Digital space. Conquered.

WHEN DIGITAL ANALYTICS MET AI AND MACHINE LEARNING

#AI#MachineLearning#DigitalAnalytics

INTRODUCTION

The Digital Economy is an amazing opportunity to positively impact people’s lives. In order to do so, and regardless of what type of industry you’re in, understanding your digital audience and the way your digital channels are performing is crucial. For many years, organizations in nearly every industry have been turning towards web analytics, digital analytics or more widely business analytics solutions to do exactly that – and many have seen a tremendous amount of success as a result. This is largely why the global business intelligence and data analytics market is expected to be worth as much as $18.3 billion by the end of the year. By as soon as 2020, there will be 1.7 megabytes of new data created for every human being on the planet every second. That data will encapsulate everything – from understanding more about audience behavior and how to meet its needs today, to proactively predicting areas of struggle, infrastructural issues and identifying the adjustments that will help you prosper tomorrow.

But this volume of data comes at a cost; particularly one of management and agility. Traditional analytical models are no longer viable. The vast, and ever-growing, amount of data captured obscures the valuable and timely insights contained within – causing business leaders to miss out on solutions to issues and opportunities for growth.

THANKFULLY, THIS IS WHERE ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNOLOGIES STEP IN.

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A NEW GENERATION OF DIGITAL ANALYTICS SOLUTIONS

Artificial Intelligence and Machine Learning may sound like concepts out of science fiction, but they’re already very real and very important. To put it as simply as possible, AI is a process that allows computer systems to be able to recognize and perform tasks that would previously require human interaction. Machine Learning is a subset of that larger concept that describes a way computer systems can automatically learn and improve from experience without being explicitly programmed to do so.

Now, if we link these capabilities back to the data issue we were discussing above, things become interesting. Think about it: your audience’s behavior on your digital channels can ultimately be translated into a huge set of data. Any click, mouse movement, swipe, keystroke or dead link can be captured, indexed, aggregated and analyzed. Thanks to Machine Learning, any deviations from a normal pattern can be identified and recognized as an incident or an anomaly. .

Likewise, aggregated systems’ metrics are nothing but data. And again, it’s a lot of data. Because although ultimately you just want your application to be reliable and easy to use, what you’re actually interested in is tracking performance and availability issues. Which, in turn, means you want to be able to monitor issues and errors impacting a specific page, offer, browser, feature, transaction, etc. In other words, you want to have a high enough level of granularity, that will enable you – when alerted to an anomaly – to segment the data and look at all the possible permutations in order to pinpoint the origin of an issue.

LET MACHINES DO THE HARD WORK

To put this into perspective, consider the state of traditional digital analytics as they exist today. They’re incredibly efficient for processing all of the data that your users are creating on a daily basis, but extracting the valuable insight hidden beneath still largely requires that ‘human element’.

Until today, business analysts needed to know – or at least estimate – what constituted ‘normal’. In other words, they had to define thresholds that, when breached, would trigger business alerts and processes.

This is no longer the case. Business Analysts can now expect technology to inform them when things go wrong, even for scenarios they had not foreseen or predicted. Again, it all comes down to technology being able to learn what ‘normal’ behaviors and patterns look like, and notify us when anomalies take place.

THERE ARE 5 STEPS TO AUTOMATING THE DETECTION OF ANOMALIES.

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LET MACHINES DO THE HARD WORK

1. COLLECT THE METRICS, AND DO IT AT SCALE.

As mentioned earlier, granularity is key here: you’ll want to collect everything. Because the larger the number of metrics you collect, the more permutations the system will be able to analyze, allowing it to understand what a normal behavior is for each one of the permutations.

Specifically, your digital analytics solution needs to be able to collect all of your digital metrics – from the client side and server side – in order to correlate technical issues that happen on your servers with behavioral issues encountered on your digital channels. Capturing and recording 100% of your digital metrics is critical if you want the system to truly understand patterns and teach itself what normal behavior is.

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LET MACHINES DO THE HARD WORK

2. LEARN HOW ‘NORMAL’ USER BEHAVIOR MANIFESTS ITSELF AS DATA

This learning must happen in the stream of data, otherwise insights can’t be in real time.

In order to learn what normal behavior is, your digital analytics tool needs to be able to take into account seasonality. In every industry, seasonality patterns will be different, but your analytics system needs to identify this seasonality in order to avoid alerting you each time there’s a peak in one of the metrics. Over time, its machine learning capabilities will understand daily, weekly and yearly patterns, and beyond.

It also needs to analyze different types of metrics – because not all metrics behave according to the same signal. Signals can be smooth, irregular, multi-modal, discreet, etc.

But if your digital analytics Machine Learning capabilities can only recognize one type of signal, they will generate a lot of false alerts, and very quickly won’t be regarded as reliable.

Lastly, to better understand normal behavior, your system will have to adapt to changes. While a given data pattern could be considered normal in one instance, any number of external changes could quickly establish a different pattern as the ‘new normal.’ Your system needs to be able to identify these changes and quickly recognize the new patterns.

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LET MACHINES DO THE HARD WORK

3. LEARN WHAT ABNORMAL BEHAVIOUR IS.

At first, any Machine Learning technology being deployed will identify a lot of anomalies. However, it doesn’t mean that all of them are important. You want your system to actually ‘reduce the noise’ and draw your attention to only the important anomalies. This involves giving a score to each anomaly by learning the behavior of the anomalies themselves and grading them on a scale from 1 to 100 representing their significance. Is the anomaly a deviation of 2% or 20%? Has the abnormal behavior happened for a few minutes and then returned to normal, or has it been happening for a few hours or days? Scoring is key to helping you focus on the important anomalies.

4. CORRELATE BETWEEN METRICS.

This will help you understand the context of an anomaly and identify its root causes. Because you’re analyzing a huge number of metrics, when you find an anomaly in one of them, you don’t want to have to manually check all the other metrics in order to understand the root cause. The automation of anomaly detection will provide you the context of the anomaly by showing you all the metrics that are correlated to that anomaly.

5. PROVIDE FEEDBACK TO THE SYSTEM BY TELLING IT WHETHER THE ALERT WAS HELPFUL OR IRRELEVANT.

By doing so, you’re helping the system become more intelligent, and more attuned to your organization’s needs.

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THE SOLUTION

Solutions like Glassbox that leverage Machine Learning and advanced AI are incredibly efficient at automatically gathering the macro, or full picture, perspective of what is going on with your digital channels.

Also consider the fact that it is virtually impossible to see all of the possible things that can go wrong, even if you’ve been operating in your particular industry for years. Murphy’s Law has a funny way of rearing its ugly head at the worst possible time, but it’s also an unpredictable beast. These types of Machine Learning and AI-based products not only help you identify issues that were not predictable and cope with unplanned scenarios in a faster and more efficient way, but they actually use that insight to become more powerful moving forward.

The burden of insight is no longer one that falls on your shoulders. You no longer have to work hard to get the insights you need to make the best possible decisions moving forward. Instead, you can focus all of that attention on what matters the most: improving your relationship with your customers.

They say that ‘what doesn’t kill you makes you stronger’. In terms of Machine Learning and AI, the more appropriate phrase may be ‘what doesn’t kill you makes you smarter’.

Ultimately, applying AI and Machine Learning to your digital analytics tools is all about the timely detection of seemingly minor anomalies, which, when addressed promptly, can have a wide and positive impact on your business. By automating this process, you create a virtuous circle of ongoing improvements and fast forward your digital transformation. That’s what we call The Glassbox Effect.

Solutions like Glassbox bring with them the most important benefit of all: the ability for business executives to spend less time on looking for issues and more time on fixing them.

These advancements don’t merely deliver data, challenging you to devote the time to find the priceless insight hidden inside; they go the extra mile and deliver that priceless insight right to your desk with a pretty little bow on top. Moreover, thanks to Machine Learning, they actually get more effective over time. In an era rife with economic uncertainty and more competition cropping up with each passing day, the importance of something like this cannot be overstated. All of this is to say that a future where business analytics are transformed via Machine Learning and AI is a bright one indeed.

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