Field Data & Analytics Service
AMOEBA supports marketing research teams end-to-end — from CAPI/CAWI fieldwork and questionnaire scripting, through tabulation and statistics, to reporting your stakeholders actually understand.
How we work
Every project moves through the same disciplined path — from data preparation to insight your team can act on.
High quality in data preparation
CAPI & CAWI — Survey To Go, KoBoToolbox & LimeSurvey. Choosing the right CAPI application for fast‑turnaround projects keeps costs under control, and disciplined scripting gets us to zero‑error data.
Tabulation — QPS, CL & Macro Tab, Python.
Statistical analysis — SPSS, XLSTAT, R, Python, AMOS, LISREL & PLS.
Deep analysis & impactful insight
Built on Tableau and Power BI, our reporting starts with structured, systematic questionnaires designed around modern statistical principles — the foundation for analysis that's genuinely comprehensive, not just presentable.
Quality control, done properly
Audio-visual QC to verify fieldwork as it happens, and dedicated coders for open-end responses, so nothing gets lost between the field and the dataset.
Reports you can reach anywhere
Accessible offline and online, with live field-progress monitoring — so you can watch your project move in real time and report on it the moment it matters.
Responsive & responsible advisory
Two decades of hands-on research experience behind every project — a team that trains as hard as it delivers. Meet them below.
Our people
Data Preparation & Statistical Analysis, Quality Control
21+ years in the marketing research industry. Builds systematic, efficient QC processes, trains smart and resilient data-preparation teams, and coaches research teams in advanced statistical analysis. Formerly with MARS, DEKA & CLOVE.
Machine Learning & Data Visualization
24+ years in the marketing research industry, specializing in data processing and tooling, with process automation through VBA and Python. Applies machine learning for faster, smarter decision-making. Formerly with MARS, Nielsen & DEKA.
Pricing
Indicative rates across data preparation, reporting, statistical analysis and advisory support. All figures are in IDR, thousands (Rp x1,000), and vary with project scope and complexity.
| Job level | LOI (minutes) | Scripting | Tabulation | Tools |
|---|---|---|---|---|
| Easy | Less than 20 | 750 | 750 | QPS, Survey To Go, KoBoToolbox, LimeSurvey |
| Medium | 21–45 | 1,000–2,000 | 1,500–2,500 | QPS, Survey To Go, KoBoToolbox, LimeSurvey |
| Advance | More than 45 | 2,000–3,000 | 2,500–4,000 | QPS, Survey To Go, KoBoToolbox, LimeSurvey |
Coding open-end: Rp 250 per open-end response.
| No | Material | Tools | IDR (x1,000) | Note |
|---|---|---|---|---|
| 1 | Dashboard achievement project | Tableau / Power BI | 1,000–5,000 | Depends on number of panels |
| 2 | Dashboard of topline report | Tableau / Power BI | 3,000–10,000 | Depends on number of slides |
| 3 | Charting | PowerPoint | 10,000 / slide | No template |
| 4 | Charting | PowerPoint | 20,000 / slide | With template |
| Material | Tools | IDR (x1,000) | Note |
|---|---|---|---|
| 1. Perceptual mapping | |||
| Map correspondence | XLSTAT | 500–2,000 | Max 10 breaks |
| Positioning & brand image | Excel | 250–1,000 | Max 10 breaks |
| Biplot | Macro Biplot | 500–750 | Max 10 breaks |
| Multiple correspondence | XLSTAT | 1,000–3,000 | Max 10 breaks |
| 2. Statistical test | |||
| Proportion test | Macro | 500 | All headers |
| Mean score test | Macro | 500 | All headers |
| Non-parametric test | Macro | from 300 | Per break header |
| 3. Factor analysis | |||
| Factor analysis | SPSS, R | 500–1,000 | By total |
| 4. ANOVA (Analysis of Variance) | |||
| ANOVA | SPSS, R | from 1,000 | By total & treatment |
| 5. Driver analysis | |||
| Correlation analysis | SPSS / Excel | 200–500 | Max 10 breaks |
| Partial correlation (impact analysis) | R | 500–1,000 | Max 10 breaks |
| Linear regression (driver analysis) | SPSS, R | 500–2,000 | Max 10 breaks |
| Path analysis | PLS | 1,000–3,000 | Max 10 breaks |
| Non-linear regression | SPSS, R | 2,000–3,000 | Max 10 breaks |
| 6. Segmentation analysis (12,000–25,000) | |||
| Cluster | SPSS, XLSTAT, R | 1,000–3,000 | Number of attributes |
| Discriminant | SPSS, XLSTAT, R | 3,000–5,000 | Number of attributes |
| CHAID | SPSS, XLSTAT, R | 2,000–3,000 | Number of attributes |
| Discriminant with covariance analysis | Latent Gold | 5,000–8,000 | Number of attributes |
| 7. Penalty analysis | |||
| Penalty analysis | XLSTAT | 1,000 | By total |
| 8. Ideal range analysis | |||
| Ideal range analysis | SPSS, Macro | 1,000–3,000 | Max 6 products |
| 9. Acceptable price analysis (PSM) | |||
| Acceptable price analysis | Excel | 500–1,500 | Max 10 breaks |
| 10. TURF analysis | |||
| TURF analysis | XLSTAT | 1,500 | By total |
| 11. Conjoint analysis | |||
| Simple conjoint | XLSTAT | 3,000–5,000 | Number of combinations |
| CBC | XLSTAT | 4,000–6,000 | Number of combinations |
| MaxDiff | XLSTAT | 3,000–4,000 | Number of combinations |
| 12. Weighting data | |||
| Weighting data | SPSS, QPS | 500–1,000 | Depth of weighting matrix |
| 13. Questionnaire validity & reliability test | |||
| Validity & reliability test | SPSS | 500–1,000 | Number of questions & sections |
| 14. Quadrant analysis | |||
| Key driver, gap & consumer value analysis | XLSTAT, SPSS | 2,500–4,000 | By total |
| 15. Structural Equation Modeling (SEM) | |||
| SEM | LISREL, AMOS | 5,000–10,000 | By total |
| 16. Brand equity model | |||
| Brand equity model | QPS + Excel, SPSS | 5,000–7,500 | By total |
| 17. Predictive analysis | |||
| Predictive analysis | SPSS, XLSTAT, R | 7,500–15,000 | By total |
| 18. Text mining | |||
| Sentiment analysis (social media, web news, etc.) | Python, R | 10,000–20,000 | By time period |
| 19. Media monitoring analysis | |||
| Media monitoring analysis | Python, R | 15,000–30,000 | By time period |
| 20. Machine learning & data visualization | |||
| ML & data visualization | Java, R, VB, web dev | 10,000–25,000 | Per project |
| No | Type of advisor | Job description | IDR (x1,000) / month |
|---|---|---|---|
| 1 | Data Processing advisor | Input, tips and problem-solving tricks for issues that arise in data preparation, scripting, data cleaning and tabulation. | 3,000–5,000 |
| 2 | Data Analysis advisor | Everything in Data Processing advisor, plus advising PM/researchers on questionnaire design with full analysis features, and support for in-depth analysis of every insight to explore. | 6,000–8,000 |
| 3 | QC advisor | Optimize CAPI software as a QC tool, manually and systematically. | 2,000–3,000 |
Note: all prices listed depend on the number of projects per month.
Want a tailored quote for your project? Reach our team directly.
AMOEBA