Proposed Marketing Strategy to Increase Pertamax Series Sales in Indonesia: An Integrated Rise Model
Bibliographic record
Abstract
PT Pertamina Patra Niaga (PPN) has experienced a downturn in the market share of its Pertamax Series—Indonesia’s flagship non-subsidised gasoline—from 26.9% in 2017 to 21.9% in 2022, even though national demand for higher-octane fuels climbed from 15.8 to 19.2 percent during the same period. This study designs a customer-driven marketing strategy to regain that share by embedding the RISE Model service-improvement cycle including SERVQUAL, Importance–Performance Analysis (IPA), TRIZ and the 7P marketing-mix framework into a single, mixed-methods research design. SERVQUAL indicated that all 51 service attributes registered negative GAP 5 scores, signalling latent dissatisfaction. IPA grand means of 4.27 (importance) and 3.40 (performance) positioned 11 attributes in Quadrant I (“Concentrate Here”), 20 attributes in Quadrant II (“Keep Up the Good Work”), 14 attributes in Quadrant III (“Low Priority”), and 6 attributes in Quadrant IV (“Possible Overkill”). Each shortfall was reframed as a contradiction in the 12 × 12 Service-TRIZ matrix. Principles such as Segmentation, Dynamicity, and Prior Counteraction generated low-cost, high-leverage ideas including QR-code pre-payment and dispenser lane exclusively for non-subsidised fuels. Combining TRIZ solutions produced the “Pertamina Signature” concept: the energy station that bundles premium fuels, seamless digital payment, revitalised hygiene standards, and lifestyle-oriented physical evidence. A coherent 7P model aligns (1) product innovation—roll-out of bioethanol Pertamax Green 95 pilots—with (2) value for money pricing via digital payment; (3) place optimisation using geospatial traffic heat-maps; (4) geo-fenced app promotions; (5) upskilled frontline personnel; (6) smart-queue processes; and (7) refreshed visual identity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".