It’s All Relative

Since CPC prices are often closely linked to the potential profitability of a sale from that keyword, the CPC price is often a mute point. A ‘bad credit history remortgage’ could be worth $15,000 profit to a remortgage broker, so having CPCs in excess of $50.00 can deliver a strong return on investment.

On the other hand, the keyword ‘New York weather’ has little commercial intention, so keywords such as this tend to benefit from low CPCs.

While this relativity of CPC prices makes CPC comparisons across sectors rather meaningless, most PPC advertisers would jump at the chance to pay lower CPCs. So below are 4 strategies I’ve found useful for achieving lower CPCs, while still maintaining a strong conversion rate.

Source: Wordstream

1. Use Long Tail Keywords

Long-tail keywords are those highly-specific, infrequently searched-for phrases such as ‘all inclusive holidays to Paris from Sydney’, which turn up in your search query report. Since they tend to have less advertiser competition, they can be considerably cheaper than more generic keywords such as ‘Paris holidays’.

What’s more, since people making long-tail searchers have arguably carried out the large majority of their pre-purchase research, and are often further along in the buying cycle, long-tail searches can have a considerably higher conversion rate.

Cheaper CPCs and higher conversion rates were exactly what I found when analysing search queries containing different numbers of words. Searches containing 4 or more words performed consistently better than searches containing fewer than 4 words.

Recommendations:

• Use Google’s Keyword Tool to research keywords which have approximately 100 searches/month. These tend to get overlooked by a large number of PPC advertisers.
• Take advantage of the ‘more like these’ feature in Google’s Keyword Tool to continue to build your list of long-tail keywords.
• Run a search query report to look at the search queries which get matched to your keywords. If they are relevant, and have a decent click volume, add them as new keywords and give them their own tailored ads. This way, instead of long-tail searches being matched to your more generic keywords, they will now be matched to longer keywords, giving you more control over ad messages and bid optimisation.

2. Research Uncompetitive Themes

Keywords which have less competition often have lower CPCs. Finding keyword with less competition often requires you to think outside the box, although with the right tools and approach you can be diversifying your keyword portfolio in no time.

Recommendations:

• Plug the URL of a competitor’s website into Google’s Keyword Tool and see what comes up. If their products and services are worded differently to yours, it can uncover some previously-overlooked terminology.
• Mine your search query report to uncover themes which you previously overlooked. Don’t worry if a search query has only a handful of clicks –you’re trying to research new themes from searches which have matched to your existing keywords, so click volumes would likely be much greater if they are added as new keywords.
• Look for numbers which frequently crop up in your search query report. If you’re selling holidays, consider how people type the dates into their search, such as ‘France holidays December 2011′, or ‘winter 2012 skiing deals Queenstown’. If you can provide tailored ads which cater for these date searches, you will likely receive high click through rates (CTR), high Quality Scores, and low CPCs.
• Use Google Insights for Search to find breakout searches. These are searches which have recently increased in popularity by at least 400%, and can be excellent opportunities for highly-profitable keyword targeting due to their relatively low competition and relatively low CPCs.
• Ask friends or family how they might search for your products and services.

3. Use Modified Broad Match

Modified Broad Match is a variation of broad match, but allows you greater control over the types of search queries which trigger your ads. By placing a plus (+) sign in front of certain words in your keyword, Google will only match your keyword to searches which contain all of the words with a preceding plus sign.

So if your keyword was ‘+cheap +deals to +Rome’, you can be sure than any searches matching to your keyword must contain the words ‘cheap’, ‘deals’, and ‘Rome’.

Modified broad match is a great way to improve the quality and profitability from your AdWords campaigns. In some recent research I carried out on modified broad match keywords, keywords with a greater amount of broad match modification tended to have considerably higher CTRs and significantly lower CPCs.

Recommendations:

• Take advantage of modified broad match. It allows you greater control over the types of searches which trigger your keywords.
• Be mindful that restricting the match types of your keywords may reduce your click volume, so compensate for this by researching additional keywords and themes.
• Broad match generation can also be a useful tool to allow your more generic non-modified broad match keywords to catch relevant search traffic which have not yet been added as modified broad match keyword.

4. Be Relevant

Despite PPC often being dubbed as saturated and highly-competitive, the truth is there are massive opportunities everywhere for PPC advertisers to achieve high profitability by providing highly-relevant and helpful ads, which engage with their target audience.

Searches on Google are now becoming increasingly diverse, complex, and unique, and users are expecting a higher degree of relevancy and helpfulness from search results. If you can be the advertiser which caters for the specific needs and requirements of searchers, visitors will reward you with their wallets. And since click through rate (CTR) is a large component of Quality Score, if you can achieve a high CTR, low CPCs will naturally follow.

Recommendations:

• Create hundreds of highly-granular ad groups, each containing only a handful of very similar keywords.
• Tailor your ad messages to your ad group’s keywords. If the ad group contains keywords related to ‘Winter 2012 Queenstown holidays’, ensure your ads also mention cater for Queenstown holidays in Winter 2012.
• Run an ad group report and find your ad groups which receive a large number of clicks. Changes are those ad groups are being matched to a large number of very different searches, so could benefit from being split out. The 10% Clicks Rule is a useful tool for quickly and efficiently identifying your ad groups which could benefit from being split out. Follow the 3 part guide for step-by-step instructions of how to do this.
• Examine your ad groups with high CPCs and low CTRs. Consider how closely your search queries match to that ad group’s ads. If a gap exists between the ad group’s searches and the ad group’s ads, consider how you can make changes to boost your relevancy.

Return on Investment is the Goal

Quality Score doesn’t sell anything. Click through rate doesn’t sell anything. CPCs don’t sell anything. Improving these metrics should not be the objective of your campaign. Improving ROI should be.

All other things equal, if you reduce your CPCs by 20%, great!

But are all other things equal? In trying to reduce your CPCs, have your sacrificed volume? Have you sacrificed quality? Are paying lower CPCs simply because visitors are now less relevant and less likely to convert? Are you now missing out on more relevant and potentially more profitable searches?

CPCs or Quality Score or CTR should not be the end goal of your PPC campaign. Return on investment should be. There’s no harm in paying 50% higher CPCs if your profit increases by 60%. So only use CPCs as a guide.

By all means use long-tail keywords, search query reports, and modified broad match as a means to lower your CPCs. But make sure your sales volume and ROI also rises as a result.

Alan Mitchell is a Google AdWords certified PPC specialist based in Melbourne, Australia, with over 4 years running successful PPC campaigns for businesses in Australia and overseas. Find out how the specialist PPC management such as the 4 PPC strategies above can help you improve your return on investment from PPC marketing, or get in touch for more information.

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.

Alan Mitchell is an experienced Google AdWords consultant helping businesses in Australia increase their return on investment from PPC marketing. For more information on how a strategic approach to PPC can benefit your business, get in touch today for a free consultation.

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.

Alan Mitchell is an experienced Google AdWords consultant helping businesses in Australia increase their return on investment from PPC marketing. For more information on how a strategic approach to PPC can benefit your business, get in touch today for a free consultation.

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.

 

Alan Mitchell is an experienced Google AdWords consultant helping businesses in Australia increase their return on investment from PPC marketing. For more information on how a strategic approach to PPC can benefit your business, get in touch today for a free consultation.

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

 

Alan Mitchell is an experienced Google AdWords consultant helping businesses in Australia increase their return on investment from PPC marketing. For more information on how a strategic approach to PPC can benefit your business, get in touch today for a free consultation.