Last week Google announced they are offering searchers the option to use SSL when they search. SSL stands for Secure Sockets Layer, and is a method of web encryption. When using Google’s new SSL page, your search terms, web history and other personal information will be encrypted, thereby improving your privacy.

With SSL, you can search and browse in full confidence, knowing that your personal information and browsing habits will never find its way to unscrupulous third-parties. When you click on a Google link, and visit an external site, because your browsing is encrypted, the site you visit will not be able to see that you came from Google – nor will they be able to see what you searched for. Advertisers therefore can’t use your personal information to provide you with ads for things you don’t need or want.

Sounds great, doesn’t it? And the more secure we can make the web, the better, right?

Not exactly.

It is only once we consider the implications for the web businesses that we realise the sheer importance of such analytical data. It is only when this data is threatened to be taken away, that we realise that SSL encryption might not be in the public’s best interests.

Let’s see why.

The Importance of Analytics

Analytics is not all bad. Okay – it does let businesses collect information about your browsing habits, your search words, your referring URLs, your city and your number of return visits, which you could argue is more information than you would like to hand over.

But we need to realise that such anonymous information is central to the efficient allocation of online resources. It is only because we freely hand over such information to website owners, that websites are as user-friendly – and online prices are as low – as they are today. None of this would have been possible if web businesses were blindfolded.

Analytics provides a market – an invisible hand which allows resources to flow to the areas which deliver the best return. It prevents wastage, and helps to efficiently connect buyers to sellers.

To illustrate how the data you hand over is the lifeblood of such a healthy online economy, let’s imagine a world where SSL is standard across the whole internet. Every page is encrypted, and none of your data is handed over.

Scenario 1 – No Keyword Data (SSL)

Suppose in this SSL world, a retailer of men’s and women’s gifts wants to know how his online marketing campaigns are working. He logs in to Google Analytics, and all he sees is a visit counter (2,500 visits), and perhaps some information on total sales (20) and total revenue ($10,000). He can’t see where these 20 sales came from; nor can he see which keywords generated those sales.

But he does know that 20 sales are coming from somewhere, so at least some of his online marketing efforts are working. And since he’s only spent $1,000 on his entire online marketing strategy, and is therefore making a healthy $9,000 gross profit, he keeps everything rolling along and heads out to lunch with his head held high.

Similarly, another gift retailer adopts a similar online marketing strategy. She generates exactly the same amount of visitors (2,500), sales (20) and revenue ($10,000), for exactly the same spend. Again, her gross profit is $9,000.

All good and well, you might think. But what could have been if keyword data was handed over to retailer 1 and retailer 2?

Scenario 2 – Keyword Data

While working his way through a delicious Penne al Forno, retailer 1 hears talk that it is now possible to see keywords in analytics. In his excitement, he cuts his lunch short and hurries back to his computer to log into analytics. Immediately, he can see that half of his visits came from “gifts for men”, and half came from “gifts for women”. No surprises there – after all, retailer 1 sells gifts for both men and women.

But look at the conversion rates for each keyword! For whatever reason, the keyword “gifts for men” is delivering the majority of his sales, revenue and profit. “Gifts for women” is somehow failing miserably.

It doesn’t take long before retailer 2 also hears word of the great news. In her similar excitement, she also logs into analytics to find that the keyword “gifts for women” is bringing in the majority of her sales.

Scenario 3 – After Optimisation

Retailer 1 then decides to take money out of his poor-performing “gifts for women” keyword, and invest it in the successful “gifts for men” keyword. Within a week, he has increased his his overall sales from 20 to 32, and his gross profit from $9,000 to $14,000, all for the same $1,000 spend.

Retailer 2 also decides to do some similar optimisation. She takes money out of the wasteful “gifts for men” keywords, and ploughs it into “gifts for women”. Her sales increase from 20 to 26, and her gross profit increases from $9,000 to $12,000. Again, all for the same $1,000 spend.

Efficient Allocation of Resources

Retailers 1 and 2 don’t know it, but what they’ve done is extremely clever. Their individual actions (and the individual actions of thousands of other retailers) have helped allocate marketing spend to the most efficient channels. Both have seen a significant reduction in wastage, and large increases in profit.

Assuming the gift industry is competitive (which is largely true due to the sheer number of ecommerce retailers), these increased profits will gradually filter through to customers in the form of lower prices.

This ‘invisible hand’, or free market of online marketing, is only made possible with analytical data. Without years of such analytical efficiency, helping to connect buyers and sellers as quickly and cheaply as possible, there is no way online prices would be as low as they are today. There is no way I would have been able to buy a pack of six iPhone screen protectors with free delivery from Hong Kong for only $0.99.

Is Privacy Overrated?

Of course, everyone has the right to protect their privacy. Measures such as SSL will prevent third-parties from seeing your search terms, analysing your browsing behaviour and perusing your social media habits without your consent. And Google’s new SSL page is undoubtedly a response to our increasing desire to keep our online activities private.

But so long as measures are taken to ensure the information you hand over is anonymous and not personally identifiable, should we really demand this increased privacy? If letting advertisers build a database of anonymous stats is all it takes to improve the online experiences and ultimately lead to lower prices for consumers, is handing over anonymous data really such a bad thing? Perhaps we have forgotten what analytics and measurement has done for us, and need to realise that data collection and optimisation is actually in the best interests for everyone.

Google AdWords gives pay per click advertisers a wealth of tools to create, test and optimise highly-targeted pay per click (PPC) campaigns. One of the methods of doing so is through match type: exact, phrase and broad.

While exact and phrase match keywords are generally more controllable than broad match keywords, broad match can open up your business to a significant number of additional customers – those who might otherwise have been missed if only exact and phrase match keywords were used.

As we consider the pros and cons of each match type, we find that a balance is therefore required between the extra visitors broad match can deliver, and the quality of those extra visitors. In trying to find that balance, we consider a technique called the Broad Match Generator, which uses broad match search queries to generate new exact, phrase and negative keywords. We see how the methodical process of regularly analysing  search query data, to continually expand keyword lists and ad text relevancy (Broad Match Generation), can help take advantage of the opportunities of broad match while still delivering a strong return on investment.

Exact & Phrase Match

Exact and phrase match keywords are typically the most favourable for search marketers, as they allow a high degree of control over the words a searcher has to make in order for their ads to be shown. If your campaign contained the exact match keyword ‘flights to Melbourne’, for example, you can be 100% sure your ad would only appear when someone searches for ‘flights to Melbourne’ exactly.

Phrase match also gives you a high degree of control, and ensures that the words ‘flights to Melbourne’ must be included somewhere in the user’s search phrase. You can therefore be 100% sure you will only receive traffic from searches which include the phrase ‘flights to Melbourne’, such as ‘cheap flights to Melbourne’, ‘flights to Melbourne from Hong Kong’ or ‘low cost flights to Melbourne from China’.

So with exact and phrase match, you have a high degree of control over the search words which will trigger your ads. You can ensure your ads will only be shown on Google for highly-relevant potential customers.

Broad Match

Broad match, however, is not so controllable. Bid for the broad match keyword ‘flights to Melbourne’, for example, and you ads could be shown when someone searches for ‘flights from London to Melbourne’, ‘Melbourne flying club’ or ‘Australian travel deals’. Basically any search term Google believes is somewhat relevant to the keyword ‘flights to Melbourne’.

Not very controllable, you might think, and you’d be right. Why would you risk receiving visitors from people looking for ‘Melbourne flying lessons’, when you can be 100% sure what you’re getting by using exact and phrase matching?

Traffic, stupid!

Broad match isn’t all bad. In fact, it can be incredibly useful. According to Udi Manber, Google’s VP of engineering, 20-25% of search queries each day have never been made before, making it almost impossible to target every potential customer using just exact and phrase match keywords.

No amount of keyword research can predict that someone might search for phrases such as ‘flight prices March 2011 Tokyo to Melbourne’, ‘airlines Melbourne business class from NZ’ or ‘flights around the world via Melbourne’. Broad match can help deliver thousands of additional highly-targeted potential customers, who would otherwise have been missed if only exact and phrase match keywords were used.

So broad match allows you to receive high-quality visitors from search terms you may have missed during your initial keyword research.

But the problem of broad match still remains. Broad match can still send you visitors from hundreds of irrelevant terms such as ‘Melbourne flying lessons’. What a waste of money.

A balance is therefore needed between the benefit of extra visitors from broad match keywords, and the relevancy of those extra visitors. Introducing the Broad Match Generator…

The Broad Match Generator

Since exact and phrase match keywords provide the highest level of control, and allow advertisers to display highly-targeted ads, exact and phrase match searches should account for the bulk of clicks in a paid search campaign. Broad match should only be used as a catch all, to pick up those specific, seasonal and somewhat abstract long-tail searches which were not added as exact or phrase match keywords during your initial keyword research, and as a tool to generate new exact, phrase and negative match keywords.

To see how this Broad Match Generator would work, let’s first look at an example of an excellent user journey.

Example 1 – Exact Match Keyword in Account

Suppose someone searched for ‘flights to Italy from Melbourne’. Also suppose ‘flights to Italy from Melbourne’ exists as an exact match keyword in your Google AdWords account. So when a search is made, your exact match keyword ‘flights to Italy from Melbourne’ is triggered. Not only that, but since the keyword has its own ad group with its own tailored ads, your ad which appears will be highly-relevant and mention the words ‘flights’, ‘Melbourne’ and ‘Italy’, as well as current pricings for the trip. The visitor is then taken through to a landing page which shows details of flights to Italy from Melbourne.

Highly relevant, highly engaging, and likely to result in high click through rate (CTR), high Quality Score, low cost per click prices (CPCs), low bounce rate, high conversion rate and higher return on investment. Fantastic!

Example 2 – Exact Match Keyword not in Account (and search is relevant)

Now let’s see what would happen if a search is matched to one of your broad keywords.

Suppose the search is ‘flights Christmas 2010 to Melbourne’, and ‘flights Christmas 2010 to Melbourne’ is not is your Google AdWords account as an exact match keyword (ignore phrase match for the moment). The search is then matched to your broad keyword ‘Melbourne flights’, and the generic ad for ‘Melbourne flights’ is triggered. The visitor is then taken through to a generic landing page.

Somewhat relevant, you might think, but far from perfect. The searcher explicitly stated they were looking for flights at Christmas 2010, so why not show ads which better answers their question?

This is where the Generator comes in.

For any broad-matched search query, first decide if it is relevant. If it is relevant, then add the search as a new exact and phrase match keyword and give the keywords its own highly-targeted ads in its own ad group.

So the next time someone searches for ‘flights Christmas 2010 to Melbourne’, your ad which will appear will mention the words ‘flights’, ‘Christmas’, ’2010′ and ‘Melbourne’, and take visitors directly through to a Christmas 2010 Melbourne flight page.

Higher click through rate (CTR), higher Quality Score, lower cost per click prices (CPCs), lower bounce rate, higher conversion rate and higher return on investment.

Example 3 – Exact Match Keyword not in Account (and search is not relevant)

But what if the search query is not relevant, such as ‘Melbourne flying lessons’?

Again, this is easy. When you find a search query which is not relevant to your business, add it (and similar irrelevant searches such as ‘instructor’, ‘jobs’ and ‘careers’) as a negative keyword, to prevent it (and similar irrelevant searches) from triggering your ads again in the future.

The result will be reduced wastage, lower bounce rate, higher conversion rate and higher return on investment.

Two Simple Steps to Ultimate Broad Match Generation

The Broad Match Generation process is very simple. On a regular basis, simply look at each of the search queries that have matched to your broad-match keywords, and make one of two improvements:

1. If the broad-match search query is relevant, add the search query as exact and phrase match keywords in their own ad group, with their own tailored ads.
2. If the broad-match search query is not relevant, add the search query as a negative keyword.

Remember, even as you add new exact, phrase and negative keywords, your broad match keywords will continue to match to more and more search terms, so Broad Match Generation is an ongoing process. However, as you increase your number of exact and phrase match keywords, you should see broad match accounting for fewer and fewer of your visitors. A higher proportion of visitors coming through exact and phrase match keywords means you’re more in control of the types of visitors coming to your site and the ads they are shown, and is a sign that your Broad Match Generation is working.

If the Broad Match Generator is carried out regularly, broad match can be extremely effective in helping to target your ads to an increasing number of highly-qualified searchers, while at the same time reducing wastage from irrelevant and wasteful searches.

Broad match should never be used as a long-term ‘set and forget’ keyword targeting strategy; instead, it should only be used to generate new exact, phrase and negative match keywords, and improve the relevancy of your ads. It should only be used as a means to an end – that end being more exact, phrase and negative keywords and better relevancy.

Broad match keywords, left alone, should never be a long-term solution.

Opportunities

As we have seen, ongoing Broad Match Generation is a great way to make use of the extra traffic available through broad match, while at the same time providing a simple and practical means to continually improve the quality of your Google AdWords campaigns. It can help you uncover new seasonal trends and long-tail opportunities (such as ‘Christmas flights to Melbourne’ and ‘flight and hotel packages Melbourne Cup 2011′), and provide you with a great opportunity to provide highly-relevant ads, tailored to these new search terms.

What’s more, since there will also be some difference between your phrase match keywords and the search queries being matched to them, phrase match also presents another great opportunity for similar ongoing refinement. Looking at the searches being matched to your phrase match keywords, and adding new exact, phrase and negative keywords, as well as new tailored ads, can help take your Google AdWords campaigns even further.

And although Google’s recently-announced broad match modifier will help to give you more control over the types of searches being matched to your broad match keywords, ongoing Broad Match Generation will still be an incredibly powerful strategy – not only to help expand your list of long-tail keywords, but also to identify seasonal keywords trends and improve the relevancy of your ads.

Broad Match Generation provides a practical means to continually provide ever more specific and relevant ads to help better cater for the growing demands of searchers and better connect with your target audience. Use it to your advantage and watch how your return on investment from Google AdWords improves.

In this third post in the Economics of PPC Pricing series, we consider the profit sharing model (you might also like to refer back to the previous Economics of PPC pricing posts on the markup model and the cost-per-sale model). By looking at the cost and revenue structures for both client and PPC agency, we discover that under the profit sharing model client and agency motivations are perfectly aligned, making profit sharing a highly efficient method of PPC compensation.

Although we infer that profit sharing is sound from an economic sense, we find it does have problems of its own in terms of implementation and conversion attribution, and conclude that profit sharing should only be considered once a strong and tested relationship has already been established between client and agency.

So let’s get started.

With a profit share deal, instead of paying the client paying the agency a percentage of PPC spend fee, or a set fee for each sale, the PPC agency receives a share of all profits they help to deliver for the client through PPC activity.

If the PPC agency helps make the client $50,000 in profit from PPC, and the agreed profit share percentage is 10%, the client will pay the PPC agency $5,000 for their troubles.

Since the more profit the agency makes for the client, the higher their fees, it is therefore in the agency’s best interests to make the client as much profit as possible. Unlike the percentage of spend (markup) model, there is no monetary incentive for the agency to spend money haphazardly on clicks to increase their commission. With a profit share model, it’s the other way around. There is an incentive for the PPC agency to reduce wastage and increase spend in areas which generate a return.

The profit share model is the only PPC pricing model which is perfectly efficient from an economics point of view. Unlike the management fee model, the percentage of spend model and the cost-per-sale model, with a profit share model both client and agency have the same common goal: to maximise client profit. As pointed out by Andreas Reiffen in his analysis on paid search profit sharing, it allows a win-win situation in which both the client and PPC agency are better off.

So the profit share model is economically sound. The point of maximum agency profit is also the point of maximum client profit. There is no other click volume which will deliver a higher profit. It provides the necessary incentives to help both client and agency maximise their bottom line.

What’s more, since a common goal is being chased by both parties, the profit share model provides a solid foundation for a long-lasting, open relationship between client and agency. It creates a platform for innovation, makes testing worthwhile and encourages the sharing of ideas to reduce clicks costs and increase click revenue. It’s in the agency’s interest to make recommendations to improve website conversion rates; and it’s in the client’s interest to share knowledge of their business with the agency to improve keyword targeting and ad copy. It’s a mutually beneficial agreement that can’t go wrong.

Or can it?

Perhaps. A client’s profit figures are sensitive information. If leaked into the hands of competitors, it could be disastrous for the client. A significant level of trust between both parties is therefore required for a profit sharing model to work.

Conversion Attribution

There’s also the issue of conversion attribution. Since the PPC agency’s fees are bases on profit generated from PPC activity only, how much the agency receives is entirely based on tracking capabilities.

Tracking generally uses cookies to measure customers who buy online within a certain time period (say 30 days), and generally ignores revenue through offline methods such as phone calls and store walk-ins. If someone clicks on a PPC ad while at work, then makes an order when at home on a different computer, or perhaps picks up the phone, that revenue is not attributed to PPC. Nor is revenue from users who have high browser privacy settings or reject cookies.

As pointed out by Econsultancy, PPC marketers are currently missing out on credit for half of the revenue their campaigns are driving, which is a huge amount. With a profit share model, agency fees are based entirely on trackable revenue, so the agency will be under-rewarded for the value they deliver. This can significantly compromise the level of investment in the client’s PPC campaigns, and the level of testing and innovation.

This bias in conversion attribution can also lead to the PPC agency reducing bids on generic ‘research’ keywords from customers earlier in the buying cycle. These keywords might not convert profitably online (according to the under-reported tracking), but may be essential for the client’s walk-in orders, branding or long-term growth. Again – not very efficient.

So although the profit share model performs exceptionally well at aligning the motivations of client and agency, it is far from a perfect PPC pricing model. A high level of trust is essential for it to work – as is accurate (and fair) revenue tracking. Until conversion attribution improves considerably, any business with strong brand or numerous different marketing touch points should use the profit share model with caution.

Are you a fan of the profit share model? Have you made it work for both client and agency? Or is it one for the future when conversion attribution improves? Share your thoughts in the comments section below.

For a business looking to hire a pay per click (PPC) agency, cost-per-sale (CPS) performance models are great. The business pays the agency a set price for each sale, so fees are entirely based on the agency’s performance.

From a client’s point of view, this is great. There is little risk – agency fees are only payable once sales come in. Guaranteed profit!

From an agency’s point of view, it’s also great. Each extra sale is extra revenue, so an agency which is confident of its abilities to deliver value from paid search is rewarded heavily (and fairly) for their efforts. Performance-related pay creates an incentive for agencies to invest their best resources and expertise into making PPC campaigns a success for their client.

Researching cheaper and high-converting long-tail keywords, restructuring ad groups to improve relevancy and regularly carrying out landing page testing to increase conversion rate become all the more worthwhile when there’s a monetary incentive. If an agency only gets paid when they deliver sales, it is worth their time and effort to deliver sales.

Sounds too good to be true. Client risk is minimal. Agencies which perform are rewarded. Agencies which don’t perform…well they are forced to perform if they are to stay in business.

So you’ve decided you want to give performance pricing a go. But how exactly would a performance deal work? And how should you go about creating one for your PPC agency?

Economics

To see how such performance deals would affect your bottom line, and the bottom line of the PPC agency you’ve hired, we’ll use a similar method to that used in Economics of PPC Pricing: Why the Markup Model is Flawed, and look at the cost and revenue structures for both client and agency.

Let’s suppose Shiny Shoes Corp launched a new range of shiny red golf shoes, which they sold online for $15 (OK, they’re very cheap golf shoes). Sales were stagnant, so in an effort to increase sales of shiny red golf shoes, Shiny Shoes Corp decided to pay a PPC agency $10 for every pair of shiny red golf shoes they sold through PPC. If the PPC agency only sold 10 pairs a month, the client would pay the agency $100 (10 x $10), regardless of whether the agency spent $50 or $5000 on clicks. If the PPC agency were to generate 1,000 sales, the client would pay the agency $10,000 (1,000 x $10) – again, regardless of whether the agency spent $50 or $5000 on clicks.

How many sales will the PPC agency deliver? How much profit will Shiny Shoes Corp make? How much profit will the agency make? Will the agency be incentivised to deliver extra sales? Is $10 per sale a fair amount, or should it be $12, or perhaps $8? All burning questions we can consider with some simple economics.

Look at the green line ‘MC (client)’ in the figure below. MC stands for manginal cost, and shows what happens to the costs of Shiny Shoe Company as sales volume increases by extra units. It’s a flat line, which makes sense – the client is paying the agency $10 per sale regardless of sales volume.

Since Shiny Shoes Corp pays the agency $10 per sale, we can also add in the agency’s marginal revenue (red line), which again is flat. At every sales volume, whether it’s 20 pairs or 2,000 pairs, the client’s cost per sale is $10 and the agency’s revenue per sale is also $10.

We also know that Shiny Shoes Corp sells the shiny red shoes online for $15, so we can add in the client’s marginal revenue. Again, it’s a flat line, since Shiny Shoes Corp receives $15 for each sale, regardless of how many pairs they sell.

Now that we have the cost and revenue structures for Shiny Shoes Corp, we can see what profit they’re making. Shiny Shoes Corp’s profit is the difference between their cost and revenue, which is represented by the green shaded area. The higher the sales volume, the higher their profit. Since Shiny Shoes Corp makes $5 profit for every sale ($15 they recieve from the customer online minus $10 they pay the PPC agency), they would want as many sales as possible!

How many sales will the agency will deliver depends on the agency’s profit, so let’s add in the agency’s marginal costs (red line). The agency’s MC is an upward sloping line, since click costs increase progressively as volume is increased.

Why is it upward-sloping?

Diminishing Marginal Returns

Well, the first few sales will come from cheap clicks from brand terms, such as ‘Shiny Shoes Corp’, as well as highly-relevant long-tail keywords, such as ‘red shiny golf shoes for sale’. Not only are long-tail keywords generally cheaper, but conversion rates for these highly-relevant keywords will also be high, meaning fewer clicks (and therefore fewer click costs) will be needed for each sale. The agency can probably bring in the first few sales for only $1-$2 each.

Once the PPC agency has saturated brand terms and highly-converting long-tail keywords, they are forced to show ads for more competitive, more generic and more expensive keywords, which might not convert as well. The higher cost per click prices and lower conversion rates for less-relevant keywords such as ‘golf shoes’, mean sales might cost the agency $8.

Each extra sale costs the agency progressively more, hence the upward-sloping marginal cost line. Diminishing marginal returns set in.

Now we have all our cost and revenue lines for the agency, we can see how many pairs of shoes will be delivered by the PPC agency, and how much profit the client and agency will make.

After a while, when all profitable keyword opportunities are exhausted, when all that is left are expensive and poorly-converting keywords such as ‘shoes’ and ‘birthday gift ideas’, the the agency will start to make a loss from any extra sales they deliver, where the agency’s Marginal Cost goes above the agency’s Marginal Revenue . At this point, the agency will reduce bids, pause keywords and cut back on sales volume until they stop making a marginal loss.

The agency’s maximum profit is where their Marginal Cost equals their Marginal Revenue, at 5,000 pairs of shiny red golf shoes. Any less than 5,000 pairs (say 4,000 pairs), and the revenue for the agency delivering extra pairs of shoes would be more than their cost of delivering those extra pairs (their Marginal Revenue would be greater than their Marginal Cost), so it would be profitable for the agency to increase volume. Any more than 5,00 pairs (say 6,000 pairs), and the agency would make a loss from these extra pairs (their Marginal Revenue would be less than their Marginal Cost), so they would be better off by reducing volume.

Maximum agency profit is therefore where the agency’s MC = MR, at 5,000 pairs of shoes.

At this point of maximum agency profit (5,000 pairs of shoes), we can see the profit for both client and agency. Looks healthy, right?

Wrong.

At 5,000 pair of shoes, there is a wastage, an inefficiency – what economists call a loss of utility. There is extra profit to be made (dark grey area), but since the agency doesn’t want to make a loss from helping the client achieve this extra profit, the extra profit goes unrealised. This makes model inefficient. There is extra profit to be made, but the model does not sufficiently extract this extra profit the market is offering.

The cost-per-sale model starts to breaks down.

What’s more, with a $10 cost-per-sale model, the fee the agency receives is a poor reflection of their efforts.

The Brand Problem

A good PPC pricing model should be an accurate reflection of the agency’s work and expertise, which is not the case with this $10 cost-per-sale model. The first few sales will be from showing ads for brand searches such as ‘Shiny Shoes Corp’, which require little agency effort and will cost the agency next to nothing. Agency profit will be high.

But as sales volume increases, and the agency is forced to invest considerable time and effort to research new keyword opportunities, agency profit will be relatively low. This and low effort/high reward and high effort/low reward problem might cause the agency to cut back on volume even further, thinking these these extra sales and tiny profit (which are highly valuable for the client) are just not worth the extra effort.

So, to recap, Shiny Shoes Corp’s decision to pay a PPC agency $10 for every sale is inefficient for two reasons:

1. The agency will stop delivering sales when the cost of an extra sale is more than $10, even though Shiny Shoes Corp would not mind paying $11, $12 or over $14.50 for these extra sales.
2. The agency’s fee is a poor reflection of the work and expertise required

Okay, so a set cost-per-sale model doesn’t work.

Separating Brand & Non-Brand

But what about a structured cost-per-sale model, such as below? Shiny Shoes Corp pays the agency only $7 for the ‘easy’ sales (brand terms), and $13 for the more difficult sales. With two different pricings, the fee the agency receives is a better reflection of the value they are adding (great!), the loss of utility is reduced (great!) and the agency now has a monetary incentive to increase sales volume to 6,000 (great!).

Awesome! It’s solved both of the problems above! Right?

Wrong.

There is still a loss of utility (the client can still make extra profit if it received these extra sales for $14 or $14.50). And although the agency is more fairly rewarded for their efforts, the agency’s red Marginal Revenue line still does not follow their Marginal Cost line closely. They are not being paid proportionately for their efforts.

How about a multi-step (or progressive) cost-per-sale model, such as below? Shiny Shoes Corp would pay $6 for the first 1,000 sales, $8 for the next 1,000, and so on.

With multiple pricings, loss of utility is tiny, and the agency is fairly rewarded at all spend levels (notice how the agency’s red MR lien closely follows the agency’s red MC line.) What’s more, sales have increased to 7,000! Great!

Such a well-structured model would be great, and as close to the Holy Grail of PPC pricing as one might hope to get. It’s fair, creates incentives, maximises sales volume and maximises total profit (client and agency).

But just as the Holy Grail is hard to find, so is such as pricing model.

Performance Models Are Difficult

A multi-step (or progressive) cost-per-sale model is great in theory, but in practice such a model might be prohibitively difficult to construct. For a progressive cost-per-sale model to work efficiently, it requires an in-depth understanding of revenues at multiple sales volumes, knowledge of brand strength, seasonality, offline marketing influences, not to mention tracking inefficiencies and bias which might exaggerate or under-report sales volume.

One scenario might be where brand strength and click costs are under-estimated by the client, such as below. Fees set too high, and the agency is over-compensated for their services. The client would make little profit, and would be better off with a percentage of spend or management fee pricing deal.

Another scenario might be where click costs and brand strength are over-estimated by the client, such as below. Fees are set too low, so the agency makes a loss from delivering any sales, and the client will struggle to find an agency wanting to manage their PPC for more than a few months. As they regularly jump from one optimistic agency to another, long-term stability goes out the window and are replaced by extra costs, effort and confusion.

A more realistic scenario might be the one below, where in an effort by Shiny Shoes Corp to closely match click costs to fees, offers a slightly under-priced cost-per-sale structure. At some volume levels, delivering extra sales would make the extra agency profit, but and at other volume levels any extra sales would be at a loss. If the agency was at point B, for example, they would be better off either increasing volume to point C, or reducing volume to point A.

Very inefficient. Sales are vastly under-delivered and nobody wins.

This difficulty in creating a cost-per-sale model and setting it at the correct level leaves the client (and agency) with a difficult decision.

A correctly set cost-per-sale deal would be great for the client, as it minimises their risk (they only pay when sales come in), as well as encouraging the agency to perform. It would also be great for the agency, who are fairly rewarded for investing their time, effort and resources into doing what they do best.

But an incorrectly set cost-per-sale model (such as the three above) could be disastrous. If the agency is making a loss, they will refuse to deliver any sales, which is a waste of time for both client and agency and a massive loss of potential sales. If the cost-per-sale fee is set too high, the client will make only a small profit a would be considerably better off with a set management fee or percentage of spend (markup) deal.

So, if you are a business looking to minimise your risk from PPC, as well as incentivise your PPC agency to perform, should you consider a cost-per-sale performance deal?

Cost-Per-Sale Tips

Firstly, be wary of offering a set fee for every sale. You will pay heavily for the agency to simply harvest the value of your brand, and will find that sales are restricted way below your most efficient point.

Secondly, be careful of offering a multi-step progressive model, unless you have accurate sales and revenue data allowing for seasonal fluctuations, offline marketing efforts and special offers, and are confident (and preferably experienced) regarding likely PPC click costs at a range of different spend levels. If guesswork makes up a large part of your research, a progressive cost-per-sale model might be more trouble than its worth.

A happy medium, especially if you a business new to PPC or cost-per-sale models, might be a two-step cost-per-sale model, which separates brand and non-brand sales. It’s not ideal, but limits the brand / non-brand problem and is relatively risk-free (you only pay for sales so are guaranteed to not make a loss from PPC). It does, however, require a decent knowledge of brand searches and likely PPC click costs, so perhaps only consider this two-step cost-per-sale model once you have collected at least a few month’s worth of brand and non-brand cost and sales data.

And remember, a performance deal does not have to be based on sales. Leads, enquiries, email sign-ups downloads or anything which is a desired outcome for your business can potentially be incorporated into a cost-per-action performance deal. Just make sure you have the performance data to back it up.

Are you a fan of the cost-per-action model? Have you made it work for client and agency? Or does it cause more problems than it’s worth? Share your thoughts in the comments section below.

Next: profit sharing. Does it work? What are its limitations? Coming soon.

 

Choosing a Pay-Per-Click (PPC) pricing model which works efficiently for both client and agency is a difficult process. A good pricing model should be simple, should create incentives for the agency to perform and should be a fair measure of the work and expertise involved.

One common model that many agencies use is the ‘markup’ model (also commonly known as the ‘percentage of spend’ model). If the agreed markup is 10%, and the client spends $30,000 on clicks, the client pays $33,000, of which the agency receives $3,000.

Nice and simple.

But does it create incentives for the agency to maximise profit for the client? Does it fairly reflect the work and expertise involved at all spend levels?

No.

Conflict of Interest

In short, the percentage of spend model is a highly inefficient pricing model for paid search management, and should be avoided. As pointed out by George Michie in his recent post on SEM Pricing Models, since the agency receives a commission on every dollar spent, there is an incentive for the agency to spend as much as possible, which can be far in excess of the point of diminishing marginal returns.

To find out exactly what George means by diminishing marginal returns, and how it creates a conflict of interest for client and agency and renders the markup model pretty much useless, let’s consider the cost and revenue structure of the client.

Look at the line CPC (marginal) in the diagram below (Cost Per Click). It shows what happens to the Cost of each subsequent Click as the volume of clicks increase. It is upward-sloping, so each extra click costs progressively more than each previous click. Makes sense – since the first few clicks are usually very cheap, and raising bids and showing for more expensive keywords is generally needed to increase click volume.

Now have a look at the line RPC (marginal). It stands for Revenue Per Click, and shows how much Revenue is generated from each subsequent Click. It is downward-sloping, which again makes sense, since each additional click is likely to be less relevant and have a lower conversion rate than each previous click (this is known as diminishing marginal returns). A rational advertiser would always go for the low hanging fruit first (the most relevant keywords), which will naturally convert better than the high hanging fruit (less relevant generic keywords).

(If this all sounds very confusing, you may like to check out Wikipedia’s articles on marginal cost and diminishing marginal returns first).

So we have an upward-sloping marginal CPC line and a download-sloping marginal RPC line.

Now, assume the client has no other costs other than paid search click costs. If the client spends $1,000 on clicks and generates $1,500 in revenue, the client makes $500 in profit (this is of course very unrealistic – but just bear with me for the sake of argument).

Look at where the two lines cross. This is the level (2,000 clicks) where the client will make the most profit.

Why?

Well suppose click volume increased to 2,100 clicks. The 2,100th click is now costing $0.85, but is only bringing in $0.55 of revenue! The client is losing money on these extra 100 clicks, so reducing click volume would increase the client’s total profit.

What about reducing click volume?

Well, at 1,900 clicks, the 1,900th click is costing only $0.65 but generating $0.85 of revenue. The client is making $0.20 profit from their 1,900th click, so why not increase click volume further, and make $0.19, $0.18 and $0.17 profit from additional clicks? It makes sense to increase click volume until no additional profit is being made – until the cost of an extra click equals the revenue of that click.

It is where the marginal CPC and marginal RPC lines meet that no additional profit would be made, and it is at this point which makes the client the most profit. It is this level of click volume that the client should aim to target with their PPC activity.

Now let’s complicate things a little. The graphs above were only concerned with marginal costs and marginal revenues – costs and revenue at the margin. They show what happens to the next click or the previous click, but they don’t show what happens on average – to the average cost per click or the average revenue per click. Average CPC and average RPC lines are therefore needed for this purpose.

Have a look at the red CPC (average) line and see if it makes sense. Like the green CPC (marginal) line, it’s upward-sloping, but flatter. Why?

Think about it for a second.

If you just spent $1,000 on 2,000 clicks, each click costed you $0.50 each on average, right? If you then decided to go crazy and purchase a few extra clicks on some expensive keywords for a hefty $4.00 each, what will happen to your average CPC price? It will increase, but not by very much, maybe to $0.41? Adding some expensive clicks will pull up the average, but only by a relatively small amount. Hence the flatness of the average Cost Per Click line.

The same is true with the average Revenue Per Click line. If a few extra keywords bring in only a small amount of revenue, it will pull down your average revenue, but only slightly, hence it’s flatness.

Still following?

Great. So we now have 4 lines which represent the cost and revenue structure of a client:

1. Marginal CPC – shows how much each extra click costs
2. Marginal RPC – shows how much each extra clicks generates in revenue
3. Average CPC – shows how much clicks cost on average
4. Average RPC – shows how much clicks generate in revenue on average

These 4 lines are all that’s needed to assess the client’s profitability.

Now remember how we decided that 2,000 clicks was the most profitable click volume for the client? Let’s see exactly how much profit the client is making from 2,000 clicks.

Well, at 2,000 clicks, the average cost per click (CPC average) is $0.30. The client is spending $600 on clicks ($0.30 x 2,000).

At 2,000 clicks, the average revenue per click (RPC average) is $1.50. The client is generating $3,000 in revenue ($1.50 x 2,000).

Minus one from the other ($3,000 – $600) and we have a healthy client profit of $2,400.

Fantastic.

Now this is where the inefficiency with the percentage of spend (markup) model comes in. Since the agency is paid a commission on every click, the agency will always want to increase click volume and spend as much as possible. As we’ll see from the following examples, this is often in excess of the point of maximum client profit.

Suppose the agency increased click volume to 3,000. The average Cost Per Click (CPC) increases from £0.30 to $0.45, and the average Revenue Per Click (RPC) falls from $1.50 to $1.20. The client is still making a healthy profit of $2,250 (revenue of $3,600 ($1.20 x 3,000) minus cost of $1,350 ($0.45 x 3,000)), although their profit of $2,250 is less the previous level of $2,400.

The agency, however, has increased their profit, since they now receive a cut of a bigger spend ($1,350 instead of $600). Assuming the markup is 10%, the agency’s profit has increased from $60 to $135, at the expense of the client’s profit.

But here’s the thing. The client is unlikely to complain – the agency is making them $2,250 of profit, after all! How is the client to know that they could be making $2,400 of profit, should the agency decide to reduce click volume? The client is none the wiser and would most likely praise the agency for their ‘efficient’ work in making them such as tidy profit of $2,250!

But why stop there?

The agency makes a cut of every click spent, so why not increase click volume further? Why not increase it to – wait for it – 4,000 clicks!

Here, at 4,000 clicks, click spend will be $3,000 ($0.75 x 4,000) so the agency’s profit will be $300 (10% of $3,000) – much better than the measly $60 or $135 from the previous click volumes of 2,000 and 3,000.

But look at what’s happened to the client’s profit at this new click volume of 4,000. Their costs are now $3,000 ($0.75 x 4,000) and so is their revenue! The client is making no profit at all! Any more spend, and the client’s average revenue will fall below their average cost, and they will make a loss. If the client is losing money, they will most likely leave the agency, or at least apply massive pressure on the agency to increase performance (reduce click volume), so any click volume in excess of 4,000 is not sustainable in the long-run.

So it’s in the agency’s interest to maximise their commission by getting the click volume as close to the point of 4,000 click where the red lines cross (where average costs equals average revenue) – but without going over, so as to keep the client happy (the client will still be making some profit).

What happens is a negotiation of pushing and pulling between the client and agency until a compromise is found – say 3,000 clicks. Exactly how close to the point of maximum client profit or the point of maximum agency profit is settled upon depends on the relative bargaining strengths of client and agency, access to cost and revenue information and countless external influences to name just a few factors at play.

What is clear though, is that whatever click volume is reached as a compromise, it will not be efficient. It is impossible to maximise the profit of both client and agency using a percentage of spend model. At every click volume, there will always be a way to increase agency or client profit by adjusting click volume.

With a percentage of spend model, there is no working together of client and agency, no common goal, no shared vision. It’s a constant pushing and pulling and conflict of interest. Time and effort is wastefully diverted into politics in an attempt to move click volume closer to one party’s optimum, not to mention the reluctance of each party to be open and transparent and share useful information with each other. Doesn’t sound like the foundations of a successful and lasting business relationship to me. Perhaps it’s why some agency churn rates are so high?

Of course, no PPC pricing model is perfect – every method will have its weaknesses. The key is to find one that works best for your goals and objectives as a business, and one which appropriately compensates the agency for their efforts in helping you develop your paid search marketing strategy. But in terms of aligning the agency’s monetary motivations with that of your business, and creating incentives encouraging them to maximise your profit from paid search, the percentage of spend model fails miserably.

My advice: use the percentage of spend model with caution.

Are you a fan of the percentage of spend model? Have you made it work for client and agency? Or does it create more problems than it’s worth? Share your thoughts in the comments section below.

Next: cost-per-sale performance models – rewarding agencies based on how they perform. Do they work? Are they efficient? Economics of PPC Pricing: Why Performance Deals Often Fail

 

Landing page selection is an art.

When it comes to choosing landing pages for paid search ads, there is only one rule which must be followed: the page must be relevant to the user’s search query.

Other than ensuring a highly relevant user journey is delivered, there are no clear rules explicitly stating how a landing page should be designed, structured and styled, nor is there a landing page formula which works for everyone. Landing pages selection is about finding out what works best for your business, products, services, target audience, keywords and ads, through ongoing testing and optimisation.

Landing page performance will therefore vary depending on countless numbers of variables, making landing page best practice ambiguous. That said, it is important to be aware of the reasoning and implications behind any landing page strategy, to enable more informed landing page selection and more insightful testing and optimisation when comparing one landing page to another.

So to better understand the issues which arise when choosing landing pages, let’s consider one common dilemma which a large number of advertisers face: whether or not to deep-link.

Deep-Linking School of Thought

Deep-linking takes the searcher to pages deep into your site, hence the name. If you are an electrical retailer, for example, and someone searches for ‘Delonghi Coffee Machine ESAM3500’, a deep-linking strategy would take the searcher directly to your Delonghi Coffee Machine ESAM3500 product page, displaying information and price details for the Delonghi ESAM3500.

The benefits of deep-linking are as follows:

1. Relevancy

The user has specified they are looking for the Delonghi Coffee Machine ESAM3500, so taking them through to your Delonghi ESAM3500 will deliver a highly relevant user journey, as per Relevancy: The Holy Grail of Search > better user engagement and a higher chance of conversion.

2. Price Awareness

Users can immediately see the price of the product, so you can ensure your price is being conveyed to every visitor > better conversion rate if price-competitive.

3. Focus

No distraction from other products and offerings keeps the user focused > reduced exit rate and reduced chance of downgrading to cheaper alternatives.

4. Efficient

Deep-linking can prevent lazy searchers from leaving the site if they can’t immediately find the product they searched for > reduced bounce rate.

Category-Linking School of Thought

Category-linking takes the searcher of ‘Delonghi Coffee Machine ESAM3500’ to your coffee machines or Delonghi category page, which includes the Delonghi ESAM3500 along with other similar coffee machines by Delonghi and other brands.

The benefits of category-linking are as follows:

1. Satisfaction

Taking the user to a category page forces the user to find the product themselves. When they do so, they gain a sense of satisfaction and will become highly engaged with the site > high user engagement > high conversion rate.

2. Environment

Users don’t feel pressured they are being ‘forced’ into buying a particular product and like choice > more relaxed > more likely to buy.

3. Comparison

Users can see other similar products, so can compare alternatives they might not have previously noticed > higher chance of buying something. Even if the customer downgrades to a cheaper alternative, it is still a sale and you have the customer for life > higher sales volume and increased customer base.

4. Authenticity

Multiple products creates a sense of credibility > higher conversion rate.

5. Time on Site

Users spend longer on the site when more products are available. People are often only willing to spend a finite amount of time and effort comparing products or services before they buy. Excessive comparison can be mentally tiring, especially if numerous variables are on offer, so maximising the time on site maximises the chance the user will stop comparing and just buy > higher conversion rate.

Landing Page Testing

It’s a tough call. Both deep-linking and category-linking deliver some strong – albeit untested and speculative – psychological arguments in favour of their method.

But no-one said it was going to be clear cut. Paid search management, after all, is about the ongoing testing and optimisation of a campaign to find the methods which work best for your business. What might work best for Joe Bloggs Electrical might not work best for Adam Smith Wired; and what might work for your coffee maker keywords, might not work for your washing machines keywords. Paid search success favours the patient, so keep testing and optimising.

So to deep-link or not to deep-link? You decide.

All too often keywords in a paid search account are evaluated based solely on their ability to generate conversions: leads, bookings or sales. If a keyword has an unacceptable conversion rate or an unsatisfactory return on investment (ROI), it is paused or its bid is greatly reduced.

Sometimes, if conversion data is scarce, click-through-rate (CTR) is instead used to evaluate a keyword’s performance. If a keyword generates only 5 clicks from 1,000 impressions, it has a CTR of 0.5% so is deemed irrelevant. The keyword is then paused or relegated to the second page of search result obscurity.

This is not the right approach.

Beyond the Click

There is more to paid search management than optimising keywords based only on conversion rates and click-through-rates. A more intelligent approach also considers what happens once a user clicks on your ad:

• How many pages are they viewing?
• How long are they spending on your site?
• How many people are immediately bouncing?
• How many people are returning at a later date?

If a keyword has a poor conversion rate and a poor click through rate, but people are viewing a large number of pages, spending a long time on your site and returning regularly, the keyword may be doing a great job at creating awareness of your products or services. Even though conversion rate may be low, the keyword may not deserve to be paused or have its bid reduced – instead it may be beneficial to increase its bid to allow the keyword to flourish and user engagement to increase.

Similarly, if a keyword is converting well but 80% of people are immediately bouncing, finding out why – perhaps by looking at search queries being matched to the keyword – could help the keyword convert even better.

How to Optimise a Campaign Intelligently

Analytics software, such as Google Analytics, are incredibly useful in helping to understand what people do after they click on your ad and land on your site. If your Google Analytics account is linked to your Google AdWords account, it is possible to see page views, time on site and bounce rate for each of your AdWords campaigns, ad groups and keywords (select Traffic Sources > AdWords > AdWords Campaigns).

Once you’ve logged into Google Analytics, look at each of your campaigns:

• Which campaigns are engaging users with a high time spent on site? Why?
• Which campaigns are bringing in untargeted users who immediately bounce back and go elsewhere? Why?
• Which campaigns are persuading users to come back later? Why?

Next, drill down to ad group level and keywords level (if you have enough data) and ask yourself the same questions. 100 visits is usually significant to make reliable, informed decisions – but be careful about drawing conclusions for keywords / ad groups with less than 30 visits.

Make a note of those campaigns, ad groups and keywords which stand out, both positively and negatively. Look for common words that regularly appear in unusually high or unusually low metrics, such as ‘cheap’, ‘discount’ and ‘free’. How have they performed? Probably very different to your more generic keywords, you would imagine, but in what way?

Look for locations, product names and other qualifiers – can you notice any trends? Are there certain keywords with a high bounce rate? Could any negatives be added to reduce bounce rate?

Record any trends and resulting changes in a log. This is your insight on user engagement, and will not only help you develop a improved paid search campaign which will better connect with users, but will also help you better understand your audience and make more informed, strategic business decisions.

How to Optimise a Campaign Even More Intelligently

If you’re feeling more ambitious, export the keyword data to Excel and VLOOKUP each keyword’s Analytics performance to its AdWords performance (select Traffic Sources  > AdWords > AdWords Campaigns > “dimension” = “keywords” > export > CSV for Excel).

That way, when you’re looking at each keyword’s costs, CTR, conversions and ROI, you can also consider page views, time on site, returning visits and bounce rate, and better understand each keyword’s impact on creating awareness and interest.

Look at keyword 8 in the example below. It has a low conversion rate and a high cost per conversion, so based on conversion performance it should perhaps be paused. But notice how page views, time on site and bounce rate are all exceptionally good. Should the keyword really really be paused? It seems to be engaging users well, so why not instead find out why its conversion is so low?

Similarly, keyword 9 is converting well, but if it’s bounce rate could be reduced, it could become even more profitable. Play around with new ideas and experiment optimising bids based on different metrics.

It’s All About Engagement

The next time you optimise your campaign, remember there’s more to paid search management than conversions. If, like the majority of Americans, you’ve ever researched your next holiday, browsed that DVD player or shortlisted those birthday presents on your lunch break at work, only to buy online when you get home (on a different IP address), you will appreciate that keyword conversion data can only get you so far.

Conversion optimisation is naturally biased towards converting keywords, so will tend to ignore keywords at earlier stages of the buying cycle, which may play an essential role in creating awareness, generating interest and engaging users with your products, services and brand.

Conversion data – and automated tools which optimise keywords based on conversion data – are not the be all and end all of paid search management. Understanding user engagement could well be, and the tools to get you started are right at your fingertips.

Browsing through your Google AdWords account, you notice some of your keywords are not showing due to “low search volume”. Hovering your mouse over the speech bubble, the ad diagnostic tool pops up:

 


 
According to Google AdWords Help, your keyword is not showing because not enough people are searching for your keyword.

“Low search volume” keywords are keywords associated with very little search traffic on Google properties. In which case, we suspend your keyword. This state is only temporary, and these keywords will be reactivated if we find that they could start delivering traffic.

So just how much search traffic is “very little search traffic”?

To find out, I decided to count every “low search volume” keyword in an AdWords account over a 3 month period. Of the 2,823 keywords that received at least one impression, 804 keywords (28.5%) were “low search volume”. That’s over a quarter of keywords.

Although most of these “low search volume” keywords were in fact that, low search volume, some had a relatively high number of searches: 108, 117, 126, 135, 156 and even 573 (see graph below). I’d hardly call 573 searches “low search volume”.

 

What’s more, although “low search volume” keywords accounted for only 3.60% of total impressions, they generated a disproportionately high 5.33% of total clicks. Their CTR was 1.72%, compared to only 1.17% for normal search volume keywords, despite a similar average position of 2.63 and 2.70 respectively. That’s a higher CTR for “low search volume keywords” by almost 50%.

5% doesn’t sound like much. So is there any point having “low search volume” keywords in your AdWords account? Or long-tails at all for that matter?

Definitely. My advice is to ignore Google’s “low search volume” advice and continue with your long-tail keyword strategy. Focusing on the long-tail will of course generate a large amount of keywords that will rarely be searched, if at all. But as I point out in 5 Benefits of Long-Tail Keywords, infrequently searched-for keywords generally have less competition, lower CPCs, higher CTRs and higher conversion rates than their short-tailed rivals. En masse, long-tail keywords can be extremely profitable.

Call me a cynic, but I think the whole point of the “low search volume” feature is a means to scare advertisers away from those cheaper, long-tail keywords, and towards more expensive, short-tails in a effort to maximise Google’s revenue (a view shared by Eloi Casali in the comments section of PPCProz’s low search volume post in June). I can’t really see any practical use for the advertiser.

 

I recently stumbled across a Google AdWords video by Derek Faylor describing how to boost AdWords relevancy. He suggests picking one keyword that is core to your business, setting it to exact match and giving the keyword its own ad group with its own tailored ads. The idea is this: if your ads closely match your keywords, you will be seen by Google as being highly relevant, so your Quality Score will increase. This will lead to a higher ad rankings, higher click-though rates (CTR) and lower costs per click (CPC).

It makes sense, and I completely agree that a highly relevant approach such as that outlined by Derek is essential to achieve great results in paid search.

However, although Derek emphasises that his one keyword per ad group strategy should only be applied to one keyword which is core to your business, there will rarely be a case where a business will only want to advertise on a single keyword. There will likely be hundreds of possible phrases that will be highly relevant to a business, and having a portfolio of hundreds, even thousands, of long-tail keywords (instead of just bidding on one or two highly generic short-tail keywords) will often achieve better results.

So is Derek’s strategy of one keyword per ad group practical if applied on a larger scale?

Let’s have a look at the pros and cons.

Advantages

1. Highly Relevant

Having one keyword per ad group makes it possible to write ads which very closely match the keyword. If the ad group contained only the keyword “brown leather shoes”, the ad could include the words “brown”, “leather” and “shoes”, possibly with prices of brown leather shoes, and take users through to the brown leather shoes landing page. However, if the ad group contained the keywords “brown leather shoes”, “blue suede shoes” and “red wellington boots”, at best, a generic “shoes” ad could be shown. Having very different keywords in the same ad group makes it impossible to create a highly relevant PPC campaign.

2. High Quality Score

As previously pointed out, having one keyword per ad group would likely achieve high Quality Scores, high CTR and strong ad rankings. Conversion rates are also likely to benefit, as highly relevant ad text will make users more pre-qualified before clicking.

3. Easy to Optimise Ads for Quality Score

If you notice Quality Score for one of your keywords is low, having one keyword per ad group makes it is relatively easy to identify which keyword / ad combination is performing poorly and make appropriate changes to improve its Quality Score.

Disadvantages

1. Unnecessary

Having every one of your keywords in its own ad group is unnecessary. If you have two keywords, “blue suede shoes” and “suede shoes blue”, what benefit is there of having each of those keywords in a separate ad group? The keywords are so similar, so you could not possibly write a more relevant ad for one if it were in its own ad group.

2. Unmanageable

Imagine a Google AdWords ad group with 2,000 keywords. If each had its own ad group, that would mean 2,000 ad groups. Imagine how difficult it would be to manage 2,000 ad groups, most of which would fail to see a single click. Since many of the keywords would be so similar, the same ads would likely be used across multiple ad groups. There would be a lot of duplication of ads and it would take forever to download reports or update changes. Quite simply, your AdWords account would become incredibly time-consuming and frustrating to manage.

3. Diluted Ad Text Performance Data

If each keyword had its own ad group, impression and click data for ads would be diluted over a greater number of ad groups, making analysis and optimisation of ads difficult and less meaningful. If , however, 10 of your very similar keywords were grouped together in one ad group, impression and click data for those 10 keywords would be aggregated for the ad group’s ads, making it easier to spot which ads are performing well and which need changing.

Balance

Although having one keyword per ad group would be nice in a perfect world, considering that there are potentially thousands of keywords that could be relevant to your business, having one keyword per ad group is taking paid search to an unmanageable level. While having one keyword per ad group is one extreme of paid search management, putting all of your keywords in one ad group is the other. The best results in paid search are achieved from a balanced approach, somewhere in between the two extremes.

So don’t just separate out keywords into their own ad groups for the sake of it. Your account will soon become unmanageable and you’ll dilute your ad text data. Instead, group very similar keywords together, even if they are broad matched. As long as your keywords are very similar, 5-20 keywords per ad group is fine. I regularly achieve Quality Scores of 9 and 10 using this approach. The key is to make sure the keywords in each of your ad groups are very similar, and that your ads are highly relevant to the ad group’s keywords.

Start with maybe 10 closely related keywords in each ad group. Once you have some data collected, use the 10% Clicks Rule to decide which of your ad groups and keywords could benefit from being split out and given their own tailored ads. It will keep your time and effort focused only on the parts of your campaign which deserve your time and effort.

As with most things in life, it’s about finding a balance. As you continually strive to improve the relevancy of your keywords and ads, make sure the strategy you are adopting is achievable and sustainable. If you put all your keywords in the one ad group, you’ll receive a poor Quality Score as users fail to engage with your ads. If you over complicate your keyword / ad group structure, you’ll end up creating a bloated paid search account and start to lose focus of your long-term goals. Find a balance that works for you.

Rule of Thumb

So unfortunatley there isn’t really a rule for the number of keywords an ad group should contain. There isn’t a ‘best’ number of keywords you should aim to have in each ad group. It’s about finding what works best for your business, for your products or services, for your set of keywords. But if you ever find yourself unsure whether a keyword should be split out into its own ad group, ask yourself this:

A keyword should only be give its own ad group if you think you could write a more relevant ad (or show a more relevant landing page) for that keyword if it were in its own ad group.

Thanks for reading. Comments and suggestions welcome.

There’s been a lot of talk about long-tail keywords in pay per click (PPC). You could say it started in the entertainment industry with Chris Anderson’s influential Long Tail article in 2004, but it wasn’t long before the concept became mainstream among search marketers.

Long-tail keywords are those low-volume, obscure, infrequently searched-for keywords that turn up in your search query reports. ‘Cheap remortgage for bad credit history’ is one example of a long-tail keyword. ‘Remortgages’ is not.

The theory goes like this:

• Long-tail keywords, en masse, can provide significant search volume (high impressions)
• Long-tail keywords have less competition than generic keywords (lower cost per click (CPC), higher click-through rate (CTR))
• Long-tail keywords are more specific than generic keywords, so ads can be better tailored to match the searcher’s needs (higher CTR, higher Quality Score, less wastage from irrelevant searches)
• People making long-tail searches are often further along in the buying cycle and more willing to buy than people making generic searches (higher conversion rate)
• These lower CPCs, higher CTRs and higher conversion rates mean long-tail keywords can be extremely profitable (lower cost per acquisition (CPA))

So are long-tail keywords all they are cracked up to be? Are they worth all the time, effort and commitment they require?

In short: yes.

Over the course of this article you’ll see exactly how search volume, CTR, CPCs, average position, conversion rate and CPA differs for searches containing different numbers of words, and how long-tail keywords can benefit your business immensely. Using three months of real Google AdWords campaign data, you’ll see that long-tail searches outperform generic short-tail searches on almost every measure, and provide a great opportunity to connect with customers which is generally not being taken by the majority of advertisers.

1. Search Volume (Impressions)

Let’s start with search volume. Do people make long-tail searches in any meaningful volume?

Look at the example below. Although 1 and 2-word searches may be under-represented in the example (the account has a natural bias towards keywords of at least 3 words), it is clear that as the number of words in a search query increases beyond 3, the number of searches made using that that number of words falls.

This isn’t surprising. You would of course expect search volume to drop as searches start becoming obscure and lengthy. It is little surprise that more people are making shorter searches such as as ‘cheap televisions Brisbane’ (3 words) instead of longer searches such as ‘low cost Sony Bravia television shops in Brisbane’ (8 words).

What is worth noting, however, is the power of these long-tail keywords en masse. Added together, searches of 5 words or more accounted for 21% of all impressions. While long-tail keywords may be individually insignificant, a PPC campaign with thousands of long-tails can be a serious source of additional traffic.

Fact: Long-tail searches have significant search volume

2. Click-Through Rate (CTR)

Another common belief among search marketers is that click-through rate (CTR) is higher for long-tail keywords. Their reasoning being:

1. Long-tail keywords have less competition, so there is a higher chance someone will click your ad
2. Long-tail keywords are more specific in their requirements, so you can write a more targeted and relevant ad to encourage the searcher to click

While the first point is perhaps rather tenuous (Google’s broad-matching mechanism often sends long-tail searches to advertisers’ short-tail keywords), the second point is definitely true. If someone searches for ‘cheap Sony Bravia 46 inch televisions’, and your ad mentions the words ‘Sony Bravia’, ’46 inch’ and ‘televisions’, perhaps with latest prices for that model, it makes sense that your ad will be more appealing than a generic ‘Sony televisions’ ad.

Let’s have a look at CTR for searches of different word counts. While searches of 1, 2 and 3 words have a relatively low CTR, CTR appears to increase significantly for searches of at least 4 words. CTR, it seems, is considerably stronger for long-tail keywords.

Fact: Long-tail keywords can achieve a higher CTR, as long as ads are tailored to the search query

3. Cost Per Click (CPC) & Average Position

Many search marketers also believe long-tail keywords are cheaper. They have less competition, fewer people bidding on them to drive up their prices, so CPCs will be kept relatively low.

So are long-tail keywords cheaper than generic keywords?

To answer this question, it is important to bring average position into consideration. Since CPCs and ad rankings are closely connected (a higher CPC typically means higher ad ranking), both average position and CPCs need to be considered together.

First, let’s look at CPCs. For searches of 13 words or more, CPCs do tend to be cheaper. For searches under 13 words, however, CPCs tend to be very similar. A 9-word search query costs pretty much the same price as a 4-word search query. CPCs do fall very slightly as word length increases from 3 to 12 words, but I would hardly call that significant.

Now let’s look at average position (a higher bar represents a higher ad ranking). For 1, 2 and 3-word searches, ad ranking is relatively low, and ads are appearing near the bottom of the first page. As word length increases, however, ads are shown significantly higher. They are appearing in the top positions.

So although CPCs were relatively similar for searches of all word counts, long-tails were often shown in a higher position.

When you think about it, this makes perfect sense. When you set a maximum CPC bid for each keyword, Google will show you as high as possible without going over your maximum bid. If long tail keywords are cheaper, Google won’t necessarily charge you less. It is in their interest to charge you as much as possible, so they will instead keep your CPCs close to your maximum bid but show you in a higher position.

Fact: Long-tail keywords are cheaper for the same ad ranking, or the same price for a higher ad ranking

4. Conversion Rate

So we’ve seen that long-tail keywords have a significant search volume. They exhibit a strong CTR and are often cheaper than their short-tailed rivals. But clicks are no good if people don’t engage with your site or part with their cash. It’s often conversion that really matters.

So are long-tail searches more likely to convert?

Many search marketers seem to think so – their reasoning being that people who make longer, more specific searches have already done their research and know exactly what they want. They are further along in the buying cycle so are more likely to open their wallet.

Let’s have a look at conversion rate for searches of different word counts.

It’s a pretty convincing trend. As the number of words increases, so does conversion rate.

Fact: Long-tail keywords have a higher conversion rate

5. Cost Per Acquisition (CPA)

Finally, what does this mean for CPA, profitability, return on investment (ROI)? Is it cheaper to acquire a customer through the long-tail?

Let’s have a look at the CPA column.

Again, there appears to be a clear trend between word count and CPA. Conversions from long-tail searches seem to be cheaper than conversions from generic, short-tail searches.

And it’s not just one or two conversions which are coming through long-tail searches, either. Remember how long-tails of 5 words or more accounted for 21% of all searches? Well, those 21% of long-tails generated a massive 40.5% of all conversions.

Fact: Long-tail keywords have a lower cost per acquisition and can be extremely profitable

Long-Tails Are Your Friend

As we have seen, the benefits of long-tail keywords are many:

1. Significant search volume
2. Higher CTR
3. Cheaper CPCs (or higher ad ranking)
4. Higher conversion rate
5. Lower CPA

Quite simply, they outperform generic, short-tail keywords on every measure.

Don’t get me wrong, long-tails shouldn’t replace your short-tail keywords. Short-tails, if used wisely, are great for building interest and awareness at the early stages of the buying cycle. Your long-tail keyword strategy should complement your short-tail strategy.

So by all means continue showing on your high-volume keywords – after all, they may be your bread and butter that keep your business afloat. But the next time you work on you AdWords account, spend some time researching relevant long-tail keywords. Try to think what people are actually searching for and use keyword tools to help. Structure your keywords into closely-themed ad groups and tailor your ads and landing pages to cater for these specialised long-tail searches.

Your Moment to Shine

Of course, researching thousands of keywords and structuring them into hundreds of closely-themed ad groups, each with tailored ads and landing pages, is by no means easy. It will take considerable time, effort and dedication, not to mention the many hours of keyword and search query analysis, ad group expansion and ad copy testing once your keywords are live.

But think about your target audience for a minute. They are calling out for someone to meet their needs in a personalised and relevant way. It’s the age of social interaction, and people are sick of seeing generic ad after generic ad. And despite many advertisers claiming they are “doing this already”, consumers are not currently getting a personalised and relevant service (see Relevancy: The Holy Grail of PPC).

If you can be the advertiser who understands your audience using search query analysis, if you can cater for their individual needs with relevant ads and landing pages, if you can be the one who makes a mark in your industry, customers will reward you with their wallet. It’s your opportunity to stand out from the competition. So take it.

Daily campaign budgets in Google AdWords are great. You simply enter the maximum you want to spend per campaign per day, then sit back and relax, safe in the knowledge that your monthly Google bill will not cause any nasty surprises.

But despite the reassuring nature of campaign budgets and the ease at which they can control your spending, they should not be used to control your spending. Instead, cost per click (CPC) bids should be your tool of choice for spend management.

More clicks, no extra spend

To understand why, suppose the daily budget for one of your campaigns is set to $100/day. Each click costs you $1.00 and your ads are showing in position 3.0. Your daily budget of $100 is being hit, so you receive 100 clicks/day.

Since your campaign is reaching its daily budget, your ads are not showing for all eligible searches. Depending on your campaign ad delivery settings, either your ads are not showing later in the day (accelerated delivery), or they are only showing intermittently (standard delivery). In either case, you are missing out on potential customers.

Now suppose you were to reduce your bids by 25%. Your average cost per click drops to $0.75 and your ads are now showing lower down in position 5.0. But since your ads are now appearing throughout the whole day for all eligible searches, you receive more clicks. Instead of 100 clicks, you now receive 120 clicks. Daily spend is now $90 (under your budget of $100).Ceteris paribus, reducing CPCs for campaigns which hit their daily budgets is likely to give you more clicks for no extra (or possibly less) spend. That’s more visitors, for no extra spend. Assuming that all clicks are of equal value to your business, and average position does not affect your conversion rate, these extra clicks are likely to mean more sales and a higher ROI.

More clicks, same CPCs

Alternatively, leaving CPCs constant and instead increasing your daily budget, will likely result in more clicks for the same average CPC. That’s more visitors, more potential customers, without paying a higher per-customer premium (CPC) for the privilege.The bottom line is this: campaigns which hit their daily budgets are inefficient.

For this reason, it is important to check on a regular basis that each of your campaigns are well within their daily budgets. If you notice any of your budgets are being reached, or ore close to being reached, alarm bells should start ringing.

Check your budgets

One simple way to keep on top of daily spending is to log in to Google AdWords, select ‘yesterday’ as the time period and compare the ‘cost’ and ‘daily budget’ columns for each of your campaigns. If any of your campaigns are hitting – or are close to or hitting – your daily budgets, either increase your budget (if extra spend is financially viable), or reduce your CPCs.

A more comprehensive approach, which looks at campaign costs a longer time period, gives you a better understanding of daily trends. By looking at a month’s worth of data (instead of just ‘yesterday’), you will be more informed and better equipped to make changes to your CPCs and budgets.

Download a campaign report

Log in to Google AdWords and create a new report. Select ‘campaign performance’ as the report type (since daily budgets are set at campaign level), select ‘daily’ as the unit of time and enter a date range such as ‘last 30 days’. In ‘add or remove columns’, ensure ‘daily budget’ is ticked. Leave all other options as default. Run the report, and export it to Excel.

Identify overspend

Once you have the report in front of you in Excel, create a new column to identify the percentage of budget spent by each of your campaigns each day, such as in the example below. Once you have done this, you will then be able to see how close each of your campaigns came to its daily budget.

Suppose you are a retailer of European holidays. You have two campaigns – one for Venice and one for Amsterdam. Both campaigns have a daily budget of $100.Looking at the % spent column, it is clear that the Venice campaign is regularly reaching its budget. Reducing CPCs for Venice keywords would likely result in more clicks for no extra (or possibly less) spend.

By all means continue to set daily budgets to protect against freak spikes in traffic which you cannot afford, but make sure CPCs – rather than budgets – are your primary tool for spend management.

Summary

When you notice any of your campaigns regularly hitting daily budgets, ask yourself a few questions:

1. Are you achieving less than satisfactory results from your current AdWords spending? If so, reduce your CPCs – you will likely see more clicks for no extra spend.
2. Are you achieving satisfactory or good results? Do you have extra funds available for AdWords? If so, increase your daily budgets – you will likely see more clicks for the same CPCs.

Either way, it’s a win-win. You will either get more clicks for the same budget, or more clicks for the same average CPC. Effective spend management is essential for a performing PPC campaign, so make regular budget checks a priority.

Welcome to the final part of the Clicks Rule special.

You may remember the 10% Clicks Rule is a technique to help identify the areas of your Google AdWords account which could benefit most from your time and effort (if not, you may want track back to Part 1: Overview and Part 2: Process).

What I want to do now is evaluate the rule using a real AdWords campaign data to assess its viability. Does it work? Does it help PPC management? Does it actually help improve results? Is 10% the right figure?

Example

Let’s start with the example I worked through in Part 2: Process.

As you may remember, we identified the ad groups which were receiving a large percentage of total broad and phrase-match clicks. In the example I used, 4 ad groups received at least 10% of broad and phrase clicks.I then suggested looking at the search queries for these ad groups.This gave me some great insight. Although search queries 54, 183, 55, 56 and 150 were relevant to my products and services, they were being broad matched to ad groups which were not relevant. Looking down the list, I found many similar examples of relevant searches being matched irrelevantly.

So I decided to create 16 new ad groups with 288 new keywords. Doing so gave me ideas of other new types of keywords, so I added them too, some in new ad groups. With these new keywords having their their own tailored ads, I could now be more sure than whenever someone searched for these search queries again, relevant ads would show.

Effects on click distribution

Let’s have a look at the before and after in terms of click distribution:

• The percentage of broad and phrase clicks going to ad group 17 fell from 22.8% to 12.4%
• The percentage of broad and phrase clicks going to ad group 27 fell from 15.8% to 11.3%
• Ad groups 30 and 26 dropped out of the top 5
• Ad group 36 (one of the new ad groups I added with new keyword ideas) moved into the top spot
• The number of ad groups receiving at least 10% of broad and phrase clicks fell from 4 last month to 3 this month
• The amount of broad and phrase clicks going to top 5 ads groups fell from 69% last month to 59% this month

(A more comprehensive comparison of ad group click percentages for both months can be found here).

So although most of last month’s ad groups are receiving a smaller share of broad and phrase clicks, one ad group (36) is now receiving more.

Not ideal, but it’s a step in the right direction. Next month, the search queries for ad group 36 can be analysed and split out into separate ad groups. If we repeat the process a few more times, what we’ll hopefully see is the broad and phrase click distribution spread over a greater number of ad groups.

Effects on actual results

Now let’s look at how overall AdWords results have changed:

• CTR increased by 22.5%
• Click volume increased by 43.2%
• CPCs stayed relatively constant (variation of $0.01)
• Average position of ads rose by 0.6
• Quality Score (weighted average) increased from 7.62 to 7.98
• Conversion rate increased by 33.8%

It seems like the improved ad group granularity, better tailoring of ads and 288 new keywords had a positive effect on CTR, Quality Score and conversion rate. Click volume also rose significantly for the same average CPC.

So great results all round.

More examples

We’ve only looked at one example so far. Let’s repeat the process for few more campaigns to see how the 10% Clicks Rule works on other campaigns.

Highlighted in red are the ad groups which are over 10% and could benefit from some insight.In examples 2 and 3, just looking at the search queries for these highlighted ad groups I found over 300 new keywords that could be added. Most were relevant to my products and services but were being matched irrelevantly.

Although examples 4 and 5 had fewer ad groups over 10%, just looking at the top ad groups helped me uncover some unnecessary broad-matching, suggesting maybe a ‘top 5 rule’ would be better to keep it relative.

Although no before and after results are yet available for these campaigns, it would be interesting to see how CTR, Quality Score and conversion rate improve over time with these ad group improvements.

Conclusions

So what can we conclude from all of this?

• The 10% Clicks Rule made it easy to identify ad groups where time and effort should be focused
• There is evidence to suggest he 10% Clicks Rule successfully helped spread the share of broad and phrase clicks across a greater number of ad groups
• There is evidence to suggest the 10% Clicks Rule helped significantly increase CTR, click volume, average position, Quality Score and conversion rate while keeping CPCs constant

So…if you are worried that too much of your traffic is being broad or phrase-matched, or worried that you are losing control over where your ads are being show, or just want to improve CTR, click volume and conversion rate, this technique could be for you.

I’m convinced of it’s use in helping to improve AdWords campaigns. I use it regularly and it really does help to quickly and easily get to the heart of broad and phrase matching. It saves sifting through mountains of data and becoming overwhelmed with analysis paralysis. That’s just me though – if you’ve tried it out for yourself and have any suggestions, good or bad, I’d love to hear your comments.

Happy optimising!

Welcome to part 2 of the Clicks Rule special.

You may be familiar with a technique I shared in recent post called the 10% Clicks Rule (if not, you may want to come back once you’ve skimmed through Part 1: Overview). In essence, the 10% Clicks Rule is a technique that aims to improve the relevancy of ads for search queries which have broad or phrase-matched to one of you keywords. Since it is impractical to give every possible keyword or search query its own ad group with personalised ads, the 10% Clicks Rule helps to identify those ad groups which are most likely to benefit from your time and effort.

Part 1 was all theory. What I want to do now is provide a step-by-step guide explaining how to identify those ad groups in your own AdWords account which could greatly benefit from your insight. All we’re trying to do here is run a Google AdWords search query report at ad group level, filter out exact match keywords (to leave broad and phrase match only) and highlight those ad groups with more than 10% of broad and phrase clicks. These are the ad groups we want to look at. So if you’re a seasoned AdWords and Excel pro, feel free to skim through the bullets or jump ahead to Part 3: Does it Work?. For everyone else who might need a little more guidance, continue reading for a detailed step-by-step guide.

Download a search query report

• Log in to Google AdWords, go to the Report Centre and click ‘create a new report’
• Click ‘Search Query Performance’
• Select ‘ad group’ as the level of detail, ‘summary’ as the unit of time
• Select an appropriate date range
• Click ‘create report’, open it once it completes and export it to Excel

Pivot the data

Once we have the search query report in front of us, we want to summarise clicks by ad group.

• Delete anything above the campaigns/ad group/search query row so ‘campaigns’ is in cell A1
• Scroll to the bottom and delete the ‘totals and overall averages’ row
• Select all data and headings
• Go to Insert, click ‘PivotTable’, the ‘OK’
• This should create a new sheet

Calculate clicks by ad group

Next we want to filter out exact match clicks and calculate each ad group’s broad and phrase match click volume.

• Make sure you can see the ‘PivotTable Field List’ toolbar on the right-hand side (if you can’t, try clicking on the blank PivotTable or go to Options > Field List) – your sheet should now look like this
• Drag ‘ad group’ into the ‘row labels’ box – this should list all your ad groups in column 1
• Drag ‘clicks’ into the ‘values’ box and ensure it says ‘sum of clicks’ – this should show click totals in column 2
• Drag ‘Search Query Match Type’ into the ‘report filter’ box which should add a drop-down filter in cells A1 and A2 – your field list should now look like this
• Click the drop-down filter, click ‘select multiple items’ and ensure only broad, broad (session based) and phrase  are ticked – like this

Calculate percentages by ad group

You should now have a list of ad groups with totals of their broad and phrase clicks. Let’s now calculate each ad group’s percentage of total broad and phrase clicks.

• Scroll to the bottom and make a note of the ‘grand total’ number of broad and phrase clicks
• Click anywhere on the PivotTable to ensure it’s selected and click Options > Formulas > Calculated Field on the toolbar
• Type “Percentage” as the name
• Type “= Clicks / total_clicks” into the Formula box, where ‘total_clicks’ is your grand total of broad and phrase clicks you made a note of earlier
• Click OK – this should add a new column with each ad group’s percentage
• Ensure the ‘grand total’ of this new column equals 1
• Change the formatting so that each number reads as a percentage

Highlight high-volume ad groups

Now that we have percentages calculated for each ad group, make a note of those ad groups…

• Right-click anywhere in column 3, go to ‘Sort’ and select ‘Sort Largest to Smallest’
• Make a note of ad groups with more than 10% of clicks

These are the ad groups with a lot of broad-matching and phrase-matching going on. These are the ad groups that could benefit with your time and effort.

The fun part

You’ve now got everything you need to start make powerful changes to you AdWords account. You can stop here and use your own intuition to make improvements to these ad groups, or continue reading for some ideas and suggestions on what to do next.

Search query analysis

• Go back to your original search query report sheet so you can see all your search queries
• In the ‘ad group’ column, filter so that only the ad groups you made a note of earlier are ticked
• In the ‘Search Query Match Type’ column, filter so that only broad, broad (session) and phrase are ticked
• Sort the clicks largest to smallest

Your search query report should look something like this.

Take a moment to familiarise yourself with your search queries. People are typing these searches to find your products or services. You need to decide what action to take. For each of your search queries, you could either:

1. Add it as a negative keyword (if it’s not relevant)
2. Add it as a new keyword in its own ad group (if ads in that ad group are relevant)
3. Add it as a new keywords in a new ad group (if ads in that ad group are not relevant and new ads are needed)

First decide if any of the search queries are irrelevant to your business (option 1). Make a note of any irrelevant searches in a separate Excel sheet – you can add them as negative keywords later.

Great. Let’s now assume that all remaining searches are relevant to your business. You now need to decide between options 2 and 3 – whether to add the search query as a new keyword in that ad group, or in a new ad group.

To decide whether option 2 or 3 would work best, have a look at the ‘ad group’ column for the search query. It is this ad group the search query is being matched to. Open up AdWords Editor and now find that ad group. Look at the ads. These ads are being shown whenever someone searches for the search query. Are they relevant? Do they mention the search query in the heading or descriptions? Could they be improved in any way to increase relevancy, Quality Score, CTR and conversion rate?

If you think the ads are relevant to the search query, add the search as a new keyword to that ad group (option 2). If you think you could write better, more relevant ads for the search query, add the search query as a new keyword in a new ad group and write better ads for it (option 3).

Great rule, but does it work?

Hopefully if you’ve made it this far you’ve managed to have a go yourself and found some juicy ways to improve your AdWords campaign. While I hope you found it simple and straightforward to follow, feel free to share your thoughts and comments.

In the final part of the Clicks Rule trilogy, I look at real AdWords examples and explore how it can actually help improve results of AdWords campaigns. Part 3: Does It Work?

Welcome to the first of a 3-part Clicks Rule special.

Here’s the theory

No more than 10% of total broad and phrase clicks in your Google AdWords account should come from a single ad group. If more than 10% of your total broad and phrase clicks comes from a single ad group, the keywords in that ad group are being over broad-matched or over phrase-matched. Too many searches are going to that ad group’s broad and phrase-match keywords, so the ad group could benefit from keyword expansion and search query analysis.

Example

Suppose you found an ad group which accounted for 18% of your total broad and phrase clicks. This ad group is a prime candidate for improvement for two reasons:

1. Ad group expansion

High-volume ad groups are perfect for ad group expansion.

Spitting out the ad group’s keywords into separate ad groups allows you to write more tailored ads for each keyword. Tailored ads are likely to have a beneficial effect on click-through rate (CTR), Quality Score, costs per click (CPC), ad ranking and conversion rate.

Since it is impractical for every keyword to have it’s own ad group (imagine how tedious and time-consuming 10,000 keywords and 10,000 ad groups would be!), the 10% rule highlights the ad groups and keywords that are likely to benefit most from being split out and having their own tailored ads.

2. Search query analysis

High-volume ad groups are also perfect for search query analysis.

Search queries are what people actually type into Google before they click on one of your ads. Running a search query report for the whole AdWords account allows to to assess whether each of your search queries are relevant to your business, and adding them as negative keywords if not.

Running a search query report at an ad group level, is even better. Not only can you decide if each search query is relevant to your business, but you can also decide if each search query sufficiently matches the ads in that ad group. If the ads in the ad group are very different to the search query, the search query could benefit from having its own ad group with its own personalised ads.

So for each broad and phrase search query you found that matches to the 18% ad group:

• If the search query is irrelevant to your business – add the search query as a negative keyword
• If the search query is very close to the ad group’s keywords and ads – add the search query as a keyword in the same ad group
• If the search query is different to the ad group’s keywords and ads and you think it could benefit by having it’s own personalised ads – add the search query as a keyword in a new ad group

Since it is impractical to look at the every ad group’s search queries, the 10% rule highlights only those ad groups which are likely to have the biggest effect for the amount of time you spend making changes.

Broad and phrase only

You may ask why look at only broad and phrase clicks? What about exact match?

Exact-match keywords give you complete control over the user’s search query. Since you can be 100% sure what the user will need to type into Google for your exact-match keyword to be triggered, you are able to write highly-targeted and personalised ads without having to worry about hundreds of different search queries triggering your exact-match keyword. It is relatively simple to look at an exact-match keyword and decide whether its ad could be made more relevant.

However, with broad and phrase match, things aren’t so simple. You could spend all day trying to write perfect ads which closely match your broad and phrase keywords, but ultimately it is up to Google what kinds of searches get matched to these ads.

For example, you could write a highly compelling ‘Cheap Sony TVs’ ad for your ‘cheap Sony TVs’ keyword. However, if the user searches for ‘Bravia 42 inch deals’ and they gets broad-matched to your ‘cheap Sony TVs’ keyword, your ‘cheap Sony TVs’ ad will appear. It will look irrelevant to the user.

A better ad would mention ‘Bravia 42 inch deals’, although this is only possible by creating a dedicated ‘Bravia 42 inch deals’ ad group. You know you can’t create a dedicated ad group for every search query, so where do you start? Where do you draw the line?

This lack of control and uncertainty with broad and phrase match can be a real problem for advertisers trying to create highly relevant campaigns. The whole point of the 10% Clicks Rule is to help regain some control, by providing a technique to help you quickly and easily get to the heart of your broad and phrase matching and make changes that are likely to have a powerful effect. It’s not meant to be a strict ‘rule’ as such, more a ‘guideline’ or ‘rule of thumb’ which I have found to work in my experience.

That’s all for part 1. Comments and suggestions welcome.

In Part 2: Process, I’ll take you through a step-by-step guide to running rule for yourself – finding those ad groups in your own Google AdWords account that could benefit from a little TLC. If you’re more interested in exactly how the 10% Clicks Rule actually works or how it can help to improve your AdWords results, skip to Part 3: Does it Work?

I’m going to focus my first post on what I believe is the most fundamental concept in PPC: relevancy. Giving users what they are looking for. Directing them to where they want to go. Answering their questions.

Why? Because paid search relevancy can pay massive dividends. Not only is a highly relevant pay per click (PPC) campaign more likely to receive a higher click-through rate (CTR), higher Quality Score, higher ad rankings, lower costs per click (CPC) and benefit from less wasted spend, but users will more qualified so bounce rates are likely to fall (the number of people who immediately ‘bounce’ back), conversion rates increase and return on investment (ROI) will ultimately improve. So a highly relevant paid search campaign is definitely a good thing.

To achieve PPC relevancy, keywords, ads and landing pages need to work together in tandem. Messages in ads need to match users’ search queries, landing pages need to match messages in ads and landing pages need to relate to users’ original searches. (For a more detailed explanation of how each component interlinks, you might like to consult Acquisio’s great article on AdWords relevancy and Quality Score).

Closely matching ads and landing pages to keywords to encourage only targeted and qualified users to visit your site is a simple theory, and one that’s been around since the dawn of Google AdWords.

So nothing new then – does that mean relevancy is no longer relevant?

Well, not exactly, for two reasons…

Impossible to achieve

Firstly, relevancy can never be achieved in its perfect form. There will always be ways a PPC advertiser can improve his keyword selection, negative keyword list, match type strategy, ad copy matching and landing page selection to give the user a more engaging and personalised experience. Just like there will never be a 10.0 film on IMDB, there will never be a perfectly relevant PPC campaign. One can only strive towards perfection – towards the Holy Grail of relevancy.

Higher expectations

Secondly, the bar of relevancy is constantly being raised. As search engine continually improve their algorithms to provide users with more relevant organic search results, so paid search ads will have to improve to keep up with the growing expectations of searchers. Ads which might have been considered ‘quite relevant’ last year may be considered ‘not so relevant’ now. Ads which sufficiently answer the questions of searchers today may not do so next year when people start to demand a more personalized and tailored service.

So not only is the Holy Grail of relevancy (a perfectly relevant campaign) impossible to achieve, but it is getting more and more impossible to achieve as we speak.

But all is not lost. No-one expects perfection, after all. Just being better than the competition can reap massive benefits for advertisers. And as I’m about to point out, getting better than the competition doesn’t need to be difficult. There are opportunities everywhere.

Opportunities

Say you’re interested in visiting Sydney and want somewhere to stay. Load up Google, search for ‘Sydney hotels’ and look at the paid search results.

Of the 10 PPC ads, 9 mention the words ‘Sydney’ and ‘hotels’. Most of the ads are calling out to the user, “I have hotels in Sydney! Come to Me!” Most of the ads are relevant to your search.

But let’s say you know a little bit more about your Sydney hotel requirements. After all, you can’t be bothered clicking through each of the hundreds of paid search results (or the 22,900,000 organic results for that matter). Come to think of it, you are interested in going to Sydney next weekend, you your refine your search query.

Search for ‘weekend breaks in Sydney’ and look at the paid search results. Although most ads mention ‘Sydney’, not a single advertiser includes the words ‘weekend’ or ‘break’ in their ads. No-one is shouting out to the user, “Yes! I have weekend breaks in Sydney! Come to me!” Every ad appear to be a generic ‘Sydney Hotel’ ad that may or may not be relevant to your weekend requirements.

Suppose, instead, when searching for ‘weekend breaks in Sydney’, you saw one of the following ads:

Suppose they took you through to a special ‘weekend break’ page, specifically designed for people looking to stay at the hotel over the weekend. Along with suggestions of local Sydney sights, activities and restaurants that could easily be fitted in over a weekend were reviews from people staying at the hotel on Friday and Saturday nights.

Would you be more likely to consider this hotel in your plans? I know I might.

More opportunities

Okay, only 36 people searched for ‘weekend breaks in Sydney’ in June. But these were 36 people who knew what they were looking for and were delivered poor, generic, one-message-fits-all ads.

‘Weekend breaks in Sydney’ is just one example. Imagine all the hundreds of similar qualified searches people could make to find your products or services. 880 people searched for ‘Sydney CBD hotels’ in June, but most advertisers fail to mention ‘CBD’ or even their location in their ads. ‘Sydney hotels the rocks’ had 390 searches, but only one advertiser mentions the phrase ‘The Rocks’ in their ads. 73 people searched for ‘3 star hotels in Sydney’ but only a handful of advertisers mention ’3 star’ or ‘3*’ in their ads. These are people who know what they want are willing to part with their cash if they can find it.

It doesn’t take long to find hundreds of other examples of keywords that have significant search volume and are being poorly served. There are opportunities everywhere.

What’s more, as demand for better search results grows, people will start making more of these 3, 4, 5 and 6-word searches and expect better, relevant, more personalised results. The winners will be the advertisers who cater for them. The losers will be the ones who don’t.

So how do I go about improving the relevancy of my AdWords campaign?

Patience

I’m not going to pretend there is a quick overnight fix (because there isn’t). A highly relevant AdWords campaign takes patience, commitment and dedication.

Nor are there techniques that work for everyone. The whole purpose of this blog is to share with you the PPC techniques I have found to work in my experience, although I recognise they will be far from the be-all-and-end-all of paid search management so I welcome your ideas and comments. Paid search is an ongoing battle to become better and better, and it isn’t going to stop any time soon.

But to keep things nice and simple, here’s a quick 5 minute run-down of the essentials of creating a highly relevant PPC campaign:

Keyword Research

Research keywords that people are searching for. There are free tools out there, such as Google’s keyword tool, so use them. Build up a comprehensive keyword list. Not just with generic, high-volume keywords, such as ‘cheap Sydney hotels’, but also with long-tail keywords such as ‘cheap hotels in Sydney CBD’ and ‘cheap hotels Sydney Darling Harbour’. Long-tails can collectively be of significant volume and provide a great opportunity for tailored ads.

Then research negative keywords, lots of them. Why waste money on clicks you know are completely irrelevant? Use the keyword tool to identify keywords that might broad match to ‘cheap Sydney hotels’. Go through each result, making a note of anything you think is irrelevant. Is your Sydney hotel miles away from Sydney Airport? If so, add ‘airport’ as a negative keyword. Keep brainstorming negatives until you have at least a hundred.

Ad Group Structure

Once you have done your initial research, group your keywords into small, closely themed ad groups of generally no more than 20-30 keywords each. Write ad descriptions that are relevant to the ad group’s keywords and include the ad group’s keywords in your ads where possible. If you think you could write a more relevant ad for a keyword if the keyword was in its own ad group, split out that keyword into its own ad group and write a more relevant and tailored ad for it.

Think of the keyword as the question and the ad as the answer. Keep asking yourself, “If I searched for this keyword and saw this ad, is it answering my question?” If not, change it so it does.

Include offers and prices that are relevant to the keyword. In your ‘Sydney Hotels Christmas 2009′ ad group, how about mentioning Christmas 2009 prices or early booking discounts?

You’re trying to make your ads as relevant as possible to the user’s searches so think about how you could angle your products or services to appeal to users searching each of your keywords.

Next, deep-link your keywords to the most relevant page on your site. Don’t have a relevant landing page for a set of keywords? Write one.

Rinse and repeat until you have hundreds of ad groups, each with tailored ads and landing pages that match the keywords they contain.

Optimization

Then get optimising. Test new keywords. New ads. New landing pages. Two keywords in the same ad group getting a lot of volume? Split the two keywords out into separate ad groups and write new ads that better match those keywords.

Run search query reports to highlight searches your keywords have broad-matched and phrase-matched to. Are they relevant? If so, ad them as new keywords in new ad groups and write tailored ads for them. If not, add them as negative keywords to prevent your ads showing for them again.

It may seem like a lot of work but it’s worth it. With patience, your CTR will start to increase. So will your Quality Score. People will start to spend longer on your site and view more pages. Returning visitors will rise as people decide to come back. Conversion rates will grow and sales volume will increase.

The Holy Grail of relevancy is not something you can achieve overnight, or achieve at all for that matter. It is only something you can strive towards. PPC success favours the dedicated. So keep testing and optimising.