Business of Web Analytics Archives - Marketing Analytics Amplified

Whether you’re new to web analytics or need a refresher course, let’s get back to basics. As more and more web analytics tools are being creating and more and more updates are being made to the popular solutions like Google Analytics, it’s always a good idea to have your web analytics basics cheat sheet up to date. So if you’re ready to learn what web analytics is or want to see if you’ve missed a few important updates recently, keep reading!

What Is Web Analytics?
Web analytics is simply put as the measurement and analysis of a website based on users’ and visitors’ actions or behaviors. The analysis of a site usually is for a business to see what’s working on its site, where the site could be optimized, and how other departments’ campaigns, advertisements, content, predictions, etc. performed.

Who Uses Web Analytics?
Depending on how large or small your company is, it could be as little as one or two people who use web analytics. Larger companies, on the other hand, can have hundreds of web analytics users. Ideally, all of a company’s departments should work together with a web analytics team (or if there isn’t a web analytics team, this might convince you to have one) to use web analytics to understand the company’s position overall online as well as its independent sectors (sales, marketing, content, risk management, IT, inventory, etc.).

Where Is Web Analytics Used?
Web analytics is used on websites and can be viewed on dashboards such as Google Analytics, given that there is tracking code implemented in the site’s HTML.

So where is web analytics actually used in a business? Let’s break it down by department! Depending on the size of your company, not all of these departments may exist (or they might overlap).

Sales – While sales teams will love to see how many products are being sold, they won’t get any additional sales until they know where the sales came from and what was driving the sale. So knowing which pages and what traffic drives leads is key to the sales team knowing which products and pages are working. And web analytics can help the sales team know where they are doing well and where they are lacking. Implementing custom tracking code using analytics tags will allow sales teams a better indication of their performance.
Marketing – Like sales, the marketing team wants to see the campaign and advertisement results. Basically, the marketing team wants to know how their efforts to market and advertise to current and potential customers performed. But in addition to that, the marketing team needs to know how to optimize and refine their efforts. Knowing about channel performance inside out is one of the biggest reasons a marketing team uses web analytics (and for this reason, marketing teams could be the largest user of web analytics at any particular company). The numbers from web analytics will tell you what happened in that specific time frame and will help paint part of the picture as to why the campaigns and advertisements performed the way they did.
Content/Content Marketing – Using web analytics to measure content and content marketing efforts is a little tricky if you don’t know which metrics and KPIs to focus on. Yes, page views and number of visitors are nice to know, but content is a lot more complex than the generic information Google Analytics and other web analytics tools spit out at you without any additional data refining. Since content marketing web analytics is not basic web analytics, we’ll go into more detail on this topic in a later post.
Risk Management – Looking at past trends helps to predict the future. Your risk management team is performing analysis on what is most likely going to happen in the upcoming quarter or year. Members in these teams will look at past data from a web analytics solution to know what performed well, what didn’t perform well, what errors were made (in any department), what trends are there, what problems kept occurring when any scheduled changes to the business will be, and so on. Again, web analytics (and big data) for risk management is a complex subject, so we’ll go into more detail in a later post.
Information Technology – Like the Risk Management team, the IT team would also use past web analytics data for forecasting things such as page speed or site speed.
Business Analysts – Web analytics will most likely be what many business analysts see every day (compared to the other departments’ occasional web analytics usage). However, only about less than a quarter of a business analyst team’s time will be analyzing the web analytics data; the rest is typically used to create analytics reports. In the upcoming posts, we’ll discuss how business analyst teams can reduce time spent on creating reports (through automation) and more time analyzing which metrics that matter.

When Should Web Analytics be Used?
You can and should start using web analytics now if you haven’t already. Google Analytics is one of the best tools out there to use, and it’s free. All you have to do is log into your Google account associated with your business and start reviewing your data. If you don’t have a log in, check out Google’s Analytics site for more help. Or if you can no longer access your Google Analytics account but you are the owner of it, check out this blog post for help to get back into your Google Analytics account.

Keep in mind that even though Google Analytics and other analytics tools provide you with a plethora of information about your site, you’ll want to customize your data through personalized segments, reports, and eventually custom tags.

Why Should I Use Web Analytics?
No business would randomly market its product to anyone and everyone it finds, so why would you forgo web analytics and miss out on a better optimized site, a better user experience, and more conversions? Web analytics provides you valuable insight about your business and customer behavior that you couldn’t find even if you did a bunch of customer surveys and other non-web methods. Businesses love information about their company and want to know what is working and what is not. Web analytics is one of the easiest ways to know the who, what, when, and where. And web analytics trends can help produce a why and a how. Web analytics provides businesses a fairly complete picture, so why would you opt out and have only a small piece of it?

How Do I Use Web Analytics?
If you’re new to web analytics, first start using one of the free and more widely used tools such as Google Analytics. There are plenty of tutorials on Google Analytics and you can even become Google Analytics certified. Once you have a better understanding of which metrics are actually useful and actionable (more on this in a later post) and know what the metrics and dimensions really mean, then you can dip your toes into paid and lesser known tools such as Adobe Analytics, Piwik, Crazy Egg, Compete, Optimizely, 4Q by iPerceptions, or any of the social media analytics tools. Eventually, you might even want to add a backup analytics tool to Google Analytics.

What Are the Most Common Analytics Terms/Metrics And What Do They Mean?
Before you get started on you web analytics quest, let’s go over some of the most common web analytics terms.

Hits – A hit on your site means that some type of file – photo, text, etc. – was downloaded or sent from your site. Google Analytics tracks many types of hits on a webpage, including page tracking, event tracking, ecommerce tracking, and social interaction hits. With web analytics, all of these hits are tracked as long as the correct tags are in place on the website.
Pageviews – Each individual page on your website that a user visits will be counted as a pageview. Even if the same user goes back to a previous page that he or she viewed, another pageview will be counted.
Visits or sessions – A session is an interaction a user makes with a site, including pageviews (visiting a page), events (clicking a button), ecommerce (purchasing a product), and social (commenting on a blog post) interactions. An individual user can have multiple sessions, depending on how long they’re on a single page (For Google Analytics, there’s a session timeout after 30 minutes of inactivity and at midnight.) and depending on how they are referred to the page or site (For Google Analytics, a timeout will occur if you go to the same site using different referrers, AdWords campaigns, etc.).
Unique visitors – A unique visitor is a distinct visitor who comes to a site and visits a page(s). No matter how many pages this visitor goes to and no matter how many different campaigns and referral sources this visitor uses to get to the site, the visitor will still be recognized as the same person for a given period of time (depends on which analytics tool you use).
Bounce rate – The bounce rate is the percentage of users who come to your site and then leave after viewing a single page of a website.
Referral traffic – Referral traffic is any traffic that is not coming from Google’s search engine, a direct view to a site (see direct traffic), or social traffic (Note: Not all social traffic is recording only in Google Analytics’ social channel. You’ll still see some social traffic from sites such as LinkedIn in your referral traffic list).
Direct traffic – Direct traffic generally means traffic from people who typed or copy/pasted one of your URLs directly into their browser and visited the site. Do note that there are some discrepancies in Google Analytics’ direct traffic, specifically its (direct) / (none).
Conversion rate – The conversion rate is the number of completed (and successful) transactions, subscriptions, sign ups, or other business goals divided by the number of unique visitors to the site overall.

Hopefully this review of web analytics helped you. If you want to know more about anything web analytics related that we haven’t covered in a blog post, let us know in the comments or on Twitter @Amplytics.

Whether you are a small business with one dedicated “web guy” to do all things related to the website or you are a larger business with a team of dedicated analysts who manage analytics, you need to have a web analytics strategy in place to leverage web analytics effectively.

Here is the fact – web analytics is hard. And it keeps getting harder as more and more devices and web properties (websites, mobile sites, and mobile apps) can send usage data that can be measured. Without a robust plan around how to use analytics, you could run into inefficiency issues, accuracy issues, and the basic inability to make the most of your web analytics data.

Why is Web Analytics Important?

Web analytics can help you understand what is happening on your website. You can use it to understand the behavior of visitors to your site, the actions they take on the site, the source of these visitors, and much, much more.

And then there are other advanced reasons to use analytics – to understand and optimize ecommerce/subscription funnels, to determine ROI of marketing spend, to measure results of A/B tests, and so on.

Regardless of what you use web analytics for, there are a few problems that are commonly seen across companies.

Typical Problems in Using Web Analytics

Here are a few common web analytics related behaviors found across tiny, small, large, and massive organizations:

Web analytics (e.g. Google Analytics) is implemented but not used
Web analytics isn’t really a priority; it is more of an afterthought
Every time a report is sent out, no real action results from it
Web analysts are very busy generating reports and have no time for ad hoc investigative questions
Big budget decisions are often made without web analytics data

If any of these statements ring true for you, please tweet us and read on!

So what can one do to use web analytics effectively without needing a web analytics degree? Read on!

The Truth about Web Analytics

There are only three things in web analytics that are important to understand. Once you understand these three things, then using web analytics becomes more of a mechanical exercise. And this applies to businesses of any size. Here are the three simple truths:

Knowing what metrics to measure is half the battle
Understanding the context to apply when measuring is the other half of the battle
Ensuring your web analytics tool is recording correct data accurately is paramount

Figuring out Web Analytics Metrics to Use

Any popular web analytics tool (Google Analytics, Piwik, Adobe Analytics, etc.) comes with a standard set of reports. Once your web analytics tags are implemented, then data starts getting recorded in your tool. At this point, you can open up the interface and pull a bunch of reports to get a read on site performance.

However, you will get the best bang for your buck and time by knowing what metrics to use when measuring performance of your website. However simple your website, you need metrics that make sense for your business and not just the ones that are common across all websites (visitors, sessions, pageviews, etc.).

So let’s walk through a quick guide on how to identify metrics that make sense for your business:

Write down how your business makes money (i.e. services rendered, products sold, subscriptions acquired, etc.)
Write out how your website contributes to that business goal (i.e. it drives phone calls, it captures potential customers’ interest, it processes transactions, it provides information about your business, etc.)
Now write out how a change in the website’s contribution changes how much money your business generates. For example, fewer phone calls means fewer appointments and fewer subscriptions means less ad revenue
Figure out which of these changes drives 80% of the impact to your bottom line. Select at most three such drivers

These will be the three metrics you can use as Key Performance Indicators for tracking the performance of your site. Let’s get just a bit fancier – The three drivers you have identified are probably absolute metrics such as visitors, subscriptions, sales, etc.

The problem with absolute metrics is that they hide the root cause for changes. A good metric is some sort of a comparison to another metric. So it is relative (a ratio or a percentage). Why? Because there is almost no scenario where a single absolute metric tells the entire story. Let’s use the example of a site that generates revenue by selling a widget. For this simple example, a potential KPI could be total revenue. And you could look at total revenue per day. A week over week change in revenue could tell whether the site is selling enough. However, it doesn’t tell you whether the change was because you sold more widgets to the same number of visitors or because more visitors came to your site and you sold fewer widgets per visitor! The point of a good metric is that you need to make it actionable. So instead of looking at just visitors or just revenue, you could create a new metric called Revenue per Visitor and track that over time. Now you are tracking two metrics with a single report! If that metric changes, then you can dig into the report to understand which of the two core components – revenue or visitors changed. Below are two charts that show the contrast between using three metrics to tell a story vs using a single actionable metric. Note that there are other issues with using RPV, but I won’t get into those now.

This is just a simple example of how to build a KPI. The key takeaways here are that a great metric is

Easy – It is simple to communicate and simple to understand
Contextual – It is relative to another metric
Actionable – It can drive further investigation or immediate adjustments

It isn’t easy to create good metrics. So start simple and generate reports using these metrics. Once you see these reports on a daily basis, ask yourself – so what? So what if my report shows an improvement or decline in a metric over time? Why should I care? Once you start poking holes in your own reports and metrics, creating a great metric will be just a matter of time.

How to Apply Context to Web Analytics

A famous web analytics guru once said “all data in aggregate is crap.” Perhaps I am paraphrasing, but he is right. The whole point of web analytics is to understand what is happening on a site and then to find the reason that it is happening. And that is only possible by slicing the data and looking at it from various perspectives. And that is what I mean by “applying context to the data.”

Context needs to be applied to standard web analytics reports as well as to ad hoc reports that are run to investigate website issues. I am going to use the example of a typical web analytics investigation to explain how context is created.

Let’s say you have seen a drop in transactions in the past week. And you’ve seen this to be a 5-7 day trend. It is very tempting for even the most seasoned analysts to just dive into analytics to solve a problem rather than talk through the approach. Instead of opening up the web analytics interface to investigate, the first step actually is to create a plan of how to approach the problem. In this particular example, we would first make a list of possible reasons why transactions dropped. Here are some things we would want to check:

Did the conversion rate change (transactions/visitors)?
Did site visitor counts drop?
Did transaction counts for a type of product change?
Was there a change in marketing tactics recently?
Was there an update to the site recently?

The purpose of such high level questions is to help understand the areas that we need to dive into. So it is just a start. Let’s say we found that there was an update to the site. Note that in the real world, it is highly improbable that only one change happened. The issue is typically a result of half a dozen things happening at the same time. But for the sake of simplicity, let’s say the only change was a site update. Then the next step will be to check whether the update to the site caused an unexpected problem. Here are five things you would want to look into:

Is this a real problem? In other words, has the presence of a problem been validated by any other means such as an automated analytics audit or a backup web analytics solution?
Does the problem show across all platforms (i.e. mobile devices, desktop, various popular browsers, etc.)?
Is it a sitewide technical problem such as slower than normal response times?
Is there a particular page in the eCommerce funnel that is now showing higher exit rates?
Is the problem limited to a particular channel (e.g. Paid Search)?

Once such questions have been formulated, then and only then should you even bother to open up a web analytics interface for investigation. Using such context, reports can now be run to see if the data trends can reveal the root cause of the problem.

As I said before, creating context is not just for investigating problems. This similar exercise needs to be executed even when putting together the daily dashboard or set of reports that you will use to track the performance of your website.

Ensuring Your Web Analytics Tool is Accurate

Ensuring that the web analytics tool continues to report reliable information is probably the most overlooked area in web analytics. Accuracy of data should be a fundamental concern for every analyst. Yet so many CMOs, CEOs, and other executives ask every day “How confident are you about this data?” Or worse, they are the ones that point out obvious problems with the data and then they lose complete confidence in the data, the analyst, and the analytics tool!

So let’s step back and ask ourselves “How do we ensure the data in the web analytics tool is accurate?” To be clear – the issue is not with the tool itself! The root causes are bad data was fed into the tool, the tool was improperly configured, or bad code broke analytics tags on the site.

So there are three approaches to addressing this problem:

Pre-Implementation Processes – Implement a dozen processes on the front side to ensure incorrect data is not fed into the tool, to ensure changes to web analytics configurations are done correctly, etc. The problem with this approach is that there are a dozen points of failure that need to be monitored and covered, so this should be used in conjunction with the other two options.
A backup analytics tool – A second analytics tool could be used to compare and confirm that the data being reported by the primary tool is directionally right. Here is a great write up about why a backup analytics tool makes sense. While this is a good solution, it still falls short in the scenario where a technical team could make identical incorrect changes in both tools. So now you end up with two tools that don’t have the correct data!
Post-Implementation Checks – A third approach that addresses shortcomings of the first two is to monitor changes made to the configuration and to web analytics tags by running automated audits every day and reviewing the changes. The problem is that no tool on the market does a good job of monitoring such changes in a cost-effective manner. We hope to change that. If you are interested in hearing more, please sign up to get notified when Amplytics launches.

Web Analytics as a Whole

Web Analytics is an essential component of any organization’s tool chest today. It is also one of the harder components to use effectively. In a nutshell, we recommend measuring what can be actioned, keeping the measurements contextual, and maintaining data accuracy. We hope that our review of web analytics strategies has provided you with enough information on how to best leverage a web analytics tool to benefit your organization. We would love to hear from you! Please drop us a line at ThinkingAnalyst@amplytics.com