**Estimated CTR**

Adthena uses a complex machine learning model to estimate nightly both CTR (click through rate) and CPC (cost per click) for every single search term, for every competitor in every position we have seen them appear in that day.

This model is constantly learning from the 10 terabytes of new data that Adthena is collecting daily, but essentially it uses the following inputs:

- What is the country?
- What is the device? (there is a separate model for mobile)
- Who is the competitor? (their size, overlap, appearance frequency)
- What is the search term? Is it brand or generic?
- How many advertisers compete on this search term?
- Is the most frequent position above the fold (top of the page or bottom)?
- What times of the day and days of the week does the advertiser generally appear on this term?
- Are there other items on the page (site links, other extensions, knowledge boxes, image search, PLAs, comparison boxes) that could draw clicks away from this advert?

**Average CPC**

The model estimates the CPC for each competitor in each position in a very similar way to estimating CTR. Is it a brand term? Is it the brand owner? How many different competitors are appearing? etc. (see above for full list).

**Average Monthly Search Term Volume**

How many people search for the term? For this, Adthena uses Google Keyword Planners’ search volume data for the previous month to establish Monthly Search Term Volume. This also affects the estimated clicks.

**Estimated Impressions**

- We calculate the
**Frequency**that we see your relevant competitors on your relevant search terms. - Using
**Frequency**(for each competitor and each search term) and the**average Google monthly search term volume**for each search term, we calculate the number of impressions of a competitor on a search term for the given period (in a trend chart it's day by day, or week by week, or month by month). - If you then sum the impressions of all search terms we've seen a competitor on, you will get the total number of impressions of the competitor for the given period. This is
**Estimated Impressions**.

**Estimated Clicks**

Multiply **Estimated Impressions** by the **CTR** for the competitor and the search term. At the end of this procedure you'll get the * Estimated Clicks* for each competitor on all of the relevant search terms they've been seen on.

**Example:**

Let’s say a search term gets 1,000 searches per month and a competitor’s advert appears on the term 60% of the time, so they generate 600 impressions. They always appear in position 2 with a CTR estimated at 5% which equates to 30 clicks per month.

The following week the competitor is seen 80% of the time fluctuating (70:30) between position 2 and position 3. The model has re-estimated the CTR for position 2 to 6% and for position 3 estimated 3.5%.

Example:

1000 searches / (30 * 7 days) * 80% frequency = 187 impressions that week

(187 impressions x 70% p2 frequency x 6% CTR) + (187 x 30% x 3.5%) = 10 clicks

Estimating Clicks gets more complicated when you include additional competitors on the same search term and additional search terms (that have different search volumes and sets of competitors).

**Share of Impressions**

= **Estimated Impressions** / ** ALL available impressions**. The latter is the

**total Google search term volume**of all relevant search terms. A competitor who appears every time Adthena searches for a specific term will have 100% Impression Share.

**Share of Clicks**

= * Estimated Clicks */

*Sum (Estimated Clicks for all relevant competitors)*

**Share of Spend**

A competitor’s spend is estimated by multiplying their estimated clicks for each search term by their estimated CPC for that search term. The total spend for all competitors can then be calculated and each competitors spend divided by the total is their Share of (the total) Spend.

**Related Articles**

How is the CTR calculated for Organic data?