Why the Food Service Industry Needs Big Data

The pandemic has tremendously expedited the need for better data analytics in the food industry. Almost every corporate enterprise is exploring opportunities to implement Big Data. But how does big data add value to an organization?

Tech giants like Google, Facebook and Amazon have the ability to invest in expensive processing power and leverage value from the huge amounts of data that they possess, but does it make business sense for smaller companies to invest in capabilities needed for Big Data analytics?

For every Google, Amazon and Facebook there are a lot of smaller companies in other sectors who are struggling to get their implementations off the ground because they don’t know how best to apply Big Data techniques.

In most food service companies the emphasis has historically been on reporting rather than on advanced analytics. The data are stored in database tables in a structured way, which limits the type of querying and analysis that can be performed on the data. This means that the data collected by the organization is not being used to its full potential.

The basic idea of Big Data is to breakdown these schemas and to integrate unstructured data from newer sources such as social media, with more traditional and structured data sources. This enables the data to be analyzed in new ways at improved data-processing speeds and in near-real time to gather a complete picture of the environment. As a result, there is a significant increase in the scope of ways to derive insights and value from the organization’s data.

Below are several benefits that the food service industry can realize by adopting Big Data methodology.

Understanding Customer Needs and Preferences

Much of today’s data is in unstructured forms like tweets, blogs, images and videos. Traditionally the business was unable to derive insights from these types of data. But using Big Data technologies, we can integrate unstructured data with structured data and derive keen insights into customer behavior and buying patterns.

We can now combine tweets, Facebook posts, blogs and other social media activities with sales and transactions data for brand, sentiment, affinity, and tendency analyses.

Some examples:

  • Real-time insights about customers, products and competitors can be obtained by monitoring Big Data available on blogs, forums, customer review sites, video and photo portals and other social media outlets.

  • Menu items which need improvements or eliminations can be determined based on the customer’s response captured through customer feedback streams such as online surveys and suggestion lists.
  • Apps based on Big Data technologies track millions of restaurant menu items and their corresponding components – they leverage analytic techniques to generate high quality and granular data for both commercial and consumer uses. This helps management gain insight about dining trends in different regions and also to predict how popular a new dish will be. Subsequently menus can be customized for a specific area or region based on factors such as ethnicity, seasonality and demographics.

Marketing and Advertising

Big Data analytics enables marketing departments to target their customer better by predicting consumer behavior and thereby increasing the effectiveness of their marketing spend.

Some examples:

  • Advertising and promotional campaign effectiveness can be measured to determine optimum promotion offers and also to predict the success rate of future campaigns.
  • Real-time measurement of campaign performance can help tune spending on the campaigns to avoid wasteful expenditure.
  • Optimize joint marketing programs with suppliers and other dealers by integrating customer information from different CRM systems to determine what menu items individual customers would like – thereby increasing the effectiveness of the campaigns.

  • Analyze meals a customer had ordered in previous visits to determine what other dishes he/she would be interested in, and make real time recommendations to increase cross selling.
  • Contact customers in the method they wish to be reached at the right location and at the right time to engage them with personalized real time offers for pin-point marketing.
  • Understand the relationships between groups, items purchased, and promotional offers.
  • Dynamic pricing by comparing competitive prices in an appropriate geographic area.

Operations, Supply Chain and Security

The Consumer Product and Retail industry lose about 3.5% of their sales annually due to inefficiencies in the supply chain.

By integrating enterprise data with relevant information from other sources (e.g., SharePoint, log files, shared drives, barcodes, etc.) and then analyzing this integrated data set, Big Data enables businesses to understand their internal functioning in ways not previously possible.

This helps in reducing wastage and supply chain costs as well as in determining ways to improve efficiency in the organization.

Some examples:

  • Use barcodes and RFID to track food from Farm-to-Fork. This ensures a smarter supply chains which helps to implement preservation programs, reduce spoilage and provide fresher food to the customer.

  • Big Data analysis techniques can be performed to find relevant clues to pinpoint possible attacks, fraudulent transactions or other security breaches by identifying relationships between multiple actions or events, and seeing how these combine to implement an attack.

    For example, we can determine dubious transactions and subsequently find employees involved in fraudulent transactions by identifying relationships between sales orders, discount coupons used in the orders, employee details and the sequence of event in that sales order.

  • Operating system and application logs record the day-to-day activity of system users and provide vital information about the health and well-being of an organization’s system infrastructure in order to maximize uptime.

    In restaurants, the efficient functioning of the POS registers, self-order kiosks, kitchen management system, in-store and wide area networks and other back office or host systems are necessary for seamless functioning of the restaurant. All of these systems generate log files, which contain information about the activity and health of each system. However it is a challenge to mine useful information from the raw log files. Big Data techniques allow us to analyze these data and mine useful information from them to proactively detect and eliminate system issues for efficient functioning of the restaurant.

The benefits Big Data can provide are enormous. In a highly competitive industry like food service, companies armed with Big Data capabilities will have the edge in the market.

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