Data Analysts are a Marketer’s Best Friend

/ Zach Heller

Marketers today need to be comfortable with data. They need to be able to understand what the data is telling them and how to make decisions based on that. The best hunches or instincts in the world cannot compete with data-driven strategies. Not in 2019.

But you and I are not the experts. We didn’t go to school to learn how to work with data (though perhaps more marketing coursework in the future will involve data analytics and statistics).

That’s why we need to befriend the people who are experts.

Data analysts and data scientists are the folks who know how to make sure your company is collecting the right data, how to review and sort through that data to get answers to critical business questions, and how to present their findings in a way that makes it easy to make business decisions.

For marketers, this skill set is a godsend. Here are just a few of the key ways marketers can work with data analysts to achieve better business results:

  1. Build real-time reporting by advertising channel that lets you view return on investment at the high level across channels and dive deeper within each channel to improve optimization decisions

  2. Gain a deeper understanding of how customers are progressing through the buyer journey and identify opportunities within the sales funnel to improve the likelihood of conversion

  3. Build more robust buyer personas with first-party data collected by your company that will help your sales team prospect more effectively and influence future branding and messaging decisions

  4. Isolate the impact of key pricing and promotional campaigns in order to determine the ideal pricing strategy that drives maximum profitability and sales

  5. Connect your data with key advertising platforms to derive greater value from AI and machine learning going forward, letting algorithms take the lead of spending decisions in order to maximize efficiency

Every marketing organization today needs people who are fully engaged with the data their company and their customers are generating. This data is a treasure trove of information that can be used to guide decisions at every level of the company.

As a marketer, you would be smart to spend more time with your company’s data analysts and data scientists, leveraging their incredible skill set to help you do more.

When Everyone is Using the Same Algorithm, What Happens to Competition?

/ Zach Heller

In the future, the company with the best algorithm will win.

But in some cases, it seems likely that we will all be working with the same algorithm. When that happens, it is unclear who wins. Do we all win? Do we all lose? How can competition exist in an arena where every company is using the same algorithm?

This was a question of some debate at a recent meeting of marketing professionals in New York City. The discussion revolved around the growing role of AI and machine learning algorithms in advertising.

The truth is, a lot of advertising exists on one of only a few large platforms. Consider the fact that 2019 is the year where more than 50% of all ad spend will occur online vs offline. And consider that Google and Facebook control nearly all of the online advertising market.

Each of those companies, along with Amazon, Bing and a large number of smaller platforms, is working on algorithms that better serve the right ads to the right people at the right time. They are doing this because the future of their business relies on advertisers successfully reaching customers and driving sales. And a better algorithm, it’s thought, will be more adept at fulfilling that promise.

But at some point, we must consider that all of the advertisers on Google are running ads on the same platform, and that platform is running the same algorithm (or set of algorithms) to determine when and where to show different ads.

Here are four potential outcomes when this happens:

1) The companies with more/better data will win out over the companies with less/poorer data

When we are all using the same algorithm, the data that we are able to feed into it might determine who succeeds and who does not. Those companies who have the ability to store massive amounts of clean data, data that feeds back into the platform (whether it’s Google or anywhere else), will be better positioned to take advantage of the algorithms of the future. With more data, the algorithm will work more effectively for that company than it may for another.

In this scenario, the real level on which companies are competing is on data.

2) The marketplace will determine winners and losers

This is potentially a dangerous, anti-competitive scenario. But one we must consider. Google and Facebook already have incredible amounts of power. And if they can use their algorithms – intentionally or otherwise – to control what companies succeed in reaching new customers and what companies do not, be careful.

I don’t think any of us wants this kind of a future. Google and Facebook will tell us the same thing. But who is preventing it?

3) Branding will become more important than ever

When there is no real competitive advantage on targeted digital advertising, companies will need to rely on other areas to compete. Brands are one possible area of competition.

While brand loyalty has been trending downwards for some time, it may be that effective branding is going to see renewed importance over time. And that’s because consumers who seek out certain brands will bypass, in a sense, what the algorithms are doing.

4) Pricing will become more important than ever

This one requires very little explanation, because it is similar to #3. And while it might sound like good news for consumers (more companies competing on price and lower prices in the marketplace overall), it spells trouble in the long run.

A price war – with each company trying to lower prices to out-offer competitors – could precipitate a race to the bottom on quality and service. It could also mean that giant companies, like Amazon, undercut competitors to the point that they become near-monopolies.

This scenario is not great of competition either.

The Possibilities Are Endless

These are only four potential scenarios. They all might come true. Or none will come true. There are thousands of other ways this could work itself out.

But if you are responsible for advertising your business, or growing your business in any way, you have to start thinking about the future of competition. Because it may not look like competition today.

Use Google Analytics to Optimize Advertising Spend

/ Zach Heller
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When you think of Google Analytics, what do you think of?

You might think of website metrics, like visits and users. You might think of website usability – bounce rates and time on site. You might even think of goal tracking – transactions and revenue.

Google Analytics does all of that, and more. Which is why it is such a great tool for marketers at companies large and small.

However, most marketers don’t think of Google Analytics as a tool to help you optimize your advertising spend. But it can do that too.

How to Optimize Your Ad Spend with Google Analytics

First, did you know that you can import cost data into your Google Analytics account? You can link your Google Ads account so that all of that data gets pulled in automatically, and then use this article to learn how to add all your other ad spend.

Once you have cost data included in Google Analytics, you can use various ‘Acquisition’ reports to dig into the performance of all your advertising channels. From paid social campaigns like Facebook and Instagram ads, to search ads on Google and Bing, to email marketing and display – you can learn more about how visitors behave on your site when they come through one of these paid channels.

You can see the number of sessions, and calculate the cost for every new visitor to your site. You can see where they go on your site, and how long they stick around. And you can see transactions, including conversion rate, revenue, and cost per transaction. In that way, you can even calculate your return on ad spend (ROAS) for each campaign – that is, how much money is this campaign delivering in revenue for every dollar you spend in advertising.

At this point, you will have a better idea which channels are working and which are not. And you can optimize your budget to spend more in those that are working, and press pause on the campaigns that are not.

But that’s not all.

Take things one step further and learn how to improve performance within each individual campaign with audiences and segments. You can identify specific behaviors in each of these visitor groups (based on the traffic source or campaign) that will help you create better onsite experiences.

Looking at landing pages, bounce rates, conversion funnels, and ecommerce data, you can collect vast amounts of data points to help you better understand how people are interacting with your site. Find the gaps, and work on improving the overall conversion process – whether its for that single campaign or all of the above.

This conversion rate optimization work – that springs from observing traffic patterns and user behavior in Google Analytics – will help you optimize your advertising efforts even further, by improving the ROAS across the board. If you get more conversions for each dollar spent, your ROAS goes up. That means greater marketing contributions and a happy boss.

Why is the Bounce Rate so High on Your Landing Page?

/ Zach Heller
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A landing page is the page on your site which people land on when they click on one of your paid ads – be they search ads, display ads, social ads, or others. And so, many marketers and the companies they represent expect that some percentage of people who land on those pages will end up leaving before the do anything else.

In analytics terms, we call that a bounce. And the page’s bounce rate is the percentage of visitors who expect your website before completing any additional action.

A high bounce rate is clearly not a good thing. But traditionally, marketers tend to be more tolerant of a high bounce rate on a landing page, where a visitor has come from an advertisement, than they would be on other pages of the site. And I am not here telling you that you should expect visitors you pay for to behave the same way as visitors who come to your site organically.

However, just because we expect higher bounce rates on our landing pages, doesn’t mean we should be okay with them. And it doesn’t mean we can’t work to lower them.

So make 2019 the year you refocus on landing pages, and cut those bounce rates in half.

How? Start by understanding why people are bouncing in the first place.

Here are five possible reasons:

  1. You are advertising to the wrong people.

  2. Your page doesn’t provide enough information.

  3. Your page is not optimized for mobile.

  4. You are not clear about what they should do next.

  5. You don’t give them any incentive to take action.

Let’s explore each of these possibilities and what you can do about them.

You are advertising to the wrong people.

If the wrong people are landing on the page, it’s no wonder that they are leaving. This may happen if your targeting is too broad, meaning that your ad is being shown to people who are not in the market for your offerings. It also can happen when you use the same landing page for multiple channels and audiences. It is a best practice to make sure your landing page is specific to each audience. To accomplish that, you may need to create multiple landing pages for each campaign.

Your page doesn’t provide enough information.

Many companies treat landing pages as teasers for a certain product or service. They provide just enough information to whet a customer’s appetite and get them to take the next step. But what you think is enough information to tease a product, may not answer the questions that most of your visitors have. And rather than take the required next step, they leave your site and go looking for alternative solutions.

Your page is not optimized for mobile.

We are living in a mobile-first world. More web activity is taking place on phones and tablets than ever before. And your landing pages absolutely must be geared toward the mobile visitor. This means focusing on load times, readability, and usability. Challenge your own perceptions of your landing pages by looking at the bounce rate for mobile users separate from desktop users. You may find that solving for mobile alone can cut your bounce rate in half.

You are not clear about what they should do next.

Some people will leave your site because they simply don’t know what else to do. A strong call-to-action is an important part of any landing page design. Once you have provided enough information to convince the visitor that they are in the right place, give them an action to take. It could be a phone call, a form to submit, a button to start the sales process, a web chat. And make it obvious. The more they have to search for it, the greater the likelihood that some will give up.

You don’t give them any incentive to take action.

Why do this now when I can do it later? That is the mentality of most consumers. It is up to you, as the marketer, to give them a reason to act now. Perhaps it’s the opportunity to claim a special offer, perhaps your offer is only good for a limited time, or perhaps they don’t want to have to wait in line. As a marketer, you are constantly fighting for attention. So don’t squander that attention when you get it by letting consumers leave without taking the next step.

To Get the Most Out of Google Analytics, First Understand What It’s Telling You

/ Zach Heller
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Arguably the single best free tool available to marketers is Google Analytics. If you know how to use it, it can tell you so much about your customers, how they are using your website, what they’re interested in, and what is causing them issues.

However, no tool is perfect. And no tool can do everything you need it to. Even something as impactful as Google Analytics must come with a few words of warning.

To get the most out of the tool, you have to first understand what it is telling you. And with a tool as powerful as Google Analytics, the biggest fear is in assuming everyone will read the data in the same way.

One Statistic, Multiple Interpretations

Let’s look at an example:

Your team is reviewing the most popular conversion paths on your website and you find that people landing on one specific page are returning to the previous page at a high rate. This is something that Google Analytics can show you quite clearly. You can see the click paths, and so you know where they are coming from and where they are going next.

It’s obvious, in this case, that there is a problem worth correcting. Something about that page is not working.

One member of the team raises his or her hand and says, “I see what’s going on here. They are clicking on a button expecting one thing and they are seeing something else. We need to more clearly explain what they will see if they click that button.”

Someone else than counters, “No, no, no. That’s not it. It’s the page they are landing on. It’s ugly and it turns people off. We need to redesign it.”

Still a third person, who can barely wait to speak, announces, “what are you talking about? It’s clear that if they’re not finding what they’re looking for, we need to give it to them. The content of this page is the problem.”

In that example, who is right? How do you know?

Inferring the Why

Google Analytics is great at telling you WHAT is happening. It’s not as good at telling you WHY.

And in the example above, each team members is inferring the WHY based on the WHAT. They may all be right, to some degree. But they may all be wrong as well. What we know for certain is that the Google Analytics data alone does not lead to any of those conclusions. Each person is taking the data presented to them, and applying their own subjective interpretation to determine what is going on.

How to Resolve the Conflict

Data, by itself, does not solve problems. It helps people make more informed decisions.

For marketers charged with improving the online experience for a company’s customers, we need both data and opinion. We need people to develop theories based on the data so that we can design new solutions and test them.

The danger is in mistaking opinion for fact. In the example above, it is a fact that people are landing on the page and then going back to where they came from. The three conclusions drawn are opinions, each worthy of exploring in more depth.

To resolve the conflict, and improve your website, you need to go further than Google Analytics. At this point in the process, after the basic theories have been established, we need to figure out how to proceed.

Do you conduct experiments with live subjects? Do you assemble a focus group? Conduct a survey? Take votes? Or simply go with the person’s gut who is in charge at the moment?

All of these are viable options. And as long as your team agrees on the facts, the opinions can and should vary. That’s what makes a healthy team, people bringing a variety of experiences and skills to the table to find a solution to problems.