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Mining Operator Data - An Overview into CHD Expert
1. Data
Challenges
and How Big Data
Mining Helps
Foodservice
Players Grow
Ways data science helps you build a
highly targeted approach to identifying
potential unknown customers with a
high propensity to buy.
2. Introduction
Bad Data Leads to Bad Decisions
Segmentation Issues due to Bad Data
Lack of Transparency Leads to Lost Opportunities
Bid Data Mining for More Accurate Lead-Generation
How Big Data is Collected and Analyzed
Case Studies
What Sets CHD Expert Apart
Solutions Toolbox : CHD Expert Bespoke Lead-Gen Lab
Contact Us
Table of
Contents
6. Bad Data Consists of the Following:
Data is Not Uniform
Data coming from multiple sources is in multiple formats.
This makes it impossible for companies to create a clear
view of the full market census.
Data is Inaccurate
It is possible that a data value can be correct, but not accu-
rate. At times, it is practical to examine against other files or
fields to find out if the data value is accurate based on the
context it is used.
Data is in Many Locations
Information exists in multiple locations creating siloes ma-
king it hard to turn the raw, unstructured data into struc-
tured data that can be easily segmented. When data is not
segmented, companies waste money on poor decisions.
Data is Incomplete
The best way to gauge your
prospect potential in a certain
market is to make sure you have
a complete, up-to-date picture
of the industry.
Data incompleteness is particu-
larly alarming because it essen-
tially stalls marketing operations
by preventing marketers from
extending their contacts base and
building the proper image of the
customer.
Data is Outdated
With restaurants opening and closing every 7 minutes, itâs
nearly impossible to keep your data updated. Outdated data
includes closed businesses, address changes, etc.
Did you know ? In less than 1 year, 20% of your CRM can be obsolete. It is essential to mo-
nitor and keep track of openings and closings within your scope of work.