“Big Data” Does Your Restaurant Need It?

     

    “Big Data” Does Your Restaurant Need It?

    “Without data you’re just another person with an opinion.”

    W. Edwards Deming, Data Scientist

    DRE Big_Data_Picture

    Big Data, promises to revolutionize the way businesses sell products and services, understand customers and optimize marketing campaigns.  How do restaurants utilize this Big Data and why should they?

    First, what is Big Data?  Big Data is made up of two different types of data and each can provide answers to different questions.

    Structured Data makes up the majority of the information every restaurant collects; this includes your daily item sales, labor costs and financial reports from your restaurant POS system.  Structured Data gives us the what data we have used forever to operate our restaurant, but without the why data we really can’t make rock solid presumptions!

    Unstructured Data gives us the why.  This information comes from many sources, in and outside the restaurant.  These sources can include obvious information such as weather, date, day of the week and time of day in addition to other sources such as employee feedback, customer feedback and third party review sites.

    The Unstructured Data helps us answer the very important why questions.  Were sales up or down due to the weather, due to a special event, due to a special promotion, due to a holiday, due to poor customer service or poor food quality?  Was labor too high or too low and how did it affect customer service and/or food quality?  Was the food quality due to poor scheduling, an absent employee or an out of stock menu item?  Was the chef off sick, or unavailable or on vacation?

    When you combine the two data streams of what and why information you create a powerful Big Data system.

    So how can Big Data help my restaurant?  Here are a couple of examples:

    Track guest purchases and their social behavior and reward your best guests with personalized rewards instead of generic rewards.  70% of businesses using personalized rewards reported increased response rates and 67% reported increased sales.

    Encourage and reward guests to visit during off peak hours, frequently called yield management. Hotels and airlines have used these methods successfully to drive up profits since the 80’s!

    Combine what and why data to reward your higher spending guests to visit when the restaurant is busy and seating is limited.  Holidays are a good example; why not maximize your holiday revenue by filling the restaurant with your big spenders instead of just filling seats?

    This all sounds great, but who has the time and knowledge to analyze all this data?  Good news! Due to the dropping price of data science and the increase in machine learning we now have the tools for automated and quantified decision making!

    By combining Big Data with cost effective tools, engineers and data scientists have created prescriptive analytics, with one known specifically as the Decision Recommendation Engine.

    What is the Decision Recommendation Engine?  Think of the Decision Recommendation Engine (DRE) as your very own business assistant.  In layman’s terms, the DRE does all the heavy lifting by poring through all the what and why big data to filter out what really matters, providing restaurateurs with recommendations to questions they didn’t even know they had. The DRE finds trends and recommends changes that will save and/or make businesses money based on their Big Data.  Most businesses – especially restaurants – just don’t have the time or data know-how to look through tables of data and know what to do.  Remember, data that is relevant today might not make any difference tomorrow.  Knowing this, the DRE outputs plain-English recommendations, quantified in dollars and cents, that restaurateurs can quickly review and decide whether or not to take action. 

    The Decision Recommendation Engine delivers on the promise of Big Data and is the first-ever prescriptive analytic tool for brick and mortar businesses.

    What Can the Decision Recommendation Engine do for my restaurant?  Think of the DRE as your very own business assistant providing you with plain-English suggestions and recommendations to improve your restaurant!

    Employee Performance – learn which employees generate or lose revenue and how to maximize revenue by staffing the right employee for the right shift. Expect 2-4% revenue gains from improved performance.

    Labor Forecasting – see how much you’re over or under staffing and how much this is actually costing your restaurant. Typical users should see profits increase 20% by controlling labor costs.

    Campaign Performance – determine how much a marketing campaign generated and determine with confidence the outcome or running this campaign vs not running it for a specific date range.

    Menu Item Pricing – determine how to price menu items to increase revenues based on customer sales patterns and guest ordering combinations.

    Adding and Removing Menu Items – using customer frequency and the popularity of items at other establishments, the DRE will let you know what you should be featuring on your menu to drive higher revenues.

    A couple of specific examples, based on quantified decision-making.  The DRE quickly learns what’s working and what’s not and with your Big Data, the DRE reports that last week you lost $177.00 on over staffing and that Bob the waiter made an extra $29.00 in new revenue.

    By utilizing DRE forecasts and recommendations, next week you can make an additional $60.00 due to better labor forecasting and if you staff Bob as a waiter on Friday and Saturday nights you’ll make an additional $38.00 in new revenue.

    A popular new item, lamb burger, is selling very heavily in your market at an average price of $12.59. However, it’s not even on your menu. Adding it could increase revenues by $318 this month.

    The DRE lets you spend more time delighting guests while the DRE does the dirty work of finding actionable insights in your troves of Big Data. 

    Watch a video here.

     

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