1. Metrics for Marketing
Data Collection Methodologies, Systems, and Considerations
There is no one methodology or system that can work for everyone, but all systems can be improved.
This presentation will introduce you to the processes, but the real work is going to be through brainstorming, applying these principles, and making an effort to expand and improve your data collection systems.

2. 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

3. Conceptualizing the Data Collection Process
Identify business goals
Identify data gaps
Select/design appropriate methodology
Select/design appropriate data management system
Collect, Clean, Validate, and Analyze data
Make effective decisions
In reality, the data collection process starts long before you actually collect the data.
A good system is conceptualized strategically from the beginning with properly defining your business goals and working backwards to determine what information you need to inform those goals.
The end goal is all about making effective decisions

4. Considerations in Data Collection
Data Source
Type of Data
Cost
Staff Resources
Quality/Accuracy
Time
Frequency
Logistics
Extensiveness of data needs
Please keep these in the back of your mind as we go through each of these components
Data source: where do you need to source the data from? Primary vs. Secondary; Farmers; Input Suppiers; Traders; Offtakers; Others?
Type of information: Quantitative vs. Qualitative?
Cost: how important is this information to your business model and what are you willing to pay for it? ROI? Equipment, Staff, Logistics (Transportation)
Staff Resources: do you have staff available for data collection and analysis? Or do you need to hire externally?
Quality/Accuracy: what margin of error are you willing to accept?
Time: how quickly do you need this information?
Frequency: how often do you need to collect this data to make informed decisions? Ongoing? Or periodic/interval?
Logistics: what means of communication is required to get this data? does it require a face-to-face interview? Transport?
Extensiveness of data needs: how many questions do you need to answer and in what depth?
Internet Access: do you need offline capability?
Can the process be automated?

5. 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
Often best to start with Secondary Sources as they are free; understand what already exists and then use primary data
Secondary sources are often flawed, however, so generating primary data independently is very important

6. Selecting Appropriate Data Type
Quantitative vs. Qualitative
Quantitative
Objective, quantifiable measurements
Semi-structured interviews with close-ended questions; self-administered through , telephone
Qualitative
Understanding context, client behavior
Unstructured, in-depth interviews with open-ended questions; direct observation; focus groups
Mixed Methods
Ultimately the best and most comprehensive methodologies combine aspects of both (mixed methods).
What good is quantitative data unless we fully understand the contextual clues surrounding the data? And what good is qualitative data unless we can pair it with something tangible and measurable for target setting
Qualitative: In some cases qualitative data can be more valuable; lower cost; smaller sample sizes

7. 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
Census: Certain information is recommended to have on a census basis: i.e. primarily client profiles (Name, ID, Telephone, Location)
Census is obviously more time consuming, so it needs to be accompanied by automated tools
There are generally trade-offs between cost and data quality, but not always
Sampling is the collection of data through a representative sub-group of beneficiaries or clients, selected at random

8. Sampling Methods Random Systematic Purposive
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

9. Example of Simple Random Sampling
Total Population Sample Population

10. 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 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
For accurate estimates, randomization is key
Key to sampling is for it to be representative
Higher degree of confidence / lower margin of error = larger sample size
Populations more than 20,000 and above yield the same sample sizes
There are a lot of considerations when determining sample sizes (range of potential responses, homogeneity of the respondents)
Estimates listed above are for calculations of percentages or proportions
If it is determined that sampling is the right approach, rather than getting lost in these calculations, it is best to consult professionals
Another approach is to figure out how many you can potentially survey with your given resources, and then work backwards to understand your confidence level and margin of error;
While you generally want to approach it from a statistical standpoint, sometimes it’s more practical to do it this way, and the tradeoff in accuracy is deemed acceptable

11. Data Collection & Management Systems
Entry
Organization
Cleaning
Validation
Analysis
Dissemination
Feedback

12. 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
Paper-based Systems
Pros: Minimal technology requirements; cheap to design and implement
Cons: Time-consuming; require extensive data entry
Third-Party Contractors

13. 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

14. Thank you!