Online Behavior - Marketing Measurement & Optimization

"Statistics are no substitute for judgement"

LOR — Thu, 06 Jul 2017 11:36:19 +0000

LOR

Revamping Your App Analytics Workflows

Hannah Levenson — Mon, 19 Jun 2017 09:45:26 +0000

Every mobile app professional today uses mobile app analytics to track their app. Yet there are some key elements in their analytics workflows that are naturally flawed. The solution is out there, and you might have missed it.

The flaw, and a fairly big one at that, is in the fact that app analytics pros sometimes focus solely on quantitative analytics to optimize their apps. Don't take this the wrong way – quantitative analytics is a very important part of app optimization. It can tell you if people are leaving your app too soon; if they're not completing the signup process, how often users launch your app, and things like that. However, it won't give you the answer as to why people are doing it, or why certain unwanted things are happening in your app. And that's the general flaw.

The answer lies in expanding your arsenal – adding qualitative analytics to your workflow. Together with quantitative analytics, these tools can help you form a complete picture of your app and its users, identify the main pain points and user experience friction, helping you optimize your app and deliver the ultimate product.

So today, you are going to learn how to totally revamp your analytics workflow using qualitative analytics, and why you should do it in the first place. You'll read about the fundamentals of qualitative analytics, and how it improves one's analysis accuracy, troubleshooting and overall workflows. And finally, you'll find two main ways to use qualitative analytics which can help you turn your app(s) into mobile powerhouse.

Exploring the qualitative

Qualitative analytics can be split into two main features: heatmaps and user session recordings. Let's dig a little deeper to see what they do.

Touch heatmaps

This tool gathers all of the gestures a user does in every screen of the app, like tapping, double-tapping, or swiping. It then aggregates these interactions to create a visual touch heatmap. This allows app pros to quickly and easily see where the majority of users are actually interacting with the app, as well as which parts of an app are being left out.

Another important advantage of touch heatmaps is the ability to see where users are trying to interact, without the app responding. These are called unresponsive gestures, and they are extremely important because they're very annoying and could severely hurt the user experience.

Unresponsive gestures can be an indication of a bug or a flaw in the design of your user interface. Also, it could show you how your users think they should move through the app. As you might imagine, being bug-free and intuitive are two very important parts of a successful app, which is why tackling unresponsive gestures can make a huge difference in your app analytics workflow.

User session recordings

User session recordings are a fundamental feature of qualitative app analytics. They allow app pros to see just what their users are doing, as they are progressing through the app. That means every interaction, every sequence of events, on every screen in the app, gets recorded. This allows app pros an unbiased, unaltered view of the user experience.

With such a tool, you'll be able to better understand why users sometimes abandon an app too soon, why they decide to use it once and never again, or even why the app crashes on a particular platform or device.

Through video recordings, it becomes much easier to get to the very core of any problem your app might be experiencing. A single recording can shine light on a problem many users are struggling with. Obviously, the tool doesn't just mindlessly record everything – app pros can choose different screens, different demographics, mobile devices or their operating systems to record from. It is also important for this tool to work quietly in the background and not leave a strain on the app's performance.

Standard workflows- totally revamped

Qualitative analytics is too big of a field to be covered in a single article. Those looking to learn more might as well take a free course via this link. For all others, it's time to discuss two main workflows where they can be used - ‘Data-fueled Optimization' and ‘Proactive Troubleshooting'.

Data-fueled optimization

Both qualitative analytics and quantitative analytics tools are 'attacking' the same problem from different angles. While both are tasked with helping app pros optimize their mobile products, they have different, even opposite approaches to the solution. That makes them an insanely powerful combo, when used together.

Employing inherently opposite systems to tackle the same problem at the same time helps app pros form a complete picture of their app and how it behaves 'in the wild'. While quantitative analytics can be used as an alarm system, notifying app pros to a condition or a problem, qualitative analytics can be used to analyze the problem more thoroughly.

For example, using quantitative analytics tools you are alerted to the fact that a third of your visitors abandon their shopping cart just before making a purchase. You identify it as a problem, but cannot answer the question as to why this is happening.

With tools like user session recordings, you can streamline your optimization workflow and learn exactly where the problem lies. You could try to fix a problem without insights from qualitative data, but you'll essentially be "blindly taking a stab".

By watching a few user session recordings, you realize that the required registration process prior to making a purchase is simply too long. Users come halfway through it and just quit. By shortening the registration process and making checkout faster, you can lower the abandonment rate. Alert, investigate, resolve. This flow can easily become your "lather, rinse, repeat."

Proactive Troubleshooting

Can you truly be proactive in your troubleshooting? Especially when using analytics? Well, if you rely solely on quantitative analytics, probably not. After all, you need a certain amount of users to actually be using the app for some time before you can get any numbers out, like app abandonment rates or crash rates. Only then will you be able to do anything, and at that point – you're only reacting to a problem already present. With qualitative analytics, that's not the case.
By watching real user session recordings and keeping an eye out on touch heatmaps, you can spot issues with your app's usability or user experience long before a bigger issue arises, therefore proactively troubleshooting any problems.

For example, by watching user session recordings you notice that people are trying to log into Twitter through your app and post a tweet. However, as soon as they try to log in, the app crashes. Some users decide to quit the app altogether. Spotting such an issue helps you fix your app before it witnesses a bigger fallout in new user retention.

Not being proactive about looking for bugs and crashes doesn't mean they won't happen – it means they might go longer unattended. By the time you spot them through quantitative analytics, they will have already hurt your user experience and probably pushed a few users your competitor's way.

Wrap-up

They say new ideas are nothing more than old ideas with a fresh twist, and if that's true, than qualitative analytics are the ‘fresh twist' of mobile app analytics. Combining quantitative and qualitative analytics is a simple process that has incredible potency in terms of your workflows and app optimization. Plus, when you understand the reasons behind the numbers on your app, you are able to make crucial decisions with more confidence.

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150 Years of Marriages and Divorces in the UK

Daniel Waisberg — Tue, 13 Jun 2017 10:17:30 +0000

Have you ever wondered how divorce and marriage rates have trended over the last 150 years? Or what reasons husbands and wives give when getting a divorce? Fortunately these, and other questions, can be answered with data. The UK Office for National Statistics make available two extremely interesting and rich datasets on marriages and divorces, providing data for the last 150 years.

Following the discovery of these datasets, I decided to uncover trends and patterns in the numbers, working with my colleague Lizzie Silvey. Two important questions were in our minds when exploring the data:

Who wants a divorce and why?
How do wars and the law impact marriage and divorce rates in the UK?

We discuss our findings in this article, but you can also drill down into the data using this interactive visualization that we created using Google Data Studio.

Divorce petitioners and their reasons

The ratio of petitioners has been stable since around 1974 (70% women and 30% men), the time at which both genders started having the same rights and divorce could be attained more easily.

In the charts below we see the trends for 'Adultery' and 'Unreasonable behaviour', the two most common reasons provided (out of five possible) - each line shows the number of divorces granted to the husband or wife for a specific reason.

In order to use Adultery grounds the petitioner must prove that the partner had sexual intercourse with someone else, which might not be simple. We can see in the chart that Adultery follows the exact same pattern for husbands and wives, but analyzing the statistics further we see that, on average, 40% of the adultery divorces are granted to husbands - since only 30% of total divorces are petitioned by husbands, it seems adultery is a particularly strong reason for men to file for a divorce.

The second chart, showing 'Unreasonable behaviour', is more enigmatic. While husbands were granted divorces in an increasing pace for behavioural reasons, and while the lines seem to be converging, there is a strange hump in the wives line. Why were wives granted a massive amount of divorces up to 1992 based on unreasonable behaviour? Could that be related to a “backlog” of cases of domestic violence (classified as a behavioural reason) that came to light after women could divorce based on those grounds more easily? Unfortunately we could not find data showing possible reasons for that.

The impact of laws & wars on marriage and divorces

When looking at the marriage and divorce trends since 1862, there were a few clear turning points.

The wars seemed to affect marriages quite significantly. Around the beginning of World War I & II we see spikes in marriages, maybe as a result of young men wanting to vow their love before going to fight. Then, during the wars, the marriages plunged as soldiers went away, and up again when they came back home.

As for divorces influenced by the wars, we can only look at World War II, as women had a limited ability to divorce after World War I. It seems the Matrimonial Causes Act 1937, which made other grounds legal (e.g. drunkenness and insanity), coupled with premature weddings (discussed above) and possibly a estrangement due to separation led to a spike in divorces starting in 1946 - who would have the heart to divorce in war times?

But what seems to be the strongest influence in divorces in the history of the UK is the Divorce Reform Act 1969 (link to PDF), which came into effect in 1971. This act states that divorce can be granted on the grounds that the marriage has irretrievably broken down, and it is not essential for either partner to prove an offense. While that explains the strong increase in divorce, we could not find a strong reason for the decline in marriages at the same time - we invite possible explanations in the comments section.

Closing Thoughts

While we couldn't bring answers as to why trends are going in a certain direction and predict upcoming changes, we believe that the data can shed new light into the British society and family relations. Hopefully with new releases of data in the future we will also be able to dive deeper and answer more existential questions.

If you are interested in exploring the data further, check the interactive visualization, created with Google Data Studio, you will find more context and charts showing trends and pattern on marriage and divorce in the UK.

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Tracking Forms Effectively in Google Analytics

Edin Sabanovic — Wed, 24 May 2017 14:30:45 +0000

Quick wins, low-hanging fruits - we're all looking for the shortest, most effective route to improve sales. As optimizers, it's what we do.

Think of the most important actions a customer can take on your website. Registering for a new account, making an inquiry about a product, filling out billing and shipping information - each of these vitally important actions are made through forms, which means: Optimizing those forms can have a big effect on your conversion funnel.

Of course, to optimize these forms, you have to understand how visitors are reacting to them, and what their behaviors indicate. And to do that, you have to measure their activity on your website or app.

Form analytics data is, perhaps, the most important information you can have to understand how your conversion funnel works. When you have access to this type of user data, you can start to see where you're losing potential customers, determine why they drop off, and create concrete steps towards funnel improvements that will reap huge rewards. All of this begins in Google Analytics and Google Tag Manager with Tracking Forms.

This article will show you how to create events in Google Tag Manager that will allow you to track the behavior of visitors interaction with forms. Having this data will enable you to optimize the forms to fit it with visitor expectations and increase the form conversion rate.

Find your ideal form to fit the purpose

Purpose 1: Simple data collection

Simple data collection forms are just used to gather basic data on visitors, like name and email address - common examples are newsletter subscriptions and short surveys. Data gathered can be used to personalize web and marketing efforts, and contribute to customer research. These types of forms can be considered micro conversions and can give important indicators of visitors' motivations (like how confident they are in your site, and whether your site looks credible enough to merit their time and attention).

Purpose 2: Lead generation

Lead generation forms are the bread and butter of B2B websites. To be really useful, lead generation forms must enable the sales department to quickly and effectively identify the strongest prospects.

Lead generation forms ask for more information, and typically offer something of value in return, like a free ebook, study, or e-course. The amount of value offered is directly related to how many form fields you can expect people to fill out.

"According to FormStack's research, only 6% of users will fill out an average of 19 (!) form fields on an order page, but people entering a contest will go nearly to the ends of the earth to submit, tolerating 10 form fields with a 28% submission rate." - Crazyegg, Little Known Form Facts That Can Increase Conversion Rates

It's a delicate balance to strike. Ideally you want only qualified leads to fill out the form, but if you add too many qualifiers, even motivated visitors may drop out or leave the form unfilled.

To avoid this outcome, you can use form analytics to determine the forms effectiveness and eliminate or change the questions that cause visitors to skip or drop out.

Purpose 3: Shipping & billing

Finally and most importantly, you need to track shipping and billing forms. For the most part, the content of these forms is mandatory. However, accurate form analytics can tell us if there are areas we need to improve.

For example, if you realize that many of the visitors drop out at shipping, it may indicate that the shipping options offered are inadequate. Visitors may need more of them, consider them too expensive, or are unpleasantly surprised at the shipping estimate.

Large dropout rates in billing forms often indicate a lack of trust in the security of the site, making customers unwilling to leave their billing information. Think of it this way: hese people want to be your customers, hey wouldn't reach your billing page if they didn't, which means that if you can fix these issues, they are sure wins.

Often, form issues are low-hanging fruits that, if fixed, can result in instant (and large) conversion increases. And to solve them, we need to track our forms and see what our users are really up to. We can do this in Google Analytics.

The Nitty-Gritty How-to: Tracking HTML Forms in Google Analytics

Tracking HTML forms is relatively easy. All you need to do is to establish events for every form. You can go through the forms in your code and add the following line at the submission button code:

ga('send', 'event', [eventCategory], [eventAction], [eventLabel], [eventValue], [fieldsObject]);

This will result in reporting an event every time a user clicks the submission button.

However, what we really want to do is to track the completion of each individual field, not just when the entire form is submitted.

To achieve this, we need to insert a custom code into the form code. You can find an excellent solution for this on the LunaMetrics blog. It is easy to implement and you only need to change a few lines, such as the name of the form you use (the formId line).

When you implement this code on your website, it fires an event every time a visitor fills in a form field or skips it. These events enable you to track the completion and abandonment of your forms.

Tracking AJAX forms - it's a li'l more complicated

AJAX is an acronym for Asynchronous Javascript and XML - it's a way for web applications, like forms, to send and receive data from the server asynchronously, without having to reload the entire page. For example, you might use AJAX forms so that if a user fails to fill out all the necessary fields, the incomplete ones will be marked in red - without erasing all of the other information they did put in.

The problem with AJAX forms is that by dynamically creating content on the same page, it makes tracking harder; the page information is rewritten every time the event happens, deleting the data layer.

So how do we track AJAX events?

For this we're going to need Google Tag Manager (GTM), and for illustration purposes, we're also going to use Gravity Forms, a plugin for WordPress (and websites that use Wordpress hosting). Don't have that exact setup? Don't worry, this use case will likely also work with minor modifications for other types of AJAX forms and other types of forms that use single page.

First off, you need to ensure that your AJAX pages populate the data layer with variables that enable you to put triggers on your tags. The easiest way to do this is to open the configuration tab of the plugin in Wordpress' admin panel. There you can set the plugin to send variables to data layers or even directly to Google Analytics.

If you have only one form you care about, this solution may be the easiest way to accomplish basic tracking.

But you might want to put in your own values in those event definition fields so you have more control over reporting. If you want to create your own events in GTM, configure the plugin to populate the data layer with variables. This is the option you can find in the plugin settings in the form of a check box. The checkboxes are located immediately below the fields shown in the previous image. If checked it will create Tag Manager variables you can use as trigger conditions to set up event tracking.

Once you configure the plugin, it is time to open Google Tag Manager. In order to configure the tags correctly, you should go into preview mode. To do this, simply click the ‘Publish' button in the interface and select ‘preview.'

Go to the page on your website that contains the form you want to track. Once you open it, you should see the bottom part of the page populating with events, such as:

Message
Page View
DOM Ready
Window Loaded

You might see other events too, depending on your individual configuration in Tag Manager.

To determine which variable should be the trigger to your tag, go into the first field of your form. If you have enabled Google Tag Manager tracking correctly in the Admin panel, you should see the following:

Google Tag Manager for the Wordpress plugin has created the #19 event highlighted in grey. It populates the data layer with following variables:

To start tracking individual events, such as form fields being filled, you just need to create a new tag, make it an event, and add triggers containing inputID or inputName values. That way, every time the visitor goes from one field to the other, an event hit will be reported to Google Analytics and you will be able to track each field directly.

Of course, you need to give the event definition values so that it is easy for you to understand and track what is happening.

Once Tracking is Enabled on your Forms, the Optimization Fun Begins

Your own users will show you where to focus first in your optimization journey, but while they'll show you where something has gone wrong, they won't tell you why - not without further research and A/B testing to verify your hypotheses.

A few form best practices may give you a jumpstart to reaching some of those quick wins.

Use a clean, clear form design that visually stands out, and is consistent with your brand.

Keep it simple - the simpler the form, the better your odds of completion.

Avoid two-column forms - they just don't get filled out at the rates single-columns do.

Asking for user age reduces conversion rates by 3%, phone numbers reduces it by 5% - avoid too-personal questions.

Don't label your "submit" button "Submit." Instead, label it with what the user will get in return for giving you their information, like "Send me my ebook!"

The most common issues that prevent visitors from completing forms are the format requirements themselves (too many or too personal), unclear instructions for how to fill out the form, and unclear expectations for what they will get if they do.

But, by and far, the issue that kills conversions fastest is this: Credibility. Users won't give you any information if they don't trust you. This lack of trust must be addressed much further up the conversion funnel, long before the visitor encounters the form. Trust indicators like posting user reviews on product pages and displaying security badges can help.

Of course, there are alternative ways to track data and analyze forms - there are dedicated pieces of software that do an excellent job. These are mostly paid software-as-a-service solutions, but you can't beat the ROI of tracking through the Google Analytics interface.

Even though it initially requires some effort, Google provides these insights free of charge. Not to mention that implementing form tracking this way has the advantage of everything being in one place.

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Partnering with data to create insightful stories

Daniel Waisberg — Wed, 03 May 2017 16:20:57 +0000

[Cross-posted from The Next Web]

Whether you are a marketer trying to persuade people, a technologist building a startup, or an executive making business decisions, data is your partner. You can use it to make better decisions and create insightful data stories inside and outside your company.

The first step is to accept your data relationship: you are partners forever. Once you understand that, there is an important consideration that will define how to tell your data stories: the context of where they live, which also defines the audience that will interact with them. In this post I will go through some important lessons I learned when visualizing and communicating data in and outside Google.

Data is your partner, live with it!

Data is no longer "next year's big thing", we have gone through that many times over and almost everyone accepts data as a valuable team member. But not everyone can understand and make use of it optimally, which means lots of decisions are still made based on intuition - if you don't believe me, check PwC's Global Data and Analytics Survey 2016, it shows some interesting numbers on how often managers use data during the decision-making process. Data education is a crooked road and we have a long journey ahead of us.

One of the reasons for that is similar to the well-known phenomenon called mathematical anxiety, where people are afraid of maths as a result of past difficulties and traumas. Every one of us have interacted with data analyses (at work, newspapers or academic research) that were created by unskilful communicators, people that might be amazing statisticians but lack the ability to convey the stories behind the numbers. That creates anxiety and could prevent professionals from even trying to understand data.

I believe the reason the data community is not growing like weeds is because professionals are not confident enough with numbers and charts. I have written about how to overcome the fear of analytics (and help others), here is a quick summary.

Never mock people for not understanding a chart

Take baby-steps towards numeracy

Make analytics more fun

When you create a visualization you may affect other people both positively and negatively. If you create a complex and unintuitive visualization, you might be creating a phobia on other people, and they will forever hate numbers and stats. However, if you create a powerful and beautiful visualization, you might be persuading another mind to join the data visualization tribe.

Below are some ideas that might help you craft better data stories, both for businesses and in general.

Stories tailored to businesses, the world, and beyond...

There are many ways to communicate data, but choosing the right format will depend on where the data will be published or presented, the context. Is it a daily performance report or a quarterly result presentation? Or a behavioral analysis using web data? Or an interactive visualization showing global trends?

I'd like to break down data stories into two main branches: business reporting or analysis, and visualizing the world. These groups can show very different characteristics, so let's look into each separately.

Business reporting or analysis

I recently had the opportunity to interview Avinash Kaushik, Digital Marketing Evangelist at Google. In our conversation we discussed techniques to create great data stories, focusing on businesses. Avinash talked about his business framework See, Think, Do, Care and the role of data visualization during the decision making process.

We also discussed data visualization (see minute 11:08), and Avinash explains how not to make silly mistakes when using data in a business context. He makes the differentiation between three main types of visualizations:

Elaborated stories presented with the intent to change people's views on a complex subject (what I call visualizing the world).
Strategic analysis of business results presented to executives.
Day-to-day reporting used to drive most small business decisions.

Avinash differentiates between analysis "packed together" with a storyteller, which allow for more complex visualizations, and day-to-day reporting, which are supposed to stand on their own and help people make decisions by themselves.

Considering the data delivery circumstances is a great start when designing your visualizations as they will inform the presentation style and level of complexity that can be used. While every visualization should strive for simplicity, a daily report (and business visualizations in general) must be extremely clean and self-explanatory, as the data storyteller won't be there to help the decision maker.

Below is a quote by Avinash summarizing his views on how to succeed with data.

"On a business context, a data visualization has to do one job really well, and it has to answer the question ‘so what?' If your data doesn't answer the 'so what' question, and if there isn't a punchy insight that drives action, all you have is a customized data puke, it looks really nice but it serves no purpose. If you want to drive change, you have to get to the simplest possible way to present the data, and once you get to it ask the so what question. After you answer it, ask if it quantifies the opportunity, if it does you are going to win."

Visualizing the world

Luckily to our society, visualizations are increasingly used in a broader context, where the goal is not to understand the business or track performance, but to educate the public and change people's minds. There are some great examples of visualizations that make a difference, but probably the most famous is Hans Rosling motion charts, where he debunks several myths about world development.

I've written about data stories in the past, discussing why it is important and providing some ideas on how to use data visualization to tell stories. Basically, here are two really important things you need on a good data story:

It stands on its own - if taken out of context, the reader should still be able to understand what a chart is saying because the visualization tells the story.
It is easy to understand - but while too much interaction can distract, the visualization should incorporate some layered data so the curious can explore.

Recently I worked on a data story with my colleague Lizzie Silvey, where we analyzed stats from the UK Office for National Statistics. We looked into Divorce and Marriage trends starting from 1862, and came up with an interactive visualization. Below is a screenshot with some of the insights on how changes in the law impacted marriage and divorce rates in the UK. Check the visualization to play with the data.

Whether you are working on a monthly report or a world-changing visualization, if you take the time to uncover and communicate the stories behind the data, you will be contributing to better decisions in your company and in society in general.

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Visualization Techniques to Communicate Data

Ben Collins — Tue, 11 Apr 2017 08:58:37 +0000

So here's the deal: you've spent a ton of time with your data and you know it inside out. You've wrangled, sliced and diced it and are now the expert with this data for this problem. You've uncovered new, actionable insights that will lead to fantastic opportunities or improve your bottom line. Great! Time to show your colleagues or your boss or your clients these findings.

You open your data tool of choice, quickly create some charts and make it all look pretty with a flashy color scheme or fancy logos. More often than not, we fly through this final stage and don't give the data visualization step the due care it needs. This is insane!

Think about it. Your charts and dashboards are most likely the only piece of information your boss or client will interact with. The only information! And yet, here we are, creating default charts and missing the opportunity to really convey our message.

Effective charts are a compelling way to show your data. The human brain is simply better at retaining and recalling information that has been presented visually.

In this article I will discuss several techniques that will help you create more effective charts to communicate the underlying data.There's no big secret here. However, by applying deliberate thought, a handful of best practices, and allocating sufficient time in projects for the data visualization step, you can make a big difference to the impact of your charts.

Plan your approach

Before firing up your favorite data visualization software, it pays to spend some time thinking about your output and your goals. Start by answering a few simple questions:

Who is the intended audience?

What medium will you use to show your charts? (e.g. slides / dashboard / report etc.)

What is the goal of this project?

For example, consider the audience who will view your chart. How long will they have to study it? How familiar are they with the data? Are they technically inclined? Do they want detailed charts, or quick summaries?

You want to optimize your message to resonate with your audience, so the more you know about them, the more likely you'll be able to achieve that.

Likewise, how you deliver your message will affect your decisions. Is it a chart in a slide deck? In an informal email? A formal report? An interactive dashboard?

Reports and dashboards are typically pored over for longer periods of time, so charts and findings can be more detailed, whereas presentations or client pitches are short and sweet, where the audience will only have a moment to understand and absorb the information.

Lastly, think about what your end goal is. What do you want your audience to do with the information you show them? For example, if you want your manager to make a cost-benefit decision for a new hire or expensive research tool, make sure your solution answers the question and facilitates making that decision.

Deliberately focus the viewer's attention

Remember, the point of your visualizations is to communicate information, and you can ensure they do that more effectively by giving prominence to the key message within your chart.

You can do this by using attributes, for example color, to highlight specific elements of your charts and focus your audience's attention there. These are known as pre-attentive attributes, and they dramatically help speed up the absorption of information.

Consider this chart showing the open rates for four newsletters that you manage. There's an important story in there, but it's difficult to see with the default colors:

However, by carefully using colors, we can bring that story to the fore:

Add context to aid understanding

Consider the two charts above, showing email newsletter open rates. The second chart also has a heading that adds context to the story. The words complement the chart and reinforce the message.

Much like writing titles for your blog posts or newsletters, think about the title of your chart in the same way. It should tell the viewer what to expect in your chart and summarize the message.

Similarly, your data may have unexpected spikes or dips, so you might want to use annotations directly on the data points or as footnotes, to make sure the viewers have all the context they need.

Reduce clutter in charts

Renowned data visualization pioneer Edward Tufte coined the term data-ink ratio to convey the ratio of ink needed to tell the core message in your display, divided by the total ink in the display. The idea is to maximize this ratio, in other words, reduce the amount of non-essential ink.

Let's see that in practice. Compare the following two charts showing Amazon's revenue between 2007 and 2016:

After decluttering, the annual revenue figures jump out at the viewer and the information is much quicker to absorb:

Avoid using overly complex charts for the sake of it

There are a lot of complex chart types out there: waterfall charts, radar charts, box and whisker plots, bubble graphs, steamgraphs, tree maps, pareto charts, etc. etc.

Sometimes these may be appropriate for specific cases (e.g. a Sankey chart to show web traffic flow) but it really comes back to the question of who your intended audience is and what medium you'll be showing your chart through.

Does this radar chart really communicate your message well? Would a simple bar chart, which is widely understood, be a better alternative?

Whenever I teach a dataviz class, I always say that a good chart should be like a good joke: it should be understood without you having to explain it.

Is that pie chart really the best choice?

Pie charts are popular and ubiquitous, but somewhat maligned by the data visualization community. Why is that?

Consider this default pie chart in Data Studio, showing website Sessions broken out by Medium:

This chart (and pie charts in general), have two main drawbacks: 1) it's hard as human beings to decipher the relative sizes of the slices (and the order and position of them affects this), and 2) the long tail is unreadable. Plus, the legend is ugly to look at.

A much better chart for data with many categories and a long-tail would be a standard bar chart. Nothing fancy here, but it's super quick and easy to read off the values, especially for the smaller categories (e.g. compare trying to understand email sessions in the pie chart vs. the bar chart).

So if you're going to use them, restrict pie charts to small numbers of categories (I'd advise three or less), and always ask yourself if a simple bar chart or table would suffice and be quicker to read.

Be careful with dual axes charts

Dual axes charts should be used with caution as they often cause confusion. It's tempting to use them when trying to chart data series with large size differences, as shown in the following image. Which series goes with which axis? Lines that overlap will also confer meaning that doesn't actually exist, because the series are on different scales.

Some strategies you can use to mitigate confusion include matching the series and axes with different colors, labeling the axes clearly and even using different chart types for the different series (line with a bar).

However, I'd still advocate only using them sparingly. It's often better to show the two series in separate charts next to each other.

When to start the y-axis at 0

For bar charts, you should always start the y-axis at 0 since the height of the bar represents the count in that category. We look at the height of the bars and compare them. If one bar is twice the height of the other, then we're going to conclude that the value of that category is twice the value of the other category, even if the axis shows otherwise.

Consider this simple example. Both bar charts have been plotted from the same data but they tell very different stories:

Vox Media created an excellent video about truncating the y-axis. With line charts we don't need to be so strict with truncated y-axes as the visual lines are used to compare trends, not actual values, as in the case of bars. Indeed you sometimes need to narrow the range with line charts to show the story.

Remember to consider the color blind

Approximately 10% of the male population and 1% of the female population identify as color-blind, and the most common type is Red-Green color-blind. So it pays to keep this in mind when designing your charts.

Closing Thoughts

Once you've created your charts, or your dashboard, pause and ask yourself these few questions:

Is it effective at communicating your message?
Is it efficient at communicating your message?
Ultimately, does the audience benefit from seeing your visualization?

There is no single right answer with data visualizations, as it will depend on many of the factors discussed above. People will come out with different charts from the same dataset, all of which could be equally effective. However, by following some best practices and thinking critically about your charts, you can improve them dramatically.

I'll leave you with some parting words from a master in this field:

"Above all else show the data" Edward Tufte

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Personalized Account-Based Marketing

Jonathan Riley — Wed, 05 Apr 2017 12:56:14 +0000

One of the primary considerations for implementing personalization is: What is the most efficient investment of my resources? What type of personalization makes the most sense for my company's needs?

Even with the help of AI and automated algorithms, creating personalization campaigns is an investment of time and money. Matching your strategy to your business model, scale, and market is therefore essential. For B2B companies, and others with smaller, more high-value segments, the more typical and broad implementation usually employed by e-tailers may not be the most effective.

Taking a highly targeted approach and marketing to a small number of clients is an emerging strategy known as Account-Based Marketing (ABM), and it has been demonstrated by market research to have a reliably higher ROI than other online marketing methods in B2B, and other industries where individual clients represent a higher value sale.

When it comes to personalization, an ABM strategy means taking a precision approach: deeper data, smaller segments, sometimes even specific versions of your landing page(s) for individual clients. It also means less focus on new visitors to the site, or those who you don't know as much about, and a concentration of resources on high-value clients you know well.

Expanding your customer knowledge for ABM Personalization

Acquiring as much useful data as possible is one of the key elements of quality personalization, and any worthwhile personalization engine is collecting a variety of IP, referral, and behavioral tracking data, by default.

However, if you're using the ABM method, there is another critical source of data that includes many parameters typically unavailable to the default mechanisms, and that is your CRM database.

Critical information that may be found in your CRM DB can include:

Role in the company, to provide relevant CTAs and promotions - If you know that the visitor is part of the IT team of the company, it' more relevant to show them more technical information, whereas the CEO may be more interested in seeing cost-effectiveness and the bottom-line.
Account status, such as a prospect or lead - Knowing the stage in the sale could determine whether to offer demos, discuss pricing, and whether to email or call the customer. If an active customer, offer upgrades and create customer retention.
Industry of the visitor, to show relevant promotions, testimonials, and use cases - If we know the industry of a client, we can provide case-studies that involve existing clients from that industry, to create a sense of trust in the company as a provider in the space.
Company size can be very relevant for understanding specific customer needs - This type of knowledge allows your marketing to be highly targeted, to speak the client's language, and establish the groundwork for closing the deal.

Starting with the fundamental CRM profile, additional behavioral tracking and referral data can give you more insight into prospect interests, or existing client needs for other products, based on which pages they viewed, for how long, where they clicked, how they arrived, etc.

How is CRM integration accomplished?

There are a variety of methods for different needs, and also types of databases. Integrating an internal DB may be different from integrating with a 3rd party service like Salesforce; a quality personalization solution will offer multiple options.

However you integrate, the key is to get the user data into your personalization engine's customer profiles, link those customer profiles to the leads visiting your site, and adapt your landing page to their specific qualities.

Case Study

Strega Software Solutions, a B2B software provider, is employing an ABM marketing strategy, due to the fact that they serve a relatively small number of key accounts within various industries. In this example, we'll see how one such visitor is delivered a tailored and engaging experience on Strega's site, via CRM-integrated personalization.

Meet Susan:

Operations Manager at utilities company
Searching for: Automation software for customer service
Not a decision maker, but one level below
Previous customer, influenced company to use Strega for marketing automation software

Before personalization, Susan would have seen the same landing page as everyone else, when she arrived at the site. However, because Strega already knows Susan through her CRM information, and this data profile has already been linked to her work IP, she sees a unique landing page tailored to her interests and role, immediately.

Because Strega is taking an ABM approach in their B2B marketing, they have placed concentrated effort into showing specific promotions and CTAs to individuals based on their role in the company, as well as whether or not they are a decision-maker. Additionally, because Susan arrived at the landing page via an ad campaign related to customer service automation, the personalization engine also recognizes this as a current or primary interest.

In Susan's case, when she lands, the site is immediately tailored to her specific interests and industry:

Banner features a testimonial quote from a utilities company
Suggested product categories are all related to her established and new interests
Energy and utilities is moved to the first slot in industries
Suggested news include an interview with a decision-maker (CEO), and a post about customer service automation
Testimonial at the bottom is related to a customer service automation product

After being alerted to her interest, an accounts manager is automatically notified that she is browsing the site, and a webhook automatically triggers a personalized email, further pushing for a demo, and also including offers for upgrades to previously purchased software, which can be included in the bundle for a reduced rate.

This is just one example of the type of targeted account based marketing that is possible with the aid of personalization informed by CRM data. When there is significant revenue on the line, knowing your customers or clients intimately, and maximizing the relevance of their experience on your site is guaranteed to drastically increase the likelihood of conversion, and to generate increased revenue for your company. Third-party SaaS personalization solutions are now making this available not only to huge corporations, but all scales of online business.

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Documentation Is the Backbone of Analytics

Baris Akyurek — Tue, 14 Mar 2017 09:49:41 +0000

So, you can't find enough time to do the stuff you want to do at work because you are very busy? Take heart. It's possible to arrange your work to spend more time on the big questions, but it'll take some work to get there. How? Read on...

Everybody wants to do cool stuff at work! We, the digital analytics folks, love doing analyses, presenting data-driven insights, exploring new tools, convincing the business to launch a new feature / product derived from our analyses, etc. I will categorize these as the "sexy" parts of the job, the parts we wish we had more time for. Now, just like any other job, there are also the not-so-sexy parts of our jobs. That's the focus of this post - the mundane (but don't go away! it ties back into the sexy part of the job at the end).

Whenever I go to an analytics event or talk to someone from the industry, the conversation is always about the aforementioned sexy parts of the job. Now that I am not at an event, I can talk more about one of the the less sexy but probably most important aspects of our jobs: documentation, the backbone of the analytics implementation process, IMHO.

Analytics Process From Beginning To End

If you think about the analytics process at your company, it would probably look something like this:

Business Problem: define the business problem you are trying to solve.Let's assume that there is a product/feature that is being built to solve this problem for the sake of clarity.
KPI Design: work closely with the product/business owner and decide on the KPIs and metrics to help identify the performance of the product/feature in detail.
Documentation:
- Business Solution Design Document: Describe in detail how you are going to capture the data you need in your implementation.
- Technical Solution Design Document: work with your implementation team (if you are lucky enough to have one)to clarify the above document in detail so that they can write the Technical Solution Document (e.g. detailed Data Layer requirements).
Requirements Implementation: implement the final solution through a TMS (Tag Management Solution) and work with developers when necessary.
Unit Testing: QA the implementation of the new features/products.
User Acceptance Testing (UAT): submit your pass/fail findings back to the team for a final UAT and update the master dictionary with the new events, custom dimensions, evars, custom metrics, etc., if all is implemented as requested.
Regression Testing and Release: do a full regression test and release the changes to production.
Data Pull: pull the data you need for your analysis.
Data Analysis: torture if needed.
Storytelling: tell a story to the business based on your findings.

Meh! Not Sexy Enough

I probably wouldn't exaggerate if I told you that (3), (5), (6) and (7) above, i.e., the documentation and QA, are often overlooked and underappreciated because they are considered time consuming! I think they are at least as important, if not more, as the rest of the items above. Let me expand on this.

I completely agree that documentation is not the most entertaining part of what we do. You know what you want to track and you can probably save time (in the short run, anyway) if you have a quick chat or type up a quick email to your implementation team and/or your developers to get the stuff you want implemented so that you don't have to "waste" your time with documentation, but I highly encourage you to document everything you implement.

Assuming that there is a release every other week, there are around 26 releases a year (times the number of platforms you have). Without proper documentation, probably nobody would know/remember what's been implemented in what release. This is the obvious reason. The second one is more strategic.

The "Urgent" Requests

Tell me if you had a conversation like the one below with one of your product managers:

Product Manager (PM): You remember we implemented this new [amazing] product to increase exposure of the XYZ product category?

You: Yes

PM: Can you get me a report/do some analysis that tells me whether this product is actually performing well? We need to talk to the sales team so that they can start selling it

You: That can be done… but when do you need it by?

PM: End of day tomorrow

You: That is not possible… I am working on [insert a high priority project here]

PM: When can I get it?

You: We'll put you in a queue and see what can we can do...

PM: It's super important because [insert a name with a big title with large salary [aka a HIPPO]] asked for it

You: OK. As I said I will see what I can do about this but I cannot get it to you by the end of day tomorrow

PM: That's too late because the presentation is this Wed.

You: If it's super important, why is this a last min. request?

PM: [Provocative response]

You: [More provocative response]

... and boom there is unnecessary tension between you and your colleague! I am sure this happens to some of you all the time (it used to happen to us a lot but not as much anymore).

One solution (at least a partial one): Self-Serve and Automation!!! Let me type that out again: SELF-SERVE & AUTOMATION. Trust me, because I'm sure you don't want to worry about these type of requests anymore.

Documentation Is the Backbone of Analytics

At autoTRADER, we've been trying to solve this problem for a while.

How can we carve up more time to do strategic analyses that can move the business forward?
How can we modify our existing processes so that the individual business units could have access to their own data and do their jobs more efficiently and faster so that they can also move faster?

And now that we have been properly documenting every single tag on the site and trained pretty much everybody who needs digital analytics data for whatever they are doing, we have enabled product / agile teams to self-serve. It's a definite win-win both for us, the analytics folks, and the product teams.

Analyze Away

Do you want to know how your amazing product has been performing? Go to the 3rd tab on the live Master Dictionary spreadsheet (ours lives in Google Drive), see how the implementation was done for your product and pull whatever you need yourself. Do you want to slice and dice the data by date, hour of day, medium, platform? Go crazy, be my guest! Now I can concentrate on my more strategic project!

We've come a long way at autoTRADER.ca. At one point there was only one person (a.k.a., me) who used to do pretty much all of the stuff above (all but coding) but now we have a larger team. Everybody has their own speciality, allowing us to execute the fix I am proposing here. Yes, you need a team to be able to self-serve; if you are a one man show, let this be an incentive for you to make the business case to staff up (more on this for a future post).

The Analysis Framework

This is the framework we've designed at autoTRADER (kudos to my colleague Kevin, who was able to articulate what we wanted to achieve in this two by two framework).

I don't know about you, but we, the Analytics group, want to spend the most of our time on the right side of the framework that has an impact on the larger business opportunities and challenges. However, we used to spend most of the time on the left side of this matrix doing "micro-analyses", where the business impact is usually smaller. I am not condescending the micro analyses at all, but our group's mandate is to focus on the right side.

In order to change this imbalance, we've come up with processes and hired resources to carve up time for the team to focus on the more strategic stuff. We document everything that goes into the implementation and we created a measurement and data dictionary (we call it the Implementation Bible) so that the teams can do their own analyses without being dependant on us. We've also trained the internal teams on our Analytics platforms - how things are tagged, how teams can create and use their own reports through various tools like PowerBI, Supermetrics, Data Studio, GA Dashboards and Custom Reports - in order to create a win-win situation for everybody. They have access to what they want, we spend our time on what we need to spend more time on...

Slog Now to Become Cooler Later

Going back to the beginning of this article, the sexy part of the job is tied to the not-so-sexy part of the job and goes back to documentation. I am aware that documentation is time consuming but it's necessary to move your business forward. I highly recommend that you think about it if you are having issues like we used to!

You don't have time to document? Sure!

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Google Analytics Segments in Data Studio

Lizzie Silvey — Wed, 08 Mar 2017 11:31:48 +0000

Not only is Data Studio now free for all (!) but, to add to our ever-growing excitement about the product, segment functionality for the Google Analytics connector is now available too!

Until now, Data Studio has provided us with the option to use filters. This functionality is equivalent to using an advanced filter within the GA interface whereby, for the data in the table or graph, the filter will narrow values meeting the condition(s) set.

Segments, on the other hand, allow for a more advanced analysis of users visiting your website. They allow you to create a subset of data by collecting users (or sessions) into that subset, which you can then query and analyse to your heart's content.

Adding GA Segments to Components and Pages

For this example, let's say we want to look at the behaviour of users who are browsing from a mobile device. In GA, there is a default segment already created called Mobile Traffic and this is set using this condition:

In the GA interface we can apply this segment, along with four other segments, to our reports to gain a better understanding of how our group of users (those browsing on mobile devices) behave on site. The good news is that you can now apply this same segment in the Data Studio interface as well.

To do so, you can either apply the segment at the page level or the component level. If you want the segment to apply to all components on the page, you can select Current Page Settings > Google Analytics Segment > Add A Segment. However, if you just want to apply the segment to one component, or a group of components, you will need to select the component(s) > go to the Data tab in the properties panel > Google Analytics Segment > Add A Segment.

At this stage, you can now pick your segment. There are three buckets that segments can fall into (see numbers in screenshot below):

Added segments: These are segments which have already been added to the report to another component or page (NB. this bucket won't show up if you've not added any segments yet.)
System segments: These are the default segments that are created within the GA interface. These include segments like New Users and Returning Users, which I'm sure you'll be familiar with seeing. The Mobile Traffic segment mentioned earlier falls into this bucket.
Custom segments: These are segments as created by you within your personal GA account… more on this later.

If you don't want to search through these buckets for the segment you're looking for, then just tap the search icon (4) in the top right hand corner and type in the name of the segment you're after and it should pop up.

Once you've selected your segment and opted to add the segment to the report, this will be applied to the page or the component(s) that you've chosen to apply it to. At this stage, you can happily go away and analyse the behaviour of your users browsing from mobile devices.

Applying multiple Google Analytics segments to a Data Studio page

However, let's not stop there; there is still more analysis to be done... you can set up identical components side by side in your Data Studio report in order to compare users visiting the site from a mobile device against users visiting the site from a desktop. So far you've added the mobile segment to the left hand components. To start with, copy the components from the left hand side and paste them onto the right hand side to get the visualisation ready. Now let's create a desktop segment and apply this to the components on the right hand side.

To do so, you need to first create the segment within GA. You can do so using the following condition:

Once that's saved in GA, you just need to refresh your Data Studio report and you can now find the Desktop Traffic segment you created under the Custom segments bucket. As noted previously, this bucket includes all the segments that you've personally created within your GA account.

Once again, you now just need to select the segment and add it to the components you're interested in. Select all four components on the right hand side of the Data Studio report (using the ctrl button) and apply the segment to all four at once. Don't forget to label the two sides of the report so that anyone viewing the report (including yourself!) will understand the data being visualised.

So now you have two different segments applied to your report! The fact that the subset of users browsing on mobile can be compared so easily to the subset of users browsing on desktop makes this a really useful feature within Data Studio. You can quickly and easily see the behaviour of these users, allowing you (if need be) to action upon the results you're finding.

From a quick glance of my report, you can see that mobile users spend a lot less time on a page on average (4 mins 38 secs) compared to desktop users (8 mins 22). If my aim is to ensure content is keeping users engaged on site, then this might mean I need to think about what to do with the mobile version of my website. Why are users less engaged with pages when using a mobile? Is content hard to read on a mobile web browser? Are there any issues with the pages loading on mobile browsers? These are just the initial questions I would begin to ask based on a quick glance of this data...there are many more that could come out of applying these segments to further components.

Synchronising Google Analytics and Data Studio Segments

One last thing... you'll notice a small icon next to the segment you've added in the properties panel.

This indicates if the segment has a sync enabled. If this is enabled, any updates to the segment within the GA interface will be replicated and synced with the segment applied in Data Studio. If you want to make sure that the segment you've applied doesn't change in definition, even when it's changed in GA, then you'll want to disable this sync. Otherwise, keep it enabled!

Happy dashboarding!

P.S. For more info on segments, head to the super helpful help centre articles on segments!

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Machine Learning Through Google Tag Manager

Online Behavior — Tue, 28 Feb 2017 12:29:51 +0000

This article was contributed by Mark Edmondson and Peter Meyer, both from IIH Nordic, specialists in online marketing and web communication headquartered in Copenhagen, Denmark. Read more about the authors in the bottom of the article.

Many analytics specialists agree that Machine Learning is going to revolutionise the digital analytics industry in the future, as all the major vendors battle to provide Machine Learning APIs that offer to surface interesting features of your data. These APIs offer cloud solutions that you can use to both scale up your own models, or take advantage of pre-trained models.

Following our presentation at Superweek Hungary 2017, we wanted to show how you could start using these services today to enhance your own digital analytics capabilities.

Among all Machine Learning techniques, we chose Sentiment Analysis since it is a common use case, but the same code with small modifications could be used for any of the machine learning APIs offered by the services we chose, Algorithmia and Google Natural Language API. For those not acquainted with Sentiment Analysis this YouTube video goes into detail about one of the approaches we use below.

Both the solutions proposed below are deployed through Google Tag Manager using the JavaScript SDKs available, and the code is available for you to try at our Github repository as well as a demo website here: https://bit.ly/Superweek2017-Demo

Case Study: Sentiment Analysis

Our use case was built for a large news website that allowed comments on their articles. We wanted to be able to monitor in real-time the reaction to the articles as they were published, as judged by the user comments beneath the article. This could then be used in the newsroom to decide which articles to highlight or demote on the homepage.

Another considered use case was for a large brand who carried user forums. A live measurement of the general sentiment on the forums under certain products could indicate when a problem or trend was starting with that product, and help inform marketing or customer support if there was a pending issue.

Maybe your users feel that the product support you provide could be better, find your products very hard to use, or have a hard time finding out how to best provide you feedback on your services. Wouldn't it be very interesting to look at numbers providing you exactly that information?

In the past, it may have taken an expensive 3rd party tool or offline analysis to get the Sentiment Analysis, but as we show below, it isn't very complicated to do it yourself, directly into your web analytics, as they happen.

The services used are both paid services, so please take that into account before using any of them for anything else but testing.

The proof of concept website

This demo was originally created for a presentation at Superweek Hungary in Feb 2017. The presentation, which includes additional examples is embedded below.

You can find the demo website at https://bit.ly/Superweek2017-Demo - it takes the code we produce below and creates a small webapp to compare the results. [Bear in mind that if it goes over the free tier it may not work until the tier resets the next day]

This site is a flat HTML page, which has Google Tag Manager deployed to provide the sentiment analysis output from the two platforms tested: Algorithmia and Google Cloud Platform. You just enter a text in the big text area, click the blue button, and then get Sentiment Analysis results returned in the two output boxes below the button. Feel free to check it out :-)

The machine learning API services

Algorithmia

Algorithmia is a cool service: a marketplace with thousands of machine learning algorithms for you to choose from. The algorithms are standardised so you can then use the same code library to run it where you need to. At present, their marketplace main categories are:

Text Analysis
Machine Learning
Computer Vision
Deep Learning

The algorithm we chose is the Stanford NLP Sentiment Analysis, which returns one value: the sentiment, which spans from 0 (Very Negative) to 4 (Very Positive).

Because Algorithmia serves as an algorithm marketplace, you can try out several before deciding which one is best for you. And if you want to, you can even put your own algorithms onto the marketplace to sell alongside the others.

Google Cloud Platform

Google currently offers a lot of different cloud products, including the Machine Learning services, and the Cloud Natural Language API. The API includes functionality for analysing entities, sentiment and syntax. Today we focus on just the sentiment part, but exactly the same approach could be used to classify topics from text.

The Sentiment Analysis service from Google provides two output values: polarity and magnitude. Polarity is a value between -1 (Negative) and 1 (Positive), and magnitude is a value between 0 (Neutral) and infinite (Strongly/Clearly). A nice description of this approach can be found in the article Sentiment analysis on tweets by Guillaume Laforge. Here is a descriptive illustration from the article:

Deploying Sentiment Analysis through Google Tag Manager

One feature of these services is that they provide JavaScript APIs, enabling you to deploy their functionality straight from a tag manager. We've used Google Tag Manager, but the same approach could work for other tag management systems. You may also choose to deploy them straight onto the webpage, but a tag manager was useful for testing concepts quickly.

Follow along with screenshots and code in this GitHub repository

Examples of both services have been adapted from the relevant documentation for each service.

Include the JavaScript libraries

To be able to use both services, we need to add JavaScript include files on the page.
The following GTM tag does exactly that, in this case the include files are put in the HEAD section of the page (code).

<script>
(function() {
    var script = undefined;
  
    // Algorithmia script
    script = document.createElement('script');
    script.type = 'text/javascript';
    script.src = '//algorithmia.com/v1/clients/js/algorithmia-0.2.0.js';    
    document.getElementsByTagName('head')[0].appendChild(script);
    // Google script
    script = document.createElement('script');
    script.type = 'text/javascript';
    script.src = 'https://apis.google.com/js/api.js';    
    document.getElementsByTagName('head')[0].appendChild(script);
})();
</script>

Calling the Algorithmia API

Once the submit button is clicked, the script below is then used to send the text typed by the user into the form, (code):

<script>
(function () {
    try {
        if (typeof Algorithmia === 'object' && {{Sentiment Analysis - Input Text}} != undefined) {
            Algorithmia.client('{{Algorithmia - Client ID}}')
                .algo('algo://StanfordNLP/SentimentAnalysis/0.1.0')
                .pipe('{{Sentiment Analysis - Input Text}}')
                .then(function (output) {
                    dataLayer.push({
                        event: 'algorithmiaSentiment',
                        algorithmiaSentimentResult: output.result
                    });
                });
        }
    }
    catch (e) {
        dataLayer.push({
            event: 'algorithmiaSentimentError',
            algorithmiaSentimentErrorMessage: e.message
        });
    }
})();
</script>

Briefly, that's what the script does:

Checks for the existence of the Algorithmia file
Sends Algorithmia the text from the input form
Receives the response and pushes a GTM event algorithimaSentiment

Once the event is returned, the API response is shown on the page like this.

To convert the numeric value returned by the service and translate it into a more verbose value, we created a lookup table below.

Calling Google Cloud Natural Language API

Bear in mind this service is currently in beta and so the code below may not work if the API changes significantly in the future.

A similar script is used when calling the Google API (code):

<script>
(function () {
    function start() {
        try {
            gapi.client.init({
                    'apiKey': '{{Google - API Key}}',
                    'discoveryDocs': ['https://language.googleapis.com/$discovery/rest?version=v1beta1']
                })
                .then(function () {
                    return gapi.client.language.documents.analyzeSentiment({
                        'document': {
                            'type': 'PLAIN_TEXT',
                            'content': '{{Sentiment Analysis - Input Text}})'
                        }
                    });
                })
                .then(function (output) {
                        dataLayer.push({
                            event: 'googleSentiment',
                            googleSentimentPolarity: output.result.documentSentiment.polarity,
                            googleSentimentMagnitude: output.result.documentSentiment.magnitude
                        });
                    },
                    function (error) {
                        dataLayer.push({
                            event: 'googleSentimentError',
                            googleSentimentErrorMessage: error.result
                        });
                    });
        }
        catch (e) {
            dataLayer.push({
                event: 'googleSentimentError',
                googleSentimentErrorMessage: e.message
            });
        }
    }
    if (typeof gapi === 'object' && {{Sentiment Analysis - Input Text}} != undefined) {
        gapi.load('client', start);
    }
})();
</script>

Briefly, that's what the script does:

Registers with the Natural Language API
Calls the API with the form text
Upon getting an answer, pushes a GTM event googleSentiment

The returned output is then displayed on the page like this:

Sending output to Google Analytics

We display the results in the demo application, but the sentiment scores can just as easily be sent straight into Google Analytics for analysis as an event. This then lets you pull out the results via the real-time or reporting APIs for your needs. Below is an example GTM code to do this.

Sending Algorithmia sentiment value to Google Analytics

In the tag below the text value ("Positive", "Negative" etc.) is sent as the label, and the sentiment score as a value. Adjust to your needs.

Sending Google sentiment value to Google Analytics

In this case, the Event label is the magnitude (0 - 1) and the value is the polarity (-1, +1). You may prefer to make another variable that multiplies the magnitude and polarity together to create one value.

Summary

You have now seen two approaches on how to implement Sentiment Analysis on a web page. The examples are quite simple, but hopefully they show how quickly you can get up and running with Sentiment Analysis on things like user comments, forum entries, and support requests, and perhaps inspire you to try other applications.

This also goes to show that you don't have to know a lot about Machine Learning, AI, Deep Learning, algorithms and such, to get started reaping the benefits of what it can do to help you. Though we do urge you to learn more about it if this catches your interest :-)

And if you do have the knowledge to create your own Machine Learning algorithms, both platforms offer a way to deploy that model at scale through almost exactly the same mechanism: Google through Tensorflow and Algorithmia through its marketplace.

We hope this triggered your interest in the subject. Feel free to get in touch if you are inspired to make anything cool on top of it, we'd love to hear from you!

About the Authors

Mark Edmondson is an Englishman in Denmark, where he lives and works with data. He grew up in Cornwall, UK, where he started his career in digital marketing in 2007, after completing his Physics masters. These days he splits his time working with digital analytics data for IIH Nordic and working with Google APIs as part of his role as a Google Developer Expert for Google Analytics. Outside work he enjoys composing music in the bunker and being a Dad. Mark blogs at code.markedmondson.me and enjoys twittering at @HoloMarkeD.

Peter Meyer has been in the digital industry since 1997, beginning with HTML, moving over front- and backend development, SQL databases and Sitecore CMS, to now implement analytics and TMS products like Google Analytics, Adobe Analytics, Tealium IQ, Ensighten Manage and Google Tag Manager. Learn more about him on LinkedIn or follow him on Twitter @pmeyerdk.

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