When a brand's media peak occurs — a surge in mentions, shares, and reactions that pushes above the rolling baseline — something happens to consumer behavior afterward. The ICDS Methodological Series has, across six prior articles, progressively mapped what that something is: an activation of the brand’s Signal Chain, progressing from media event to informational search, attitude formation, transactional search, and ultimately behavioral intent. The key insight from previous articles is that this chain does not fire uniformly: its activation depends on the expressive intensity of the peak, the Balance of Expression within it, and the category type of the brand.

What the series has not yet examined is whether the chain fires differently depending on where in the world the brand is located. The present article poses that question directly: controlling for peak magnitude and expressive structure, does consumer search responsiveness to brand media peaks differ systematically across countries? And if so, what explains those differences?

The question is practically important. Global brand managers routinely compare the “media efficiency” of campaigns across markets: how many incremental search queries, website visits, or purchases did a media spike generate per dollar spent or per million impressions reached? Implicit in such comparisons is the assumption that one unit of media intensity produces a comparable consumer behavioral response regardless of geography. Our data suggest this assumption is incorrect — and that ignoring it produces systematically biased cross-market assessments.

The question is also theoretically important. If responsiveness differs across countries, something about the information environment, cultural dispositions, platform ecosystem, or consumer processing habits must account for the difference. Identifying those drivers is a necessary step toward a general theory of how media fields translate into consumer behavior across different societal contexts.

We approach this question as a pilot investigation rather than a definitive causal study. The FMCG corpus is geographically broad — 26 countries spanning five macro-regions — but shallow in most individual markets (typically two to three brands per country). The results reported here are therefore directional evidence that motivates hypotheses for a larger dedicated study, rather than conclusions warranting strong causal claims. We are explicit about this throughout.

The most relevant prior evidence comes from Liadeli, Sotgiu, and Verlegh’s (2023) meta-analysis of owned social media effectiveness published in the Journal of Marketing. Synthesizing data from 152 studies, the authors demonstrate that owned social media has a stronger effect on sales in countries with high power distance — a Hofstede (1980) cultural dimension measuring the degree to which less powerful members of society accept the unequal distribution of power. The proposed mechanism is that high-power-distance cultures are more receptive to branded communications that fulfill materialistic and status-related needs, making the brand’s social media presence a stronger purchase cue. The authors also show that mobile phone penetration amplifies the effect of owned social media on sales, and conclude that a one-size-fits-all global social media strategy is likely suboptimal.

This finding is the closest existing precedent to our research question. It demonstrates that the national context systematically moderates how brand social media activity translates into commercial outcomes. However, the Liadeli et al. study examines owned content — brand-controlled posts with pre-determined structure and intent — rather than the organic, uncontrolled media peaks that are the unit of analysis in the ICDS framework. The mechanisms driving variation in owned content effectiveness (status signaling, platform penetration, advertising receptiveness) may differ from those driving variation in responses to earned, UGC-driven peaks. The ICDS signal chain begins with a media event that the brand does not control; the behavioral consequences of that event in different national contexts remain unmapped.

Complementary evidence comes from cross-national studies of social media and brand relationships. Baskentli et al. (2016), in a three-country study, found that the positive effect of social media interaction on brand relationship quality was more pronounced among consumers high in uncertainty avoidance — the cultural disposition to seek structure and avoid ambiguity. Consumers from high uncertainty-avoidance cultures were found to invest more in processing brand-related information and to form stronger brand relationships as a result of that processing. This finding is directly relevant to our dependent variable: if high uncertainty-avoidance consumers process brand information more thoroughly, we would expect their search response to brand media peaks to be more sustained and category-sensitive.

Connell et al. (2023), however, introduce an important qualification: in their netnographic study of brand communities, national cultural dimensions did not significantly moderate the engagement behaviors of consumers who were already engaged with a brand. The moderation operated at an earlier stage — who became engaged in the first place — rather than at the level of how engaged consumers subsequently behaved. This distinction matters for our research design: we are studying the aggregate search behavior of a national market in response to a peak event, which captures both engaged and non-engaged consumers, making cultural moderation more likely to be visible than in a study restricted to already-engaged brand community members.

A consistent theme in the cross-cultural social media literature is that the behavioral implications of social media use differ not only because of cultural values but because of structural differences in the information environment itself. Three structural factors are particularly relevant to our research question.

The first is information saturation — the density of brand-related and non-brand-related content in the user’s social media feed. In highly saturated markets, individual brand peaks are harder to detect above the noise floor, and the cognitive resources available for processing any single peak are correspondingly lower (Song et al., 2017; Lee et al., 2016). The concept of social media fatigue describes the downstream consequence: as saturation increases, consumers shift toward more superficial, peripheral-route processing, reducing the probability that any given media event will activate the deeper cognitive engagement required to produce a durable behavioral effect.

The second structural factor is platform ecology — the set of platforms through which consumers encounter brand information and subsequently act on it. In Western markets, the Google search ecosystem is the dominant channel through which brand-related information-seeking behavior is expressed and measured. In emerging markets, this relationship is less certain. DataReportal’s Digital 2025 report documents that nearly 70% of Nigerian internet users cite brand discovery as a primary reason for social media use — a figure far exceeding Western benchmarks — yet the search behavior expressing that discovery intent may occur on WhatsApp, YouTube, or local platforms rather than on Google Search. This platform fragmentation creates a systematic measurement artifact: Google Trends, the primary data source for the ICDS search indices, will systematically undercount consumer responsiveness in markets where Google search is not the dominant expression of information-seeking intent.

The third factor is the novelty premium — the degree to which media peaks represent genuinely new information relative to the consumer’s existing knowledge of the brand. In markets where a brand has low baseline awareness, a media peak carries a larger marginal information value: it tells the consumer something they did not previously know. In markets where the brand is already deeply embedded in consumer consciousness, the same peak carries less new information and may produce a correspondingly smaller behavioral response. This mechanism is consistent with the DataReportal finding that consumers in less developed economies are more likely to use social media to learn about brands, suggesting a higher novelty premium for brand-related media events in those markets — a premium that should, in principle, produce higher behavioral responsiveness.

Against this background, the specific gap that the present article addresses can be stated precisely. Prior research has established: (1) that owned social media effectiveness varies across countries, moderated by power distance and mobile penetration (Liadeli et al., 2023); (2) that cultural dimensions, particularly uncertainty avoidance, influence how deeply consumers process brand social media interactions (Baskentli et al., 2016); (3) that information saturation and platform ecology create structural variation in how social media signals are received and processed (Song et al., 2017; DataReportal, 2025).

What has not been studied is the cross-national conversion rate of organic brand media peaks into consumer search behavior — the specific behavioral outcome that the ICDS Signal Chain identifies as the measurable proxy for attitudinal processing. This is the contribution of the present article. We do not claim to resolve the causal question of what drives the differences we observe; our corpus is too small for that. We claim to document the fact of variation, to propose the most plausible explanatory mechanisms, and to introduce a measurement instrument — the Country Search Responsiveness Index — that makes the variation analytically tractable.

The analysis is based on the ICDS FMCG monitoring corpus, which contains 66 brand-peak observations spanning 26 countries and 10 product categories. One observation (McCain Foods, Canada) was excluded as an extreme outlier in search index values (raw Δt0 exceeding 400 index points), yielding a working sample of 65 observations. The geographic distribution is: Western markets (USA, UK, Germany, France, Switzerland, Netherlands, Austria, Sweden, Denmark, Italy, Canada) — 32 observations; Asia (India, Indonesia, Philippines, South Korea, Thailand, Vietnam, Singapore) — 18 observations; Latin America (Mexico, Brazil) — 7 observations; Africa and Middle East (Nigeria, South Africa, Kenya, UAE, Saudi Arabia) — 7 observations; Turkey — 1 observation.

For each observation, the dataset contains: total monthly mention volume and deviation from rolling baseline (peak magnitude); expressive intensity and Balance of Expression; three search indices (brand, product category, main competitor), each normalized to the pre-peak baseline period (t−1 = 100); geo-context classification (WORLDWIDE vs. country-specific); peak type classification (global, regional, local event); and geo-reliability rating. All search indices are derived from Google Trends relative search volume data, indexed to the pre-peak period.

A key methodological contribution of Articles 6 and 7 is the introduction of a three-level search index architecture that enables cross-country comparisons that were not possible with a single brand-level index. The three indices are:

Brand index (Bt): Google Trends relative search volume for the brand name, indexed to t−1 = 100. This is the primary behavioral outcome variable.

Category index (Ct): Google Trends relative search volume for the product category, indexed to t−1 = 100. This captures ambient category-level search dynamics that the brand does not control.

Competitor index (Compt): Google Trends relative search volume for the main competitor brand, indexed to t−1 = 100. This captures how competitor brands benefit or are displaced by the focal brand’s peak.

The normalized search index (Nt = Bt − Ct) measures the brand’s search performance relative to its category, isolating the brand-specific effect of the media peak from ambient category-level variation. Crucially, the brand and category indices show near-zero correlation across the corpus (Spearman ρ = +0.10, p = 0.41), confirming that brand peaks do not systematically elevate their categories — they create isolated brand-specific search impulses. This independence is an empirical result in its own right, and it validates the use of the normalized index as a cleaner signal than the raw brand index.

To enable cross-country comparisons that are not confounded by differences in peak magnitude, we introduce the Country Search Responsiveness Index (CSRI) — a normalization of search response by the size of the triggering media peak. It is defined as:

A positive CSRI indicates that a media peak converts into lasting search interest that persists beyond the peak month. A CSRI of zero indicates no net conversion. A negative CSRI indicates that post-peak search interest falls below the pre-peak baseline, suggesting the peak produced a temporary spike followed by rebound-below-baseline disengagement — a pattern consistent with audience fatigue or disappointment following the peak event.

The CSRI is computed at the individual brand-peak level and then aggregated by country and by macro-region. Country-level aggregates are interpreted with appropriate caution given typical sample sizes of one to three observations per country.

Three limitations of the current design must be stated before presenting results, as they are essential context for interpreting the findings.

Google Trends as the measurement instrument: All search indices are derived from Google Trends, which measures relative search interest on the Google Search platform. In markets where Google is not the dominant search engine (notably Russia, where Yandex dominates, and China, where Baidu dominates), or where brand discovery primarily occurs on social platforms rather than search engines (which DataReportal documents for Nigeria, Indonesia, and Brazil), the Google Trends signal will systematically undercount actual consumer information-seeking. This creates a measurement artifact that we expect to produce artificially low or negative CSRI values for affected markets. This is a measurement problem, not a behavioral one: consumers in these markets may be highly responsive to brand media peaks, but their response is expressed in channels that our measurement instrument does not capture.

WORLDWIDE vs. country-specific geo-context: 49% of observations in the corpus (primarily large Western brands) use WORLDWIDE as the Google Trends geography, meaning their search indices reflect global search interest rather than country-specific interest. The remaining 51% use country-specific geo-contexts. This asymmetry means that direct numerical comparisons between Western brands (mostly WORLDWIDE) and non-Western brands (mostly country-specific) should be interpreted with caution, as they compare different units of measurement.

Sample depth: With an average of 2.5 observations per country, country-level CSRI estimates have high uncertainty. The analysis should be understood as a mapping of the hypothesis space — identifying which countries and regions show patterns worth investigating with a dedicated larger study — rather than as a definitive measurement of country-level responsiveness.

The first result of the analysis replicates the core finding of Liadeli et al. (2023) in a different empirical context. Cross-national variation in search responsiveness is substantial and directionally consistent with prior literature on owned social media effectiveness. Western markets and Asian markets show positive average CSRI values; Latin American and African/Middle Eastern markets show negative average values.

Dt0 and Dt1 = mean change in brand search index from pre-peak baseline. CSRI Median = median of individual brand CSRI values within region. Durable = share of brands with t1 > t0. Intensity = mean expressive intensity of peaks.

The durability pattern is notable: across all regions, approximately 56–59% of brands show a growing post-peak search trajectory (t1 > t0). The universality of this proportion across regions confirms the earlier finding that the delayed-response mechanism is not a Western phenomenon — it operates across geographies. What differs is not the direction of the temporal profile but its magnitude and the platform through which it is expressed.

The consistency of durability rates across regions (57–59%) in the face of large CSRI differences is one of the most analytically interesting results in the dataset. It suggests that the underlying attitudinal mechanism — the delayed conversion of media exposure into information-seeking — operates universally, but that its magnitude of expression in Google Search depends on market-specific structural factors.

Prior research predicted that emerging markets would show higher social media responsiveness to brand communications due to higher brand discovery motivation, higher novelty premium, and lower existing brand saturation. DataReportal (2025) documents that nearly 70% of Nigerian internet users actively use social media to learn about brands — a figure that substantially exceeds Western counterparts. Yet our CSRI data show the opposite: Africa/ME yields the most negative median CSRI in the entire corpus (−1.88), and Latin America follows (−0.74).

We argue that this negative CSRI is not a behavioral finding but a measurement artifact, and that distinguishing between these two interpretations is one of the core contributions of this paper.

This interpretation is supported by several converging pieces of evidence. First, the share of brands showing a durable post-peak profile (t1 > t0) in Africa/ME is 57% — essentially identical to Western markets (59%) — despite the large CSRI difference. If consumers in these markets were genuinely less responsive, we would expect fewer brands to show the sustained-response pattern, not the same proportion. The pattern of temporal dynamics is present; only its magnitude in Google Search differs. Second, Pew Research (2019) documents that in Nigeria and Kenya, social media use is concentrated among higher-education, higher-income users — a subset that may express brand search intent differently from the broader population captured in social media mention data. Third, the specific platforms dominant in these markets (WhatsApp in Nigeria and Kenya, YouTube across sub-Saharan Africa) are either unmeasured by Google Trends or measured incompletely.

India presents a distinct and analytically productive pattern within the Asian cluster. With eight observations — the largest country-level sample outside the United States — India shows a near-zero mean search response at t0 (+0.1) but a substantial positive response at t1 (+8.1), with 62% of Indian brands showing a growing post-peak trajectory.

The Indian pattern is consistent with both a longer purchase deliberation cycle and the specific category composition of the Indian sub-corpus: six of eight brands fall in Cautious or Routine categories (cosmetics, household chemicals, dairy), where the ICDS framework predicts delayed responses. The temporal lag in India — where the search response at t0 is virtually zero but grows substantially by t1 — may also reflect mobile-first information-seeking behavior: consumers in India primarily access the internet through smartphones (fewer than half own computers according to DataReportal, 2025), and mobile-first information behavior tends to occur in shorter, more distributed sessions that accumulate over time rather than producing a concentrated immediate spike.

The India pattern also provides an important methodological lesson: evaluating media peak effectiveness at t0 alone would lead to the conclusion that brand media peaks in India are essentially ineffective. Evaluating at t+1 reveals a pattern of substantial and durable search activation. This has direct implications for how global brands assess campaign effectiveness in India — an issue we return to in the practical implications section.

One of the more unexpected findings from the analysis concerns the role of peak type rather than country of origin. We classify peaks as global (brand-event visible worldwide), regional (country- or region-specific event), or local event (hyper-local trigger). The normalized search index at t+1 differs substantially across these types:

Global peaks produce modestly positive normalized search effects. Local events — despite their small sample (n = 5) — produce the strongest brand-specific effects. Regional peaks are associated with negative normalized search indices, meaning the brand underperforms its category during and after regional peaks.

We propose a contextual specificity hypothesis to explain this pattern: local and global events create a specific, identifiable brand signal that consumers can act on through targeted search. Regional events — which by definition are visible in some markets but not others — may produce awareness without providing the contextual hooks (a specific product, a specific claim, a specific cultural moment) that motivate information-seeking. The brand becomes salient but not actionable.

This finding has a practical implication that cuts across the country-level analysis: the geographic scope of a media event may matter as much as its country of origin in determining search conversion. A global brand experiencing a local event in India may see stronger search conversion than the same brand experiencing a regional marketing push in a Western market.

A consistent finding from Articles 2 through 6 is that expressive intensity — the share of evaluative mentions in the peak — predicts search response in the full corpus, but the relationship is concentrated in Cautious product categories. We examine whether this pattern holds differentially across regions.

No correlation reaches conventional significance thresholds in any region, reflecting the small within-region samples. However, the direction of the Asia coefficient (+0.39 for t+1) is noteworthy: it is the strongest tendency toward significance in the corpus, and its direction is consistent with the global finding that expressive intensity predicts delayed search response. If the Asian sub-corpus were expanded to 30–40 observations, this coefficient would likely reach significance given the observed effect size.

The Africa/ME coefficient at t0 (+0.46) is the largest in the table and also fails to reach significance, but its direction — combined with the balance-vs.-normalized-search tendency of ρ = +0.71 (p = 0.07) documented in Article 6 — suggests that in low-saturation markets, the structural composition of the media field may be more predictive of behavioral response than in high-saturation Western markets. This is consistent with the signal clarity hypothesis: in a less noisy information environment, the quality and structure of the signal matters relatively more than its volume.

Global brand management teams face a recurring measurement challenge: how to compare the “media efficiency” of brand campaigns or organic events across geographically dispersed markets. A standard practice is to compute a ratio of behavioral outcomes to media input — impressions, peak mentions, or share-of-voice — and compare these ratios across countries. This practice implicitly assumes that one unit of media input produces a comparable behavioral effect regardless of geography, a premise that our results demonstrate to be empirically unfounded.

The bias introduced by this assumption is not random: it systematically overstates the efficiency of markets with high Google Trends responsiveness (predominantly Western markets) and understates the efficiency of markets where consumer response is expressed through non-Google channels. Applied globally, it will consistently produce the impression that Western campaigns are more “efficient” than campaigns in Africa, Latin America, or markets where WhatsApp and YouTube dominate brand discovery.

The Country Search Responsiveness Index, as defined in Section 3.3, provides a country-level correction coefficient that partially addresses this measurement bias. The principle is analogous to purchasing power parity in economics: just as GDP comparisons across countries require correction for differences in price levels, media effectiveness comparisons across countries require correction for differences in the conversion rate from media peaks to Google Search behavior.

A practical application would proceed as follows. Suppose a global brand manager wants to compare the search impact of a product launch event in Germany versus India. The raw brand search indices might show Germany producing +8 index points at t+1 and India producing +4 index points. Without correction, Germany appears twice as effective. But if Germany’s CSRI is +0.15 and India’s CSRI is +0.45 — reflecting that India converts peak media into search at three times the per-unit rate — the comparison reverses: India’s +4 points represent a higher-efficiency conversion, not a lower one. The raw index comparison is biased by market-specific structural factors; the CSRI-adjusted comparison isolates the brand-specific performance from those structural factors.

The following table presents CSRI estimates for countries with more than one observation in the corpus. Countries with a single observation are included for completeness but should be treated as preliminary signals rather than stable estimates.

The most robust estimates are for the USA and India, where sample sizes are sufficient to provide meaningful averages. The USA median CSRI of +0.10 represents the benchmark for a mature Western market: a 10-percentage-point media peak above baseline produces roughly 1 index point of additional brand search at t+1. India’s median CSRI of +1.35 indicates substantially higher per-unit conversion, even measured exclusively through Google Trends. Markets with suspected measurement artifacts (Nigeria, Indonesia, Philippines) show extreme negative values that are inconsistent with the documented high brand-discovery motivation in those markets, confirming the measurement hypothesis.

The CSRI as currently implemented is a proof-of-concept rather than a production-ready instrument. Validating it as a practical benchmarking tool would require several extensions. First, expanding the corpus to a minimum of ten brand-peak observations per country, drawn across multiple product categories and peak types, to produce stable country-level CSRI estimates. Second, developing a multi-platform search index that incorporates YouTube search, Instagram Explore, and local search platforms (Yandex, Baidu, Naver) in addition to Google Trends, reducing the measurement artifact for non-Google markets. Third, validating CSRI against an independent outcome variable — ideally actual sales data from a subset of brands — to confirm that CSRI differences reflect genuine variation in consumer responsiveness rather than only platform measurement artifacts. Fourth, testing whether CSRI is stable over time for a given market (i.e., whether India’s higher responsiveness persists across different brand categories and different years), which would justify its use as a correction coefficient.

The most theoretically important result of this analysis is the separation between two dimensions of cross-country variation that prior literature has not distinguished: the universality of the response pattern and the locality of its magnitude. The delayed-response pattern — in which brand search interest grows from t0 to t+1 in the majority of observations — holds across all regions at remarkably consistent rates (56–59%). The direction of the behavioral effect is universal. What varies is how large that effect is when measured through Google Search.

This separation has a direct theoretical implication: the universality of the pattern suggests that the underlying cognitive mechanism — which the ICDS framework identifies as progressive attitude formation following media exposure — is not culturally or geographically contingent. Consumers in Nigeria and consumers in Germany appear to undergo similar attitudinal processing when exposed to brand media peaks. What differs is the channel through which that processing is expressed behaviorally. This is a measurement problem, not a theory problem, and it suggests that the ICDS Signal Chain model is more globally generalizable than its current measurement infrastructure allows us to demonstrate.

The directional patterns in our data are broadly consistent with predictions derived from Hofstede’s cultural dimensions framework, though we cannot test these predictions formally given our sample sizes. The higher CSRI in India relative to the USA is consistent with both the higher uncertainty avoidance score of India (as documented in Hofstede’s framework, India scores moderately on uncertainty avoidance, reflecting active information-seeking before significant purchases) and the higher novelty premium for brand information in a market where many brands are less thoroughly saturated in consumer consciousness. The higher CSRI in South Korea and Thailand — both markets with high collectivist orientation and active social media information-sharing cultures — is consistent with the prediction that in collectivist societies, brand media events trigger more active communal information-seeking behavior.

The Liadeli et al. (2023) finding that power distance moderates owned social media effectiveness on sales appears partially relevant here as well: markets with higher power distance (India, Philippines, Indonesia, Nigeria) should show stronger responses to brand social media signals. Our CSRI data confirm this for India but not for the African and Southeast Asian markets, where the measurement artifact hypothesis overwhelms any cultural signal. This illustrates an important methodological lesson: cultural dimensions frameworks generate valid predictions about consumer behavior, but those predictions can only be tested when the measurement instrument is calibrated to the local platform ecosystem.

The practical implications of this analysis for global brand managers cluster around three strategic questions.

The first is benchmarking: how to compare media campaign effectiveness fairly across countries. Our CSRI framework provides a conceptual solution — normalizing observed search responses by the country’s structural responsiveness coefficient — but implementing it requires building the corpus of historical brand-peak observations that makes country-level CSRI estimates reliable. This is a data infrastructure investment, and we argue it is a necessary one for any brand operating in more than three or four national markets.

The second is timing: when to measure the outcome of a media peak. In India, the search response at t0 is near zero while the t+1 response is substantial. In Western markets, the immediate and delayed responses are more balanced. A measurement protocol that evaluates campaign impact only in the peak month will systematically undercount India’s performance relative to Western benchmarks. The appropriate measurement window differs by market.

The third is platform mix: what channels to monitor as behavioral outcome indicators. The negative CSRI values for several African and Southeast Asian markets are not evidence that brand media peaks fail to activate consumer behavior in those markets. They are evidence that Google Trends is the wrong instrument for capturing that behavior. A robust global media effectiveness measurement system must integrate platform-native search data (YouTube analytics, Instagram Insights, WhatsApp message volume where available) with Google Trends data, and must apply country-specific weighting based on the actual platform distribution of brand information-seeking in each market.

Sample depth. With 65 observations across 26 countries, the per-country sample averages 2.5 observations. Country-level CSRI estimates have high variance and should be treated as directional approximations. Causal claims about country-level responsiveness require substantially larger samples, ideally 20 or more observations per country.

Single measurement instrument. All search indices are based on Google Trends. The measurement artifact identified for Africa/ME and parts of Southeast Asia is a real constraint on the conclusions that can be drawn about those markets. Resolving it requires multi-platform search data that is not currently available in the corpus.

Observational design and endogeneity. The brands in the corpus are not randomly assigned to countries or peak types. Larger, better-resourced brands are overrepresented in Western markets, and the nature of the peaks (marketing campaign vs. scandal vs. viral UGC) differs across regions. These confounds make causal attribution difficult. The relationships documented here are correlational patterns that motivate hypotheses, not causal estimates.

WORLDWIDE geo-context for Western brands. The WORLDWIDE Google Trends aggregation used for most Western brands in the corpus does not isolate country-specific search behavior. This means Western CSRI estimates reflect global search patterns rather than the behavior of consumers in the brand’s home country, potentially diluting the country-level signal for large multinational brands.

Absence of informational/transactional search split. As in prior articles in the series, the search indices used here are aggregate brand search indices rather than decomposed informational and transactional query types. The ICDS theoretical framework predicts different temporal profiles for these two query types, but their systematic collection awaits the planned KeywordTool Pro data expansion.

The Country Search Responsiveness Index (CSRI) introduced in this article provides a conceptual instrument for making cross-country media effectiveness comparisons that correct for these structural differences. At the country level, the USA offers a baseline CSRI of approximately +0.10 (one index point of additional brand search per 10 percentage points of peak intensity), while India exhibits approximately +1.35 — thirteen times higher per-unit conversion when measured through Google Search. These differences are large enough to substantially bias global benchmarking analyses that ignore them.

In filling the gap identified in the literature review — the unmapped territory of cross-national search responsiveness to organic brand media peaks — this study simultaneously confirms prior findings (cross-national variation in brand social media effectiveness is real and substantial) and extends them (the measurement infrastructure for capturing that variation is as important as the behavioral theory explaining it). The signal conversion problem is not only a cultural question but a platform question, and resolving it will require the integration of multi-platform data sources that current global media measurement practice has not yet standardized.