In this article, we’ll take a closer look at nuances in data measurement and various analytics software in action.

Looking at nuances in data measurement  

Digital analytics tools were never intended to work as accounting systems or sales registers. They were made to collect and quantify interactional user data into easily usable insights and reports. Over the years, these tools’ data collection methods have evolved. In turn, the way specific data points are measured also changed. 

Let’s say you changed your tape measure from imperial (measuring in inches) to metric (measuring in centimeters). The length of a desk might be reported as 39.4 in one and 100 in the other. The length of the desk didn’t change, but how you measured it has. 

Try switching between different analytic tools. Often, you’ll see that your numbers may be different, but trend lines remain similar. Each tool counts things slightly differently; the same issue frequently applies when upgrading software.

At one point, unique users were counted by combining the total number of unique IP addresses that accessed a website in a given period. Eventually, organizations started using firewalls/proxy servers, requiring all internal users to access the internet with a single IP address. How unique IP addresses were counted didn’t change, but the count of unique users dropped dramatically.

Counting of unique users evolved into using a combination of IP address, OS and browser (type and version), then the addition of a persistent cookie to better estimate unique users. Once again, no matter how you count unique users, if the user cleared their cookies and cache or switched computers (office vs. home vs. phone), no analytics tool will have provided an exact number. Nowadays, tools take other factors into account when counting unique users.

Dig deeper: Data analytics: Your stack’s past and limitations

How to think of your analytics data

Your analytics software is imperfect because of many factors beyond its control. Users might be blocking cookies or other tracking methods. Internet blips might prevent data from reaching the data collection server. The best way to think of your analytics data is by viewing it as a poll of user activity.

Everyone is familiar with polls at election times. A typical U.S. presidential election poll surveys approximately 10,000 people (or less) out of 150+ million eligible voters (0.006% of voters). This is why when news broadcasters report on the poll results, you hear something along the lines of “This data is accurate within 4 percentage points 4 out of 5 times.” This equates to it being off by more than 4 percentage points 20% of the time.

When it comes to your digital analytics tools, most analytics professionals estimate the loss of data to be no more than 10% and most likely around 5%. How does this translate into data accuracy?

If your site received 10,000 sessions in a reporting period but for various reasons, you could only capture data on 9,000 sessions, your data would be accurate within a margin of error of less than 1%, 99 times out 100. 

In other words, 99 times out of 100, your data is accurate and 1 out of 100 times, it is off by more than 1%. Simply put, your data is accurate, but it is not perfect (precise) and will not match your sales records.

Such data is more than accurate enough to determine which marketing efforts — SEO, paid ads, sponsored posts, social media marketing, email marketing, etc. — are working and even which ones drive traffic versus drive sales.

Dig deeper: Don’t apply wishful thinking to your data

Analytics in action

While analytics data may be accurate, even being off a small percentage in precision can call your analysis into question. This is especially true when the difference between two data sources changes. 

The key is to monitor the data and, where possible, compare it. If there is a sudden change in accuracy, you need to investigate. For example, was your website recently changed? Was this change properly tagged to capture the data?

A client once added a pop-up to their Shopify account after an order was placed but before the thank you page was generated. Their analytics tool records sales only when the user receives the thank you page. 

With the pop-up in place, the order still went through, but many users didn’t click through the messaging. As a result, a large percentage of sales were suddenly not being captured as no thank you page was generated. There wouldn’t have been an issue if the pop-up appeared after the thank you page.

Below is an example of monitoring sales and orders between Shopify and Google Analytics 4 (GA4). We can see how much data is being lost because of various factors. Using Shopify’s analytics as a record of true sales and comparing it to data collected via GA4, we see the following:

The daily variations in total revenue and orders varied from virtually 0% to nearly 13%. Overall, in these 24 days, GA4 reported 5.6% less revenue and 5.7% fewer orders. This data is accurate, especially when applied to marketing efforts to see what drove the user to the site to make the purchases. 

Should this company use GA4 to report sales? 100% no! That’s what accounting software is for.

If your organization demands even more accurate data, there are methods to push data directly to most analytics tools (server side). This avoids issues with user browsers and cookies. 

While sales data may be more accurate, other soft measurement aspects of user interaction may drop (e.g., scroll tracking). This is a complex and time-consuming method to implement for most organizations. 

You must ask yourself, “is this extra effort necessary just to capture another 2-5% of sales revenue in my analytics reports?”

Understanding your analytics data

Everyone needs to have faith in their analytics data. The key is ensuring your analytics software is installed and configured correctly. Understand that it can’t capture everything. 

Your analytics software simply takes a poll with a sample size of over 90%. This makes the results highly accurate (on target), if not 100% precise (actual numbers).

The post Accuracy in digital analytics: What marketers need to know appeared first on MarTech.

After completing numerous UA to GA4 migrations, one issue that appears in almost all instances relates to categorizing existing marketing links into Google’s newly defined channels.

Old online marketing links with UTM parameters are no longer being categorized the same way. But why? 

In this article, we’ll discuss the common reasons for miscategorization in GA4 and tips to make sure your marketing campaign links are properly tagged and tracked.

Miscategorized links in GA4: Common reasons

While there are several reasons for miscategorization, sometimes it is caused by using a “custom channel” that GA4 no longer supports. Other times, it is due to invalid values entered into the marketing link’s source or medium UTM parameters.

While GA4 hasn’t changed or added new UTM parameters, they have become stricter about the values they accept. Failure to meet these specific settings will cause traffic generated from your marketing efforts to be categorized as “Unassigned.”

Formatting campaign URLs: What marketers need to know

To understand the problem and the solution, let’s review the proper format (as per Google’s requirements) for a marketing/campaign link.

The URL format must be (in all lowercase): 

https://www.ourcompanysite.com/?utm_source=source&utm_medium=medium&utm_campaign=campaing_name&utm_id=optional_campaign_id&utm_term=optioanl_term&utm_content=optional_content

In the above example, optional parameters are indicated.

When the values of the required utm_source or utm_medium parameters do not match specific values, the traffic is recorded in the “Unassigned” channel.  

To view the values assigned to that traffic within GA4 simply add the second dimension “source / medium” to your channel report.

Dealing with ‘unassigned’ traffic

In the report below, someone on the marketing team defined the medium as a “post,” which is not one of Google’s defined values for utm_medium and hence the traffic is categorized as “Unassigned.”

Below are examples of where someone on the marketing team just wanted to define the source but not the medium for various marketing efforts or incorrectly assigned an invalid medium.

You may notice that Google discourages using spaces in the parameter values and even hyphens (“-“) as an alternative. Instead, an underscore “_” is the preferred replacement. 

While failing to follow these new recommendations won’t impact GA4 reports today, with the constant changes taking place, it may in the future. 

The new format will become more critical when your organization starts using Google’s BigQuery data warehouse to store your analytics data beyond the 14-month maximum online availability — an inevitable undertaking.

Defining source and medium parameters

The two parameters that GA4 focuses on to categorize traffic correctly are:

• utm_source: The traffic referrer (i.e., google, newsletter4, billboard).
• utm_medium: The marketing medium (i.e., cpc, banner, email).

So, if you set the utm_source to equal “My Email List” and the utm_medium to equal “EMail,” GA4 may categorize it as “Unassigned” instead of “EMail.”

If you’re scratching your head about this, here’s the reason. 

According to GA4 documentation, the following criteria must be met for the traffic to be considered from the email channel:

• Source (utm_source) = email|e-mail|e_mail|e mail
• Medium (utm_medium) = email|e-mail|e_mail|e mail

For those not familiar with regular expressions (or regex), let’s break this down.

• Note there are no capitalizations allowed.
• Either the utm_source or utm_medium must contain one of the values provided where the “|” indicates “or.”

In the example provided, while the utm_medium contains “EMail,” having both an uppercase “E” and “M” means it does not equal “email” as per the GA4 specification. (While Google may convert all parameters to lowercase, you can not rely on them to fix your errors 100% of the time.) 

Having the name of an email list as the value for the utm_source parameter is still permissible, provided that the utm_medium equals “email.” (This is not ideal, though. The email list name is better assigned to one of the optional parameters or, at a minimum, made all lowercase with the spaces removed.)

Also, while in the example of acceptable values from Google, it is permissible to use “e mail” (with a space), it is not recommended and should be avoided.

Similar specifications are available for all 19 GA4 channels. A complete list of each channel and the acceptable values can be found here.

Pay attention to paid channels

While we don’t have to worry about the non-advertising channels (i.e., organic search, organic social, direct, referral, etc.), we need to concentrate on all the other ones. 

Paid channels require special attention. The new ones include: 

• Paid Social.
• Paid Shopping.
• Cross-network. 

All paid channels with “Traffic is Google Ads” in the requirements imply that the utm_medium value is either set to “cpc”, “ppc” or “paid.” 

The value of the utm_source must match the site value that Google has determined as a search/social/video site vs. something else based on Google’s defined list. 

(Here is an Excel spreadsheet you can download with the complete list of the utm_source values Google uses and how they are categorized.)

For example, if your URL has utm_source=blogger and utm_medium=cpc, it will appear as “Paid Social” in GA4. 

If any other value is used for utm_medium beyond “cpc”, “ppc” or “paid” it will appear as simply “Organic Social.”

The spreadsheet from Google is constantly being updated, so be sure to download the latest before assuming how any new advertising partner will be categorized.

What happens to ‘unassigned’ campaign data?

While we need to be proactive in aligning all our marketing links to the new Google Analytics requirements, what can be done with the data that has already been captured and reported as “Unassigned” or perhaps it was a custom channel you defined for Universal Analytics?

This can be addressed when data is extracted from GA4 or BigQuery via Google’s Looker Studio (formerly Data Studio) or any other business intelligence tool. 

Within these, you can define your own logic where utm_source and utm_medium equal the values you defined in your UA custom channel and assign it to its own channel. (Hence, a custom channel.) 

Dig deeper: MarTech’s in-depth GA4 coverage

Adapting to GA4’s campaign tracking standards

Universal Analytics and Google Analytics 4 differ in many ways. There are technical reasons behind these differences, all with good intent.

Different isn’t always a good thing for marketers who are used to the traditional approach of tracking marketing campaigns. But one thing we can easily address is how our online marketing efforts are tagged to ensure they get classified into our preferred channels. 

Addressing this before your migration from UA to GA4 will help lessen the headache of the transition and the efforts required to fix them after you have made the inevitable move.

The post Campaign tracking in GA4: How to ensure your links are properly tagged appeared first on MarTech.

I was thrilled to be present 25 years ago when Sir Tim Berners-Lee first announced the Web Accessibility Initiative back in April 1997. But while awareness of accessibility issues may have increased and various government legislations have mandated it (for some), anyone involved in the field of accessibility knows that digital campaigns as a whole are lacking.

Many people don’t think about accessibility beyond seeing ramps added to buildings. When they find themselves on crutches or using a wheelchair, only then do they become concerned with physical accessibility.

Accessible design — when combined with advances in technology that may hinder accessibility and an aging population who can no longer read small print — is becoming (and should be) front-of-mind for everyone in the marketing community.

I interviewed three accessibility authorities on the subject to find out the current state of things and the best way to ensure that accessibility becomes part of all digital projects. 

Our experts are:

• Meryl K. Evans, CPACC, Speaker and Accessibility Marketing Consultant.
• Lea Scudamore, Lead SEO, Aimclear.
• Kim Krause Berg, CPACC, QA Analyst, Accessibility and Human Experience Design, BMTX.

What percentage of digital campaigns do you feel involve any level of accessibility thought and or testing?

The experts responded in an almost unanimous response of “none to almost none”. Evans was the most optimistic, estimating no more than 10% while Scudamore estimated 5%. She went on to expand:

“I am still seeing light grey fonts, red fonts, and other colors of fonts that do not have the high contrast that makes it easy for everyone to see. Many ads have very small fonts that also make them hard to read. Inaccessible content is still far too common. Many landing pages pop up over the website, and many pop-ups, landing pages and shopping carts are not reachable without a mouse, which makes them inaccessible.”

What is the most common aspect of accessibility that digital marketers forget?

Berg stressed: 

“It’s more of a lack of training than being forgetful or neglectful. Marketers are focused on able-bodied target markets and SEO and less inclined to consider how people access content. There’s a gigantic segment of the population using assistive technology or accessibility settings on their computers and mobile devices whose needs are ignored. They are simply not getting promotions because there are barriers preventing them from accessing content.”

Evans emphasized: 

“The most common aspect of accessibility that digital marketers forget is using the headings on blog posts and web-based content. They tend to enter headers and subheaders, and then format them to look the way they want. This deprives their content of search engine juice.

Search engines give a higher priority to headers and subheaders than using proper headings as in <h1>, <h2>, <h3>, etc. When you don’t use headings, the headers are treated like a paragraph. 

Think about reading an article in the newspaper or online. Do you scan the headlines, subheadings, images, and bullets? Most of us do. It’s how we get the lay of the land. Online content with <h#> headings provide the lay of the land for people using screen readers. Without them, they can’t skip around the content.”

Dig deeper: The cost of ignoring website accessibility

Have you ever seen a digital campaign where you felt the marketers and designers did a good job from an accessibility perspective?

Both Evans and Berg couldn’t think of a single campaign that did a good job with accessibility, Scudamore did identify a single one:

“U.K. Unilever a few years ago. From their ad campaigns to their site, they did a really nice job and a seamless experience from the ad to the landing page to the sale. They have slipped a bit. I think there have been some leadership changes there, and perhaps accessibility isn’t the priority it still should be.

PurpleTuesday.com is also a good example. However, if you use the WAVE Evaluation Tool it will appear that the site has errors. It doesn’t. This shows that tools can only test a site through math, accessibility evaluations need both tools and people to review. We need marketers to up their game and knowledge about accessibility so we don’t rely on automated tools that have varying degrees of accuracy.”

If you could make digital marketers and designers implement a single aspect or key aspects of accessibility into their campaigns, what would it be? How could they test it to be sure they did it correctly?

Evans recommends looking at color contrast: 

“Campaigns are usually very visual. So, a single aspect I’d recommend they check is the color contrast. They can do that easily with a free color contrast tool like Colour Contrast Analyser.”

Berg also encouraged the use of tools for testing:

“Imagine what your campaign sounds like without the visuals. The bulk of the delivery is with the use of images, UI layout, calls to action and text. Without images, would you know the purpose of the promotion? Are the images described with alt text?

Use a built-in browser web dev tool extension to remove all images and see if there is anything left to communicate the promotion and then provide the opportunity in multiple ways, such as text, links, buttons with your keywords and verbs.”

Scudamore highlighted that accessibility testing shouldn’t be an afterthought:

“Brands must bring people with various abilities to the table at the start of the development of any campaign, website, app, etc. They must keep them on board to test as the project develops.

As an industry, we have got to stop wasting money trying to retrofit poorly developed projects. Too many brands and agencies lose time and opportunity by not considering accessibility as an imperative (as opposed to being an option, at best — or ignored, at worst).”

Dig deeper: Optimizing the online experience for disabilities improves it for all customers

Are there any tools or websites you’d recommend to help digital marketers evaluate/test their campaigns prior to launch?

Tools are tools and the most important tool any digital marketer has is the one located between their ears. That said, some handy utilities/tools to provide information to our brains were provided.

Scudamore recommends “cozying up with the Web Content Accessibility Guidelines (WCAG). Each step of the way. Every time there is a function or a measurable goal, we need to check how it should be done accessibly with the WCAG” as well as a contrast tester plugin from WebAim.org.

Berg also recommended WebAim.org and their WAVE browser extension. She also maintains a list of recommended resources.

Evans provided the following list:

• W3C Easy Checks (manual review).
• Use a color contrast analyzer for color contrast.
• For web content, WAVE Browser Extensions, WAVE Web Accessibility Evaluation Tool, and/or Accessibility Insights for Web using the FastPass option.
• For docs, Microsoft Word’s built-in accessibility checker.
• For presentations, Microsoft PowerPoint’s built-in accessibility checker
• Grackle has multiple tools

Berg’s final suggestion:

“This is not about features. It’s about delivering promotions in ways that more people will understand, perceive, and be motivated to choose. They can’t make decisions when they can’t see, hear, or comprehend marketing content and page layouts.

View your digital marketing strategies on as many computers and mobile devices as possible. Turn the page from portrait to landscape view. Magnify the page up to 200%. Be sure your marketing investment is not a big content blob when the page is requested with practices you may not have considered.”

Evans made the point, “If you want to reach more people, then make your content accessible.”

This was substantiated by Scudamore. As she pointed out:

“The American Institutes for Research estimates the spending power of people with disabilities in the United States to be $490 billion in disposable income for workers aged 16 to 64 — the after-tax dollars for basic necessities such as housing, food, and clothing. In the marketplace, PWD — as well as their families, friends, and advocates — wield considerable spending power.”

Dig deeper: How to make your content more accessible to the visually impaired

Incorporating accessibility in digital marketing efforts

It is this untapped market where we can expand the reach of our digital marketing efforts to not only increase revenues but increase our customer base.

If we are the first to tap into an untapped audience and do a great job, we’ll also create loyal customers. It’s time to take the advice of our accessibility experts and start including accessibility as part of our digital marketing efforts.

The post How to make web accessibility a part of digital marketing efforts appeared first on MarTech.

Over the last 10 years, as the industry evolved alongside the evolution of the internet, the web, and digital apps, there has been a growing and positive trend to do a way with the “Jacks-of-all-trades”(and masters of none) and engage specialists instead. One of the key skills that the digital marketing industry is sadly ignoring, however, is that of the User Experience (UX) expert.

The absence of UX experts on digital marketing teams shows that those in charge of these teams still believe that everyone can be all things. Just as digital marketing teams tend to rely on the digital analytics team to help evaluate the success or failure of their marketing campaigns, or to help optimize the campaigns after they are launched, they should be engaging with a UX expert before the launch to ensure there is nothing in the experiential aspects of the campaign that will hold it back from its potential.

To delve into the signs that you need to engage an UX expert as part of the digital marketing process, we asked one of the world’s leading UX experts, Jared Spool from UX design school Center Centre – UIE to answer a few questions on UX and digital marketing.

What percentage of digital campaigns do you feel involve any level of UX thought and or testing?

I would have no idea. I would hope all of them do, but I’m sure they don’t. So, it’s definitely a number below 100%. Since I know some do, it’s above 0%. So, I’d say the likely range is between 1% and 99%. Beyond that, it would be hard to narrow it down.

What would be a sure sign in analytics reports that a digital campaign has failed from a UX perspective?

Unfortunately, you can’t tell from analytics whether a campaign has succeeded or failed from a UX perspective. To understand the UX, you need to really understand the experience of the users. (That sounds obvious, but you’d be surprised how many people don’t really know what their users’ experiences are.)

Let’s say you have a simple campaign that drives people to a landing page, with the intent of getting them to sign up for whatever the landing page is intended to sell. Now, a lot of folks will tell you that you could look at the conversions to see if there was a failure. Unfortunately, conversions only tell you half of a story.

Conversions tell you whether someone converts (signs up) or doesn’t. The analytics could tell you how many people visited the landing page and how many converted. Dividing the latter into the former would be your “conversion rate.”

However, this assumes that every visitor should convert. What about the people who legitimately shouldn’t? Maybe they didn’t understand what was offered by the campaign, yet when they landed on the page, they suddenly realize this isn’t the offer for them. Should they convert?

If they do, you might have a disgruntled customer on your hands. Or you’ve overinflated your number of people who signed up. This means there’s four possible combinations of people who come to your landing page through your campaign:

1) Those that should sign up and do. (Yay!)

2) Those that shouldn’t sign up and don’t. (Also, this should be a ‘yay!’)

3) Those that should sign up, but don’t (Hmmm.)

4) Those that shouldn’t sign up, yet do. (Uh-oh.)

If your efforts try to optimize for #1 (this is standard “conversion rate optimization”), you’ll end up ignoring the intentions of #2 and #3. When you optimize for conversion rate, success isn’t measured in terms of what makes your customers happy, but what’s good for your wallet. 

Unfortunately, there’s no analytics in the universe that can tell you about #3 or #4. The only way to learn about these is to do hard-core user research (which is a fancy technical term for “talking to your customers”).

What is the most common aspect of UX that digital marketers forget?

Simple: talking directly to their customers and prospects. Having conversations. Learning what they need and what they don’t.

People try to use digital marketing campaigns to replace having salespeople. And there’s lots of good reasons to do that. Yet the one advantage a salesperson has is they usually have to talk directly to customers and prospects. Those conversations are research into what the customer and prospect really need. And the salespeople are always learning.

Once you eliminate the salespeople from the equation, digital marketers often don’t replace that research with anything. The absence of research has a technical term for it: guessing. If you’re guessing what your customers and prospects want, you’re probably doing it wrong.

Have you seen any digital campaigns recently where you felt the designers did a good job from a UX perspective?

Sure. But you can’t isolate things to a campaign. When we’re talking about user experience, we’re talking about their total experience. 

Let me give you an example: Insurance companies try to get people to switch to their product from someone else’s. (This is because, in many places, people must have insurance. The market isn’t growing. The only way to grow your business is to steal someone else’s customer.)

If the marketers see their business as a commodity business, they see the biggest differentiator is price. Yet the biggest reason people switch insurers isn’t price. It’s the quality of the service they get during a claim. Someone has a bad claim experience (the company makes it really hard to get the claim settled satisfactorily), then they’ll switch instead of renewing. Where do they go? Someplace they believe offers better service.

Therefore, the UX of the insurance purchase has virtually nothing to do with the campaign that gets them to switch. It has to do with the quality of service. By the way, do you know what the No. 1 way people learn about better quality service? Not from ads, because every ad claims their company’s service is great.

It’s from their friends. Word-of-mouth advertising is the No. 1 influence on who people choose for their next insurer. What drives word-of-mouth? Not clever campaigns. No. It’s great service. So, the best thing UX people can do to help digital campaigns is to make sure the overall service is the best quality service.

If you could make digital marketers and designers implement a single aspect of UX into their campaigns, what would it be? How could they measure it to be sure they did it correctly?

It would be to deliver high quality service at every touchpoint. How would you measure that? By talking to customers and prospects to make sure you’ve delivered high-quality service everywhere.

Any other thoughts you’d like to share on UX and digital marketing?

My thought is that UX is digital marketing. A great user experience is the number one driver of every marketing metric. Investing in better UX is the best way to improve digital marketing. Not just of the marketing campaign experience, but of every aspect of the product or service.

When you invest in better UX, you improve the experiences people have. You improve the way they talk about you and your services. You make everything in your marketing efforts easier. 

It’s far easier to market a product or service everyone thinks is great than a product or service that nobody thinks is great. So much of the heavy lifting in marketing is because the company hasn’t made the investment in UX that they need to.

The main takeaways

To sum up the points and perspective Jared Spool shared, UX should be and needs to be part of every digital marketing strategy. Failure to incorporate UX efforts into digital campaigns, typically results in poorer performing campaigns which could have been much better performers.

Unfortunately, there are no KPIs or simple analytics measurements to inform you that your campaigns would be helped from UX nor that integrating UX into your campaign development process will help your campaign performance. At best you could compare a current campaign with UX versus an earlier campaign without it.

The post Why UX is critical to digital marketing appeared first on MarTech.

Long before the first web banner ad appeared (Oct. 27, 1994, in Wired magazine), marketers wanting to help their clients with their marketing efforts embraced the marriage of analytics and marketing. Over time, that marriage has evolved, and the capabilities of analytics tools have as well.

At one time, marketing reports were, “Look at the number of site visitors the campaign generated!” or “See how many page views we were able to get!” These were the common uses of analytics. Eventually, as analytics tools improved, the ability to attribute online sales to specific marketing efforts became possible.

During these 30-plus years, one thing remained constant in marketing’s interpretation of web-based analytics: A campaign drove X visitors to the site. They viewed so many pages, which led to a given number of sales. Essentially, a basic user flow. Each step on the site during the visitor’s journey was seen as fluid and easy to follow.

As marketers, we need to start getting our brains in shape for what is coming with the next generation of analytics tools and techniques. The new generation of analytics tools no longer process user activity recording (log file) but instead store specific events in a database. If you haven’t heard about “Event-Based Analytics,” you will soon hear about it everywhere.

Back in October 2020, Google released Google Analytics 4 (GA4). It was in Beta mode, but any user signing up for Google Analytics was automatically enrolled into GA4. You had to know your way around GA to set up the old Universal Analytics (UA). While GA might be the most popular analytics tool out there, Adobe Analytics has been doing “Event-Based Analytics” for a while, along with several other analytics tools out there.

While the official date by Google forcing everyone to switch over to GA4 hasn’t been announced, rest assured it is coming, and it is time to start thinking about “Event-Based Analytics,” and how it differs from what you are used to and some of the advantages contained within it.

Defining Event-Based Analytics

“Event-based analytics is the method of tracking and analyzing interactions between users and your product, also known as events.”

What does this all mean to marketers? We need to rethink how we present analytics data as part of our marketing reports.

In the past, when we’d talk about a user’s journey, say, “They came from this campaign, landed on this page, visited these pages and made a purchase of $XXX.XX.”

With Event-Based Analytics, we’ll still see which campaigns brought visitors to the site. Following them on which pages they viewed is not as easy, but tracking the individual steps in the checkout process becomes much easier.

With Event-Based Analytics, we get a product view of what transpired more than user flow.

For example, we can create a segment for a specific campaign and see individual steps (think of it as stepping stones, a user can easily jump from one to the other or skip over some of them). In an ecommerce site, we’ll see how many units of each product were added to shopping carts and how many were purchased. You won’t see if they add a product to their shopping cart, then come back later and remove it or decrease the ordered amount. Event-Based Analytics will generate a report that looks something like this:

Event-Based Analytics and segmentation

A powerful feature that becomes available with Event-Based Analytics is enhanced segmentation. While order analytics tools offer some level of segmentation, you’ll now have much more flexibility when it comes to defining them. Segmentation will provide you with the ability to separate prospects and customers into specific groups based on how they engage with your product.

Below is an example of how with Event-Based Analytics user engagement by different channels of acquisition can be generated.

With Event-Based Analytics, you’ll most likely not see a bounce rate measurement being reported. Why? Because the simple act of viewing a page is an event. Most analytics tools now record time on page (an event is triggered every X seconds) via timers and not just from the timestamp between page views and they also will track user scroll on a page (engaging). To simplify this, if a user spends X seconds on a page or starts scrolling then they didn’t bounce, but they engaged. We now have to think of “engaged sessions” versus “non-engaged sessions”. A single page view, with no scrolling and spending less than X seconds is a “non-engaged session.”

Dig deeper: What is customer journey analytics?

Using Event-Based Analytics to increase revenue

With an ecommerce website and mobile app, a site visitor (perhaps from a marketing campaign) opens the website and browses a number of items before adding an item to their cart. It could be days later, they log back in on the mobile app and complete the purchase. Now in your analytics platform, the above behaviors or events might look like this: “User Sign Up,” “Search for Items,” “View Item Details,” “Add Item to Cart,” and “Purchase Complete.” On many older analytics tools, you wouldn’t see this connected journey, but would see that a user came from X campaign, added items to the shopping cart, and then stopped. Another user “magically” logged in via the app but bought stuff without even adding them to the shopping cart.

Event-based data can generate questions that lead to product changes and adjustments. After reviewing data from the above example, we could be asking:

1. The percentage of users that complete the checkout in a single session?
2. Does conversion differ by item or brand?
3. If users didn’t convert, where did they do? (abandoned the site, continue to view other information, etc.)
4. How long does it take (in minutes or days) for conversion?
5. Do users face a payment error or other issues (events) during the checkout process?
6. If they didn’t purchase immediately, are they gone forever?

You might be able to answer the above questions with your existing analytics tools, but with event-based analytics it becomes much easier.

Event-Based Analytics and data warehousing

Combining your Event-Based Analytics data with a data warehouse puts your data on steroids. You may have noticed that each event is essentially a data point that can easily be exported to a data warehouse.

Simply by exporting your data, you now have the power to manipulate and process your raw data. Previously, you had to work with the data available within your analytics tool.

For example, with an ecommerce site, you are likely tracking a unique customer ID. This by law is an anonymous ID (no way to link to specific personally identifiable information). Within your database, you can execute a customer lookup and start to see how much specific customers are ordering and when. How about generating a report of customers who left items in their shopping cart for more than 2 weeks? As a marketer, you could then generate incentive based emails, or even have their assigned sales rep give them a call to see what’s up. It is in this power of the combined data in the data warehouse that truly allows event-based analytics to drive up sales.

Reporting is further enhanced and made easier using your data visualization tools when accessing the data warehouse. You no longer need to connect multiple data sources and show individual reports. Connecting your data visualization tool to the data warehouse, allows data to be presented in unified tables and graphs.

If your organization hasn’t already implemented event-based analytics, start making plans to do so. If you’re currently running Google’s Universal Analytics (UA), start preparing for when they announce the date for turning off UA and forcing you to switch to GA4. As a recommendation to all UA users, it is time to start running GA4 in parallel, if for no other reason, to familiarize yourself with it and to start seeing the power that it brings with it.

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Why is having a current marketing plan that is disseminated and known by your entire marketing team and other teams within your organization critical and how does this have anything to do with your corporate analytics? For the simple reason: Without one, how does the marketing team actually know what they should be doing. More importantly, how can success be measured?

The key to marketing success is to merge a marketing plan with a measurement plan into a unified plan.

Key measurement elements of a marketing plan

The first step in developing or validating a marketing plan is ensuring the marketing department’s mission statement aligns with the corporate mission statement. This is where many marketing teams make their first mistake. If the two don’t align properly, then how can the marketing department effectively obtain corporate buy-in and ensure their marketing efforts are effective in helping the organization meet its overall goals?

Once the marketing department has validated its mission statement, it needs to define specific objectives. Then, it is time to merge these elements with a measurement plan that defines specific marketing tactics that can be planned, budgeted for, approved, executed and measured.

 Clearly define these tactics. Examples can be:

• More posts (paid and non-paid) on specific social apps (i.e. Facebook, Twitter, Reddit, etc.)
• More engagement with the public on social media sites
• Creation of branded ads 

Perhaps the most difficult task in this process is that of defining the appropriate Key Performance Indicators to measure how these tactics measure up. Some KPI in support of the above examples might be:

• Increase in branded organic search traffic
• Overall increase in organic search traffic
• Increased activity/engagement on corporate social media accounts including click-throughs on posts
• Increase click-through rates on branded ads
• Increase in online sales by specific channels (organic search, branded campaigns, social accounts, etc.)

When defining your KPI keep in mind the following four factors that make up a useful KPI:

1. Must utilize obtainable data
2. Must relate directly to the marketing objective
3. Should not be an absolute value but a ratio or comparison. For example, a KPI for improved customer engagement might be to increase average session duration. Or, measure average time on site comparing period one to period two. Or, a KPI for measuring effective campaigns on the “average number of orders per 100 sessions” by campaign
4. Must be easily reportable and understandable by the target audience.

With the KPIs in place, ensure your analytics account is configured correctly. Ensure that you can accurately – without too much effort – report on the identified KPIs. Have all the required channels been defined? Don’t just rely on default channels from your analytics tool. Make sure the marketing activities to support the marketing department’s mission statement are realistic and approved.

Marketing measurement plans are typically in a layout grid format. Do a quick search and you’ll discover many suggested layouts. My favorite is simple:

With a merged marketing measurement plan in place, the next task is to align your corporate analytics to capture appropriate data and making it reportable becomes an easier task, as well as getting buy-in from other departments.

Imagine if your marketing plan didn’t include a KPI on sales by channel? Could you get your IT group to make the necessary coding changes to push transaction values to your analytics tool? Frequently they simply default to saying: “Sales information is available from our ecommerce tool.” While true, you can extract sales data from a backend tool, in virtually all cases, you can’t attribute those sales to specific marketing efforts. It is only with an approved marketing plan in place can you apply leverage and get this data integrated with your analytics.

What about custom channels? Do you need to segregate paid social, from non-paid social (your team’s participating on social sites on your own posts), from public sharing of your content? Yes, these are three unique social channels that should be tracked and reported on, if your company is utilizing these channels as part of their marketing plan.

You can create custom analytic reports that demonstrate how effective various marketing efforts are in support of not only the marketing department’s mission statement but also the corporate mission statement. This allows you to easily evaluate and adjust with objectively to demonstrate just how successful these efforts are to the c-suite.

Remember that marketing mission statements are a living and breathing thing. The world of online marketing is constantly changing as are the tools that help execute marketing plans and those that measure results. Plan on reviewing the mission statement at a minimum annually and possibly quarterly or semi-annually if appropriate for the organization. Don’t forget to get analytics audited by an independent auditor on a regular basis to ensure the data being collected is as accurate as possible, and that all data that should be collected is reported.

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What is cross-domain tracking?

According to Google Analytics, “Cross-domain tracking makes it possible for Analytics to see sessions on two related sites (such as an ecommerce site and a separate shopping cart site) as a single session.“ In simple terms, cross-domain allows you (a business analyst, analytics analyst, business owner, etc.) to view a website visitors session as they navigate from one domain to another as part of a single customer journey from the point of acquisition to conversion.

When and when not to implement cross-domain tracking

Implementing a cross-domain tracking solution isn’t the answer to poorly configured websites. We’ve received requests for it to solve this problem. “If you go to our site with domain.com everything is fine, but you also get there with www.domain.com and everything is also fine, but as you navigate the site, sometimes a user gets the www and sometimes they don’t. We want cross-domain tracking to fix this in our analytics reports.” Our answer is yes cross-domain tracking can help, but you’re better off having your admin fix it with one line in the .htacess file to either show the www or not show it every time.

Another favorite request is, “We have a few sites domainA.com, domainB.com and domainC.com and want to see how many people navigate between them.” This may sound like a perfect reason to implement cross-domain tracking, but when you dig a bit deeper with the client and ask them, “Do you have links between your sites?” or “Are the sites related in some specific way?” and you get the answer “No!”, then what they are asking for isn’t cross-domain tracking, but rather “session stitching” which is far more complex to implement.

What cross-domain tracking, is truly intended for is connecting the data flow between related sites. For example, perhaps you have all your marketing landing pages on a sub-domain of www1.domain.com and clicking on the call to action takes the user to a different domain (perhaps to complete a form) (i.e., ecommerce.domain.com) and once they’ve completed this task they are then returned to your public site of www.domain.com with additional conversion opportunities. In this customer journey, a visitor would encounter three domains and as a business owner, you need to know which ads drove conversions and potentially if running A/B testing on landing pages which landing pages yield conversions. This is the perfect scenario to implement cross-domain tracking.

Perhaps you operated multiple domains in support of a common target audience (each one specializing in different services) that do link to each other and the services promoted on each of them. Once again, this is a perfect reason to implement cross-domain tracking as part of a roll-up analytics report.

Final use of cross-domain tracking

While a bit of a stretch, if your organization operates multiple websites that aren’t linked together you can through some custom reporting and the use of Attribution Modeling and Multi-Channel reporting, view if a user visited associated websites (including which ones) before converting on the final one. This last option can be extremely tricky to implement, expensive and fraught with holes that may limit the reliability of the data. However, to some people, a bit data is better than no data at all.

Ultimately, talk to your analytics provider and ensure that the need for cross-domain tracking is truly in line with your corporate measurement plan. Then and only then, is it going to be worth the investment of time and money to implement it.

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To segregate traffic driven to your site from various social media properties, you’ll need to configure custom channels in your analytics tool. (For a detailed explanation on how to do this in GA, see the Marketing Land article The Google Analytics Social channel is broken. Here’s how to fix it.

Defining custom social channels

Before starting any configuration changes, first decide not only the names of these new channels, but what they represent for your clients (both internal and external).

I’d recommend setting up the following three channels.

Paid social: Consists of any paid ads you are running on any social media property to drive traffic to your website.

Promoted social: All activity performed by your social media team where no additional marketing fees are required. Typical activities that fall under this channel include typical posting to your social media channels.

Organic social: Any activity that the general public (people not on your payroll) drives traffic to your site from social media. This includes a person clicking on a “Share This” icon on your blog post or perhaps just including a link to your site in a spontaneous social media post.

Once you’ve defined your social media channels, you need to define the medium definitions (for GA the utm_medium parameter value) to be used your generate a custom URL to track (see Google URL Builder). The great news is you don’t need to do anything for organic social.

Here are some typical medium definitions (required by your analytics software) or make up your own. Note you can use more than one to refine your analysis at a later date.

Paid social: Paid-social, Social-PPC, Social-CPC, Social-display, Promoted-post

Promoted social: Psocial, Promoted, Post, Tweet

Remember, these changes to your analytics tool are permanent and will remain in place unless deleted. They will not impact historical data.

Repeating the benefits of custom channels

With this additional information, you’ll be more effective at evaluating which content is performing best and you’ll be able to compare its performance across the organic, paid and promoted social channels.

Another advantage is it will be much easier to compare conversion rates and goal achievement between different channels. In this example, a Data Studio report was created to showcase different goal conversions by all defined channels.

It is only by segregating paid from organic from promoted social activity that a complete picture of which activity drives which type of conversion. From the above chart, the Paid Search channel generated the most contact forms being submitted, while social (organic) and paid social drove zero forms being submitted. In this case, it’s important to look further into your analytics reports for assisted conversions (does a site visit generated from Paid Social come back to the site later through another channel) to determine the influence of Paid Social on contact forms being completed or other conversion points.

Any questions?

I hope you found this useful, and hope you’re as thrilled as I was to be able to segregate my client’s social traffic into meaningful refined channels. We are now able to put a true value on all our social marketing efforts (paid or promoted) and to calculate a realistic ROI which makes all business owners happy.

The post Back to basics: Measuring your social media efforts with unique acquisition channels appeared first on MarTech.

But it’s critical that marketing and analytics professionals start to examine one of the last vestiges of old-school analytics: reporting bounce rate. While bounce rate still holds some value, its actual meaning and how it’s applied can vary among individuals and organizations. Yet its potential to reveal great insights is largely ignored in most organizations.

In an effort to explore how organizations currently use bounce rate in their reporting, I conducted an unscientific survey of a mix of analytics and digital marketing professionals. The first question they were asked was “Do you currently include bounce rate in your regular analytics reports?” At one time, the response would have been over 90 percent “yes,” but when I conducted the survey last month, it was only 68 percent.

This survey demonstrates that, like any metric, bounce rate is just a number. As I always say, “Numbers out of context are meaningless.” Many professionals fail to put bounce rate into context, and hence, render this metric meaningless.

Why is bounce rate becoming meaningless as part of reported KPIs? Think about these two examples:

Example 1:

An organization spends $100,000 to drive traffic to a specific landing page (e.g., PPC, SEO). The bounce rate is 95 percent, but the 5 percent who don’t bounce go on to convert and generate $250,00 in sales with a net profit of $50,000 (Revenue – marketing costs – cost of goods).

Is the bounce rate of 95 percent good or bad? It’s merely an indicator of potential further profits if the bounce rate can be lowered, but at what cost?

Example 2:

An organization spends $100,000 to drive traffic to a specific landing page (e.g., PPC, SEO). The bounce rate is 50 percent. Yet, of the 50 percent who didn’t bounce, only 20 percent (10 percent of the total generated traffic) go on to convert and generate a mere $150,000 in sales with a net loss of $50,000 (Revenue – marketing costs – cost of goods).

In this case, the bounce rate of 50 percent may be an acceptable bounce rate — or is it a bad bounce rate? It’s merely an indicator that something needs to be done to obtain a more profitable audience or perhaps narrow the marketing effort to a smaller number of people who could increase the bounce rate, but at a lower cost to obtain that traffic, turning a marketing loss into a profit.

The confusion over bounce rate between digital marketing and analytics professionals is evident in how they answered the second question of the survey: “Which most closely matches your definition of ‘bounce rate?‘”

The survey contained four possible answers, plus a fifth option to provide their own definition. For 46.9% of respondents, the choice was the most common definition of “Single Page Visit.” The answer of “They came, they saw, they vomited and left” (18.4 percent) combined with “They came and did nothing” (6.1 percent), both of which essentially classified bounce rate as a “came, looked around and left,” came out to 24.5 percent.

What’s more shocking — but perhaps explains why it’s losing favor as a KPI — is that 4.1 percent either didn’t know what a bounce rate is or how it’s calculated.

An enlightening part of the survey is that 24.5 percent of respondents chose to enter their own definition of bounce rate. As expected, these answers were extremely diversified, reflecting the issues we face in today’s web design in using bounce rate as a KPI.

How can bounce rate be used effectively?

With various clients, we’ve examined the typical user experience, and it goes beyond a bounce being a single page visit. The problem many content sites face is that they generate lots of organic search traffic to specific pages. Once the page is read, the user leaves the site.

Without any measurable action, this is a bounce, and time on page is zero seconds. Knowing that users come to read content, it makes senses to set up specific measurable events on the page to turn a bounce into an interactive engagement metric for these pages.

This problem also exists with “infinite scroll” pages, where a user can scroll down to multiple content blocks that effectively are multiple pages. A user could potentially scroll past three, four, or even five screens without tracking a specific event; the visit will be considered a bounce.

The solution in both cases is the creation of engagement events. These events can be either or both of the following: scroll interaction or a timer.

Scroll interaction

With “scroll interaction,” if a user scrolls past a specific percentage of the page, an event is recorded in analytics. Typically these are set at 25, 50, 75 and 100 percent. Of course, these percentages are easily configurable to a site’s specific needs. The added benefit is that when an event is triggered, the time it’s triggered is also recorded, generating a more accurate time on page than without it.

Time triggers

The second option (typically used for content sites) is to set a timer. The timer event is initially triggered after a predetermined number of seconds — typically 15 or 25 seconds to record the first event, and then an event is triggered every 10 or 15 seconds to ensure a more accurate time-on-page calculation.

The most difficult part of a time trigger is the setting of the initial event. One has to determine how long it takes a typical visitor to determine (without scrolling) that this page is a waste of their time and they close the session. Generally, some trials and testing are needed to get this set correctly.

Impact on bounce rate

By setting events (timers, scrolls, viewing a video, plus others types of interactions), the bounce rate reported in your analytics takes on a new and greater meaning. You’ll get a more realistic bounce rate, and time on page becomes more accurate as well.

I implemented this solution on a 100 percent content-based site. The sales team was shocked to see the bounce rate drop from over 80 percent to below 5 percent. Several advertisers started questioning the bounce rate, saying it was impossible to have one that low. But showing advertisers just how far down the content a typical user reads and where various ads were located (became visible during a typical visit) made the job easier on the sales team to sell ad positions lower down within the content.

Note that by tracking when a site visitor starts reading (scrolls to a specific percent of the page), and then how many reach the bottom of the article, a more accurate bounce rate can be calculated. Only 17 percent of article visitors bounced (failed to start reading), and of those who started, nearly 100 percent read to the end of article.

Only by digging deeply into the meaning of all those KPIs we see nearly every day in our marketing dashboards, understanding how they’re calculated, and then configuring them correctly can we truly extract value from our analytics.

The post Bounce rate: Important metric or junk data? appeared first on MarTech.

To meet this growing demand, successful marketers are making greater use of digital analytics. Yet many marketers either don’t fully understand the differences between analytic tools available or are simply burying their heads in the sand and hoping their clients don’t know either.

Being able to distinguish between at least three types of analytics and knowing which to use, and when, can mean the difference between the success or failure of any marketing campaign. Using these tools effectively can make you a superhero to your clients.

To understand the role analytics must play in your marketing strategy, think of analytics as your marketing campaign’s GPS (Global Positioning System). It’s a tool that will help you navigate campaigns successfully from point A to point B.

Analytics is typically broken down into three categories: historical, real-time and predictive. Knowing which to use and when can be compared to the different types of GPS systems available. Let’s examine each one and its role in implementing a successful marketing plan.

Historical analytics

All marketers are familiar with historical analytics. It dates back long before the subjective analytics that Burma-Shave used to measure the effectiveness of its roadside billboards in the 1920s or when AC Neilsen first started measuring the difference in audience size of various radio programs in the 1930s.

Today, Google Analytics is the most common of these tools. It’s great at telling you what happened yesterday, last week, last month. How did our marketing efforts perform?

When used correctly, it allows you to adjust your marketing plan, as historical evidence demonstrates you’re not going to reach your goal in time. This is the equivalent of a traditional GPS (one that you might have purchased a few years ago), where you input your desired destination, and it maps out a route based on published speed limits and calculated distance.

If you encounter unusual road traffic, road closures or other obstacles along the way, you can request a new route, and you’re off again.

Real-time analytics

Savvy marketers are now starting to embrace real-time analytics. No longer do they have to wait until the next day or week to see the impact their marketing efforts are having.

Real-time analytics is most commonly used in digital marketing but can be available for other media. It tells you in real time if those new ads are generating traffic and conversions, or how much engagement is being generated via social media campaigns in real time.

When used correctly, real-time analytics not only allows for quicker adjustments to marketing plans, it can also save thousands of dollars by killing efforts that fail right from the start or by giving a boost to those that are working. This ensures a higher ROI than if you waited a few days to review the data.

It’s important to remember that despite the “real-time” label, it’s still historical data. The difference is that it’s not a day or more old, but only seconds or minutes old.

Once again, Google Analytics with its “Real-Time” analytics is the most commonly used one within the marketing field.

Using the GPS analogy, real-time analytics aligns itself with Waze or any modern GPS that accesses real-time traffic data. Depending on what might be happening on the road a few miles ahead, these systems are constantly (in real time) calculating the best route for you and, when necessary, will revise the route, ensuring you arrive at your destination in the quickest time possible.

Predictive analytics

The best way to explain predictive analytics is that it’s the next generation of analytics, leveraging the power of historical and real-time analytics.

In the old days, analysts would gather all the available historical data, do a regression analysis to plot a graph and calculate a trend line to predict the future. Today, analysts can leverage the power of real-time analytics, making near-instant adjustments to their forecasts and allowing a nimble marketing team to make adaptations to their campaigns on the fly, avoiding potential disasters.

Returning to the GPS analogy, imagine a GPS that looks at historical traffic patterns. It plots your best route based on the day of week and time of day (Google Maps does this now) but then makes near real-time adjustments based on real-time data and your personal preferences (Perhaps you have a route you always prefer).

We all know that even when using a GPS like Waze, the best route right at this moment isn’t always the best one in 15 minutes. From experience, we may know (we can predict) that in perhaps 10 or 15 minutes, a certain highway is going to come to a standstill, and we need to take an alternative route. That’s predictive analytics at work.

We see predictive analytics at work every day when we log into Facebook — based on posts we’ve liked, reacted to, clicked on, shared, hid, marked as spam, commented on and more. Each time you go to your news feed, Facebook uses predictive analytics to determine, out of the thousands of potential posts ready to go to your feed, which ones you should see and ranks them according to what it thinks you want to see. It does the same for promoted posts that appear in the news feed.

What’s ahead?

Through predictive analytics, we as marketers (in the near future) will predict which ads will respond to which demographic and then adjust them based on real-time data. We will have the ability to showcase specific products to prospective clients, all based on custom algorithms derived from predictive analytics.

When it comes to analytics, I think back to my business 101 class many years ago and the story of the ice cream vendor. The vendor needs to know how much ice cream he’s going to sell on any day of the week to ensure proper inventory. He knows he sells more on hot days and nearly nothing on cold and rainy days.

With predictive analytics, he can incorporate weather forecasts against historical data to adjust inventory and marketing messages to ensure the optimal success of his business.

As marketers of today and the future, we need to start leveraging the power of all three forms of analytics to ensure campaign success.

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