Podcast ¦ CX Files: Steve Mosser – Sensée – Flexible WFM & Total Footprint Optimization

Access the full podcast series here
Summary:
This podcast episode features an interview with Steve Masa, the Chief Executive and Head of Innovation at Sansei, a UK-based DPO company that focuses on work-from-home solutions. The conversation revolves around workforce management and the development of flexible scheduling, particularly in light of the COVID-19 pandemic. Sansei’s approach, termed “total footprint optimization,” involves breaking down work shifts into smaller time chunks and offering agents the flexibility to work from home. This allows for greater efficiency and cost savings for clients while providing job security and training investment for agents. The podcast also discusses the limitations of gig CX models and the importance of investing in agents for sustainable workforce management.
Key Points:
– Sansei’s total footprint optimization approach focuses on workforce management and resourcing optimization, offering micro staffing and flexibility in scheduling to maximize productivity.
– The company provides clients with highly productive resources by breaking down eight-hour shifts into smaller time blocks and scheduling them only when necessary.
– Total footprint optimization also involves holistic optimization of the working environment, from inside the company’s contact centers to home agents, based on clients’ specific needs and pain points.
– Sansei’s approach offers continuous benefits to organizations, allowing for elastic resource allocation and scalability to support clients’ demands throughout the year.
– The company emphasizes the importance of providing full-time contracts and investment in agent training, differentiating their approach from gig CX models, which may lack sustainability and incentivize inefficiency.
– Sansei’s methodology not only benefits clients by increasing efficiency but also provides agents with work-life balance, job security, and better scheduling satisfaction.
– Traditional workforce management, which focused on filling desks and achieving efficiency within the office environment, is no longer effective post-pandemic.
– The COVID-19 pandemic highlighted the need for new tools and methodologies that cater to the flexibility and work-life balance promised by work-from-home models.
– Sansei’s approach enables clients to have aligned teams with efficient scheduling and workforce planning while maintaining steady-state operations and minimizing scheduling inefficiencies.
– The podcast emphasizes the need for a paradigm shift in workforce management, moving away from traditional models and adopting more flexible and efficient approaches tailored to the specific needs of clients and agents.
Key Statistics:
– Sansei’s micro staffing approach provides 16 times more flexibility than traditional workforce management methods.
– Their annualized and lunarized contracting model allows for elasticity of resource allocation, accommodating different demand patterns based on day of the week, week of the month, and month of the year.
Key Takeaways:
– Sansei’s total footprint optimization approach to workforce management offers clients increased efficiency, cost savings, and flexibility, while providing agents with full-time contracts, training, and job security.
– Gig CX models may lack sustainability and incentivize inefficiencies. Sansei’s approach focuses on creating win-win scenarios for both clients and agents.
– The traditional workforce management approach that focused on filling office desks is no longer effective in the post-pandemic era.
– The COVID-19 pandemic highlighted the need for new tools and methodologies that cater to the flexibility and work-life balance promised by work-from-home models.
– Sansei’s approach allows for aligned teams, efficient scheduling, and improved workforce planning, leading to better colleague and team engagement.
– A paradigm shift is needed in workforce management to adopt more flexible, efficient, and tailored approaches based on the specific needs of clients and agents.

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