So you have a #1 ranking brand term should you also buy it in paid search?  I recently wrote the article about the 81% of all paid ads that don’t have an accompanying orgnaic listing.  That was a byproduct stat from teh main purpose of the research which was to reassure advertisers that running paid ads for high ranking organic terms is still an excellent strategy – which I agree with 100%.

Why does Google care if you do or don’t?  Well, it is all part of Google’s Profict Maximization – this, in m,y opinion is the siggle largest budget waster only second to braod matching terms.   For many adverisers, their top ranked terms are their branded terms and where a significant share of the click volume goes.  For one of our clients it was as much as 83% of all of their paid search clicks.   This means there is significant click volume (Google Revenue) that would be lost if they just turned them off.

There are some key strategies where you need them.  Back in the day were you could really protect your trademark this was not a big issue but now that you can’t there is often a stragic way to do it but there is also a right ans wrong way.  The following is a case it shows you need to do the research and it also shows the financial impact to Google if you just turn it off.

This is a major national brand in the travel space who was buying their company name “Acme” in paid search both in broad and exact match. Their average paid position fluctuated between #1 #2 and #3 but typically wanted it in the #3 position.    They obviously rank #1 for their brand name and have 6 highly relevant site links.

Scenario 1 – Broad Match Brand Name

Organic Performance:

They received 409,872 visits from Google via the organic results (94.35% click rate based on paid search impressions and ranking #1) that resulted in a 4.06% conversion rate generating $7.3 million in revenue.

Paid Performance: 

They had 434,531 impressions for which received 19,191 clicks (Average CPC was $1.28) for a media spend of $24,541.   They had a 4.42% click rate and a 1.19% conversion rate generating just over $9,000 in revenue.  This resulted in a negative Return on Ad Spend (ROAS) of -$15,535.

Analysis:

The problem in this case was they were running a “price match offer” along with copy that indicated “they offered he cheapest airfare” which did not sync that well in with the searchers intent for a broad-based branded navigational phrase.  At first the agency wanted to just change the ad copy to make it more in line with the navigational query but ultimately after looking at scenario 2 below, they opted to kill the broad match and go with exact match terms since we had identified 2,387 exact match variations including nearly 2,000 misspellings.   The result was putting just over $24,000 worth of click costs back into the pool for other words.

This is the prime reason that some advertisers want to turn off these ads.  The paid team argues that they have to be there for branding purposes but in this case the paid actually pushed people into the organic.  The learning here is as quickly as possible you want to get words into exact match situations and then make sure the context matches.  In the scenario below the advertiser swapped their broad match of the brand to over 2,387 exact match variations taking the average CPC from $1.28 in the broad match to between $0.04 to $0.32 for the exact match terms saving nearly $30,000 a week thereby allowing significantly more clicks for other words where they had budget restrictions.

Cost to Google: 

About $24,000/month or $288,000 a year.  Note: Google will ultimately make this up n other keyword clicks but this helped increase the media efficiency of this advertiser exponentially.

Scenario 2 – Exact Match Brand Name

So while in scenario 1, buying a #1 ranked branded term using broad match did not make sense what happens with an exact match for the same term? The paid search ad in this case was a reference to them being a leading travel site and having the lowest prices.

Company Name – Exact Match vs. Organic Performance
Source: Back Azimuth Search ROI Analysis

Organic Performance:

Using the same 409,872 organic visits this means that our organic click rate was 55.27% with a Conversion rate of 4.06% generating the same $7.3 million in revenue.

Paid Performance

In this exact match case the paid click rate was 42.36 percent (nearly 10 times that of Broad Match).  The Average Cost per click was a negligible $0.04 per click.  We drove 314,229 visitors from paid search The conversion rate was 3.43% which is great resulting in sales of nearly $300,000 generating a positive ROAS of $285k.   So with paid in exact match and the #1 organic listing Acme had nearly a 97.63 percent share of clicks.  This my friends is the definition of 1 + 1 = 3.

Analysis:

For half the paid search media budget they achieved a significant increase in visits from paid search.  This gave them the brand protection they wanted along with an increase.  The following months have shown that in this case of exact match the paid and organic seem to be working together.  Due multiple competitors also showing up for the brand name and to the collaborative success of paid and organic the client will not turn off or day part the paid to see what share of traffic would go to organic without the paid.

What is your situation?

If you don’t know what your situation is you are a perfect candidate for our Keyword Management Suite or new Search Performance Analysis Solution.

How many of your top ranked keywords are under performing?  If you are like most companies, your not tracking it so you may not know.

The goal of most SEO’s is to achieve top rankings and high conversions.  Unfortunately, once we get the top rankings little is done to maximize it or even make sure the correct page is ranking.

Many SEO’s have stopped doing ranking reports due to personalization or the inability to show any real performance improvement from it.  I can understand both of these which is why I developed a report in the application called “Top Ranking keywords that Suck”  – well, that is the working title until we can figure out what we want to call it.    We originally started with the tongue twister you see below “ Top 5 Ranking Keywords with Less than 5% share of clicks”

When I have done SEO for clients I wanted to focus on the immediate low hanging fruit that drives incremental revenue.  I have talked at conferences in the past where I found keywords that were ranking and simply fixing the snipped resulted in not only increased traffic and revenue but on one case a PPC click cost reduction of $26k in one month.

The following is the use case that I created for this function in the application.

Tool Use Case: Identify keywords that are currently ranking well (top 3 or 5 positions) but getting a low share of clicks (less than 5% of the total search volume or less than the average click rate for its position).   This would allow the Search Marketer to quickly identify underperforming words to review snippets for relevance as well as messages and offers.

In our first generation we simply called this out on the dashboard and allowed you to click in and see the details and do sorts on the data.  The default view is performance sorted by highest CPC but you can sort as below – by highest Google demand.

The new version of this report we are calling, as mentioned above, “Top Ranked Keywords that Suck” – in this example, this client has a fairly experienced and robust SEO team.  In our analysis across a few beta clients we are finding similar patterns:

In this example, the client has 1.4 million keywords in the database.  Of those keywords, 101 currently rank in the top 3 positions of Google but are getting less than a 5% click rate.  If they could increase the clicks from Google for keywords to 5% that would be in increase of 103,678 visits a week.

[Why 5% - we use a simple rationale for these.  10 organic and 10 paid = 20 chances of being clicked.  If a searcher clicks just 1 listing we have a 5% chance of being clicked.  No complex formula of paid and organic, national brand, message, universal search results – just simple probability. ]

Actionability of the Data

I have to say that whenever I do a prototype for a new function I am haunted by the voice of Avinash Kaushik in “don’t just Puke Data – offer Actionable Insights” – based on his “Actionable Dashboards” presentations.  this was no different.  How can someone process 101 results? We are still working on ways to just mine the uber nuggets of data – see “Crowdsourcing Questions” below.

In our tool we can sort these by Google Search Demand, Revenue Per Visit, or Conversion Rate or any other number to identify which of the 101 that we want to fix first.  Typically the user is choosing those that have the highest demand and the lowest click rate.   The typical user of the tool is trying to identify 5 or 10 keywords a week to try to improve.

Action 1:  Sort by highest Google Search Demand – this helps us find the keywords with the greatest opportunity overall.

Action 2:  Sort by revenue or Revenue Per Visit (RPV) to identify keywords that generate the most revenue per visit.  This is a good variable since our goal of this look is to find words not getting the share of visits.

The following are key insights when we do this:

.acme is a brand misspelling keyword and there are clearly not 695k searches for it but Google associates that to the brand name.  We are now dealing with this by filtering out keywords with keyword type = Brand Misspelling

Cheap Tickets – in our demo case, they are ranking #2 in Google and only getting 6,492 clicks or 2.6% share.  A 5% share would be 12,500 or 52% of our goal opportunity.  Even a 1% increase in traffic would

Note: In this case if we average the actual click rate of this client for keywords with an organic position of 1, 2 or 3 without paid search influence it is 4.32% so not far off the 5%.  We can assume the actual click rate would be higher if we could count those 103k clicks we should be getting at 5%

In the next generation of the application, for each keyword phrase we will return the snippets for the top 5 positions to review them and apply a “reason for non-performance”  In doing beta tests we started to understand why some of the words are ranking and others are not.

Mitigating Factors

As with everything in search, there are never absolutes.  In working through the unique percentages of clicks for a company like IBM we may need to have more robust scoring moving forward.   The following were interesting findings from an exercise with Lee Moore, Global Search & Syndication Manager from IBM.  He wants the ability to flag those not performing for one of the following reasons and re-weight the variables.

1. Context of the listing – for example, There are x searchers for SOA and IBM, while ranking #4 is only getting about 1% of those searchers.  Looking closing we can assume it is due to “context.”

• #1 ranking listing is “Society of Actuaries” which has been around a while
• #2 is Wikipedia for the technical term Service Oriented Architecture
• #3 is the popular TV show “Sons of Anarchy” which is abbreviated SOA and most of the cast and followers in social media refer to the show that way.
• #4 IBM Service Oriented Architecture (SOA) solution
• #5 is Oracle which is a competitive placement.

So we can assume a large share of  the “Searchers Intent” and clicks are related to “Actuaries” and Sons of Anarchy and not expected to go to IBM.  Since this is also generic, a site like Wikipedia may give a better “What is SOA” than IBM or Oracle if they are looking to the technical variation of the acronym.

1. Message – Not related to this IBM case but if the query and “searchers intent” is price based and the price is out of line this will cause a reduction in clicks.  If the message is branding and they want to buy that may reduce clicks.
2. Paid Search – If there is or is not a paid search ad for the company may impact click rate.  Many studies show that both a paid and organic lead to increased brand awareness or assumption they re bigger resulting in a click on one or both.
3. Branded Keyword – branded keywords tend to get clicked more than non-branded.  For example, Dell Laptops vs. Laptop Computers.  In this case listing 1 and 3 are branded for “other” brands and not for Service Oriented Architecture.  It can be a battle of brands between IBM and Oracle.
4. Gibberish Snippet – already addressed but added in for completeness, if the snippet is bad for IBM or any listing the potential of clicks drops exponentially.

Base on these factors, we need to factor in some sort of automatic or manual override of the percentage based on an analysis of the listing so that it is not counted against the company going forward.  We can also use the same analysis to help identify the need for and priority of corrective action.

Crowdsource Questions:  Post any suggestions in the comments section below:

1. Should we restrict the rank to top 3 or top 5 positions?
2. Is a search demand of 500 the right cap? If we lower it, how do we filter out those in mass?
3. Since these are ranked high this will draw clicks from paid search.  Due to bad snippets there should be increased clicks on paid.  Not sure how to calculate yet but am thinking of a “Cost of Not Clicking calculation that would show that the delta in the 5% is made up with paid clicks.
4. Should we offer facets to allow user to choose which position, search demand and which click rate?
5. Should these filter variables be user editable in an admin area?