Metrics for Marketing
Data Collection Methodologies,
Systems, and Considerations
Goals for this Session
• To approach data collection strategically and
systematically
• Identify appropriate, practical methods for diverse
data collection needs
• Understand and analyze the costs associated with
data collection
• Commit to next steps in data collection
Conceptualizing the Data Collection Process
Identify business
goals
Identify data gaps
Select/design
appropriate
methodology
Make effective
decisions
Collect, Clean,
Validate, and
Analyze data
Select/design
appropriate data
management
system
Considerations in Data Collection
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•
•
•
•
•
•
•
•
Data Source
Type of Data
Cost
Staff Resources
Quality/Accuracy
Time
Frequency
Logistics
Extensiveness of data
needs
Identifying Data Sources
• Secondary vs. Primary
• Secondary Sources
– Government agencies (Ministries of Agriculture, Trade; Bureaus of
Statistics)
– Donors, donor-funded projects (ZOI Reports)
– Online databases, existing studies
• Primary Sources
–
–
–
–
Farmers
Agrodealers
Traders
Buyers/off-takers
Selecting Appropriate Data Type
• Quantitative vs. Qualitative
• Quantitative
– Objective, quantifiable measurements
– Semi-structured interviews with close-ended questions; selfadministered through email, telephone
• Qualitative
– Understanding context, client behavior
– Unstructured, in-depth interviews with open-ended questions; direct
observation; focus groups
• Mixed Methods
Data Collection Methodologies
• Census vs. Sampling
• Census: Collect data on all subjects (i.e. clients,
beneficiaries)
– Appropriate for small populations (<250)
– Appropriate for information that can be quickly/easily ascertained (i.e.
Name, ID, Gender, Age, Telephone, Location)
– High quality, high cost
• Sampling: Collect data on a representative sub-group
of your population
– Appropriate for large populations
– High quality, low to medium cost
Sampling Methods
• Random
– Gold standard; high level of representation; highest accuracy of results
– Logistically more challenging, resource intensive
– Example: Off-taker randomly selects smallholder suppliers to sample plots
and estimate harvest volumes
• Systematic
– A form of random, probability sampling
– Can be done without lists, but subject to bias
– Example: Input supplier interviews every 10th customer entering a store to
determine reasons for/not purchasing improved seed
• Purposive
–
–
–
–
A form of non-probability sampling
Not representative; results cannot be generalized
Subject to bias, but cost-effective
Example: A financial service provider interviews select youth clients to
understand specifically the barriers to youth accessing finance
Example of Simple Random Sampling
Total Population
Sample Population
Sampling: Basic Principles
• The goal of sampling is representation achieved through
randomization; to generalize results to a broader population.
• Accuracy of data measured through confidence level/margin of
error.
• For populations greater than 20,000, a sample size between
350-400 is generally sufficient.
• Sample sizes vary for quantitative vs. qualitative data collection.
Population
Confidence Level (95%)/
Margin of Error (5%)
Confidence Level (90%)/
Margin of Error (10%)
500
218
60
1,000
278
64
5,000
357
67
10,000
370
68
25,000
379
68
Data Collection & Management Systems
Collection
Entry
Feedback
Dissemination
Organization
Analysis
Cleaning
Validation
Data Collection Systems
• Off-the-Shelf Applications
– Pros: Quick to implement
– Cons: Rigid in structure; often do not allow for flexibility
– Variable in cost
• Custom Applications
– Pros: Customized user interface; meets all data needs
– Cons: Requires infrastructure, programming, maintenance, support; needs may
change over time
– High cost
• Hybrid Systems
– Pros: Customizable; flexible to changes in data needs
– Cons: Need in-house programming expertise
– Variable in cost
• Paper-based Systems
– Pros: Minimal technology requirements; cheap to design and implement
– Cons: Time-consuming; require extensive data entry
• Third-Party Contractors
Data Collection Costs
• Staffing (Training, Piloting, Collection, Analysis)
– Enumerators
– Programmers
– Analysts
• Logistics
– Vehicles, fuel
– Per Diem
• Equipment
– Mobile Devices
– Databases/Servers
• Software
– Data Collection
– Data Analysis
Equipment
8%
ODCs
15%
Salaries
36%
Ground
Transport
18%
Per Diem
23%
Thank you!