What is Share of Search (SoS)?

Share of search (SoS) is a brand's share of all brand searches in its category: searches for one brand divided by searches for all brands in the competitive set. Popularized by Les Binet and James Hankins through the IPA in 2020, it is a low-cost proxy for brand demand and a leading indicator of market share.

How share of search is calculated

Share of search = (searches for your brand / searches for all brands in the category) × 100

For example, if your brand receives 10,000 monthly searches and all brands in the category together receive 100,000, your share of search is 10%.

In Binet's definition, the denominator is the sum of brand searches for the competitive set, not all category searches. Generic queries such as "running shoes" are excluded, because the metric compares brands against each other (IPA, Share of Search). Some tools use a wider denominator that includes generic category terms; that variant answers a different question and should be labeled accordingly.

Origin and evidence

Les Binet presented share of search as a predictive measure at the IPA's EffWorks Global conference in October 2020, which started the IPA's work on the metric. He and James Hankins then developed it further, and Hankins led an IPA follow-up a year later (Hankins, 2021). Their main findings:

• Leading indicator. Across the categories they analyzed, share of search tended to move ahead of share of market. The lead time varied by category and could be long; the IPA reported up to a year for cars.
• Correlation, not causation. The 2021 IPA material states explicitly that the relationships are correlations. Share of search reflects demand; it does not prove what caused it.
• Smoothing. Monthly brand search data are noisy, so the IPA work recommends looking at rolling averages rather than single months.

The appeal is practical: search data are available almost in real time, for every competitor, at little cost, whereas brand tracking surveys are slow and expensive.

Data sources and their limits

• Google Trends gives relative search interest on a 0–100 scale. Google normalizes each data point by total searches in that place and period, and adds noise for privacy, which is most visible for low-volume terms (Google Trends Help, FAQ). Compare all brands within the same query so they share one scale.
• Google Ads Keyword Planner gives approximate absolute search volumes, useful for checking the scale of the Trends index.
• Google Search Console shows queries for your own site only, so it cannot provide the competitor side of the ratio.

Three choices drive most of the result: which brands form the competitive set, which brand-name variants and misspellings to include, and how to handle brand names that are also common words. Document these choices and keep them fixed over time.

Share of search vs. related metrics

• Share of market is the brand's share of actual sales. Share of search is a demand signal that tends to lead it, but the two are not the same.
• Share of voice is the brand's share of advertising spend or exposure in the category. The gap between share of voice and share of market is a separate, well-known planning concept.
• Branded search volume is the absolute number of brand searches. It moves with seasonality and overall category demand, whereas share of search removes those common movements by comparing against competitors.

Share of search in marketing mix modeling

For marketing mix modeling (MMM), share of search and brand search volume are useful in three ways, each with a caveat.

1. As a control for underlying demand. Search query volume can capture demand that would otherwise bias the estimated effect of paid search. Google researchers showed that conditioning on search query volume can correct the selection bias in paid search estimates under certain assumptions (Chen et al., 2018).
2. As an outcome of brand media. TV, video and other brand-building media raise brand searches; see how TV affects search engine marketing. If brand search is added as an ordinary control variable, it can absorb part of the effect of those channels and make them look weaker than they are. Model it as a mediator, or use it with care.
3. As a leading indicator. Because share of search tends to lead market share, it helps to explain and anticipate changes in branded search volume and base demand that sales data show only later.

In practice, share of search is most valuable next to an MMM and incrementality tests: the MMM estimates what each channel adds to sales, and share of search shows whether the brand is gaining or losing demand relative to competitors. Sellforte's MMM separates incremental media effects from base demand.

Using share of search well

• Track it monthly with a rolling average, and compare year over year to remove seasonality.
• Segment by country or region when markets differ; a national figure can hide regional changes.
• Read changes against your media plan and competitors' launches, not in isolation.
• Validate the relationship with your own sales and market share data before using it as a target.

Key takeaways

• Share of search is a brand's share of all brand searches in its competitive set, popularized by Binet and Hankins through the IPA in 2020.
• It tends to lead market share, but the evidence is correlational, and the lag varies by category.
• In MMM it can control for demand or act as a leading indicator, but treat it as partly caused by brand media.

Related terms

• Branded search advertising
• Search engine marketing (SEM)
• Incrementality
• Organic visibility

Further reading on Sellforte

• The spillover effect: how TV affects search engine marketing
• How to measure upper-funnel marketing in ecommerce
• What data do I need for marketing mix modeling?

References

1. Binet, L. (2020, October). Share of search as a predictive measure [Conference presentation]. IPA EffWorks Global 2020, Institute of Practitioners in Advertising. https://ipa.co.uk/knowledge/effectiveness-research-analysis/share-of-search
2. Hankins, J. (2021, October). Share of search: One year on [Conference presentation]. IPA EffWorks Global 2021, Institute of Practitioners in Advertising. https://ipa.co.uk/media/11056/effworkglobal-2021-share-of-search-thinktank-final-version-final.pdf
3. Chen, A., Chan, D., Perry, M., Jin, Y., Sun, Y., Wang, Y., & Koehler, J. (2018). Bias correction for paid search in media mix modeling (arXiv:1807.03292). arXiv. https://arxiv.org/abs/1807.03292
4. Google. (n.d.). FAQ about Google Trends data. Trends Help. Retrieved 2026-10-30, from https://support.google.com/trends/answer/4365533

Authors

Lauri Potka is the Chief Operating Officer at Sellforte, with over 15 years of experience in Marketing Mix Modeling, marketing measurement, and media spend optimization. Before joining Sellforte, he worked as a management consultant at the Boston Consulting Group, advising some of the world’s largest advertisers on data-driven marketing optimization. Follow Lauri in LinkedIn, where he is one of the leading voices in MMM and marketing measurement.