This webinar explores achieving effective client feedback programmes by blending quantitative and qualitative research techniques. Ethan Murphy explains how combining both data types provides complementary insights, whilst Susie Green shares Bird & Bird’s transition from predominantly quantitative feedback to a balanced approach.
Market research reveals professional services firms are shifting towards more frequent, integrated feedback methods rather than annual surveys, enabling prompt issue resolution and opportunity identification. Online surveys gain popularity by incorporating both quantitative and qualitative questions, enhancing insight depth.
Key findings demonstrate that firms effectively blending data types gain richer, more actionable insights. However, technology integration challenges persist, with many firms lacking integrated dashboards and advanced analytics capabilities. The discussion highlights employee feedback as a crucial yet underutilized resource for understanding client service challenges.
Advanced analytical techniques like key driver analysis and segmentation offer significant potential for targeted client strategies but remain underutilized across the sector.
This webinar explored how professional services firms can optimize their client feedback programs by effectively blending quantitative and qualitative research methods. Whilst in-depth interviews remain the most popular data collection method, leading firms are increasingly embedding both approaches within single tools—adding qualitative follow-up questions to surveys and quantitative rating scales to interviews. Bird & Bird shared their transformation from biennial "big bang" surveys to an always-on model that combines interviews and surveys at multiple touchpoints (post-pitch, post-project, mid-matter), with all data integrated into a single reporting platform and forward-looking questions about share of wallet and future challenges driving partner conversations. The session highlighted novel collection methods including client panels, employee feedback surveys to identify root causes of issues, 360-degree approaches with mutual feedback, and forms to capture informal insights from emails and conversations. However, CLIMB benchmark data revealed significant technology gaps—less than half of firms use central dashboards for tracking KPIs and fewer than 20% leverage AI text analytics for qualitative data despite the manual burden. The speakers demonstrated advanced analytical techniques including key driver analysis to prioritize improvements, segmentation to create actionable client personas, and AI tools that enable deeper qualitative exploration whilst dramatically reducing manual coding time.
Most firms still use traditional methods (in-depth interviews remain most popular), but leading firms increasingly embed both approaches within single data collection methods—adding qual questions to surveys (e.g., "why this rating?") and quant scores to interviews (CSAT, NPS).
Moved from "big bang" biennial surveys to an always-on model combining interviews and surveys at multiple touchpoints (post-pitch, post-project, mid-matter), integrating all data into a single reporting platform and using forward-looking questions like share of wallet and future challenges to drive partner conversations.
Client communities/panels for proposition testing, employee feedback surveys to identify root causes of client issues and practical solutions, 360-degree approaches where both client and law firm teams give mutual feedback, and forms to capture informal feedback from emails and conversations.
CLIMB data reveals less than half of firms use central dashboards for tracking KPIs, and fewer than 20% use AI text analytics for qualitative data despite the manual burden of analyzing interview transcripts—representing a major missed opportunity for scaling qualitative insights.
Key driver analysis (regression models identifying which factors most impact satisfaction), segmentation and cluster analysis (creating client personas like "opportunities" vs "retainers"), and AI tools for automated theme/sentiment analysis that enable deeper qualitative exploration without the manual coding burden.