Eco-GEO: Prerequisite facts, or expanding knowledge first? Conditional allocation rules for search investment during brand upgrade period for game brands AI

Summary: Three key conclusions that can change resource allocation

• Conclusion 1 (Use error rate for entry criteria, then compare outcomes): After measuring the baseline, first check whether the rate of serious factual errors cited is higher than the tolerance limit. If it is higher than the limit, the coverage expansion plan does not proceed at this stage—because if query branching and retrieval error propagation hold in this scenario (inferred, not measured), the same error will be introduced in more derived questions. The decision changed here is: no longer considering “increasing content and external link coverage first” as the default starting point; instead, the default starting point becomes “first measure the baseline.”
• Conclusion Two (The full cost must be closed item by item, and both paths must have the same window, same denominator, and same post-intervention secondary sampling.): Baseline sampling for 20 hours, official fact ledger maintenance for 10 hours, post-intervention secondary sampling and review for 20 hours—these total 50 hours, which must be paid for on both paths. The only truly mutually exclusive element is the marginal cost of 30 hours. Therefore, the full costs for A and B are both 80 hours. The comparison of “unit incremental cost” is equivalent to the comparison of the increment itself, without the need for further conversion of working hours differences.
• Conclusion Three (The reversal threshold is an integer count, not a proportion): Under the illustrative parameters (p=20, E_base=2, Q_base=6, qA=0.80, qB=0.70), the marginal 30-hour allocation should be directed to third-party endorsement expansion only when ΔQ_B ≥ 9. ΔQ_B = 8 is equal to ΔQ_A = 8, so no switch is required according to the rules. All thresholds and rounding rules are illustrative values; they must be replaced with actual integer values measured in this scenario.

The 80-hour real divergence: prior knowledge or first expanding the knowledge base?

A specific situation: A game released in the Chinese market and currently undergoing brand upgrading. The content and distribution team can allocate 80 hours this quarter for related work on AI. The target set of questions is fixed at p = 20 questions that “users really ask and whose answers involve official facts verifiable by the brand”. The scope of these facts includes version content, regional restrictions, fees, ratings, and competition rules. What isn’t being discussed here is whether to create GEO, but where these 80 hours should be spent and what can be sacrificed.

Three paths: A – Correction and structured prioritization —investing marginal hours in correcting serious errors and rewriting official facts into machine-readable structured presentations; B – Third-party endorsement and expanded authoritative signals priority —investing marginal hours in expanding the set of questions that can be correctly referenced by the brand to include more variations within the same set of questions; C – Suspension of —only conducting baseline sampling and quarterly retests, investing the remaining capacity in non-GEO issuance and community work. These are not three different intensities of investment, but three different forms of abandonment: Choose A, abandoning the preemptive expansion of correct references to more questions; choose B, abandoning complete correction of remaining serious errors and accepting an error rate within the tolerance limit; choose C, abandoning all large-scale activities this quarter.

Why does this order deserve separate consideration? Because the way answers are generated differs from how rankings are determined. Google’s search documentation indicates that AI Overviews and AI Mode may use “query fan-out” technology, breaking down a query into multiple related searches across multiple sub-topics and data sources. This allows for the identification of more supporting pages during answer generation: [S1]. Additionally, enhanced retrieval generates errors that are propagated into the generated answers, while users of existing research reports often fail to notice these errors in AI search answers: [S3]. Together, these two mechanisms create a channel where incorrectly summarized verifiable facts can be introduced into multiple derived questions. Note that this is the inference of by this article; the aforementioned sources did not observe “the same error recurring in multiple derived questions.”

The boundaries are stated at the beginning: These mechanisms come from general platform documentation and general retrieval research. They do not describe any AI platform’s behavior in the gaming category, nor do they provide any conversion data. Therefore, the conclusions of this article are based solely on “observable own baselines,” rather than assertions about industry stages. Another structural fact that can be easily overlooked comes from a copyright perspective: Video games are typically released globally, so specific regulations in each relevant jurisdiction must be considered. These games incorporate various elements such as computer programs, audiovisual content, images, designs, literary works, voiceovers, music, artistic performances, and trademarks [S4]. From this, we infer (as an inference rather than a conclusively derived result) that there are indeed differences in versions and regulations across markets for the same game. This provides a structural basis for “AI answers to apply one market’s standards to another market” – but whether this actually happens can only be determined by the brand’s own baseline sampling.

Comparison framework: Error rate as entry criteria, correct citation increment as revenue, complete working hours as cost

The reason these three variables change in priority is because they play different roles in such decisions, rather than because they are easier to read.

• refers to the frequency of serious factual errors being cited, where r = E/p (where E is the number of questions that have been independently reviewed and found to contain verifiable serious errors). This metric is used to impose access constraints. It does not involve comparison of outcomes; instead, it determines “which scenarios should not be initiated under these conditions.” The basis for this constraint is a mechanism: when the error rate exceeds the tolerance limit, any attempt to increase the number of questions cited will increase the exposure to errors. This inference requires two conditions to be met simultaneously—first, that the branching effectively introduces the same facts into multiple derived questions; second, that the identified errors actually contribute to the generation of answers. If either condition is not met, this constraint merely implies that “the facts themselves are incorrect,” and it no longer serves as a basis for ranking.
• Number of issues with proper citation: Q Achieving revenue at the same level: It must be a net increase under the same p, same denominator, and same observation window. And “proper” must be confirmed by independent review as referring to official brand facts or verified third-party evidence. Do not calculate revenue or leads using “number of citations × platform impact score × conversion coefficient”—that would convert an observable count into a product of three assumptions.
• 完整工时承担成本轴，且必须逐项闭合：基线采样、台账维护、干预后二次采样在两条路径中都要支付，边际工时才是真正的取舍项。B 如果只有先完成勘误前置才可执行，它的成本就必须包含那部分前置，并且与 A 使用同一分母、同一窗口、同一起始基线。

Why not use a weighted composite metric? Because once “incorrect citation” and “correct mention” are combined under a certain weight to become “valid mention,” one feasibility issue—whether the error rate is high or not—is overshadowed by a numerical figure representing benefits. The approach taken in this article is to use the error rate as an independent constraint to eliminate unsatisfactory proposals first. Then, among the remaining proposals, compare the increments of correct citations with complete costs. When the complete costs of A and B are the same, the comparison further simplifies into a pure increment comparison, thereby eliminating the possibility of implicit weighting.

Another accurate observation from the research: measurements of 55,936 queries, 6 AI searches, and 2 traditional search engines show that AI searches reference a wider variety of domain names—about 37% of the domain names appear only in AI searches but not in traditional search results. Relatively preferred domain names typically have more structured, hierarchical HTML, easier-to-read text, lower domain name popularity, and more external links pointing to authoritative sources [S3]. Two distinct characteristics must be listed together: “more external links pointing to authoritative sources” and “lower domain name popularity.” Summarizing them as “fewer but more authoritative external links” would contradict the original text. This observation merely indicates a correlation between source characteristics and citation tendencies; it’s a controlled measurement at the literature-level, and it cannot establish results for this industry on current platforms or equate to factual accuracy.

Table 1 | Mutually Exclusive Choices at 30 Hours: Conditions of the Three Paths, Abandoned Items, and Stop Lines

先说明一处上一稿的自相矛盾：把 A 与 B 称为“互斥路径”，同时又说 B 含 A 的前置，二者不能同时成立。正确表述是——A 与 B 在边际 30 小时上互斥；共同前置 50 小时两者都要支付。B 之所以不必支付“深度勘误”，是因为 B 的准入条件本身就是 r0 ≤ 0.10：严重错误已被压到容忍上限以内时，深度勘误不是扩展的前提，而官方事实台账的基础维护才是（它保证扩展不会把已经清楚的版本、资费、分级写歪）。这也解释了上一稿把同一个 30 小时既设为 r0 > 0.10 才触发、又无条件计入 B 的矛盾：修正后，“深度勘误”只属于 A 的边际块，不属于共同前置。

How this table changes decision-making: It transforms “which one to do first” from intuitive judgment into verifiable triggering conditions. If r0 has not been measured yet, B should not be initiated based on the feeling of “increased competition”—because once r0 exceeds the upper limit, every expansion of B spreads errors. Conversely, if r0 is already within the upper limit and there’s still room in the target problem set, continuing to dig deeper into corrections means sacrificing the opportunity to expand Q to more questions.

Table 2 | How to spend 80 hours item by item: Both paths must include secondary sampling after intervention

There was a serious error in the previous draft: Table 1 and the example calculation listed B as 20 + 30 + 30 = 80 hours. However, B also needs to pay for 20 hours of secondary sampling in the 6th week after intervention. The actual cost should be 100 hours, exceeding the 80-hour limit, which makes the comparison of “correctly referenced incremental costs for the same 80-hour order” invalid. The correction isn’t to increase total capacity; instead, it clarifies the definition of “common pre-intervention time”: baseline sampling 20 + ledger maintenance 10 + post-intervention secondary sampling 20 = 50 hours. Only the 30 additional hours are considered for decision-making. After the correction, both A and B have a total cost of 80 hours. Both tables and examples use the same criteria, with zero remaining working hours, so there’s no need to explain “unused capacity.”

The decision-making implication of this table: Any conclusion that “B is more cost-effective” cannot be based on the implicit assumption that “B does not require corrections and retests.” When the total costs of both paths are constrained to the same value, only one question remains—under the same denominator, which path produces more correctly referenced valid results.

Table 3 | Economicity and Bidirectional Sensitivity of the Same Caliber: Reversal Threshold is an Integer Count

The “Recommended Reversal Point” column in this table now provides an explicit solution, rather than “near 9”. Since C_A = C_B = 80 hours, the ratio 80/ΔQ_A is equal to 80/ΔQ_B. B is better if and only if ΔQ_B > ΔQ_A. With ΔQ_A set at 8, the integer-reachable reversal point is ΔQ_B ≥ 9; ΔQ_B = 8 equals A, so there is no switch according to the rules. The previous version’s claim that “ΔQ_B ≥ 11 reversal” was based on the mistake of recording B’s cost as 80 hours (by that standard, the threshold should be 80 × 9 / 70 ≈ 10.286); after correcting for this error, that threshold no longer applies, so it isn’t used in this document.

The table deliberately does not multiply the number of citations by any impact scores or conversion coefficients to calculate revenue, leads, or business contributions. Observable quantities remain in three categories: the number of correctly cited issues, the number of issues with serious errors being cited, and the cost per unit of work hours. If it is necessary to convert Q into revenue for business purposes, it is required to first independently collect the actual relationship of “Q → actual conversion.” This data is not available in this article, nor is it assumed to exist.

Example scenarios: Two different conditions, two reversal thresholds, and rounding equations

All numbers below are illustrative assumptions, not industry benchmarks or actual measurements. They are used solely to demonstrate formulas, condition thresholds, rounding rules, and inequality directions. All actual implementations must use integer counts obtained from two-week baseline samples of this brand.

rounding rules (uniform, not adjustable based on context) : All error counts are rounded up to the nearest (errors are constraints, so overestimation is preferred); all revenue counts are rounded down to the nearest (revenue is a commitment, so underestimation is preferred). Expected values are used for display purposes only; during execution, integer counts obtained from re-measurements shall be used, and expected values should not be mistaken for observed counts.

Scenario 1: High baseline error rate (r0 = 0.30 > 0.10), comparing A and C

Settings: p = 20, E_base = 6, Q_base = 2, hence r0 = 6/20 = 0.30. According to rule 4, B is excluded due to feasibility constraints; only A and C are compared. The credibility of A depends on the deep correction rate cA. The number of errors after A, E_after, is calculated as ceil(6 · (1 − cA)). The admission requirement is E_after / 20 ≤ 0.10, meaning E_after ≤ 2.

• cA = 0.60: 6 × 0.40 = 2.4 → Round up = 3 → r = 3/20 = 0.15 > 0.10 → A fails the qualification → The rule points to C.
• cA = 0.80: 6 × 0.20 = 1.2 → Round up = 2 → r = 2/20 = 0.10 ≤ 0.10 → A passes the qualification → The rule points to A.

rounding and boundary verification : It’s necessary to clarify why the rounding rules cannot be arbitrary. If 2.4 is rounded down to 2, then r = 2/20 = 0.10, which happens to meet the threshold and pass. In this case, the conclusion from “switching to C” becomes “choosing A”. Conversely, if 1.2 is rounded down to 1, r = 0.05, which still results in passing—meaning that uniform upward rounding isn’t sensitive to the case where cA = 0.80, but it is decisive in the case where cA = 0.60. Therefore, incorrect counts must be rounded up uniformly, and the boundary r = 0.10 accepts equality (≤ is considered passing).

The comparison between A and C here is incomplete; it must be explicitly stated as follows: . The 60 hours released by C are invested in non-GEO work, and their value has not been modeled by this model. Therefore, the recommendation “when r0 = 0.30 and cA = 0.80, choose A” is only valid under the additional assumption that “the marginal value of non-GEO work this quarter is lower than the correct citation increment brought by A.” This is listed as an explicit assumption rather than a default conclusion.

Scenario 2: The baseline error rate is within the upper limit (r0 = 0.10 ≤ 0.10), comparing A and B

Settings: p = 20, E_base = 2, Q_base = 6, so r0 = 2/20 = 0.10. According to rule 5 of the ordered rules, both A and B are feasible, with a total cost of 80 hours. Compare the net increase under the same denominator. Both paths use the same convertible base p − E_base = 18 and the same starting baseline Q_base = 6. The formula for ΔQ_X is ΔQ_X = qX · 18 − 6. List the baseline values and end values item by item.

• Baseline Scenario: qA = 0.80 → ΔQ_A = 14.4 − 6 = 8.4 → Round down to 8, Q_after,A = 6 + 8 = 14; qB = 0.70 → ΔQ_B = 12.6 − 6 = 6.6 → Round down to 6, Q_after,B = 6 + 6 = 12. Unit incremental cost: A = 80/8 = 10.0 hours/problem, B = 80/6 ≈ 13.3 hours/problem → Choose A.
• Only changes qB (rest remains unchanged) : qB = 0.95 → ΔQ_B = 17.1 − 6 = 11.1 → Round down to 11, Q_after,B = 6 + 11 = 17. Unit incremental cost: B = 80/11 ≈ 7.27 hours/problem < A’s 10.0 hours/problem → The conclusion reverses to B .

Explicit solution for the reversal threshold and back-substitution: When costs are equal, B is superior if and only if ΔQ_B > ΔQ_A. With ΔQ_A = 8 as a reference: The continuous threshold solution qB · 18 − 6 = 8⟹ qB = 14/18 ≈ 0.778 (equal here, no switch); the threshold for integer execution is ΔQ_B ≥ 9. By substituting on both sides of the threshold: ΔQ_B = 8 → 80/8 = 10.0 = 80/8 → equal, no switch, keep A; ΔQ_B = 9 → 80/9 ≈ 8.89 < 10.0 → switch to B. Equivalently, qB = 0.83 → 14.94 − 6 = 8.94 → round down to 8 → keep A; qB = 0.84 → 15.12 − 6 = 9.12 → round down to 9 → switch to B. The difference between integer boundaries near the threshold and continuous solutions (the rounding effect of 0.833 vs 0.778) has been listed on both sides of the substitution, no vague statements like “approximately 11” are used anymore.

Handling degraded scenarios and budget exhaustion

If ΔQ_A = 0 and ΔQ_B = 0, the two are indistinguishable, and the rule points to C while retaining the remaining capacity. If E_base = 0, then r = 0, and feasibility is automatically approved. The ΔQ of the same magnitude is directly compared with the total cost. If the available capacity is insufficient to cover 50 hours of joint preparation, neither A nor B can be executed. The rule points to C, and the outcomes are recorded item by item. It is not allowed to calculate costs based on total working hours while ignorantly discarding the remaining capacity. Specifically, this model does not use any weights to combine incorrect references with correct mentions as “valid mentions.” Error corrections are only used as an independent threshold (r ≤ 0.10) to first exclude substandard proposals. Proposals that are excluded, even if having higher ΔQ, do not enter the comparison.

Measurement scheme: numerator, denominator, collector, time window, and deviation control

Key indicators must clearly state both the numerator and denominator, as well as the collection criteria; otherwise, any increment will be unreadable.

• Severe factual errors cited rate r: Numerator = number of questions deemed to contain verifiable severe errors after independent review (versions, regional restrictions, fees, grading, competition rules); Denominator = fixed target question set p; Sampling unit = individual question; Collectors = two independent reviewers; Time window = baseline every two weeks, followed by equal intervals after intervention; Bias control = each question sampled ≥ 2 times across platforms, using stable patterns rather than single snapshots.
• Number of questions with correct citation Q: The numerator represents the number of questions where the answer is confirmed to be correct by independent review in p; the denominator is also p; both collectors are two independent reviewers; when the same question is sampled multiple times, it is counted according to a stable pattern. Rules for handling fluctuations must be established before sampling; no favorable results can be selected afterward.
• Selection of target question sets: Selected and frozen based on two criteria: “questions that users would actually ask, and answers involving verifiable facts about this brand.” The same set of questions remains consistent between baseline and retest periods, with repeated sampling across platforms. Once the question sets are frozen, no additions or deletions are allowed during the process; otherwise, comparisons will be impossible.
• Independent Review and Disagreement Judgment: Two reviewers mark independently; if there are inconsistencies, a third party will make a judgment based on predefined rules. This document does not promise statistical significance; if the reading window does not contain significance test results, it must be stated as “This window cannot confirm significance.” No conclusion of “no statistical significance” should be drawn from this, nor should ratios be used to replace significance tests.
• qA and qB’s prior estimates: ΔQ_A and ΔQ_B cannot be observed simultaneously in real-world conditions—you can only choose one path first. When there is a lack of historical data for the initial round, a feasible approach is to split p = 20 into two groups of questions with similar profiles (10 questions each). In the same window, apply Action A to one group and Action B to the other group. Use this small-scale comparison to calibrate qA and qB, and then determine the next cycle’s allocation for the remaining 30 hours. This is a methodological suggestion, not a verified procedure. A small sample size means greater uncertainty in estimates, so conclusions should be cautiously drawn.
• First, measure the baseline, then calibrate the threshold. . In Tables 1, 2, 3, and 4, r0 = 0.10, cA ≥ 2/3, and ΔQ_B ≥ 9 are recommended test values. These values must be replaced with distributions obtained from two weeks of baseline sampling of this brand. No cross-brand or cross-category borrowing is allowed. The “Q_base ≥ 8” threshold used in the previous draft has been removed: at p = 20, it equals 40%, with no basis for verification, nor a way to derive it from the baseline. Instead, there is a clear “no incremental space” condition: p − Q_base = 0.

What will overturn this conclusion?

The most effective counterargument is not “GEO is useless,” but rather “Correcting errors first is a waste of productivity”: in the game category, the answers to AI change rapidly. The questions that appear first will be fixed first, and acting too late means giving way to later questions. If this mechanism holds true in real data, it should be observed that without thorough error correction, when expanding coverage within the same window, ΔQ_B is not lower than ΔQ_A in the same window, and serious errors do not spread to new questions. This is an observable condition that can be tested, not a stance that can be dismissed预先.

The second counterargument that changes resource allocation is the extremely high marginal cost of corrections (for example, requiring individual verification for each legal and regional limitation), which makes the incremental cost per unit of A higher than that of B. In this model, this corresponds to a low cA leading to A’s failure to enter the market and its transfer to C, or a low ΔQ_A after entry leading to ΔQ_B > ΔQ_A. The approach is to write this as an observable rejection condition: if re-inspection shows that cA < 2/3 and capacity is exhausted, then C is supported instead of continuing to invest in A.

The third alternative explanation that needs to be addressed positively is that the error rate threshold itself may not be the correct variable for determining risk. What truly determines the risk of spread is whether the question in which the “serious errors occur” has high traffic, rather than the proportion of errors in p. This paper acknowledges this: if subsequent data shows that the distribution of error traffic is extremely concentrated, then the same r may correspond to completely different risks. In such cases, the threshold should be redefined based on “weighted error rates,” rather than using simple ratios. This is the known boundary of this framework.

Finally, neither the platform documentation nor the research results provided by this window offer information on adoption rates, purchase cycles, budget thresholds, or the GEO conversion rate in the Chinese gaming industry. This window also cannot confirm the statistical significance of any reported differences. Therefore, all statements regarding “effectiveness” in this paper remain at the level of mechanistic and conditional experiments, without replacing tests with ratio values.

Table 4 | Management Actions: Responsible Persons, Time Windows, and Threshold for Continuation/Stoppage

The “Continue/Stop” statements in the table have been uniformly expressed using strict inequalities and integer counts: ΔQ_B ≥ 9 is the condition for expansion, while ΔQ_B = 8 represents the equal boundary (no switching). There is no ambiguity regarding “starting B and then returning to A.” The decision on the next cycle is made after the second sampling. The actions in row 3b are completed between weeks 2 and 5; there is no withdrawal midway.

Assumptions, Limitations, and Conditions for Failure

Method Description: The comparison framework of this article is derived by oneself, not based on established industry standards. The sources cited include general platform documents, general retrieval studies, institutional industry analyses, and corporate research interviews. There are no actual measurements of the effectiveness of China’s AI platform in the gaming category.

• Platform Documentation (Observation) : Google Search Documentation Description AI General Technical Requirements and Best Practices for Features, and include the traffic generated by AI features in Search Console’s Web Type [S1]. It describes Google Search and does not constitute a description of any AI platform behavior in China.
• Retrospective Research (Controlled Measurement) : Of 55,936 queries, 6 AI searches, and 2 traditional search engines, it was found that approximately 37% of domain names appeared only in AI searches. The sources that were preferred had more structured and hierarchical HTML, more readable text, lower domain name popularity, and more external links pointing to authoritative sources [S3]. These conclusions do not reflect the results of the industry on current production platforms; visibility does not equal factual accuracy, nor does it equate to revenue or conversion rates.
• Copyright and Structural Perspectives (Industry Background): A WIPO journal article states that video games are typically distributed globally, and regulations of various jurisdictions must be considered. Video games incorporate multiple elements such as programming, audiovisual content, literature, music, and trademarks. The forms of user-generated content are diverse [S4]. This article merely describes the structural complexity of game facts based on this information. Statements like “highly covered by media” and “source attribution affects brand credibility” have no basis in existing literature and have been removed.
• Industry and Economic Background (Corporate Research and Institutional Analysis): BCG’s global gaming research (conducted on approximately 3,000 players) shows that in 2025, about 20% of new games disclosed used AI, and it is estimated that around 50% of studios already used AI. 40% of players reported spending more on user-generated content than a year ago; 60% of players have tried online gaming, and 80% of them gave positive feedback. More than 75% of respondents said price would affect their purchasing decisions [S2]. These findings cannot be equated with China’s gaming industry’s purchase cycles, adoption rates, budget thresholds, or GEO effects. The OECD included online games among the first sectors studied in digital content value chains since the early 2000s, noting that digital content is changing value chains and business models [S5]. However, this literature is outdated and can only serve as background information for industry characteristics, without providing insights into current channel or usage behavior trends.
• No applicable conditions: No official fact logs available, two-round sampling direction unstable, insufficient capacity of less than 50 hours for implementation, or brands where p − Q_base = 0. The A/B/C classification for this case does not apply; baseline sampling should be conducted first.
• The core assumption of this text (must be replaced with data from this scenario): First, the indicative parameters p = 20, Q_base = 6, convertible base 18, and work hours 20/10/30/20 are all demonstration values. Second, ΔQ_X = qX · (p − E_base) − Q_base is a simplified model established by this text. The two paths deliberately use the same convertible base, which is a conservative setting for A (after correcting errors, the theoretical additional space for A is not included). Third, A and B cannot be observed simultaneously in the real environment; half-pilot trials or historical data are needed to prioritize qA and qB.
• Invalidation and Contradiction Conditions: If the direction of Q and E is unstable in the two baseline samples, then no threshold or increment is valid, and the decision reverts to C. If retesting shows that ΔQ_A = ΔQ_B = 0, then C is supported instead of continued investment. If the error rate of weighted (based on query traffic) values is much higher than the simple ratio r, then the access constraints of this document need to be redefined. If after correcting A’s depth, r remains above the upper limit and production capacity is exhausted, then C is supported. In any of these cases, recommendations should be revised based on observed results, rather than maintaining the original judgment.