Metrics for Marketing Data Collection and Analysis Tools

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

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, Email Studies suggest that SMS systems are good for up to 6 questions; Beneficiary name, ID registration, Yields, Sales

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

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

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

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

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

Data Visualization: Histograms

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

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

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

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

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

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