1. Metrics for Marketing
Data Collection and Analysis Tools

2. Goals
Identify specific data collection and analysis technologies and tools to fit your business needs
Generate ideas for leveraging new technologies to improve and expand data collection and analysis
Identify strategies for maximizing data quality

3. Data Collection Technologies
Smart Phones, Tablets
Used for semi-structured interviews collected on mobile devices
Short Message Service (SMS)
Basic messaging, price reporting, basic data collection
Interactive Voice Response (IVR)
People tend to stay on the phone longer, answer more questions
Computer Assisted Personal Interviewing (CAPI)
Used for self-administering surveys
Problematic where internet is unavailable, unreliable
Other: Mail,
Studies suggest that SMS systems are good for up to 6 questions; Beneficiary name, ID registration, Yields, Sales

4. Data Collection Tools Software Barcode Scanning (Traceability Systems)
iFormBuilder
Snap Surveys
Survey Monkey
Formhub*
RapidPro (SMS, IVR)
Barcode Scanning (Traceability Systems)
FarmForce
SourceTrace
Sensors (weather, soil)
Mobile Money
Drones
There are many different platforms
Need to consider internet necessities; web-based vs. off-line capabilities
Drones can collect infrared data
Weather sensors can measure humidity, rainfall, and can be used to leverage crop insurance

5. Analysis Software Quantitative Analysis Qualitative Analysis
Statistical Packages (SPSS, Stata, SAS, R*)
MS Excel
Qualitative Analysis
nVivo, Atlas
Spatial Analysis
ArcGIS, QGIS*, GmapGIS*
Satellite Imagery
Financial Analysis
Accounting Software
Satellite Imagery is often used for precision farming; uses remote sensing to detect variability in crop and soil conditions

6. Data Analysis Disaggregation by key variables Statistical analysis
Gender, age, crop, income level
Important to know during questionnaire design
Statistical analysis
Basic descriptive statistics (average, median, range, standard deviation)
Inferential statistics (t-tests, regressions)
Data cleaning
Identify outliers (basic statistical functions, filtering, sorting)
Identify data gaps
Trend Analysis
Operational analysis
Technical assistance and training by agronomist
Area planted

7. Data Visualization Graphical Spatial Also useful in data cleaning
Proportions: Pie charts
Trends: Bar, line graphs
Distribution: Scatterplots, histograms, boxplots
Spatial
Beneficiary coverage, density
Distance to markets, roads
Pest, disease incidences
Also useful in data cleaning

8. Data Visualization: Bar/Line Graphs
Hawaiian Production of Fresh Pineapples
2000
2001
2002
2003
2004
2005
MTs
321,143
293,021
290,299
272,155
199,581
192,323
$000s
101,530
96,337
100,616
101,470
83,104
79,228

9. Data Visualization: Histograms

10. Data Visualization: GIS Maps
Density analysis of particular crops
Potential additional analysis includes distance to markets

11. Data Quality Common data quality issues Mitigation
Lack of farmer records
Handwriting/data entry
Outlier data
Under/overestimate areas under production
Mitigation
Well trained enumerators
Well designed tools and questionnaires
Triangulate data across multiple sources
Incorporate question checks; program reasonable ranges
Make questions required
Identify, correct, or if necessary, eliminate outlier data

12. iForm Demonstration: Key Features
Required fields
Data validation checks
Input masks
Images
Question routing
Conditional questions
Offline capability
Required Fields: make a question required before submitting a survey; to help ensure completeness of data collection
Data Validation Checks: program likely ranges to quickly highlight outlier data for the data collector
Input Masks: force the user to enter data in a specific format to maintain consistency (i.e. phone numbers)
Question Routing/Conditional Questions: ability to skip irrelevant questions based on responses to other questions
Offline Capability: ability to collect data/surveys when offline

13. Takeaways Effective analysis starts with accurate, basic data points
Data quality is key
Good data is not free; need to invest in technologies and establish well-designed systems
The future of data collection is in mobile and automated tools for real-time data
While technology is key in data management, it is not a silver bullet
While data quality is key, it is also important to be able to make sense of, and see through, “noisy” data

14. Additional Resources Data Collection & Analysis
MozTarget
ConsultUS
MeasureAfrica
ETC
Custom Application Development
TechnoBrain
SmagMedia
AgTechXChange
Resources on data collection methodologies, survey design, software

15. Thank you!