Based on the eight research principles summarised so far and to meet the requirements of the three stakeholder groups (outlined in the second diagram below), CoachBot collects and processes the following team data.

Antecedent conditions

Not all of the CoachBot functionalities may be relevant for every type of team. By asking a few simple questions about the type of team, CoachBot can ensure teams focus their efforts where they’ll have the biggest impact. A team’s antecedent conditions, such as poor team design, can reduce the impact of CoachBot interventions, even when these are targeted and well executed. Rather than taking these as inevitable circumstances, organisations have a duty to determine whether the basic structural conditions that enable team coaching interventions to be fruitful are in place.

CoachBot processes and behaviours

These data are collected to ensure CoachBot interventions (foundations and habits) are having the desired effects; building the foundations and habits of high performing teams. 

Direct impacts of CoachBot

These data are collected to prove that CoachBot interventions (foundations and habits) have a positive impact on psychological safety and teamwork engagement, which have been proven as moderators of team performance.

Market factors and organisation factors

CoachBot doesn’t collect these data as standard. These are shown in the model above as a reminder that teams do not exist in a vacuum; they exist in real-time amongst fluid organisational circumstances and market factors, making the measurement of any coaching intervention a challenge.

Team performance 

CoachBot collects data on team members’ perception of their team’s performance as a unit. More robust quantitative data on team performance cannot be collected in product but can be collected and analysed internally by any companies who wish to conduct a deeper research.

Proving the impact of team coaching interventions is hard

As mentioned, measuring the impact of any type of coaching intervention is far from straightforward. Now consider measuring the impact of team coaching interventions, that are determined jointly by organisation, team and individual factors. For this same reason, amongst others, improving team performance is not a simple, linear journey.

How much data is the right amount of data?

The Saberr team iterates on CoachBot’s design and data capture on an ongoing basis to deliver value to three stakeholder groups:

  1. Instant value to end-users; the teams using CoachBot. How do the datasets collected help teams improve their performance?
  2. Organisational value to buyers; the central admins implementing and monitoring CoachBot.How does the data collected help buyers understand the impact CoachBot is having?
  3. Research value to Saberr.  How does the data enable us to remain at the forefront of team coaching technology? 

These different drivers mean Saberr faces trade-offs that need to be navigated carefully. Saberr has potential to collect a wide range of robust team data (for example, we could ask a dozen questions to measure psychological safety alone). However, we also place great focus on a team’s user experience of our products. After all, if teams don’t want to use our product we will not have a sustainable data collection process.

Become part of the future of teamwork 

The data collected in CoachBot will enable Saberr, over time, to understand a team’s needs before they become problematic and provide the best solution given that team’s context.

In order to continue generating new insight and analysis, we welcome partners for a unique project to both improve and understand team performance better. Combining performance coaching with cutting edge research this project would both improve performance of teams and generate insight understand drivers of team dynamics in your organisation.

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