Artificial intelligence (AI) technology presents businesses with an unmissable opportunity to deliver positive digital experiences to their customers, but in order to do so – regardless of sector – organisations must first understand their digital audience and how their digital channels are performing.
The way to do this is through data collection and analysis, both of which are growing at an exponential rate; by 2025, the global datasphere is predicted to grow to 163 zettabytes – ten times the 16.1 ZB of data generated in 2016. This ever-growing quantity of data will unlock new business opportunities, and customer experiences, by enabling organisations to understand more about customer behaviour and identify potential pain-points and areas of improvement.
However, this volume of data comes at a cost in terms of management and agility. Traditional models of web and data analytics are no longer viable in this new and expanded digital ecosystem. The vast and ever-growing amount of data captured has the potential to obscure the valuable and timely insights that might be gleaned from them, causing business leaders and data analysts to fail to identify solutions to key issues relating to Customer Experience.
Thankfully, this is where AI steps in. Alongside machine learning, these technologies form the basis of a new generation of digital analytics solutions. Put simply, AI is a process that enables computer systems to be able to perform tasks that would previously require human cognitive ability and interaction. Machine learning is a subset of AI that denotes the way in which computer systems can automatically learn and improve from experience, without being programmed to do so.
By linking these capabilities to the issue of data collection and analysis, keeping in mind the size of the current and future datasphere, organisations can convert data sets into actionable insights that reveal customer online behaviours and improve digital experiences.
Every click, mouse movement, swipe, keystroke, and dead link can be captured, indexed, aggregated, and analysed. And thanks to machine learning, anomalies can be recognised in real-time and flagged to IT teams, business executives and customer service representatives. Moreover, these anomalies can be correlated to business impacts, and IT fixes can be prioritised according to the revenue loss attached to them.
Compared to the state of digital analytics today, these advances are revolutionary. Current processes are efficient in data collection; however, extracting actionable and valuable insights from this data still requires human intervention. The same is true for alerts to anomalies in the digital customer journey.
Formerly, business analysts would be required to know what constituted a ‘normal’ customer journey, and be aware of thresholds that, when breached, would trigger pre-defined alerts and processes. In an AI-enabled ecosystem, business analysts can rely on technology to alert them to customer pain-points and system anomalies. The ability for technology to do so comes down to AI and machine learning, which learn what ‘normal’ customer journeys look like.
These alerts are essential in improving customer experiences, as issues on the webpage or mobile app are flagged with the IT team, which can then correct the problem, and customer service representatives, who are able to provide up-to-date advice and reassurances to any customers who get in touch.
At Glassbox, we have set up a five-stage process for automating the detection of anomalies using AI:
Collect metrics at scale
Data granularity is essential when it comes to setting up automatic alerts; the larger the number of metrics a business can collect, the more permutations its AI-enabled system will be able to analyse. In order to correlate technical issues affecting servers with CX issues affecting digital channels, a successful digital analytics solution must be able to collect metrics from both the client and server side.
Understanding normal data behaviour
It is essential for digital analytics to be set up to account for seasonality. No matter the industry, seasonality will affect data patterns on digital channels, and so effective analytics systems need to identify this seasonality in order to avoid sending unnecessary alerts each time there is a peak in one of the metrics. This is an area in which machine learning comes into its own by learning to take into account daily, weekly, monthly and yearly patterns.
What does abnormal behaviour look like?
For an anomaly detection system to operate correctly, it has to reduce the noise and only draw attention to the important issues. It is recommended to assign a score to each anomaly, for instance grading them on a scale of one to 100 based on the level and duration of the deviance, thereby allowing organisations to focus only on the anomalies that matter.
Correlate between metrics
Since the datasphere is growing larger and larger by the day, it would be time – and labour – intensive to manually check each and every metric when an anomaly is found in one of them. An automated anomaly detection system will analyse the context of an anomaly by correlating the metrics.
Finally, to constantly improve the process, provide feedback to the detection system by telling it whether an alert was helpful or irrelevant. In doing so, businesses are able to help the system improve and become more intelligent and attuned to the needs of the organisation.
Advancements in AI go further than enabling businesses to collect and harness more data; they bring tangible improvements to the digital customer experience. By automatically detecting anomalies throughout all digital channels, AI-enabled systems can deliver priceless insights that become smarter and more accurate over time.
The usefulness of this cannot be overstated, especially when you consider the unprecedented rate of growth of the global datasphere.