Why Analytics Are the Key to ROI with Unified Communications (UC)

The business case for UC should be built on improving productivity, and to validate that plan, IT will need performance metrics. This Insight Brief will explain how AI-driven analytics can help measure productivity, but also identify challenges IT will first need to address.

• Only 27% of U.S. users have a mobile UC client installed (source: Frost & Sullivan), and only 34% of U.S. users report using their mobile UC apps. (source: 451 Research)
• As mobile-centric digital natives come to dominate the workplace –the desk phone is becoming the last resort rather than the first resort for voice.
• Your UC deployment will not gain adoption, and productivity will not improve if you don’t have a plan to drive adoption.
• Employees aren’t asking for UC, they don’t know what the term means – IT needs to show them why UC is a better way of working.
• Data is becoming the currency upon which business decisions will be made.
• Mounting concern that the organization actually knows very little about its workers, and that IT knows very little about what actually drives productivity.
• Unified Communications initiatives fail because the rationale was based on cost savings. There will be some cost savings, but that’s the wrong reason to invest in UC.
• Buying decisions were often driven by what’s best for IT, but that approach won’t work here; instead, IT needs to focus outward on what workers need and what’s best for making the business more successful.
• Artificial Intelligence remains more hype than reality, and because it’s poorly understood –and poorly explained by vendors – businesses either rush into it driven by FOMO, or they sit on the sidelines until it’s more proven.
• Productivity has always been difficult to measure, but what’s not widely known is how today’s AI capabilities can bring new layers of insight that IT could not previously capture.

All businesses can benefit from Unified Communications, but its potential is often not reached because decision-makers don’t fully think things through. Unlike a phone system, where you know exactly what you’re getting, UC is a platform that enables better ways of working. However, your employees aren’t asking for UC, they don’t know what the term means, and they can’t see or touch it – the onus is on IT to show them why it’s better so they’ll adopt UC.

One reason why UC initiatives fail is because the rationale was based on cost savings. There will be some cost savings, such as consolidating the use of multiple conferencing services across the organization, but that’s the wrong reason to invest in UC. With legacy technology, buying decisions were often driven by what’s best for IT, but that approach won’t work anymore. Instead, IT needs to focus outward on what workers need and what’s best for making the business more successful.

When thinking along those lines, the rationale for UC becomes very clear. Employees struggle to work effectively with legacy communications tools – they want to be more productive, but really don’t know how. Furthermore, management needs them to improve productivity because they know that’s key to driving growth in a hyper-competitive marketplace. As such, the business case for UC should be built on improving productivity, and to validate that plan, IT will need performance metrics. This Insight Brief will explain how AI-driven analytics can help measure productivity, but also identify challenges IT will first need to address.

THE OPPORTUNITY – Leveraging Analytics to Improve Productivity

Artificial Intelligence remains more hype than reality, and because it’s poorly understood – and poorly explained by vendors – businesses either rush into it driven by FOMO, or they sit on the sidelines until it’s more proven. Both approaches carry risk for the business, and the starting point for success with AI needs to be defining clear use cases. AI is a highly adaptable framework of powerful technologies like Machine Learning and Natural Language Processing, but you’ll only get business value when applied to specific needs and use cases.

Productivity is a broad term, so it’s very difficult to measure, and that’s a big challenge for UC. Conversely, the power of UC comes from having a singular user interface where all modes of communication are seamlessly integrated. As such, it’s the ideal environment to capture data around how work gets done, and that makes UC an incredible engine to support analytics, which in turn will produce the metrics IT needs to show a great ROI. Never before have businesses had these capabilities, and by connecting the dots between UC and analytics, the power of these two together should be evident to IT.

The starting point is to identify use cases where AI can improve productivity, either by having workers adopt new capabilities for better or faster outcomes, or use applications more effectively so less time is wasted trying to communicate. To illustrate, consider the following examples:

• The success of UC depends on migrating workers away from using communications applications in a standalone fashion, and on to an THE OPPORTUNITY – Leveraging Analytics to Improve Productivity Insight Brief: Why Analytics Are the Key to ROI with Unified Communications (UC) 4 integrated platform – using metrics to track this migration provides the foundation for demonstrating the speed of adoption, and from there, to track how the efficiency of communications improves when using UC.
• Determining which communications modes – and mix of modes – are the most efficient for different types of tasks or team engagements – voice, text, email, video, etc.
• Assessing which applications best support productivity across different working environments, namely inoffice, home-based, and in mobile settings.
• Determining the strength or value of each worker’s inputs when working in teams, such as following or leading, quality of ideas contributed, level of participation, the efficacy of meeting, assigned tasks or milestones, etc.

How AI Enhances Existing Metrics

To various degrees, metrics around these use cases can be tracked by IT now without the aid of AI. These could come from network utilization data, but the dataset would be limited, and IT simply has higher priorities to manage. The UC space has never developed easy-to-use productivity metrics to validate the investment, and this gap isn’t about to be addressed by IT. To be fair, productivity has always been difficult to measure, but what’s not widely known is how today’s AI capabilities can bring new layers of insight that IT could not previously capture.

For example, by searching keywords for context within the company, AI could dramatically reduce the time needed to source information or documents, as well as organize it for use by a team. Another use case would be searching across all modes – voice, data, video – to identify the right subject matter experts when collaborating across multiple locations. A more advanced application would be sentiment analysis, where the intonation of speech, body language or style of writing would help determine how effectively each team member is contributing to the project, especially those working remotely.

In terms of the opportunity here, these examples show two things. First, since AI is data-driven, you can track metrics that you couldn’t capture before. In the absence of useful productivity metrics, this presents new possibilities for IT to measure the ROI on UC. Secondly, if you see value in these new metrics, this may reinforce a mounting concern that the organization actually knows very little about its workers, and that IT knows very little about what actually drives productivity.

THE CHALLENGE – Capturing Data to Drive Productivity Analytics

As businesses move along the digital transformation path, data becomes the currency upon which decisions will be made. Management is increasingly relying on data to prioritize investments for improving customer satisfaction, where to allocate marketing dollars, how much office space is needed, which types of people to hire or fire, evaluating employee performance, etc. When considering UC, it’s not hard to see how collaboration – and really all workplace activities – can impact these decisions.

The classic maxim that you can’t improve what you don’t measure fully applies here and most businesses simply have very little workplace data. One reason is that until recently, this type of data did not have much perceived value, so no effort was made to collect it. Perhaps more importantly, even if its value was understood, the tools didn’t exist to capture and analyze it. Technology certainly has evolved, and UC has a big role to play by virtue of centralizing so many flows of digitized communication and information sharing. Applying AI to this creates a great combination to capture exactly the kind of data needed to drive the above decisions, so all the pieces are in place. That said, it’s not enough just to deploy UC, and suddenly have a perfect, complete dataset.

Learn more about incorporating productivity analytics into your business

The combination of UC and AI-driven analytics checks all the boxes for capturing data to improve productivity and provide a solid ROI, but there’s one critical piece missing. Aside from the broader challenge cited earlier – getting workers to adopt UC – once UC is deployed, you won’t be capturing very much of the most valuable form of data – voice.

When it comes to real-time communication, voice is the richest mode, and outside of in-person interaction, it is usually the preferred mode. No other mode conveys as much meaning or produces the most immediate outcomes in terms of translating ideas into actions. Given its frequency of use, along with effectiveness for getting things done, it stands to reason that voice-based data can yield valuable metrics for improving productivity.

Until recently, the vast majority of voice activity was telephony-based, usually running over the PBX. For a variety of reasons, desk phones have lost their primacy in the workplace, and the downward trend of usage will only continue if not accelerate. Even though telephony vendors still report strong IP phone shipments, usage is clearly declining. As mobile-centric digital natives come to dominate the workplace – they’re at 50% now – the desk phone is becoming the last resort rather than the first resort for voice.

While this broad pattern is not about to change, there are still many job functions and work modes that rely heavily on desk phones, so there certainly is data there to be captured. The same can be said for VoIP activity using softphones, and while adoption has never been great, PC-based calling still produces valuable voice-based data.

As such, fixed-line modes of telephony – PC and desk – can support your analytics efforts to some degree, but the biggest challenge lies with mobility. Everything seems to be mobile-first now, and there’s little doubt this is the preferred mode for voice, at least among digital natives.

At face value, it would appear that a mobile client on a UC offering would be the gateway to capturing all this rich data.

The reality is quite different, and IT needs to recognize this for analytics to have the hoped-for impact to improve productivity. First, with the advent of BYOD, employees are using their personal devices in the workplace, and this makes it difficult to capture data from work-related conversations. Secondly, even when UC is deployed, the adoption of mobile apps is quite low. According to Frost & Sullivan’s Hosted IP Telephony study, only 27% of US users have a mobile UC client installed, and that level was only slightly higher in the EU at 33%.

Another study from 451 Research had similar findings, with only 34% of US and UK users reporting using their mobile UC apps. So, even when you have the right tools in place and in use, you’ll only be capturing a fraction of voice activity. The overall takeaway here is that voice activity isn’t declining; rather it’s just migrating to new places. Desk phones still have utility in the office, but for many workers, the mobile phone is more convenient – even when at their desks. This may not sound like a promising scenario for voice analytics, but there is a way forward.

THE STRATEGY – What You Need to Do

This Insight Brief may be technology-focused, but the value of technology investments is primarily based on having a plan, and the ideas you bring to the table when mapping out a strategy. Your strategy needs clearly-defined objectives around which the use cases for AI can be built. That has been the focus of this analysis, and the conclusion rests on steps you can take to address the challenges outlined above.

Think about the best-case scenario – the end-goal you should be working towards when thinking about UC. If all your workers used their mobile UC app for all their workplace voice communication, IT would have an incredibly valuable stream of data that ML and NLU could finetune to produce the best productivity metrics you’ve ever seen.

This may be an unrealistic expectation, but it’s the way you need to be thinking, otherwise, your UC deployment will not gain adoption, and productivity will not improve. Voice isn’t the only communications mode used to be productive, but it’s the most widelyused, especially for the most important needs. Other communications modes will provide valuable data inputs for UC analytics, but voice inputs will be the richest. To get the best results possible for capturing voice inputs, here are three things you need to do:

1. Improve The Desk Phone Experience

The desk phone will never regain its primacy, but there are many use cases where it is superior to all other options for voice. Telephony is an integral THE STRATEGY – What You Need to Do Insight Brief: Why Analytics Are the Key to ROI with Unified Communications (UC) 9 part of any UC platform, and the more desk phone usage you can drive, the richer your productivity metrics will be. One way to do this is to invest in better phones with updated features like touch screen, color display, video support, HD, etc. You don’t have to do this with every endpoint, and to start, just target workers who regularly lead team-based projects.

2. Rethink Your BYOD Policies

Workers aren’t about to change their mobile habits, but clearly, you’re losing a lot of valuable data when business communication occurs on personal devices. Some companies mandate that employees must use their business device for all workrelated communication, but that’s difficult to enforce. IT can do things to ensure that work-related resources can only be accessed when logged in on a business device or a business-based client on a personal phone. Again, this is less a technology problem as it is about devising tactics that support a strategy for capturing as much communications data as possible when using mobile devices.

3. Get the Best Mobile UC App Possible

Mobility has long been an after-thought with UC, and given how central this is to capturing the data you need to create intelligent productivity metrics, you need to be strategic here. The starting point is to make this a dealbreaker when evaluating UC vendors. Find out how central this is to their value proposition – and if it is, they should be able to provide proof points and references from customers showing high adoption rates among end-users.

At your end, you also need to think about ways to motivate or even incentivize workers to use their mobile UC clients. For starters, IT should make this central to the messaging and training provided when deploying UC across the organization. Gamification shouldn’t be ruled out either – there is an endless variety of ways to use fun and friendly competition to drive new behaviors. Prodoscore has seen an 800% increase of adoption rates for UC mobile app deployments that have been stagnant of use after years of low adoption.

J Arnold & Associates, an independent technology analyst practice, produced this Insight Paper, which was sponsored by Prodoscore. The contents herein reflect our conclusions drawn from ongoing research about AI and the collaboration market. For more information, please contact us by email: jon@jarnoldassociates.com.