In the modern age of high-paced digital transformation, data providers must stay ahead of the competition by using data analytics and machine learning to understand foodservice market trends. Knowledgeable restaurant technology providers have the capabilities to accurately identify consumer needs, accurately forecast consumer preferences, and track operational and marketing performance metrics. They must be able to evaluate data from various sources, compare trends across multiple markets, and be able to predict key players in the foodservice market. This guide to evaluating and understanding restaurant research offers an overview of the necessary steps to assess restaurant industry data.
Data Sources & Tools for Restaurant Research
The first step to understanding restaurant research is to find the right data sources and tools. With an expansive array of data available, it’s important to be acquainted with the essential data sources and research tools that can make analyzing the foodservice market easier.
Primary data, such as sales records, industry surveys, and consumer surveys is an invaluable source of information and analysis. This type of data is highly specific and insightful and provides insights from the customer’s viewpoint. Additionally, secondary data such as market research reports, industry publications, industry news, industry blogs, and web search results can provide a broad overview of the foodservice market.
Data processing tools can assist in collecting, analysing, and processing data obtained from these various sources. These tools not only automate manual tasks and ensure accuracy, but can also be used to discover insights from the collected data that may have otherwise been overlooked.
Data normalization & cleaning
Data normalization and cleaning are fundamental components of restaurant research and must be considered when evaluating any data source. Data normalization refers to the process of ensuring that data is consistent and can be used across different data sources. This process involves cleaning the data of any redundancies, duplicates, or irrelevant data.
Data cleaning also eliminates any errors or inconsistencies that can be found in the data set. It can be difficult, time-consuming, and expensive to correct errors and inconsistencies that can be hidden within the data without proper data analysis tools.
Data Analytics & Business Intelligence
After obtaining, normalizing, and cleaning the data, it’s time to run data analytics and utilize business intelligence to make sense of the data. Data analytics helps to uncover key insights that can be used to pinpoint new opportunities or identify potential risks within the foodservice market.
Data analytics and business intelligence can be used to understand customer trends, benchmark performance data, and create predictive models for future performance. By conducting data analysis, restaurant technology providers can identify gaps in the market and gain a competitive edge in the industry.
Analyze & Interpret the Data
The most important step is to analyze and interpret the data collected. This involves identifying the key insights that can be used to improve organizational decisions and operations.
The analyst or data scientist must look at the data from multiple angles, contextualize the data, and use creative problem-solving techniques to identify potential solutions. Typically, this process involves utilizing cognitive intelligence, predictive analytics, and machine learning to find correlations between data, even if there is no visible link.
Report & Insights
After data analysis is complete, a report on the insights and recommendations can be generated. This report should be written in a way that it can be interpreted by all stakeholders, regardless of their technical background.
In the report, the analyst should explain the methodology used, list the findings, draw conclusions, offer recommendations, and provide clear courses of action that can be taken to leverage the insights gained.
Last ideas
Data analysis and business intelligence is essential for restaurant technology providers to be successful in the competitive industry. It’s important to understand the right sources of data, the importance of proper data normalization and cleaning, the tools necessary to conduct deep analysis, the creative problem-solving techniques needed for success, and the key considerations for producing actionable insights. With the right tools, techniques, and tips, restaurant technology providers can lead the industry through data-driven research and analytics propelling their operations forward.
