DATA CLEANSING & MAPPING SERVICES

Over the past decade, GuestMetrics has invested more than one hundred thousand hours to “map” every conceivable abbreviation and mis-spelling of every item sold in bars, restaurants, hotels and stadiums across the United States, resulting in a data dictionary that is more than twenty eight million items and growing. Our data dictionary matches each item on more than 1.7 billion checks collected annually to the specific beer, wine, spirits, cocktail or food item it is supposed to be describing.

A very simple example of this Data Cleansing & Mapping process would be mapping "BUD LITE".  It should be simple, however across thousands or enterprises and locations "BUD LITE" has been spelled more than nineteen different ways from "Budweiser LT", "Budwiser LT", "Budweiser Lite",  “BD-LT”, “BUD-LT”, “BUD LITE”, "BD-LITE", you get the picture.   Now multiply this by the 34,000 different beer brands, 39,000 spirits and cocktail brands, 19,000 wine brands and tens of thousands of food items that GuestMetrics tracks every day and you start to get the scope of the problem or the level of effort.

Fortunately we now have a system that takes our ten years of mapping and streamlines the cleansing and mapping process for our clients. Our system catches any new items and spellings introduced at tens of thousands of locations and adds them to our data dictionary daily.  By itself, our powerful data dictionary is not sufficient for unlocking the black box that is the hospitality industry. For every beer, wine, spirit, cocktail, and food item that we map the check item to (using the data dictionary), we also assign the “meta” level attributes that describe the specific brand, such as the brand family it belongs to, the supplier that manufactured the brand, its price class, its category and sub-category, its origin, as well as many other key attributes. Only with these meta-level attributes is it then possible to analyze the trends that can be used to inform strategic decisions.

WE ASSIST CUSTOMERS IN THE FOLLOWING MANNER:

  • Defining data classifications and meta-level attributes
  • Identifying cleansing and mapping requirements
  • Building and customizing client's data dictionary