How Much More Could You Make If You Got Rid Of Your PPC Agency?

Paid Search agencies typically charge a percent of spend for their services. So if you’re spending $50K to $100K a month, your fee could be anywhere from 6% – 8%. What if instead of paying the fee, you managed your paid search account yourself, took the money you were paying to the agency and rolled it into your media spend?

Let’s say you’re spending $100K on media and your agency has the task of getting a return on ad spend 4:1. At an 8% fee, the agency makes $400K in revenue with your account and you pay them $8,000.

But If you took the $8K and added it into your cost, you’re real ROAS would be 3.7 ($108K / $400K). So if you took over the account and spent $108K and were able to do no better than a 3:7 ROAS, it’s a wash – you’d still make $400K in revenue.

But what if you could do just a little bit better than 3.7 ROAS with your $108K budget? A 3.75 ROAS would make you $5K more. Over the course of a year that’s $60K in incremental revenue. Not bad. The return grows the higher the ROAS. If you do just as good as the agency at a 4:1 ROAS, you’d make $384K more in a year.

You could take what you’re paying the agency, put it into media spend ( the same net expense as you already have), have the account perform 4% worse than what they’re doing (3.8:1 ROAS) and make  $124K more in a year.

ditch the ppc agecny and make more money

Caveat 1: there is a diminishing return on incremental ad spend which may make that extra $8K a month not return at an equal ROAS as the rest of the account.

Caveat 2: Could you really keep an ROAS higher than 3.7:1 on your own when the agency was doing a 4:1? Maybe (probably).

A good way to find out the level of skill and effort managing your own paid search account would take is to look at the percent of revenue that these three categories make up: brand terms, product listing ads, and non-band terms.

Chances are, the majority of your account’s revenue is coming from brand terms. Second to that is product listing ads. In fact, there’s a good chance 75% of your paid search revenue is made up of those two sources. And the thing about brand terms and product listing ads is that they take little effort to maintain. There is very little skill involved to keep 75% of your paid search revenue flowing in at the ROAS it is now. The typical paid search account looks something like this:

percent of revenue ppc

And the ROAS of the non-brand terms is probably 2:1. The reason your account has a 4:1 ROAS is because brand terms and PLAs return so high that it rounds out the account after taking in all the bad non-brand return.

So the real question is, can you nix the agency, take the fees you’d pay to them and wrap them into your spend, and then get your non-brand terms to perform at a measly 2:1? Worth considering.

Diminishing Marginal Benefit Of Web Analytics Reporting

Heretofore is my Law of Diminishing Marginal Utility Of Web Analytics Reporting: The larger the distribution list of your web analytics report, the less valuable that report is to any individual.

dimishing marginal value of report distribution

Wherever there is web analytics there are big, watered-down reports being generated and sent to lots of people every monday – it doesn’t have to be this way. Sending out reports to the entire company starts with the best intentions. Everyone wants to know what is going on with the website. Requests are made to include a little bit of everything for everyone: overview of total traffic and conversion rate, ROAS for the marketing team, best selling products for merchandising, cart conversion funnel for the UX team, social likes and tweet counts for the brand team, site response times for web development, customer service stats, and the list goes on. Pretty soon it’s a report that takes hours to create and when it arrives Monday morning it is swiftly ignored by everyone in the company.

One of the basic productivity concepts of David Allen’s book Getting Things Done, is to spread your actions over all the contexts you are acting in. Your daily actions happen in some very clearly separated areas, called contexts: @Home, @Work, @Computer @Store, for example. It doesn’t do you any good to remember to buy toilet paper when you’re at work. So on your to-do list you can tag your action to buy toilet paper with @store, and when you are in that specific context you only need to focus on those applicable actions. The same concept can be applied to web analytics reporting.

The right data needs to be available at the right time to the right person. If any of those three don’t match up, the report is just noise.

venn diagram right data to right person

  1. Only data that is actionable on behalf of the person consuming it is needed. Consider only consuming information that applies to your circle of influence. The point of web analytics data is to incite change. If you do not have access to levers then what good does the data do you if you can’t pull them? Maybe someday you will have access to the levers and the data will wait happily for you until then.
  2. Monday is an arbitrary day to look at your reports unless you only make decisions on what to do next on Mondays. Your job probably requires looking at the data that matters to you at different times, all the time.
  3. The right data is discovered when you have the right person in the room and you’re able to discuss what business objectives they have that can be mapped to trackable site behaviors and what levers they are in charge of.

Looking at the criteria of A, B and C, a weekly mega-report is pointless. Full of nothing helpful to anyone. instead, individualized reports tailored to the user, available and accessible when the user needs them, is the only rational approach.

Right Now Most Of That Data In Your Web Analytics Interface Can Be Ignored

Web analytics tools suffer from a lack of constraint. With so many things that can be tracked, all of it is and served up all at once to the unsuspecting user who opens up the reporting interface. The amount of data creates a nagging feeling that will start to follow you around, telling you that you’re not using the tool to it’s full potential. Pretty soon you start coming up with business objectives to match the reports you have instead of focusing on your business objectives and then reporting on them second. Eventually you’re looking at everything and not deciding on anything.

Just because there are top entry pages, cart additions, time spent per visit, device types, paths, internal search terms, referring URLs and on and on, it doesn’t mean you need to pay attention to them. At least not right now, and especially not all at once.

From Seth Godin:
“If you build your company with the policy that you’ll never run an ad, it makes it even more important that you build a remarkable product–you’ll never be tempted to compromise and try to make it up with hype. Same thing goes for organizations that refuse to pay bribes. By eliminating situational decisions and grey areas, it changes strategies from the top down. Or perhaps you’re not willing to pay overtime, regardless of the emergency, regardless of how late the project is… it makes it far more likely projects won’t be late, because they’re designed to ship without emergency… Rigidity is rarely your friend, but well understood boundaries make decision making a lot easier.”

Focus on one site section or source of traffic along with a couple metrics. Make changes to that site section or inbound traffic source. Measure your success and then move to the next one. All those other metrics and reports in the interface will happily continue to gather data and wait for you to make them relevant.

Paying For SEO Is Like Paying For An Oil Change

There are those who say that say SEO is a con and those that defend it saying it helps many, many people.

I think the debate is summed up well with the following analogy. Just replace ‘oil change’ with ‘SEO’ and ‘your car’ with ‘your website’:

I pay to have the oil changed on my car. Of course, I could do it myself. It would be cheaper, I should know my own car and working on it would give me the best results since it’s my car. But I choose not to, and so do many other people – 7 billion dollars a year worth. Am I and all these people stupid? Don’t we not know we could do it ourselves? Is the oil change industry a huge con?

Of course not.

People choose to not change their own oil for many valid reasons:
They don’t have the technical (albeit simple) know-how and have no desire to learn it
It’s time consuming
Its dirty

Is it possible that the workers in an oil change shop take advantage of their customers by selling them things they don’t need? You bet they do. It happens every day. That doesn’t make it right, but unfortunately that’s what happens when there is an imbalance of information.

Web Analtytics Increases Uncertainty As Much As It Does Confidence

With so much data available with web analytics tools, one would think that businesses would become more confident in their understanding of themselves and their customers. But more data leads to just as much uncertainty which is described with a perfect analogy in this New York Times article.

Here’s the analogy:

“The larger the island of knowledge grows, the longer the shoreline — where knowledge meets ignorance — extends. The more we know, the more we can ask. Questions don’t give way to answers so much as the two proliferate together. Answers breed questions. Curiosity isn’t merely a static disposition but rather a passion of the mind that is ceaselessly earned and nurtured.

Mapping the coast of the island of knowledge, to continue the metaphor, requires a grasp of the psychology of ambiguity. The ever-expanding shoreline, where questions are born of answers, is terrain characterized by vague and conflicting information. The resulting state of uncertainty, psychologists have shown, intensifies our emotions: not only exhilaration and surprise, but also confusion and frustration.”

Exhilaration and surprise on one side, confusion and frustration on the other – as any web analyst would attest – sums up web analytics perfectly.

Attention Is Monetizable But Shouldn’t Always Be

The reason apps that don’t have any revenue but millions of users are worth so much is because “attention is monetizable”. The assumption is that once advertisers are able to access all of that attention they’ll pay a lot for the privilege for putting their ads in front of it. But money shouldn’t always follow where the most eyeballs are. It’s possible that all the attention in the world can’t make up for a medium that is just bad for advertising.

I really like Bob Hoffman’s idea that the best mediums for advertising are those that are entertainment mediums like TV and Radio because you are accustomed to entertainment media carrying advertising. The internet is part communication medium, part information medium, and part entertainment medium. Meaning that when you are using it for any reason other than entertainment the advertising gets in the way, is ignored and is ineffective.

From Bob:

“What if you picked up the telephone and instead of getting a dial tone you got an ad?

What if you picked up a dictionary and instead of finding a definition you found an ad?

In both cases you’d be angry. Why? Because the telephone is a medium of communication and you don’t want to be slowed down. The dictionary is a medium of information and you don’t want to be sidetracked.”

Those high value/high attention apps are more like telephones and dictionaries. This isn’t to say that advertisers won’t spend a ton of money on ads in front of that attention – the ways advertising budgets are squandered are infinite – I’m just saying it could have been better spent elsewhere.

The Biggest Threat To Success In Omni-Channel Marketing

For retailers the omni-channel approach is to let the customer decide how they want to shop – whether it be online or in store, mobile or desktop, or any other combination – and then accommodate those preferences in the most frictionless way possible.

The biggest reason an organization struggles with developing an omni-channel approach isn’t as much about technology but misalignment with incentives within the organization.

The ecommerce team is incentivized to reach a revenue target within it’s allocated budget. Therefore, it’s also incentivized not to promote omni-channel behavior from it’s customers. Any visitor that the ecommerce team spends money on driving to the site is considered a waste of money if they don’t buy on the web site. The revenue targets that the team is held to are completely derived from sales on the website only. So they have every reason to not promote the idea that the customer could go in the store. The same goes for the retail marketing team. Their advertising efforts are designed to draw visitors to the store. Every sale that happens online is one that doesn’t happen in the store and affects their bottom line.

As a result, many marketing tactics are left unrealized – especially the efforts of using the internet to drive in-store sales. This is why no online marketing department wants to spend money on search ads on mobile – those people aren’t buying on the website, their buying in the store. Hence Google’s preoccupation of educating advertisers about all the different ways that customers use Google to decide to buy – instead of just focusing on when customers decide to buy on their desktop computers.

The solution here is to put both offline and online store teams on the same budget and revenue target. Easier said than done.

How To Come Up With Questions To Ask Web Analytics Data

It’s said that the successful use of web analytics requires asking the data the right questions. So where do you get the right questions to ask? Some questions start with data and some questions end with data.

Recognizing a trend or change in the data can lead to a good question. This requires creating key performance indicators and watching them as time goes on. You may notice that the amount of purchases that include a sale item is increasing year over year and you want to answer the question as to why – which may lead to an insight that then leads to a change in how your marketing dollars are being spent, how product is merchandised or a page is displayed.

Or a question might first come from outside of the data. Looking at a competitor’s site causes you to wonder what it would be like to do what they are doing. Or something bothers you about the old looking design of your product detail page and you wonder what would happen if you changed it. Those questions then lead you to look at data to validate your questions.

A healthy combination of both approaches – watching your data for trends and watching the competitive landscape for trends, can and should be employed to get the most out of web analytics to improve your online business.

Content Marketing Quality Versus Quantity

You want to create some content (videos, info-graphics, blog posts, podcasts, social posts & tweets, etc.) in hopes that it will drive traffic, improve search rankings, give utility to your brand in the mind of the customer and sell stuff. But you have a limited budget. You had to decide between a little content that has a lot of polish and high production costs versus more content but lower quality.

I say go for quantity over quality every time.

The first reason to put quantity over quality is that there’s no correlation between how much something cost to make and how interesting it is. A hundred million dollar movie can bomb and a video made for free can go viral.

You’re also competing with 300 hours of video uploaded to YouTube every minute, 2 million blog posts published a day and more than 3 billion Facebook posts a week. In other words, there is a lot of other content vying for attention. The more content you can make with unique titles, different perspectives and varied angles the better chances you have at finding the people looking for the content you make.

So, if there’s so much competition and money isn’t related to interestingness, why do brands choose quality over quantity nearly every time? Over-the-top production values are a place to hide. It’s easy to cover up boring with something pretty, but it rarely works.

Unintended Consequences Of Key Performance Indicators

Narrowly focusing on key performance indicators can have unintended consequences,
optimizing for the wrong things at the expense of something else.

For nearly every metric that an organization can focus on with web analytics, there is potential for backfire. Here are a few:

Engagement is a buzzword these days that refers to making an experience “sticky”, or that the user finds content so compelling that they develop a stronger relationship with a brand. Metrics like average time on site and page views per visit are often used to measure engagement but these metrics can be wrongly translated as frustrated visitors who can’t find what they are looking for.

Increasing visits to a site can’t be wrong can it? Not unless in an effort to reach a certain level of visits you engage in spammy tactics like click-bait headlines or spend lots of marketing dollars that in the end produces lots of unqualified traffic. Why praise increased visits if none of it takes the actions on the site that make you any money?

Measuring your advertisements reach refers to how many people are seeing the ad. The more the better is not necessarily true. In the age of precise targeting online, who sees your ad is more rewarding than how many.

Increased conversion rate is every site owner’s goal, but an unhealthy obsession can lead to marketing that only targets people who are the most likely to buy, and the most likely to buy are those people who are already familiar with your brand. Left unchecked, you never gain new customers as your marketing preaches only to the converted.

A high bounce rate is never good but continually changing content to decrease bounce rate can turn into an inverted curve – bad to better to bad again. Content goes from irrelevant to more generally applicable to banal and watered down.

Increasing revenue at all costs is the most obvious short-sighted metric and the result is what you see in many failed business: lower quality, less return customers, decreased goodwill, lower customer satisfaction, decrease in perceived brand value, the list goes on.