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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".