Staffing is one of the most critical and challenging aspects of merchandising. You need to balance the demand for customer service, sales performance, and operational efficiency with the availability, cost, and productivity of your staff. How can you use data analytics to optimize your staffing levels and achieve your merchandising goals? Here are some tips to help you.
Before you dive into data, you need to have a clear idea of what you want to accomplish with your staffing strategy. Do you want to increase customer satisfaction, conversion rates, or average order value? Do you want to reduce labor costs, turnover, or shrinkage? Do you want to improve inventory management, merchandising standards, or store layout? Define your specific and measurable objectives and align them with your business and merchandising goals.
Once a business’s goals have been communicated, labor budgets ( set by store position) should be established to assist in meeting those goals. Usually Sales volume/revenue will dictate fluctuations in labor expense. However managers can employ different strategies to manage labor: *cross train personnel for different roles ( stock/sales personnel as cashiers) *utilize split shifts based on store sales activity to maximize store coverage to meet customer demand. *utilize over night shifts for restocking, processing replenishment deliveries, merchandising etc *for labor shortages utilizing management for certain tasks, employing temp agencies, offering incentive pay/vacation days to existing staff to increase staff coverage
An example I have used is to first survey your team on what do they like to do, what are they best at doing, and what would they like the opportunity to do. With this information it can help you strategically assign staff based on what is most important for that job or task. For example, a task that has a lower priority but someone has expressed wanting to learn that area may be a great opportunity to expanding knowledge while giving opportunities for growth in your team. In another example, you may ha e a high priority task were you need to assign someone that is already skilled in that area to ensure deadlines or quality is met. Leaders underestimate how much data you can gather from just asking your people!
Ter objetivos claramente definidos é o primeiro passo para garantir uma execu??o de qualidade. Quando você tem que definir a estrutura do seu time, é fundamental que você tenha em mente quais objetivos você precisa atingir e come?ar suas análises e planejamentos com essas prioridades.
Knowing about your goal is prerequisite before deciding on your staffing requirements. Skill level of your requirement or automation component can be decided with reference to your goal. Past data can give you a reference point example if you increased your staffing requirements to x(Staff) what outcome you got on your deliverable in past.
Optimizing staffing levels through data can boost your short-term profitability and ensure your long-term growth by proactively engaging the talent you'll need to meet your future strategic needs. The growing availability of big data — especially in the realms of HCM and analytics — positions HR leaders to become strategic partners with financial leaders to control costs, optimize employee performance and grow the business. Here are four major big data capabilities that can impact business growth. 1. Benchmarking 2. Data exchange 3. Predictive analytics 4. Analyzing patterns It's the precision and predictive quality of big data that can help you optimize your staffing — benefiting both cost control and growth.
To optimize your staffing levels, you need to collect and analyze data that reflects the factors that influence your objectives. For example, you may want to track data on customer traffic, sales volume, basket size, service time, staff performance, labor costs, employee engagement, and customer feedback. You can use various sources of data, such as POS systems, footfall sensors, RFID tags, online surveys, and employee reviews. You can also use data analytics tools, such as dashboards, reports, and predictive models, to help you visualize, understand, and forecast your data.
Reviewing historical data is the first step. >POS audit trails to determine peak and low volume periods >know key launch dates for product lines > awareness of holidays and overall product seasonality that could impact business >LY labor and productivity actuals to set labor budgets >weather and it’s impact on LY business and future business Regarding labor: >utilize split shifts to maximize coverage during peak volume periods > utilize associate incentives to increase staffing levels > rotate labor between stores/departments > cross train staff in other responsibilities to increase coverage to meet client demand >Schedule top performers during peak periods to maximize sale potentials >utilize drop off/pickup service where applicable
Para avaliar os seus resultados é necessário que você estruture suas ferramentas de coleta de dados. Indicadores das lojas, com seus dados de venda podem ser um início promissor para que você inicie suas análises e rode testes A/B trazendo mais objetividade para seu negócio.
I think that defining specific and measurable objectives is crucial for a successful staffing strategy, It's essential to align these objectives with overarching business and merchandising goals. Whether it's enhancing customer satisfaction, improving conversion rates, boosting average order value, minimizing labor costs, reducing turnover, mitigating shrinkage, refining inventory management, maintaining high merchandising standards, or optimizing store layout, having clarity on these goals helps tailor the staffing strategy to achieve them efficiently.
Optimising staffing levels involves a comprehensive approach leveraging various data sources. Collecting data on customer traffic, sales, service metrics, staff performance, and feedback is key. Analyzing this through tools like dashboards and predictive models helps in making informed decisions to streamline operations and enhance overall efficiency.
Collect and organize data: Gather data on employee schedules, workload, customer traffic, and other relevant factors. Ensure the data is accurate and comprehensive.
Based on your data analysis, you can adjust your staffing levels to match the demand and supply of your business. You can use data analytics to help you optimize your staffing decisions, such as how many staff to hire, train, and retain, how to schedule and allocate your staff across shifts, departments, and locations, how to incentivize and reward your staff, and how to communicate and engage with your staff. You can also use data analytics to monitor and evaluate the impact of your staffing adjustments on your objectives and make further changes as needed.
I totally agree, it’s important to analyse the role of every staff member. - Data can help in staff movements, Analyse peak & non peak traffic hours to offer best customers experience
Data analytics is a powerful tool to improve and optimize staffing efficiency if the right set data sets are used. For example in a q-commerce business Data analytics can give an hourly update on order volumes and the algorithm learns patterns to forecast accuracy in staffing accordingly to demand and supply curve. This can be tweaked for specific promotions and events to adjust staffing accordingly. Modern data analytics has not only improved accuracy in staffing planning but, also improved efficiency while optimizing costs.
Again review historical data first ( LY sales performance , labor productivity, key launch dates etc) to assist with setting hiring targets. Meet training deadlines to ensure staff readiness and ensure staff comfort level with sale techniques to maximize volume potential. Constantly review sales reports to proactively adjust labor to meet client demand. Be prepared to reduce again based on daily revenue reports or weather patterns. As mentioned previously use of split shifts, associate incentives, cross training of department staff, accelerated training classes and rotation of staff between stores/departments all contribute to labor management.
Todas a??es desempenhadas podem dar origem a dados que v?o trazer insights poderosos. Para abordagens de decis?es centradas em dados, todas a??es desenvolvidas devem ser comparadas com grupos controle para garantir que os insights coletados sejam extrapoláveis.
Identify key performance indicators (KPIs): Determine the metrics that indicate staffing requirements, such as average customer wait time, service level agreements, or employee utilization rates.
Optimizing your staffing levels is not a one-time event, but a continuous process of testing and learning. You can use data analytics to help you conduct experiments and measure the results of your staffing changes. For example, you can test different staffing scenarios, such as increasing or decreasing the number of staff, changing the mix of skills or roles, or introducing new training or technology. You can then compare the outcomes of your experiments with your baseline data and see what works best for your business.
Toda abordagem baseada em dados traz em si oportunidades de ganhos com o uso de testes. Quando você tem redes de lojas, você tem à sua disposi??o, uma linha de laboratórios, onde você pode testar solu??es diferentes para seus problemas e mensurar os resultados reais, de forma objetiva, que cada uma dessas solu??es traz.
Staff performance is being watched on daily basis,weekly & monthly.You have to aware the problems of your staff and provide them friendly environment to improve their efficiency. Rewarding for seekers and help to weakers. This way you can get better performance
Analyze historical patterns: Use data analytics techniques to identify patterns and trends in staffing needs. This can involve examining historical data to understand peak hours, seasonal variations, and other factors that impact staffing requirements.
Data analytics can help you optimize your staffing levels, but you also need to be aware of the changing environment and customer expectations. You need to keep your data updated and relevant, and be ready to adapt your staffing strategy to the new trends and challenges. You also need to consider the human and ethical aspects of your staffing decisions, such as the well-being, motivation, and diversity of your staff, and the privacy, security, and trust of your customers.
One should research digital options ( beyond the traditional brick and mortar) that could impact business growth as well as drop off/pick up service.
Utilizar a massa de dados à sua disposi??o é uma oportunidade de aprender com cada a??o realizada. Investir o tempo e a energia necessárias v?o permitir que você teste diversas abordagens e analise seus resultados e extraia aprendizados importantes.
Build predictive models: Develop predictive models using machine learning algorithms to forecast future staffing needs based on historical patterns and other variables like promotions or events.
Just monitor the current sales trend of the store and then compare to previous years same sales. Evaluate the previous years sales for labor hour. All annual data should be analysed to help scheduling this year.
Schedule staff using workforce tools based on forecasts, adjusting in real-time as needed. Train agents based on data insights, offer flexible shifts, and incentivize performance. This ensures efficient staffing levels that meet demand while maintaining service quality.