5 Must-Have AI Capabilities Enterprise Teams Need for Modern Website and App Optimization

Liam Burns
Written by Liam Burns
June 05, 2020

Your analytics data is arguably one of the biggest “make or break” factors for running a successful digital optimization program. In fact, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable as a result.

But it takes more than just collecting and analyzing data to truly move the needle on engagements and conversions. Your teams need highly actionable insights that leave no stone unturned during optimization efforts.

Here’s the problem: 42% of CX professionals claim their analytics systems don’t meet current needs, while others only partially match expectations. This raises a concerning trend that many enterprise level digital teams don’t have a full view of how to optimize websites or apps due to limited capabilities of industry-standard solutions.

Which aspects of your own web analytics and digital tools are holding back the productivity of your optimization team?

The Challenge of Obtaining Qualitative Customer Insight at the Enterprise Scale

Many of the widely adopted digital solutions bring a range of foundational data-driven insights to your practitioners. Unfortunately, they also possess some severely limiting intelligence gaps which restrain website or app optimization success.

After surveying over 300 companies, 69% reported they use web analytics while 48% use session replays or heatmaps, yet less than half (48%) found each of these tools ‘very effective’ at measuring online experiences.

You may think of web analytics as the backbone of your data-driven optimization strategy. Industry standard solutions like Google Analytics or Adobe Analytics provide your analysts baseline results like conversions, engagements, or traffic necessary for evaluating the performance of your digital properties at scale.

However, your end-users will run into problems identifying the specific experience issues behind this data, which hinders their ability to address specific issues. For instance, if your web analyst notices a dip in engagements on your product pages it’s clear there’s an issue, but probably not obvious why. Here’s where your investigative tools come into play.

With digital tools like session replays and heatmaps, analysts have a ground-level view into experience issues behind your web analytics data. Session replays enable your team to playback and review specific user sessions to definitively identify poor experiences impacting your web analytics metrics. Meanwhile, your heatmaps bring an aggregated view of the trends and patterns from user journeys across your site.

But if you’re an enterprise with millions of monthly user sessions, your team will quickly become bogged down by the manual and time-consuming nature of these tools. Imagine trying to identify the poor experiences causing product page engagements to drop-off by reviewing millions of user sessions – that’s nearly impossible. This presents a scalability and efficiency drawback for many organizations.

While these methods are foundational in any optimization program, your web analysts and optimizers will struggle to develop qualitative analysis at scale to address user experience (UX) flaws. This dynamic contributes to just 3% of digital analysts saying they can act on all the customer data they collect, while 21% say they can act on very little of it.

To combat the limitations of web analytics and digital tools, enterprise organizations should take advantage of modern analytics powered by highly advanced technologies like artificial intelligence (AI) and machine learning (ML).

AI Closes the Gap, Providing Qualitative Insight at a Quantitative Scale

AI and ML are growing annually at 36.65% and 19.7% respectively, and it’s for good reason.

With AI performing data collection and experience analysis across every user session automatically, your analysts and optimizers can operate with proficiency and velocity. These capabilities have convinced 64% of CX leaders to already use or plan on implementing AI and ML technologies.

If your organization fails to take advantage of modern analytics powered by these innovations, your practitioners will face unnecessary hurdles undermining their optimization efforts. This puts your organization at risk to falling behind competitors, as 84% of executives and analysts view this level of intelligence as a competitive advantage.

Just imagine your web analysts and optimizers cutting out lengthy hours of tedious data analysis of your session replay data and reducing endless rounds of UX testing to just a single provision. This is a glimpse into your AI-powered digital optimization team.

These technologies are no longer nice-to-have capabilities, they’re becoming must-haves for successful website and app experiences.

How AI and Machine Learning Enhance Website & App Optimization Projects

Data is now your ultimate competitive weapon in CX, and highly intelligent analytics extract more impactful data for your practitioners to leverage. Currently, insights-driven organizations grow at an average of more than 30% each year, and by 2021, they are predicted to take $1.8 trillion annually from their less-informed peers.

Equipping your web analysts and optimizers with analytics featuring automated data analysis and highly prescriptive insights will simplify digital optimization projects. But what specific criteria are essential for modern digital experience analytics?

Here are 5 must-have AI and ML capabilities your team needs from their analytics:

  • Automatically analyzes millions of user session data points
  • Pinpoints critical user sessions impacted by experience quality
  • Measures digital behaviors to evaluate the user’s state of mind throughout their journey
  • Instantly scores every user session to definitively quantify user experiences
  • Consolidates analytics data across all integrated tools into a single report

According to Forrester’s TEI Study of Decibel, digital experience analytics with these modern features can save end-users 20,000 hours of analysis and reporting, and their highly granular insights drive a 449% ROI.

Analytics with AI and ML shoulder the heavy lifting for your digital team, which enables new levels of efficiency for website and app optimization programs. Key business stakeholders will take notice, as 72% of decision-makers believe AI will enable people to focus on more meaningful work while 65% of workers believe AI frees them from menial tasks.

However, deploying digital experience analytics isn’t enough to win in customer experience. Your digital team needs to constantly adapt and update their digital strategies as user behaviors and expectations continue to change.

Keep your team fully prepared with your copy of the 9 New Rules of Website and App Optimization to update and nearly perfect your digital customer experience methodology.

Topics: Analytics, Behavior, Data, Digital Experience
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