A Guide To Understanding Web Analytics in Digital Marketing

Understanding Web Analytics is the key for Successful Digital marketing Performance Evaluation! Still following our TCEO (Think, Create, Engage & Optimize) Strategy of Digital Marketing, planning, research and strategy are important for executing digital marketing campaigns that resonate with audiences. As part of executing these strategies, you need to create digital assets, and then use various channels to drive traffic to those assets, and build relationships with your visitors. I have comprehensively explained all these tactical processes in my previous posts based on respective digital marketing strategies & topics. Now we are at the last stage of TCEO model; The Optimize Stage!

As you are carrying out all your digital marketing tactics, it’s important to know: are they actually working? And how might they all work better? This is where Optimise comes in: the process by which we track, analyse and optimise our digital assets and campaigns to achieve the best results. (Yes, that’s track, analyse and optimise, or TAO, so you’ll never forget it.)

As Eric Schmidt, Executive Chairman of Google, said: “The Internet will transform advertising because of its trackability, not its beauty.” Because most actions online are recorded, we can build a rich, data-driven picture of how our digital assets and online campaigns are performing. Digital allows us to use this data to test and improve our marketing efforts – and then release new, better versions of campaigns rapidly.

With user-friendly reporting interfaces and free, powerful web analytics tools such as Google Analytics, there is no excuse for not knowing what’s happening in your campaigns. This post on Understanding Data Analytics on Web will help you with the concepts and definitions you need, and will also help you to set objectives and key performance indicators (KPIs) so that you know what you need to measure. (Referencing: eMarketing: The essential guide to marketing in a digital world)

It’s easy to rely on web analytics tools to report the ‘what’ without applying your brain to understand the ‘why’ – and the ‘what next’. The post on Conversion Optimisation takes you through the steps of turning data into something useful: insights that let you transform website visitors into active customers.

Getting into a routine of using data to understand how your visitors are behaving and how you might influence that behaviour is what conversion optimisation is about. You need to use the web analytics data not just to report on how campaigns and digital assets are performing, but also to understand how to make them perform better.

Take for instance; you’ve opened up a new fashion retail outlet in the trendiest shopping centre in town. You’ve spent a small fortune on advertising and branding. You’ve gone to great lengths to ensure that you’re stocking all of the prestigious brands. Come opening day, your store is inundated with visitors and potential customers.

And yet, you are hardly making any sales. Could it be because you have one cashier for every hundred customers? Or possibly it’s the fact that the smell of your freshly painted walls chases customers away before they complete a purchase? While it can be difficult to isolate and track the factors affecting your revenue in this fictional store, move it online and you have a wealth of resources available to assist you with tracking, analysing and optimising your performance.

To a marketer, the Internet offers more than just new avenues of creativity. By its very nature, it allows you to track each click to your site and through your site. It takes the guesswork out of pinpointing the successful elements of a campaign, and can show you very quickly what’s not working. It all comes down to knowing where to look, what to look for, and what to do with the information you find.

In this post, you will learn:

• How to set solid objectives, goals and measurements for your digital marketing campaigns
• How to capture web analytics data, and what data you can find
• Techniques and guidelines for analysing data to better understand your web visitors Working with data

In the days of traditional media, actionable data was a highly desired but scarce commodity. While it was possible to broadly understand consumer responses to marketing messages, it was often hard to pinpoint exactly what was happening, and why.

In the digital age, information is absolutely everywhere. Every single action taken online is recorded, which means there is an incredible wealth of data available to marketers to help them understand when, where, how and even why people react to their marketing campaigns.

This also means there is a responsibility on marketers to make data-driven decisions. Assumptions and gut feel are not enough – you need to back these up with solid facts and clear results.

Don’t worry if you’re not a ‘numbers’ person – working with data is very little about number crunching (the technology usually takes care of this for you) and a lot about analysing, experimenting, testing and questioning. All you need is a curious mind and an understanding of the key principles and tools.

Here are Some Data Concepts You Should Be Aware of

1. Performance Monitoring and Trends

Data analytics is all about monitoring user behaviour and marketing campaign performance over time. The last part is crucial. There is little value in looking at a single point of data – you want to look at trends and changes over a set period.

For example, it is quite meaningless to say that 10% of this month’s web traffic converted – is that good or bad, high or low? But saying that 10% more people converted this month, as opposed to last month, shows a positive change or trend.

While it can be tempting to focus on single ‘hero’ numbers and exciting-looking figures (‘Look, we have 5 000 Facebook fans!’), these really don’t give a full picture if they are not presented in context.

2. Big Data

‘Big data’ is the term used to describe truly massive data sets – the ones that are so big and unwieldy that they require specialised software and massive computers to process. Companies like Google, Facebook and YouTube generate and collect so much data every day that they have entire warehouses full of hard drives to store it all.

Understanding how it works and how to think about data on this scale provides some valuable lessons for all analysts.

• Measure trends, not absolute figures. The more data you have, the more meaningful it is to look at how things change over time.
• Focus on patterns. With enough data, patterns over time should become apparent – consider looking at weekly, monthly or even seasonal flows.
• Investigate anomalies. If your expected pattern suddenly changes, try to find out why – and use this information to inform your actions going forward.

3. Data Mining

Data mining is the process of finding patterns that are hidden in large numbers and databases. Rather than having a human analyst process the information, an automated computer program pulls apart the data and matches it to known patterns to deliver insights. Often, this can reveal surprising and unexpected results, and tends to break assumptions.

Data mining in action: Target

US retail chain Target uses data mining to market specific products to consumers based on their personal contexts. Target gathers a range of personal, psychographic and demographic data from customers and then analyses this against their shopping habits. They analyse their data and predict the products customers might be interested in, based on their lifestyle needs and choices (Duhigg, 2012).

Each customer is given a unique code called the Guest ID number. This is linked to all interactions the customer has with the brand, from using a credit card or calling in to the help line, to opening an email. They then gather data, including the following:

• Age
• Marital status
• Whether the customer has children, and how many
• Estimated salary
• Location
• Whether they’ve moved house recently
• Which credit cards they use
• Which websites they visit

Target is also able to buy data on their customers from other companies to supplement this, like:

• Ethnicity
• Employment history
• Favourite magazines
• Financial status
• Whether they’ve been divorced
• Which college they attended
• Their online interests
• Their favourite coffee brands
• Political leanings

For example, Target markets baby and pregnancy-related products to expectant moms and dads, from as early as the second trimester. How do they know to do this? Using this data, combined with customer shopping habits, Target has created a pregnancy-predictor model, determining whether a woman was pregnant (sometimes even before she knew it herself).

One father of a teenage girl complained to the store about sending his daughter marketing materials of this nature – but apologised to the retailer a short while later, after discovering that his teenage daughter was in fact pregnant, indicating that the predictor model did actually work (Duhigg, 2012).

However, Target has decided to be a bit subtler in how they use their valuable insights!

4. A World of Data

Another consideration to keep in mind is that data can be found and gathered from a variety of sources – you don’t need to restrict yourself simply to website-based analytics. To get a full picture of audience insights, try to gather as wide a variety of information as you can.

Some places to look:

• Online data – aside from your website, look at other places your audience interacts with you online, such as social media, email, forums and more. Most of these will have their own data-gathering tools (for example, look at Facebook Insights or your email service provider’s send logs).
• Databases – look at any databases that store relevant customer information, like your contact database, CRM information or loyalty programs. These can often supplement anonymous data with some tangible demographic insights.
• Software data – data might also be gathered by certain kinds of software (for example, some web browsers gather information on user habits, crashes, problems and so on). If you produce software, consider adding a data-gathering feature (with the user’s permission, of course) that captures usage information that you can use for future updates.

• App store data – app store analytics allows companies to monitor and analyse the way people download, pay for and use their apps. Marketplaces like the Google and Apple app stores should provide some useful data here.

• Offline data – and don’t forget all the information available off the web – such as point-of-sale records, customer service logs, in-person surveys, in-store foot traffic, and much more.

Setting Objectives, Goals and KPIs

The key to success for any website or online campaign is designing it with specific, unique, clearly defined objectives in mind. These are used to measure the success of the website or campaign, and are crucial to maintaining focus within online activities. Sometimes, words such as ‘objective’ or ‘goal’ can be used in different situations with slightly different meanings.

For the sake of clarity, this post will use them as described below.

• Objective: The objective of a website or online campaign is aligned with the strategic outcomes of the business. The objective answers the question, “What do we want to achieve with this marketing campaign?” For example, an objective might be to increase sales of a product, grow brand awareness or increase website traffic.

• Goal: The goal of a website or campaign in web analytics refers to an action that a user takes on a website or a type of user behaviour. This action could be making a purchase, signing up for a newsletter, or viewing a certain number of pages in a visit. A completed goal is called a conversion. Goals are derived from objectives, and answer the question, “What do we need users to do in order to achieve our objective?”

• Key Performance Indicator (KPI): Key performance indicators or KPIs are metrics that are used to indicate whether objectives are being met. There are many metrics to be analysed, and determining which are important will help to focus on what really matters to a particular campaign. KPIs relate closely to goals, and answer the question, “What data do we need to look at to see if goals are being completed?” For example, if your objective is to increase website traffic, you may look at the number of website visitors, the percentage of new visitors, and how long users stay on the site.

• Target: Targets are the actual target values that KPIs need to meet in order for the campaign to be declared a success. If one KPI is ‘newsletter subscriptions’, then a target might be ‘100 subscriptions every month’ – if one month falls short at 70, this will quickly reveal that a fix is required.

Figure: Illustrating Objectives, Goals KPIs & Targets

Although a website has an ultimate goal, the process of achieving that goal can be broken down into several steps. These are called events or micro-conversions. Analysing each step in the process is called funnel analysis or path analysis and is critical to understanding where problems in the conversion process may lie.

For example, on a hotel website, the ultimate goal is that visitors to the site make a booking on the website with a credit card. Each step in the process is an event that can be analysed as a conversion point.

Event 1: Perform a search for available dates for hotels in the desired area.

Event 2: Check prices and amenities for available hotels.

Event 3: Select a hotel and go to checkout.

Event 4: Enter personal and payment details and confirm booking (conversion).

One naturally expects fewer users at each step. Increasing the number of visitors who progress from one step to the next will go a long way to improving the overall conversion rate of the site.

Figure: A Conversion Funnel

Here are Some Examples of Objectives, Goals and KPIs for Different Websites:

1. Hospitality eCommerce site, such as expedia.com

Objective: increase bookings

Objective: decrease marketing expenses

Goal: make a reservation online

KPIs: Conversion rate, Cost per visitor, Average order value

2. News and content sites, such as news24.com

Objective: increase readership and level of interest

Objective: increase time visitors spend on website

Goal: a minimum time on site

KPIs: Length of visit, Average time spent on website, Percentage of returning visitors

KPIs help you to look at the factors you can influence in the conversion process. For example, if your objective is to increase revenue, you could look at ways of increasing your conversion rate (that is, the number of visitors who purchase something). One way of increasing your conversion rate could be to offer a discount. So, you would have more sales, but probably a lower average order value. Or, you could look at ways of increasing the average order value, so that the conversion rate would stay the same, but you would increase the revenue from each conversion.

Once you have established your objectives, goals and KPIs, you need to track the data that will help you to analyse how you are performing, and will indicate how you can optimise your website or campaign.

Tracking and Collecting Data

Currently, there are two main approaches for collecting web analytics data: cookie-based tracking, and server-based tracking. A third option, called universal analytics, is set to dramatically change how data is gathered and analysed. Universal analytics is one of the most exciting examples of non-cookie-based server tracking.

How Information is Captured:

• Cookie-based Tracking

The most common method of capturing web analytics is to use cookie-based tracking. Here’s how it works:

1. The analyst adds a page tag (a piece of JavaScript code) to every page of the website.
2. A user accesses the page using their browser.
3. When the browser loads the page, it runs the page tag code.
4. This tag sends an array of information to a third-party server (like Google Analytics), a service that stores and collates the data.
5. The analyst accesses this data by logging in to the third-party server.

The data gathered this way can capture a wide array of factors about each visitor from their device, operating system and screen resolution, to their long-term behaviour on your website. This is currently the most common option for most website tracking.

• Server-based Tracking

Web servers are the computers that websites are stored on so that they can be accessed online. Server-based tracking involves looking at log files – documents that are automatically created by servers and that record all clicks that take place on the server. A new line is written in a log file every time a new request is made – for example, clicking on a link or submitting a form. (Referencing: eMarketing: The essential guide to marketing in a digital world)

Server-based tracking is very useful for tracking mobile visitors (since many phones cannot execute the cookie-based JavaScript tags) and is also essential for universal analytics, discussed below.

Comparing the Options:

Because these two options use different methods of collecting data, the raw figures produced will differ. For example, caching occurs when a browser stores some of the information for a web page, so that it can retrieve it more quickly when you return. Opening this cached page will not send a request to the server. This means that the visit won’t show up in the log files, but would be captured by page tags.

Website analytics packages can be used to measure most, if not all, digital marketing campaigns. Website analysis should always account for the various campaigns being run. For example, generating high traffic volumes by employing various digital marketing tactics such as SEO, PPC and email marketing can prove to be a pointless and costly exercise if visitors are leaving your site without achieving one (or more) of your website’s goals. Conversion optimisation aims to convert as many of a website’s visitors as possible into active customers.

• Universal Analytics

Google recently announced a new feature in its analytics suite called universal analytics. The biggest problem web analysts have faced up until now is that they can’t actually track individual people – only individual browsers (or devices), since this is done through cookies. So, if Joe visits the website from Chrome on his home computer, and Safari on his work laptop, the website will think he’s two different people. And if Susan visits the site from the home computer, also using Chrome, the website will think she’s the same user as Joe.

An additional concern is that cookies are on the decline. Most modern browsers allow users to block them, and many mobile devices simply can’t access or execute them. With growing consumer privacy concerns, and new laws like the EU Privacy Directive (which requires all European websites to disclose their cookie usage), cookies are falling out of favour.

Universal analytics allows you to track visitors (that means real people) rather than simply sessions. By creating a unique identifier for each customer, universal analytics means you can track the user’s full journey with the brand, regardless of the device or browser they use. So, that means you can track Joe on his home computer, work laptop, mobile phone during his lunch break, and even when he swipes his loyalty card at the point of sale.

Crucially, however, tracking Joe across devices requires both universal analytics and authentication on the site across devices – in other words, Joe has to be logged in to your website or online tool on his desktop, work laptop and mobile phone in order to be tracked this way. If he doesn’t log in, we won’t know it’s the same person.

You can see:

• How visitors behave depending on the device they use (browsing for quick ideas on their smartphone, but checking out through the eCommerce portal on their desktop)
• How visitor behaviour changes the longer they are a fan of the brand – do they come back more often, for longer, or less often but with a clearer purpose?
• How often they’re really interacting with your brand
• What their lifetime value and engagement is

Another useful feature of universal analytics is that it allows you to import data from other sources into Google Analytics – for example, CRM information or data from a point-of-sale cash register. This gives a much broader view of the customers and lets you see a more direct link between your online efforts and real-world behaviour.

The Type of Information Captured

KPIs are the metrics that help you understand how well you are meeting your objectives. A metric is a defined unit of measurement. Definitions can vary between various web analytics vendors depending on their approach to gathering data, but the standard definitions are provided here.

Web analytics metrics are divided into:

• Counts – these are the raw figures that will be used for analysis.
• Ratios – these are interpretations of the data that is counted.

Metrics can be applied to three different groupings:

• Aggregate – all traffic to the website for a defined period of time.
• Segmented – a subset of all traffic according to a specific filter, such as by campaign (PPC) or visitor type (new visitor vs. returning visitor).
• Individual – the activity of a single visitor for a defined period of time.

Here are Some of the Key Metrics You will Need To Get started on With Website Analytics

1. Building-block Terms

These are the most basic web metrics. They tell you how much traffic your website is receiving. For example, looking at returning visitors can tell you how well your website creates loyalty; a website needs to grow the number of visitors who come back. An exception may be a support website – repeat visitors could indicate that the website has not been successful in solving the visitor’s problem. Each website needs to be analysed based on its purpose.

• Hit – one page load (though this is an outdated terms that we recommend you avoid using).
• Page – unit of content (so downloads and Flash files can be defined as pages).
• Page views – the number of times a page was successfully requested.
• Visit or session – an interaction by an individual with a website consisting of one or more page views within a specified period of time.
• Unique visitors – the number of individual people visiting the website one or more times within a set period of time. Each individual is counted only once.

o New visitor – a unique visitor who visits the website for the first time ever in the period of time being analysed.

o Returning visitor – a unique visitor who makes two or more visits (on the same device and browser) within the time period being analysed.

Figure. A breakdown of new versus returning visitors in Google Analytics.

2. Visit Characteristics

These are some of the metrics that tell you how visitors reach your website, and how they move through the website. The way that a visitor navigates a website is called a click path. Looking at the referrers, both external and internal, allows you to gauge the click path that visitors take.

• Entry page – the first page of a visit.
• Landing page – the page intended to identify the beginning of the user experience resulting from a defined marketing effort.
• Exit page – the last page of a visit.
• Visit duration – the length of time in a session.

Figure. Visitor behaviour in Google Analytics.

• Referrer – the URL that originally generated the request for the current page.

o Internal referrer – a URL that is part of the same website.

o External referrer – a URL that is outside of the website.

o Search referrer – a URL that is generated by a search function.

o Visit referrer – a URL that originated from a particular visit.

o Original referrer – a URL that sent a new visitor to the website

• Clickthrough – the number of times a link was clicked by a visitor.
• Clickthrough rate – the number of times a link was clicked divided by the number of times it was seen (impressions).
• Page views per visit – the number of page views in a reporting period divided by the number of visits in that same period to get an average of how many pages are being viewed per visit.

3. Content Characteristics

When a visitor views a page, they have two options: leave the website, or view another page on the website. These metrics tell you how visitors react to your content. Bounce rate can be one of the most important metrics that you measure. There are a few exceptions, but a high bounce rate usually means high dissatisfaction with a web page.

• Page exit ratio – number of exits from a page divided by total number of page views of that page.
• Single page visits – visits that consist of one page, even if that page was viewed a number of times.
• Bounces (or single page view visits) – visits consisting of a single page view.
• Bounce rate – single page view visits divided by entry pages.

4. Conversion Metrics

These metrics give insight into whether you are achieving your analytics goals (and through those, you overall website objectives).

• Event – a recorded action that has a specific time assigned to it by the browser or the server.
• Conversion – a visitor completing a target action.

Figure. Goal conversions in Google Analytics.

5. Mobile Metrics

When it comes to mobile data, there are no special, new or different metrics to use. However, you will probably be focusing your attention on some key aspects that are particularly relevant here – namely technologies and the user experience.

• Device category – whether the visit came from a desktop, mobile or tablet device.
• Mobile device info – the specific brand and make of the mobile device.
• Mobile input selector – the main input method for the device (e.g. touchscreen, clickwheel, stylus).
• Operating system – the OS that the device runs (some popular ones include iOS, Android and BlackBerry).

Figure. Mobile device categories in Google Analytics.

Now that you know what tracking is, you can use your objectives and KPIs to define what metrics you’ll be tracking. You’ll then need to analyse these results, and take appropriate actions. Then the testing begins again!

Analysing Data

In order to test the success of your website, you need to remember the TAO of conversion optimisation:

Track – Analyse – Optimise

A number is just a number until you can interpret it. Typically, it is not the raw figures that you will be looking at, but what they tell you about how your users are interacting with your website. Because your web analytics package will never be able to provide you with 100% accurate results, you need to analyse trends and changes over time to understand your brand’s performance.

Key Elements to Analyse:

Avinash Kaushik, author of Web Analytics: An Hour a Day, recommends a three pronged approach to web analytics:

1. Analysing behaviour data infers the intent of a website’s visitors. Why are people visiting the website?
2. Analysing outcomes metrics shows how many visitors performed the goal actions on a website. Are visitors completing the goals we want them to?
3. A wide range of data tells us about the user experience. What are the patterns of user behaviour? How can we influence them so that we achieve our objectives?

1. Behaviour

Web users’ behaviour can indicate a lot about their intent. Looking at referral URLs and search terms used to find the website can tell you a great deal about what problems visitors are expecting your site to solve.

Some methods to gauge the intent of your visitors include:

• Click density analysis – looking at a heatmap to see where people are clicking on the site and if there are any noteworthy “clumps” of clicks (such as many people clicking on a page element that is not actually a button or link).
• Segmentation – selecting a smaller group of visitors to analyse, based on a shared characteristic (for example, only new visitors, only visitors from France, or only visitors who arrived on the site by clicking on a display advert). This lets you see if particular types of visitors behave differently.
• Behaviour and content metrics – analysing data around user behaviours (e.g. time spent on site, number of pages viewed) can give a lot of insight into how engaging and valuable your website is. Looking at content metrics will show you which pages are the most popular, which pages users leave from most often and more – providing excellent insight for your content marketing strategy, as well as discovering what your audience is really interested in.

A crucial, often-overlooked part of this analysis is internal search. Internal search refers to the searches of the website’s content that users perform on the website. While a great deal of time is spent analysing and optimising external search – using search engines to reach the website in question – analysing internal search goes a long way to exposing weaknesses in site navigation, determining how effectively a website is delivering solutions to visitors, and finding gaps in inventory on which a website can capitalise.

For example, consider the keywords a user may use when searching for a hotel website, and keywords they may use when on the website.

Keywords to search for a hotel website may be:

• Cape Town hotel
• Bed and breakfast Cape Town

Once on the website, the user may use the site search function to find out more. Keywords they may use include:

• Table Mountain
• Pets
• Babysitting service

Analytics tools can show what keywords users search for, what pages they visit after searching, and, of course, whether they search again or convert.

2. Outcomes

At the end of the day, you want people who visit your website to perform an action that increases your revenue. Analysing goals and KPIs indicates where there is room for improvement. Look at user intent to establish if your website meets the users’ goals, and if these match with the website goals. Look at user experience to determine how outcomes can be influenced.

For Example: After performing a search, 100 visitors land on the home page of a website. From there, 80 visitors visit the first page towards the goal. This event has an 80% conversion rate. 20 visitors take the next step. This event has a 25% conversion rate. 10 visitors convert into paying customers. This event has a 50% conversion rate. The conversion rate of all visitors who performed the search is 10%, but breaking this up into events lets us analyse and improve the conversion rate of each event.

3. User Experience

In order to determine the factors that influence user experience, you must test and determine the patterns of user behaviour. Understanding why users behave in a certain way on your website will show you how that behaviour can be influenced to improve your outcomes. This will be covered in my next post on Conversion Optimisation.

Segmentation

Every visitor to a website is different, but there are some ways in which we can characterise groups of users, and analyse metrics for each group. This is called segmentation.

Figure. Default segments in Google Analytics.

Some Segments Include:

• Referral Source

Users who arrive at your site via search engines, those who type in the URL directly, and those who come from a link in an online news article are all likely to behave differently. As well as conversion rates, click path and exit pages are important metrics to consider. Consider the page that these visitors enter your website from – can anything be done to improve their experience?

• Landing Pages

Users who enter your website through different pages can behave very differently. What can you do to affect the page on which they are landing, or what elements of the landing page can be changed to influence outcomes?

• Connection Speed, Operating System, Browser

Consider the effects of technology on the behaviour of your users. A high bounce rate for low-bandwidth users, for example, could indicate that your site is taking too long to load. Visitors who use open source technology may expect different things from your website to other visitors.

• Geographical Location

Do users from different countries, provinces or towns behave differently on your website? How can you optimise the experience for these different groups?

• First-time Visitors

How is the click path of a first-time visitor different from that of a returning visitor? What parts of the website are more important to first-time visitors?

In-page Heat Maps

Software such as Crazy Eggcan show you exactly where users click on a web page, regardless of whether they are clicking on links or not.

Figure. Heat map options offered by CrazyEgg.

It produces information that helps you to know which areas of a website are clickable, but attract few or no clicks, and which areas are not clickable but have users attempting to click there. This can show you what visual clues on your web page influence where your visitors click, and this can be used to optimise the click path of your visitors.

There are many factors that could be preventing your visitors from achieving specific end goals. From the tone of the copy to the colour of the page, everything on your website may affect conversions. Possible factors are often so glaringly obvious that one tends to miss them, or so small that they are dismissed as trivial. (Referencing: eMarketing: The essential guide to marketing in a digital world)

Changing one factor may result in other unforeseen consequences and it is vital to ensure that you don’t jump to the wrong conclusions.

Analytics Tools:

The first thing you need is a web analytics tool for gathering data. Some are free and some need to be paid for. You will need to determine which package best serves your needs. Bear in mind that it is possible to switch vendors with server-based analysis software without losing historical data, but it is not as easy to do so with cookie-based tracking.

Below are some leading providers:

• Google Analytics (cookie-based tracking)
• ClickTracks (server-based and cookie-based tracking)
• AWStats (server-based tracking)
• Webalizer (server-based tracking)
• SiteCatalyst

When it comes to running split tests, if you don’t have the technical capacity to run these in-house, there are some third-party services that can host them for you. Content Experiments is a built-in feature of Google Analytics that lets you run split tests quickly and easily, and even helps you to figure out whether your results are statistically significant. A basic split test calculator is also available.

When you use cookie-based tracking, you need to add code tags to your web pages – and these need to be maintained, updated and changed occasionally. Google Tag Manager makes it easy to add and work with these tags without requiring any coding knowledge. Other professional tag management tools include TagMan, Ensighten and Tealium.

Wrapping Up:

The ability to track user behaviour on the Internet allows you to analyse every level of a digital campaign, which should lead to improved results over time.

The foundation of successful web analytics is to determine campaign and business objectives upfront and use these to choose goals and KPIs (grounded in solid targets). Web analytic packages come in two flavours – server-based and cookie-based tracking – although

some packages combine both methods. Data can be analysed to discover how users behave, whether outcomes have been achieved, and how appealing the user experience is. Testing to optimise user experience can demonstrate ways in which to influence user behaviour so that more successful outcomes can be achieved. Segmenting the audience allows specific groups of users to be analysed.