How can you ensure your compensation and benefits survey is accurate?

Indeed a proper and clear perspective on the final outcome for the client/your company is essential, along with crystal clear objective on your benchmark, “you can’t benchmark a tech start-up with large oil and gas corp”! . Yet also, being aware of the talents’ supply and demand for specific roles is very critical and most of the time it could be missed during surveys. Talents pool could various from on location to another , and from industry to another .

Creating an accurate survey is a complex task. Salaries within organizations often vary due to numerous factors such as experience, roles, and internal policies. Organizations may not always disclose precise data, leading to a range of reported figures. While surveys aim to provide a general understanding of industry standards, it's advisable to interpret the results with a degree of flexibility. For the most accurate insights, direct communication with individuals from other organizations is recommended. Even then, it's essential to acknowledge a potential margin of error, typically around 90% accuracy.

Salary scoping need's to be completed by those in a position who know what remuneration is being paid. You need real examples from the market you are assessing for analysis. Understand the variables; workplace, culture, location, skills and market conditions. Using survey data only does not take into account the depth understanding of variables. Having provided salary scoping services for 32 years, I would certainly not be setting salaries and wages based on most surveys I have seen without a complete understanding of the aforementioned variables. Instead identify who most likely possesses the depth of knowledge needed, have them scope the salaries and then put the checks and balances in place to test the findings.

Para fazer pesquisa salarial, precisamos considerar a competitividade salarial e os benefícios oferecidos pois muitas vezes os benefícios tornam o pacote muito mais atrativo . Atualmente, esta é uma prática bastante comum para a maioria das empresas. Normalmente, solicito o apoio de outras empresas do mesmo segmento atuante no mercado, lembrando de considerar o porte da mesma e a estrutura interna. Também busco informações nas associações da empresa como AABIC, SECOVI (atualmente), enquanto na GMAC buscava informações na Febraban.. Mesmo com todo esse cuidado conseguimos ter uns 90% de assertividade. Tudo para “chegar a um nível salarial coerente e compatível com a realidade da empresa e do mercado”,

Ensuring the accuracy of a compensation & benefits survey is crucial for obtaining reliable and meaningful data. We have to: Define Clear Objectives Target Relevant Participants Maintain Anonymity Use a Well-Designed Survey Instrument Consider Benchmarking Regularly Update the Survey Utilize Multiple Data Sources Verify Data Accuracy Encourage Honest Responses Conduct Regular Surveys Engage Stakeholders By carefully planning, designing, and implementing your compensation and benefits survey, you can increase the likelihood of obtaining accurate and valuable information for your organization.

The source of data is very vital. Especially to the clients as they are more keen to know specifically about their own industry. The best way is to encourage maximum participation. This provides reliable and robust primary data, which provides vital and meaningful insight. We need to work on building our credibility as the participants are often reluctant to share their primary compensation-related data with external agencies with low credibility. To begin with, the participants may be less in number, however quality of the report and and ability to maintain confidentiality help in increased participation. Often a very high cost of compensation report charged by the established players is a deterrent to many small and new organizations.

Analytics are only as good as the raw input data so important to ensure you have a representative sample of data within your own company and this is accurate and current. The same goes for and external source data you use for comparison. Also important to ensure there is an ‘apples to apples’ comparison based on role, seniority, location and geography. Different people have different definitions of what comprises benefits and compensation so ensure these are as similar as possible

Quality of data is key. Reliability and reputation of the source of the data is very important as well. Getting data from multiple industries is important for example if you are undertaking the survey exercise to be more competitive or to stem attrition, you need to have information on the industries that you are losing valuable employees to.

I want to add: having led numerous HR projects, I often observed a missing approach to data authenticity - deep conversations with employees. It's time-consuming, but these rich discussions can uncover nuanced information that large scale surveys may miss. Adding this layer of 'human data' often reveals fresh insights critical for effective decision making.

To enhance the accuracy of a compensation and benefits survey, employ rigorous methodologies such as stratified sampling, ensure job classifications are well-defined, validate data through cross-referencing multiple sources, uphold participant confidentiality with secure processes, and iteratively update the survey to align with dynamic industry benchmarks.

Another crucial aspect that's often overlooked is contextualizing data. The best interpretation of data is enriched by understanding the context from which it came. For instance, understanding company culture or industry norms can provide valuable insights missed when focusing only on quantitative analysis. Remember, data tells a story, but only if we consider its context.

Need to be aware of the market, economic trends, location, company size, sector as well as the job families and job levels as this guides validation of data. Year-on-year reliability is important, and this is entails referencing the data reported in the compensation survey over multiple years to ensure the trend makes sense. When conducting statistical analysis of the data attention should be paid to outliers and whether or not this can impact the results of the survey.

While data validation is crucial for understanding compensation benchmarks, your judgment plays an equally important role in determining appropriate pay levels for your employees. Simply relying on average industry pay data may not be sufficient, especially if your company operates in a particularly competitive or specialized market. In today's dynamic job market, talent is increasingly mobile, willing to consider opportunities across industries. To effectively attract and retain top talent, you need to identify the right comparison group, not just rely on generic industry averages. This could include companies of similar size, operating in the same geographic region, or those with similar business models and competitive landscapes.

Another crucial aspect that's often overlooked is contextualizing data. The best interpretation of data is enriched by understanding the context from which it came. For instance, understanding company culture or industry norms can provide valuable insights missed when focusing only on quantitative analysis. Remember, data tells a story, but only if we consider its context.

Checking the data is super essential for accurate surveys. Look closely at the data to find any weird or wrong stuff. Use special checks to find and fix mistakes and strange data. Doing this makes sure your data is dependable and can be trusted.

When conducting statistical analysis of the data attention should be paid to outliers and whether or not this can impact the results of the survey. Data should include lower, mid-point and upper scales. Insights to interpret the data and guide decision making are also important.

Un ejemplo que puedo contribuir para poner en práctica en la encuentra de beneficios y satisfacción relacionada al análisis de la información vista desde los segmentos y demográficos en donde nos permite reconocer más allá de una transacción del que se solicita ¿cómo se vive y experimentan las condiciones relacionales en enloquece de compensaciones. Ahí nos daremos cuenta la importancia de equilibrar el para quien nos dirigimos y la naturaleza del desarrollo laboral. Tomar mejores asignaciones y reconocimiento del valor o solución desde la singularidad del segmento, comunidad o posiciones.

Essencial para análise de dados é neutralidade, pois ouvir depoimentos, entender as pesquisas oficiais, buscar informações diversas, pode ser complexo. Neutralidade e visão macro serão fundamentais para conseguir ter uma compreensão da complexidade dos dados e dos cenários que os dados darão de possibilidade.

Na minha experiência, após coletar os dados de remuneração e benefícios, é importante analisá-los para identificar tendências e padrões. Isso pode ser feito usando uma variedade de ferramentas e técnicas, como gráficos, tabelas, proporções e índices. Também é importante comparar os dados do cliente com benchmarks e práticas recomendadas para identificar pontos fortes e fracos.

Lo que he podido observar de los años que he realizado estudios de encuestas de mercado de C&B, es lo importante que puede llegar a pesat los Beneficios más allá del salario específico. Lo que hay que ver es la estrategia analítica para poder desglosar de la recompensa total por rol o cargo, cuánto equivale al salario y cuánto a beneficios traducidos en montos de dinero. Normalmente las políticas de C&B consideran la ejecución del reajuste salarial anual en relación al salario base; los componentes adicionales tienen otras vías de adecuación o reajuste, ya sea por convenios sindicales o actualizaciones de políticas de C&B. Es un must tener claridad del desglose para la planificación estratégica de budget asociado a People cost.

In preparing a summary report it should entail objectives methodology, data and key findings as well as recommendations and suggestions on how best the data can be used by the client.

A riqueza da ciência é a possibilidade de sempre se ampliar, para isso o compartilhamento, a troca de informação e conhecimento cria uma base sólida dentro da comunidade.

Na minha experiência como consultor, aprendi que é importante que a comunicação seja feita de maneira clara, concisa e envolvente. O cliente deve compreender os resultados da pesquisa e as implicações para sua empresa. Aqui estão algumas dicas específicas que podem ajudar a tornar a comunicação mais eficaz: 1) Use linguagem simples e direta. Evite jargões técnicos. 2) Foque nos resultados mais importantes. Não tente apresentar todos os dados da pesquisa. 3) Use recursos visuais para ilustrar os pontos principais. 4) Conte histórias ou exemplos para tornar a pesquisa mais interessante e relevante. 5) Esteja preparado para responder às perguntas do cliente. 6) Mantenha contato com o cliente após a apresentação do relatório.

After carrying out your survey, there is need to communicate your findings to the decision makers. You are expected to be as objective as possible. Back-up your findings with data from your findings as this will enhance objectivity. As a HR professional you can go ahead to recommend solutions based on the findings. However, your recommendation has to be a subtle one and should not be imposed on the decision makers.

Watch for your own bias when translating data into insights. Step back, slow down your decision-making and reflect before reaching your insights and making any recommendations. Remain aware that as humans we often look, unconsciously, for the results that have most meaning for us.

Your inputs to the management on the required inputs and stages of change . It may be by correction in band ,collating positions and kras into jobgroup and linking them to the ROI in balance sheet

I believe relying on primary data is difficult and time consuming hence companies can take help of good consultants for accurate and tailored data. It is also very important to implement changes basis on parity received so that we can retain our good talent.