How can you use data analytics to choose the best site for your business?

Data analytics/ location analytics mean the process of aggregating, analyzing, and forecasting data sets that are highly correlated with location intelligence to help with a scientific site selection process.

📊 El análisis de datos es solo la punta del iceberg de un trabajo sostenido y silencioso, que a larga termina de demostrar que lo nuestro no es solo vender o alquiler inmuebles corporativos, sino que involucra una investigación y conocimiento del mercado. En el caso de Cushman & Wakefield trackeamos información constante de los mercados de oficinas, retail y logística. Esos datos van a parar a un BI que sinceramente es único en el mercado y muy valorado por nuestros clientes.

Data Analytics is a great tool in today's competitive market. - Market Analysis: Utilize data analytics for understanding market trends, demand patterns, and customer demographics. - Competitive Landscape: Assess competitor presence and market saturation to find strategic advantages. - Accessibility & Connectivity: Analyze site proximity to infrastructure for optimal access. - Risk Assessment: Use data for environmental, regulatory, and economic risk evaluation. - Financial Modeling: Project ROI based on construction costs and potential earnings. - Stakeholder Feedback: Integrate analytics with feedback for social and economic impact analysis. - Sustainability Analysis: Evaluate sites for environmental impact and green building potential.

Comprender la importancia de elegir la ubicación correcta para su negocio es fundamental. En un mercado competitivo, la elección del sitio puede ser el factor decisivo para el éxito. Como especialista en inmologística, mi enfoque se centra la toma de decisiones sobre ubicación. El análisis de datos es crucial; a través de este proceso, identificamos patrones de tráfico, preferencias demográficas y costos operativos específicos de la ubicación. Esto no solo minimiza riesgos financieros, sino que también maximiza el potencial de ingresos al situar su negocio en un entorno que se alinee perfectamente con sus objetivos y el perfil de su clientela.

El análisis de datos es todo el proceso de normalización, conglomerado y la interpretación de forma sencilla para la toma de decisiones de los negocios, indicadores de productos, índice de gastos y beneficios dentro del presupuesto de la empresa, entre otros más indicadores.

In my experience, data such as sales, revenue, expenses, inventory, customer feedback, employee engagement, and environmental impact can be obtained from your own business operations. These internal data sources provide insights into your organization’s performance and processes.

Las fuentes de datos son diversas dentro de un entorno empresarial, fuentes como los de CRM para la captación de clientes, fuentes de un ERP dónde se tienen datos de las operaciones del negocio como ingresos y gastos.

There are multiple sources, first most liked is public opinion of industry veterans then data company reports and social media trends, last but not least is global trend.

One of the primary sources of data is internal data, which includes information from various aspects of a company's operations such as sales, revenue, expenses, inventory, customer feedback, employee engagement, and environmental impact. This data is crucial as it provides a comprehensive understanding of the company's performance and allows for identification of patterns and trends. Internal data is also readily available and can be easily integrated into data analytics tools.

Data emerges from diverse sources shaping strategic decisions. Market reports, government databases, and public records provide foundational insights into economic trends and regulatory landscapes. Property listing platforms furnish details on available spaces and pricing. Tenant and lease data, obtained through property management systems, unravel occupancy patterns and rental yields. Emerging technologies like IoT and sensors offer real-time metrics on building performance and usage. Social media platforms and industry publications contribute to sentiment analysis and market sentiment. Harnessing data from these multifaceted sources empowers professionals in CRE to navigate complexities and make informed, data-driven decisions.

Alongside a prescriptive analysis, a qualitative and contextual analysis can also identify systemic or structural issues, such as undeclared student expectations, which would be difficult to identify in a purely quantitative approach This is an asset when it comes to proposing more innovative real estate solutions in higher education.

Firstly, descriptive analysis is essential in understanding the current situation and identifying patterns and trends in the data. By summarizing and visualizing the data, it becomes easier to interpret and draw conclusions. This type of analysis is particularly useful for identifying any outliers or anomalies in the data, which can provide valuable information for decision-making. Furthermore, inferential analysis takes the analysis process one step further by testing hypotheses and making predictions based on the data. This is where statistical techniques such as regression and correlation come into play. By using these methods, we can estimate the effects and relationships between different variables in the data.

In commercial real estate, analyzing data is a strategic process. Define objectives, employ statistical tools for efficient processing, and utilize predictive analytics for foresight. Evaluate market trends, property performance, and investment opportunities. Visualization tools simplify complex data presentation. Regular reviews ensure adaptability to evolving conditions. This systematic approach transforms data into a powerful asset, guiding informed decision-making in the dynamic commercial real estate landscape.

To analyze data effectively: 1. Descriptive Analysis: Use charts, tables, graphs, and dashboards to summarize and visualize data. Interactive dashboards provide real-time insights. 2. Inferential Analysis: Apply statistical techniques like regression, correlation, and ANOVA to test hypotheses and make predictions. Use advanced software and machine learning for deeper insights. 3. Prescriptive Analysis: Optimize and recommend actions with models like linear programming and decision trees. AI and advanced tools enhance recommendations. Enhancements: Ensure data quality with cleaning and validation, encourage cross-department collaboration, and stay updated with new techniques and tools.

Factors to compare for business site selection includes; -corporate tax level -demand for your business represented by characteristics of residents including income level, age group, and jobs -Market cycle and trends

Rigorous scoring of the various criteria of the future real estate site is essential. It is also important to take into account emotional data: the prestige or otherwise of the address, the quality of nearby businesses, the perception of public transport and the access route by future users, etc.

To compare different options of real estate, the weighted scoring of lots of factors and data becomes a key factor to the right decision.

I think that it is fundamentally important to create metrics that can be applied generally to all compared sites. In this way the comparison is more objectively done.

The market factors of demand, supply, growth, segmentation, and differentiation are also crucial to consider when comparing sites. A site located in an area with high demand for a particular product or service can lead to success, while a site with low demand may struggle to generate sales. It is also essential to analyze the supply of similar businesses in the area and the potential for growth in the market. Understanding the market segmentation and how a site can differentiate itself from its competitors is also vital in making an informed decision.

Visualize location intelligence and urban intelligence related factors on a map, use radar charts for detailed comparison between two or three sites that are narrowed down.

Visualization even of future to be built projects in full 3-D became critical decision factor in the last Years. If such sources can be included early in a decision process, this will be a big competitor advantage.

To effectively communicate the results of your data analysis and site comparison to stakeholders such as managers, investors, employees, and customers, you should: Define Your Audience: Tailor your message to the specific needs and interests of each stakeholder group. Choose the Best Format: Select the most appropriate format for presenting information, such as reports, presentations, or dashboards. Use Visuals: Illustrate data with charts, graphs, and infographics to make complex information easily understandable. Tell a Story: Use clear and concise language to convey the findings and their implications.

Presentar los datos en Dashboard con herramientas de BI con los indicadores más importantes para el negocio

The most important thing when it comes to communicating is to know your audience and build your visualizations based on that. There needs to be quick glance visuals and then more crunching type of presentation.

Deve - se considerar o conhecimento do negócio ao qual a análise de dados está sendo aplicada. O principal objetivo de uma análise de dados bem feita é responder as questões do negócio, se o conhecimento técnico do tema não for aliada à um conhecimento de causa este objetivo não é atingido.

En la actualidad se está utilizando la herramienta de SAP Analytics Cloud con una fuente de CRM para ver datos demográficos de los clientes y mejorar la captación de nuevos prospectos de clientes para la empresa.

Most important factor is the correct utility of data by executing policy’s without delaying. Because trends changes with times.

This article is open ended to cover all bases but the critical thing before starting any feasibility study or analytical appraisal of locations is to have a board approved strategic vision and clear purpose. This would determine the scale, resources, co dependencies and budget for the endeavour. A design thinking approach framework would help surface best scenarios based on identified hypotheses to test. The Return on Investment would be determined on the right scalable size at the best price with flexible terms including duration, contraction or expansion, fit out cost, attraction of best talent, convenience of customers/clients to procure services / goods priced to compete well with competitors and ease of access plus more variables.