The Science

The global leadership function started to measure team effectiveness through a structured questionnaire co-developed with McKinsey, Harvard Business School, and the in-house Research team. Beginning with the 50 highest performing teams, the research group captured 360 feedback on the team, from those inside the team, and from key stakeholders who knew the team well.

The research group ran a statistical factor analysis which identified 8 statistically significant factors which demonstrated moderate to strong positive relationships between team behaviours and team performance.

Next, the reliability of the diagnostic tool was reviewed and validated by a global business psychologist company over 6 months to find out the level of systemic variation within the tool alongside testing its face validity. With validity and reliability established, the diagnostic was rolled out across 500+ teams as part of its leadership transformation journey.

Following the diagnostic development and leadership programme delivery, the research group looked for correlations between the financial performance of 1000+ teams and the results from the team diagnostic. The analysis from the team diagnostic showed exceptional performance was not a naturally occurring phenomenon for most teams, with only one in eight teams assessed as high performing.

The financial metric used as a proxy for economic performance was percentage of on-target annual bonus awarded to the team leader (from CEO and down 4 hierarchical layers) sourced from Reward and Pay systems. The critical insight from the multiple regression analysis was that top quartile teams delivered a 22.8% higher economic impact, when compared with teams where none of the habits were above the median. This uplift was almost +4X the +6% uplift in economic performance achieved by leaders who attended conventional leadership programmes.

These insights were the catalyst for Rob to establish Meta Team and refine the high-performance team model, refresh the diagnostic, the question set, and build out the practices needed by teams to unlock their next level of performance.

Since 2016, our work in the field has helped us build out Meta Team’s global norm group including teams from Australasia, Southeast Asia, South Asia, North Asia, Africa, the Middle East, Europe, North America and Latin America. The types of organisations in our norm group ranges from early stage FinTech’s based in Sydney, eCommerce Sales Leadership teams in the US, IT delivery teams in Hungary, global functional leadership teams from one Asia’s biggest bank, alongside other global FMCG, Pharmaceutical and Asset Management leadership teams.

The diagnostic data we have captured from c20,000 responses has allowed us to strengthen, simplify and re-baseline Meta Team’s diagnostic tool. The assessment is now shorter, more focused and time-efficient by eliminating two-thirds of the items where correlation was <0.3. The resulting diagnostic tool seeks feedback on 32 team micro-behaviours which map directly to Meta Team’s model of high-performance teams.

The greatest breakthrough from Meta Team’s extensive field work has been our growing understanding about how teams take their performance to the next level at speed. Time and again, we found teams made significant and fast performance gains when their team journey matched their team type, the team pathways was based upon the results of their diagnostic, and was sequenced on robust logic so they tackled their priority habits in an optimised order.

Meta Team’s first response to these breakthrough insights was developing five team types (Leadership teams, Delivery teams, Project & Agile squads, Advisory teams, Other teams). Starting with variance analysis of our 20,000 responses, we have built statistically valid norm groups for each team type where we hold sufficient data.

With the team types and norm groups established, Meta Team then set about building an algorithm to sequence a unique journey for every team. The algorithm uses each team’s diagnostic results, the norm group for their team type, and our codified intelligence on the optimal sequencing of Meta Team’s 8 habits. As our data set grows we are refining, enhancing and developing further archetypes – next in our pipeline are Start-up teams and Sales Leadership teams.

We’ve found the integration of the diagnostic results, team types and sequencing algorithm accelerates every team’s performance, by matching the priority team habits with the team’s bright spots and areas for growth. Each team programme we run makes impact at speed, consistently achieving significant changes in team cohesiveness, candour and collaboration.