Collaboration platform providers, like Microsoft and Cisco for example, promise great insights using the analytics tools they offer. But is the big data gathered useful? I will moderate a virtual birds-of-a-feather session during Enterprise Connect next month to discuss how organizations use unified communications (UC) analytics and what additional capabilities would be valuable.
A couple of years ago, we started to see a shift in the use of collaboration tools for one-on-one meetings. By replacing the standard phone call (and “phone tag” interactions), the meeting tools allow users to show up when ready, share screens, add video if desired, have meetings appear on calendars, etc. Then the pandemic hit, which forced increased adoption of collaboration. According to Gartner, collaboration tools usage jumped from 55% of users pre-pandemic to 79% as of August of 2021, a 44% change (increase). Concurrently, many people jumped from occasional use to constant use as working from home (WFH) drove usage rates.
What Works for One Doesn’t Work for All
Merging analytics from multiple tools may be difficult but critical. In this earlier No Jitter post, I questioned the completeness/accuracy of the data, especially for staff that uses more than one collaboration tool. One researcher claimed that nearly 42% of companies use more than one collaboration tool. But what happens when we return to in-office meetings and aren’t using a collaboration tool to facilitate a group discussion or a one-on-one session? There’s a danger in jumping to conclusions without in-depth understanding.
Most products offer statistics , such as the average number of messages exchanged per month and the average number of collaborators users per team. But to me, this feels like “so what” numbers. Do you chide those below an average to encourage more usage? Or are heavy users flooding others with unnecessary messages? Are teams too large or too small? What do such statistics have to do with actual productivity?
In any case, what works for one successful employee is not automatically the best approach for all employees, even those with the same job classification—especially if some users favor personal meetings versus a collaboration tool.
The big challenge is comparing the UC analytics and productivity of your high performers to draw accurate conclusions to help increase productivity for others. Rather than making wholesale conclusions, the data can guide an in-depth exploration of the link between the statistics and outcomes. One company found that the most successful sales team frequently collaborated with the product management team. As a result, the sales team was more informed about product capabilities, production issues, and roadmap plans—which leveraged into better interactions with customers. This finding was followed by specific examples and training, rather than a simple mandate to the other sales teams to “collaborate more” with the product management team.
The Starting Point: UC Collaboration Platforms
The analytics available within UC collaboration platforms may be a starting point. Data gathered from these tools may also be integrated with other business information, such as accounting data, customer relationship management systems, etc.
Real-time analytics may be a valuable tool eventually. Imagine an AI-based speech and video analysis tool picking up on reactions (sentiment) as the meeting progresses. If everyone in the meeting had video, could the analytics count the number of eye-rolls? The apparent agreement or confusion over a controversial statement? Or the percentage of multi-taskers?
The analytics providers claim most of the data is typically de-identified to prevent misuse by those inclined to micromanagement. But the source data still contains details. A CCS Insight employee survey report from January indicated that nearly half of workers (46%) in the U.S. and Europe are uncomfortable with the prospect of an employer monitoring their productivity via digital tools when working from home. “We’re opposed to workforce surveillance,” Microsoft said, which has a comprehensive suite of analytical tools.
Statistical awareness can influence group behavior when no personal identifications are present. For example, years ago, I installed a call accounting system for a client. After 90 days, the statistical results, including average call length, were shared with employees without identifying individuals. The following month, average call time significantly dropped as employees became more self-aware of their behavior—knowing it was being captured and analyzed. But the employees also knew that individual statistics existed and detested the personalized scrutiny.
UC statistics providers tell us what a great tool they offer. Instead of putting my faith in the salesperson telling me about their sliced bread, I would rather hear from those making toast and eating sandwiches. Thus, our Enterprise Connect session is for businesses using analytics, planning to, and even those wary of analytics. We plan to discuss specifics, such as:
- What types of information did you get that otherwise were not available to you?
- Which analytics are the most valuable?
- What types of actions or changes did you put in place based on the analytics?
- Were measurable results achieved?
- How were analysis results presented to staff, and how did they react?
- What type of analytics do you find misleading?
- What type of analytics do you wish you could get but don’t yet?
I hope you can join us on Wednesday, March 23, at 1:30 p.m. ET!