How Russian polypropylene appears in the algorithmic choice of domestic converters
Packaging manufacturer · feedstock purchase for the production cycle
74 out of 100 — this is how much the object influences the choice in this scenario0 — the buyer never sees you, 100 — you are recommended first
2025
Confidence: lowderived estimate- Basis
- annual corporate spend
The category has no standalone public report: the value is derived from the parent market by segment share, so it is directional rather than exact.
What to change to be chosen
We found three possible directions for growth. We estimated how each of them could affect the buyer's choice and potential revenue.
Together ≈ $0.4 mn a year
SIBURRussiaH17 moPass technical evaluation.
Shortlist7681AI answers out of 100 that name SIBUR≈1 every 6 yearsnew annual contracts with converters≈ $0.2 mn a year in these contractsScenario estimate- SIBURRussiaH27 mo
Win the comparison with foreign grades.
Shortlist7680AI answers out of 100 that name SIBUR≈1 every 7 yearsnew annual contracts with converters≈ $0.2 mn a year in these contractsScenario estimate - SIBURRussiaH36 mo
Enter converters' shortlist.
Shortlist7678AI answers out of 100 that name SIBUR+3%to revenue from the buyer's decision journeyScenario estimate - NizhnekamskneftekhimNot assessed
The market is measured, but there is no report or growth hypotheses for Nizhnekamskneftekhim. We build them to order.
What task is the buyer solving?
Pick a choice scenario — a specific task of the buyer.
See which options AI systems consider, whom they recommend and how the participants' positions change across the stages.
Language shapes the choice
We study the same task in Russian and English to see how language affects AI-system recommendations and companies’ positions.
Why does SIBUR appear in 94% of answers but get only 65% of recommendations?
We traced how SIBUR passes the five stages of choice in AI-system answers. See where its position weakens most and how competitors perform.
Measurement conditions: S1 «Buying commodity grades for the production cycle» · journey «Problem-first» · Russia · languages RU, EN · AI systems: 7 · Q1 2026
Height of a column is the share of answers where the object passes the stage. The narrowing between columns is the loss.
A stage is analytical markup of a step, not a required order of conversation: a journey may start with a recommendation and come back to comparison.
Key finding
SIBUR is named first on commodity grades, but on specialty grades the AI systems still point to foreign ranges.
Point of maximum loss
The largest loss occurs between the stages «Shortlist» and «Recommendation»
SIBUR · journey «Problem-first»
Why the position is lost
Choice factors that keep the object out of the final recommendation.
Position depends on the journey and on the scenario
Probability of a final recommendation for SIBUR for each decision journey, in both scenarios.
The scenarios are different economic tasks. Their levels may be set side by side but not subtracted: the gap between scenarios is not a deficit to be closed.
Who takes the place
Tile area and colour show the win index on this market — shortlist and recommendation. The participation index is in the table view.
What to change to be chosen more often?
Recommended for SIBUR · RussiaWe identified what keeps the company from winning against competitors and proposed concrete actions to strengthen its position in the market.
Choose the solutions you want to implement. GolOps will help organise the work and check the result.
- 01
Disclose the specifications of all your grades
Publish technical data sheets for every product in the range, including specialty grades.
Forecast+5pts to the indexPotential effect≈ $9 mn / year - 02
Prove your quality holds from batch to batch
Publish batch-to-batch consistency data so converters can compare you more easily with foreign grades.
Forecast+4pts to the indexPotential effect≈ $7 mn / year - 03
Show buyers real examples of your grades in use
Collect examples of plants already making products from your grades and publish them.
Forecast+2pts to the indexPotential effect≈ $4 mn / year
Every solution you add raises the plan’s potential
How the forecast is calculatedforecast · model
- 01 addresses the loss factor “Datasheets are not disclosed for every position” · 40% of answers
- 02 addresses the loss factor “Batch-to-batch consistency data is limited” · 32% of answers
- 03 addresses the loss factor “Few applied converting case studies” · 17% of answers
This is a sample analysis for an object that entered the universe of this measurement. The universe is assembled anew at every scan of the category: its composition and the positions within it may change. Forecast, not a measurement. The upper bound is the loss at the narrowest transition, distributed across the weights of the loss factors; stage-passing points are carried into index points one to one, and the conversion coefficient is not yet established by the methodology. The money figure uses a working calibration: CCI is measured inside the category universe rather than against the whole market, so 25 index points are taken as roughly 2 percentage points of market share. The actual gain is obtained empirically only — by repeating the same benchmark after the intervention.
Check the conclusions against the raw observations
Study the data, reproduce the conclusions or find what we missed.
How the measurement works- 170
- observations
- 7
- AI systems
- 2
- languages · RU, EN
- Q1 2026
- measurement
Polypropylene × Russia
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