Brand trackers are expensive to field, so most teams read them a few times a year. Between waves, you want an earlier signal of whether people are becoming more or less interested in your brand than in your competitors. Share of search is one of the cheapest ways to get it, because the data is public, monthly and available for every brand in your category. This guide covers how to calculate share of search, how to read it, and where it fits in a wider competitive intelligence program.
Key takeaways
- Share of search is your brand’s branded searches divided by those for every brand in your competitor set, over the same period.
- It is cheap, frequent and available for competitors, which is why marketers adopted it.
- It is commonly reported to move before share of market. Treat it as an indicator, not a forecast.
- The hard part is the query list: brand names, misspellings and product names, defined once and kept stable.
- Read it next to brand tracking to see whether search interest reflects real consideration.
What is share of search and why do marketers use it?
Share of search measures how much of a category’s branded search activity belongs to your brand. If people searched for the four running-shoe brands in your set 100,000 times last month and 30,000 of those searches were for yours, your share was 30%.
Marketers adopted it for three practical reasons. It is cheap, since search volume estimates are public. It is frequent, with monthly data rather than one tracker wave a quarter. And it covers competitors, which most of your own data does not. Someone who searches for a brand by name is showing interest, so a rising share suggests the brand is drawing more of the category’s attention.
How do you calculate share of search?
The formula is simple:
Share of search = branded searches for your brand ÷ branded searches for all brands in the set × 100
The work is in deciding what goes into each number:
- Define the category and competitor set. List the brands a buyer would realistically choose between, and keep the set stable.
- Build a branded query list for each brand. Include the brand name, common misspellings, product and sub-brand names. Exclude terms that mean something else.
- Choose one source and one region. Use the same search-volume source and market for every brand.
- Normalize. Sum each brand’s queries for the period and divide by the total across all brands, so the shares add to 100%.
- Track monthly, report quarterly. Monthly data shows when a shift began; quarterly figures smooth out seasonality.
- Compare with share of market. Put the result next to your sales or panel share for the same period and watch the gap over time.
Share of search example: how did four brands move between two quarters?
The numbers below are illustrative. Four brands compete in a category; the table shows each brand’s monthly branded search volume for the first half of the year.
| Brand | Jan | Feb | Mar | Apr | May | Jun |
|---|---|---|---|---|---|---|
| Brand A | 42,000 | 40,000 | 44,000 | 41,000 | 42,000 | 41,800 |
| Brand B | 30,000 | 31,000 | 29,000 | 35,000 | 36,500 | 37,300 |
| Brand C | 18,000 | 17,000 | 19,000 | 20,000 | 20,200 | 20,600 |
| Brand D | 10,000 | 10,000 | 10,000 | 8,800 | 8,500 | 8,300 |
| Category total | 100,000 | 98,000 | 102,000 | 104,800 | 107,200 | 108,000 |
Step 1: Sum each quarter
Brand A’s Q1 total is 42,000 + 40,000 + 44,000 = 126,000. The category total for Q1 is 100,000 + 98,000 + 102,000 = 300,000, and for Q2 it is 104,800 + 107,200 + 108,000 = 320,000. The category grew by 6.7%.
Step 2: Divide by the category total
| Brand | Q1 searches | Q1 share | Q2 searches | Q2 share | Change |
|---|---|---|---|---|---|
| Brand A | 126,000 | 42% | 124,800 | 39% | −3 points |
| Brand B | 90,000 | 30% | 108,800 | 34% | +4 points |
| Brand C | 54,000 | 18% | 60,800 | 19% | +1 point |
| Brand D | 30,000 | 10% | 25,600 | 8% | −2 points |
| Total | 300,000 | 100% | 320,000 | 100% |
Brand A’s Q1 share is 126,000 ÷ 300,000 = 42%. In Q2 it is 124,800 ÷ 320,000 = 39%.
Step 3: Read the change
Brand A’s search volume barely moved, down 1% from 126,000 to 124,800. Yet its share fell 3 points, because the category grew and Brand B took most of that growth: its searches rose 21%, from 90,000 to 108,800. Watching only its own volume, Brand A’s team would have seen a flat line and missed the shift. That is the point of measuring share rather than volume.
How is share of search different from share of voice and share of market?
The three metrics share a structure, your number divided by the category total, but measure different points in the buying process.
| Share of search | Share of voice | Share of market | |
|---|---|---|---|
| What it measures | Branded searches for you vs. all brands in the set | Your advertising or media presence vs. competitors’ | Your sales vs. total category sales |
| What it reflects | Interest and demand from buyers | What brands put out | What buyers actually bought |
| Typical source | Public search-volume estimates | Media spend or mention counts | Sales, retail or panel data |
| Competitor data | Freely available | Estimated | Often paid or delayed |
Share of voice is an input, share of search is a response, and share of market is the outcome. Our guide to share of voice covers how to measure the input side.
Does share of search predict share of market?
Marketing-effectiveness work has commonly reported that, in established categories, share of search tends to move with share of market and often moves first. The reasoning is that people search for brands they are considering before they buy, so a change in interest shows up in search before it shows up in sales.
Treat that as a reported observation, not a law. How closely the two track varies by category and by how cleanly brand names can be searched. Before you rely on it, plot several years of both metrics from your own history and see whether one leads the other.
What are the limits of share of search?
- Ambiguous brand names. A brand named after a common word or a place picks up unrelated searches. Exclude them and you undercount; include them and you overcount.
- Navigational searches from existing customers. Many branded searches come from current users trying to log in, find support or reorder. A large customer base inflates the share without new demand.
- Small categories. When monthly volumes are in the hundreds, estimates are rounded and noisy, and one month can swing the share several points.
- Events and PR spikes. A recall, a viral moment or a news story drives searches that signal attention, not preference. Mark these months and read through them.
How do you combine share of search with brand tracking?
The metric tells you that interest moved, not why, or whether that interest is positive. A brand caught in a recall can gain searches while consideration falls.
Brand tracking fills that gap. Put the search trend next to your brand awareness tracking results for awareness and consideration. If both rise together, the interest is likely real demand. If searches rise while consideration is flat or falling, look for an event or a navigational effect first. To keep both views together, you can track brand health, consideration, sentiment and competitors continuously and see what changed and why.
When the two disagree, the competitor analysis framework helps you work out what a rival did that might explain the shift, and market trend analysis helps you separate a brand-level change from a category-wide one.
What are the common mistakes?
- Tracking volume instead of share. As the example shows, flat volume in a growing category is lost ground.
- Changing the query list or competitor set midstream. Every change breaks the trend. If you must, recalculate the full history on the new definition.
- Mixing sources or regions. Volumes from different tools or markets are not comparable.
- Reading one month as a trend. Wait for two or three months in the same direction.
- Treating it as a forecast. It is an early indicator of interest. Confirm it with research and sales data before you change budgets.
What should you do next?
Start small: pick your competitor set, build the query lists and calculate it for the last 24 months, so you have a baseline and can see seasonality. Then add it to your regular competitive review. With mTab, you can see competitor launches, pricing and perception shifts monitored across research and market sources and turn what you find into an executive brief on what changed, why it matters and what to do.