there's a pervasive thought throughout companies that analytics “is not my job” or that the only time it's useful is determining the success or failure of a campaign after the fact.

If data isn't the foundation of every marketing decision made by each and every person, then you're not truly a data-driven company. Teach others how to use the data not just when it suits them, but as part of the overall company culture – in planning, prioritizing, during the progress of a campaign, and throughout optimization up to launch and beyond.  Above all, analytics should never be used to tout one person's idea as better than another's. Through all this data, everyone is learning whether something works, whether it doesn't, or whether it merits more study.

Every single piece of information is a potential lesson.

Action Steps for Becoming a More Data-Driven Company

So now that we know what's needed, how do we turn this information into action?  Here are a few steps to integrating a more data-driven flow into your workplace:

• Set your data free – Let everyone from key decision makers to operational leads, partners, suppliers and vendors have access to relevant information


• Make analytics for everyone – Connect all your sources so that every department sees the big picture and can drill down for more details


• Let people create their own insights – You'd be amazed at the ideas and perspectives that percolate to the top when people have the freedom to make their own analyses and insights based on the situation presented


• De-centralize decision making – Let things be done openly. Test, track, monitor and report. Don't require lots of needless hoop-jumping just to try out some ideas.


• Rely on systems that everyone can use, not a black box configuration that is recreated each year by a new analytics lead

Remember, analytics is going to be new to many, many people. So it's understandable that there would be some uncertainty or outright trepidation in getting accustomed to it. Furthermore, company culture shifts don't happen overnight, so gradually easing into this new form of doing business helps ensure that everyone is on the same page and understands what's expected of them as it relates to their role in moving the company forward using data insights.

With that being said, however, it's always a good idea to learn from those who have successfully migrated over to a more data-driven mindset. Has your company embraced its data-driven roots? How did the process go? What parts were easiest to get people acclimated to and where did you struggle? Share your insights with us in the comments below!

About the Author: Sherice Jacob helps business owners improve website design and increase conversion rates through compelling copywriting, user-friendly design and smart analytics analysis. Learn more at iElectrify.com and download your free web copy tune-up and conversion checklist today!