Top Five Digital Marketing Trends to Look For in 2019

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As a paid digital optimization firm we saw 2018 as a year of innovation in the industry. The rapid pace of change with both new and existing players is setting the stage for 2019 to be exciting, dynamic, and fast-paced. There is a lot of change in the air, but here are five big trends that we think will impact most advertisers in 2019:

1. Rise of Amazon

Amazon took steps in 2018 to consolidate their advertising options for retailers, manufacturers, and (most excitingly) for people not selling through Amazon because they are not a fit for the Amazon marketplace. In particular, the launch of a platform-based way to manage Amazon programmatic through “Amazon DSP” will likely ensure the rise of Amazon into the top three advertising networks in 2019. If you divide programmatic providers along data lines, Amazon completes the triumvirate of Google (owning search and web behavioral data), Facebook (owning personal and social engagement data) with a massive player owning the world’s biggest treasure trove of consumer interest and behavior data.

2. Video, video, everywhere

It wasn’t that long ago that when you said “video advertising” it was a solid euphemism for YouTube. Not anymore. Video content is everywhere from Instagram stories, to Facebook feeds to classic display inventory, to Connected TV. Video content is riveting, engaging, unrivalled for storytelling and is emerging as compelling content across digital advertising. Combine that with fast and easy content creation tools and 2019 will likely see a massive increase in video advertising content. If you spent the last few years solving for mobile, the next question is solving for video content creation.

3. Programmatic transparency in display

The land rush of display inventory moving to programmatic exchanges is essentially over with the frontier now booming with virtually all the traditional display ad inventory that exists. As we move into the next phase of maturing programmatic advertising there is a push for increased transparency of the underlying data in the exchanges. While viewability was a key focus in 2018 that will continue in 2019, we are also likely to see dollars shift to exchanges that provide the most data and controls over performance KPIs such as per-user viewthrough information. Some claim that blockchain is the solution, but some DSPs, such as Acuity and Dstillery are already closing these gaps with existing technologies.

4. Rush migration of remaining traditional media to programmatic

While display has almost all moved into the Programmatic mansion, the new rush is with traditional OOT and network Cable/TV inventory. In 2019 expect to see much more traditional TV inventory move into the programmatic exchanges allowing digital marketers to target OOT, Connected TV and maybe direct even more traditional TV inventory. With the explosion of independent content delivery services being announced (Disney, NBC Universal, etc.) expect some of these to explore programmatic advertising as well as subscriptions as alternate revenue models.

5. Embracing of AI tools

In 2019 virtually all digital advertising will utilize Artificial Intelligence tools for ad placement and delivery. This is not a bold prediction, but a reality check reflecting the state of digital at the end of 2018. Whether it is Google’s ad-serving algorithms, the proliferation of Lookalike audience building, or conversion-based bidding algorithms, the face of digital is AI. Marketers need to be looking for ways to test, adapt, and restructure best practices to take advantage of the new world of AI-driven marketing as well as to proactively look for ways to push the use of AI-based tools in new directions. AI is no longer what is coming, it’s here.

It is impossible to make a short list like this without missing big trends (Podcasts? The death of Snapchat? Facebook Search Advertising?). By distilling this to a Top Five we think we’ve identified large trends that are so far advanced already that they cannot help but influence digital marketing in 2019. Have a different idea? Let us know!

When KPIs are Blinders: The Dangers of Local Optima

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Recently we encountered a strange situation.  One of our clients had a top of the funnel KPI that looked like it was going sideways with regards to efficiency, but their revenues were going gangbusters. We use this metric for optimization and it is the core number by which everyone from their Board of Directors on down judge the efficiency and success of the digital campaigns.

Suffice it to say, this was not good.

Interestingly enough, the revenue numbers were setting new records, which made us question what was happening with our long standing and go-to KPI. So we started checking off the boxes. Was the revenue coming from the current marketing investment? Check. Were we paying what our financial models said was appropriate to the audiences? Again check. Was the top of the funnel KPI consistent over time and audiences?

Hmm. No, no it wasn’t. Aha! Changes to user engagement paths had affected the top of the funnel over time, and the average KPI from the past was no longer aligned with revenue and profit.

Our go-to KPI had become a Local Optimum, and we needed to address it quickly.

I first came across the term Local Optima in Eliyahu Goldratt’s classic novel on business optimization The Goal.  Essentially, a local optimum is a metric that may apply to a “neighborhood” of criteria, but is uncorrelated with the entire system. Goldratt used the uptime of a single machine in a factory as his example, where running any machine at 100% capacity other than the one machine that throttles throughput for the entire factory will cause inefficiencies and losses for the entire system.

Goldratt and his Theory of Constraints thinking dictate that “Measurements of local optimum behavior should be abolished and replaced with holistic measurements.”  If we had a Local Optimum on our hands, attempts to “correct” for this KPI would not be mildly bad, it would almost assuredly hurt the profitability of the entire company.

So we threw out the KPI and went back to first principles: “Marketing is a financial investment in a financial outcome”. What could we look at that would be correlated to revenue production over time?

It turned out that slightly down-funnel was another metric that turned out to be 100% correlated to revenue production and, in fact, was the one to which we calibrated our bid models.  We were able to show that the sideways direction of the top-of-funnel KPI wasn’t a problem, but was actually a natural side-effect of using the “true” KPI to back-calculate to the top of the funnel. By shifting the conversation from our most visible KPI, the conversation then quickly shifted to how to take advantage of this knowledge. How could we change our reporting, conversation, and client-side metrics to align? We lucked out. Our client is extremely savvy in understanding their own cohort data and financial targets and rapidly escalated the conversation to the C-suite and their Board.

We talk consistently about using Profit as a KPI. In reality, as digital marketers we often have to use early engagement numbers as proxies for true Profit. It is always good to step back and assess the relationship to Profit in case a Local Optimum has snuck into the room as it did in this situation.

Why We Are Focused on Amazon Advertising

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Last month, Fortune reported that Amazon is rapidly becoming one of the world’s largest ad networks, with over $2 billion USD in advertising revenues in Q2 2018 alone.  Their rapid appearance on the world advertising stage is only one of the reasons that we’ve been putting our sights on Amazon for some time now.  Here are our three main reasons to be all about Amazon:

1. Amazon Has Something for Everyone.

In the past, Amazon advertising has largely been focused on on-site internal advertising on Amazon.com.  Amazon has long been a go-to source for feed-based promoted product advertising. This type of internal Amazon advertising has been great for retailers and manufacturers looking to build Amazon as a channel partner. It is also highly effective as long as you watch the math as Amazon charges both for the advertising as well as their platform fees for each sale. Recently, however, Amazon has matured their offerings with the rollout of Amazon DSP to a wider audience.  I’ll speak more about Amazon DSP below, but one strength compared to other Amazon offerings is that you don’t have to sell on Amazon to use Amazon DSP.  In fact, all kinds of companies selling products, services and more are using Amazon DSP, Amazon’s programmatic solution, to profitably acquire customers from advertising delivered in programmatic exchanges across the Internet.

2. Amazon is Investing in Their Platforms

There is nothing like a few billion in revenue to attract resources, and Amazon is no exception.  Amazon is in the midst of a significant platform consolidation. Amazon’s previously disparate solutions for products, manufacturer solutions, and programmatic, and that encompass display, product, video, and store advertising have been combined into a single-login platform. This makes it far easier to know about potential offerings, to leverage knowledge across offerings, and to track and report on Amazon Advertising as a whole (well, maybe someday) We’re quickly on the road to agencies and advertisers taking full advantage of Amazon as a professionally executed network alongside Google and Facebook. This is still in the very early stages, but with hints that other Amazon platforms like Twitch advertising might follow, we are bullish.  For example, billing solutions are not yet integrated across offerings but we hope that the recent addition of single sign-on is a sign that further integration is to come.

3. Amazon DSP is Unique (as a DMP)

Programmatic is evolving faster than any other segment of Digital Marketing.  We have largely moved from the main trend being web publisher migration to exchanges. While this is mature in display, migration to programmatic it is still in early stages in traditional media like TV. Some of the newer programmatic trends involve access to data through DMP (Data Management Platform) integration in the advertiser DSP (Demand Side Platform). This and the use of AI are on the cutting edge of developments we are seeing in programmatic.  Amazon DSP (formerly Amazon AAP in a long line of product name changes) utilizes Amazon’s massive warehouse of customer demographic, product interest, and shopping data to inform targeting for ads delivered in other exchanges. Unique data for targeting is the currency of the new world of Digital Marketing. And since the abuse of social media data has curtailed targeting options in Facebook, Amazon has the ability to leverage tremendous personal data without violating the privacy protections of their users. We’re bullish on the data Amazon can bring to the table for advertising far outside of products sold on Amazon.com.

 

Amazon is changing the Digital Marketing landscape. Because of this we actively sought partnership as a firm capable of in-house programmatic management of all the Amazon platforms including Amazon DSP. We’ve invested in external and internal tools to bridge gaps in Amazon’s still-evolving reporting and to bring our deep financial optimization to Amazon’s advertising products. We’re excited about these opportunities and fully expect that Amazon advertising will be our biggest single growth network in 2019.

What Magritte Has to Tell Us About Marketing Data

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In the winter of 1928-29 a Belgian painter living in Paris painted what looked like an advertisement for pipe tobacco. The painter, René Magritte, painted a caption below the large pipe stating “This is not a pipe” creating confusion for viewers and a bit of a stir in the Paris art scene at  the time.

The title of the painting is “The Treachery of Images” and Magritte intended it as a reminder that representational art is a reflection of the artist’s view of the object,  not the object itself. His interpretation was that  the painting of the pipe is not actually a pipe. You can’t smoke it, fill it with tobacco, or put it in your pocket; it is a representation of reality.

Magritte’s message is particularly apt in our view of marketing data. As marketers, we use our marketing data all the time in measuring and assessing user behavior related to digital advertising and engagement. But it is extremely important to remember that marketing data is an imperfect representation of user behavior and not a perfect simulation.

There are three main reasons that marketing data is imperfect. The first is in the nature of tracking. Tracking is technically limited in its scope and reach.  At best, tracking can measure engagement from the same device over time, or the same multi-device account over time. This only works if the tracking is implemented correctly in the first place and is not disabled by the end user. Because of the technical limitations of tracking there will always be engagement that is not tracked because of multi-device use, the amount of time for which the tracking is active (cookie window), or because of personal opt-out at the user level either by choice or by browser pre-set settings.

The second main source of imperfection in marketing data is in user behavior.  As humans we have a wide variety of choices and methods of interacting with advertising. Some of us choose to avoid interacting with advertising as much as possible while some of us behave in the opposite fashion. Some of us will choose not to click on ads at all, some of us don’t hesitate. Some of us need to heavily research purchases, some don’t. There is not one engagement path that is adhered to by all users yet often our data is interpreted through a “single funnel” lens that introduces inaccuracies in interpreting data.

The last main source of data imperfection is in data integrity. The cleaning and analyzing of data must align with the knowledge being sought. De-duping rules will differ by business model. Attribution will vary based on external factors such as affiliate payment rules (and will still never tell a 100% accurate story). Data dropout can cause interpretation issues.

So what is a marketer to do?

The best thing you can do is to fully utilize data to test your own model of reality.  The best data firms don’t blindly use data but instead use it to inform their perception of reality.  For example, if you know that a percentage of social users engage with your brand without clicking on ads and instead show up as brand search or No Referrer traffic, you should try to assess: how much, how can that magnitude be assessed and what impact does it have on my decisions as a marketer if I can hypothesize about non-measured (but real!) activity.

Magritte’s point  is well-made. Our data is not reality, but an imperfect reflection of reality biased by user behavior, technical limitations, and process. Our best response is to acknowledge and accept this and use it to our advantage.

5 Hidden Dangers in Annual Budgets for Marketing

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Ah, the smell of the end of the Fiscal Year! Budget Planning for Marketing is in the air and the smell of burning remaining budgets lingers, backed by the sound of inter-departmental standoffs.

It doesn’t have to be like that.

Annual Budgeting is a time-honored process, but I’d argue that in today’s world annual budgets for marketing do more harm than good. Flexible budgeting based on hitting financial KPIs and working within cash-flow limits is a much better way to go.  So roll up your sleeves and get that coffee, because today we’re talking about the five main reasons Annual Budgeting can harm your marketing program!

1. Cost vs. Profit

Annual budgeting attempts to control costs while managing resources for the continued profitable execution of the business. Unfortunately, the two common tools for reviewing financial performance, the P&L (past-looking) and pro forma (forward looking) are poorly equipped to understand marketing’s direct contribution to bottom-line growth as they both separate cause (investment in marketing) from effect (profit realized from marketing).  This is solely because marketing has always been treated as a fixed cost, along with salaries and rent. But quantitative marketing, and particularly quantitative digital marketing has changed that.  Done right, data-driven marketing creates a clear and predictable relationship between investment and return, often optimizing to financial KPI’s such as Contribution Margin (post-marketing gross profit) that, if scaled, will improve the profitability of the company. Annual budgets, by definition, cap the potential upside of profitable opportunities that might exceed the estimates of the company at the time of budgeting.

The Better Way: Utilize strict measurements on the generation of profit from marketing where marketing is expected to increase Contribution Margin over time (how much time is predictable, but is based on sales/evaluation cycles and will vary widely by company). Combine these with cash flow controls so as not to overspend in advance of revenue. For example, in a SaaS company we might be able to predictably increase future Monthly Recurring Revenue (MRR) in an acceptable payback period with a greater ad spend, but monthly spend would still be capped as a percentage of current MRR for cash management reasons.

2. Limits on Course Correction

Budgets are a plan for the future, but the more detailed they are, the more difficult it will be to change course, even in the face of compelling evidence that you should do so. Budgets are often difficult to change on purpose, as the control inherent in the budget planning process is one way that leadership executes the course for the company.

The problem is that detailed knowledge of opportunities doesn’t live at the top of the organization, it often lives deep inside, at the levels of tactical decision making. As opportunities surface, the entire budget planning process, people, and leadership control needs to be challenged even to make a minor change. It sounds crazy, but is extremely common to hear “We know we should make this change and doing so will increase profit, but it just isn’t in the budget.”

In a recent example, one of our clients realized that they had given us the wrong remarketing budget and that they had no more budget for the remainder of the fiscal year. And even though we have clear data illustrating the profitable results of the investment in remarketing, the line managers decided they would rather forfeit that profit than challenge the budget process. This does not help their company’s bottom line.

The Better Way: Put money behind goals with limits on expenditures. Educate line managers on P&L level financial KPIs and enable them to move resources to improve those financial KPIs within cash flow limits. This often means addressing local optima, KPIs that don’t correlate well with financial outcomes but are intended as measures of performance separate from revenue and profit. In marketing, most traditional measures (Bounce Rate, Time-on-Site, etc,) are local optima. Local optima are blinders to good prioritization of activities that improve the business and should be viewed with caution.

3. Arbitrary Channel Blindness

Marketing is complex. Actually, that isn’t nearly as true as the fact that the human interaction with marketing is complex. Yet internally we fall in love with single-source attribution to evaluate marketing channels. Nothing feels better than knowing that this one customer came from this single source. It makes us confident in crediting programs and teams, and makes it easy to allocate budgets.

Of course, it’s dead wrong.

We know from our own behaviors that people engage in interactions with marketing campaigns that are far more complex than our tracking can fully capture. We see ads in multiple locations over a period of time. We are remarketed to. We see an ad, don’t click on it, and then later do a brand search. We switch devices before engaging with a web site. We’re human and so are our customers.

Single source reporting creates a false sense of security, and like the CEO who exclaimed, “This Brand traffic is great! Let’s move all our budget to that!” it often masks cause and effect within the complexity of real human behavior. (If it is not clear what the issue is with that comment, please reach out to me.)

Budgets, of course, make this worse the more granular they get. In addition to making my last point worse in that they can limit course corrections to better performing areas, channel budgets reinforce the false view that channels act independently of one another, and that all channels act in similar fashion with similar functions.

The Better Way: Have your financial KPIs be holistic, evaluating black/white success only when looking at the whole picture of paid, unpaid, earned, unearned customer acquisition. The channel numbers are directional and need to be evaluated carefully. Your Cost-Per-Acquisition numbers in Search and Social shouldn’t be the same, as Social typically drives 5x to 10x more out-of-channel activity that breaks source tracking. Data needs to be used to its fullest extent, but with thoughtfulness, experimentation, and a constant understanding that data collection is limited with regard to user intent. When changes need to be made, or experiments run, the budgets should easily and flexibly accommodate those changes.

4. Slow Learning

In the Mad Men days of traditional media, learning about campaign performance was slow, when it could be measured at all. Annual budgeting caused no significant harm, because annual budgets often aligned with annual media buying.  In a world where success was often obtained by the most demographically-correct eyeballs for the lowest price, demographic targeting and price negotiation were everything.

No wonder people had time for all those martini lunches!

Modern digital advertising is a world of constant measurement and constant experimentation. The limits of the budget process in slowing change and limiting complex understanding have one extremely dangerous but often overlooked effect: They slow learning. Gains in digital media are often discovered through testing (certainly this is the financially responsible approach), so how many test cycles you engage in is one control over success. A process that by nature limits the speed of testing can have a highly detrimental effect on performance that is likely completely invisible to those engaged in the budgeting process.

The Better Way: Create the freedom for testing within the budgeting process. 10% of the marketing budget that is solely for experimentation and is not held to financial KPIs will pay off dramatically in rapidly finding ways to scale profits.

5. Incorrect Time-Frames for Budget Evaluation

Fundamentally, annual budgets operate at an arbitrary time frame that is out of sync with marketing decisions that need to be made in a financially optimized, highly data-driven campaign. Quarterly budgets are not much better but will be closer to the learning cycles in most campaigns.

While the longer the budgeting cycle, the worse the problem is, the fundamental problem is that line-item budgeting for marketing is an inherently bad practice. In a world of 24/7 live auctions, shifting competition, constant learning and improvement, budgets are sub-optimal.

The Better Way: There is a better way, and many organizations already do it this way. Financial KPIs tied to cash flow-based caps given to engaged teams with line-level decision making work far better than a classic budgeting process. The best, most nimble companies we work with work off of pro formas that are not set in stone but that are live working documents, recast constantly based on evolving data.

When done right, digital marketing contains an amazing set of tools for the predictable scaling of profits. What is required is for companies to support the financial practices that allow this to happen as quickly as possible, provide financial transparency and clear financial KPIs to their marketing teams, challenge their teams to be thoughtful about complex data and behaviors, and stand back. Maybe it is simple after all.

15 Years of Profit-Driven Marketing

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In 2003, Google was still a scrappy startup, digital marketing largely meant Search or un-tracked banner ads, and Working Planet co-founders Soren Ryherd and Vida Jakabhazy had an idea that paid digital advertising could be optimized to financial KPIs.

We were a little early.

While Working Planet began rapidly building and optimizing digital campaigns it wasn’t until the economic downturn of 2007-2008 that CMOs really began being tasked with the financial performance of their digital campaigns. And as hindsight clearly shows, digital quickly became the safe haven for investment in marketing because it had three things that companies loved: trackability, the ability to easily change budget allocations, and a platform for rapid testing.

But we pushed it further from Day One. We stated that, done right, financial KPIs were not only an outcome of marketing, but a key INPUT into the optimization to profitability. If you knew the value of an audience as measured in real profitability, then you could pay only what an audience is worth. Of all the amazing transformations in digital over the last decade and a half, the thing that amazes us the most is that people still talk about “The ROI of digital” as if it were fixed, when the ROI from digital is 100% based on the choices you make in the execution and optimization of digital campaigns.

Throughout the last 15 years, we’ve been quietly pushing the curve, developing new practices to apply financially-based optimization to all digital marketing. What started in Search has evolved as the industry has evolved, and we now manage substantial ad spend in Search, Social, Display, Retargeting, Programmatic, Video, and more. We’re excited about the ongoing rapid migration of radio and television to the Programmatic exchanges and are managing campaigns in those areas as well.

We are exceedingly proud of our history. We have been part of so many success stories. From small companies that have become large significant players in their industry, to clients that have been acquired after we achieved successful results for their founders (and investors), to our serial entrepreneurs who have brought us along for the ride in their subsequent ventures, we have been privileged to work with a wide variety of amazing people and organizations. And we cannot overlook the increasing number of professional marketers and business executives we have now worked with at multiple companies. We’re very happy to be their secret weapon.

We are also very proud of our team, the Working Planet family of current and former Planeteers who have worked to create something special in the industry: a marketing agency whose teams see the tangible results of their work every day as measured in the real financial success of their clients. The Working Planet teams bring passion, enthusiasm, and curiosity every single day and working with a talented and curious group of smart individuals is as satisfying an experience as one can have as marketers and business owners.

So what is next for Working Planet? Expect to see more from us. We want to share more about what we firmly believe is the right way to do marketing. We continue to grow and have big plans on that front, both in growing the size of our firm and growing the scope and success of our clients. And we continue to keep a close eye on the industry, using our embedded approaches to innovation and measurement to find new opportunities as all of digital marketing continues to evolve. Holographic advertising you say? Bring it.

Soren Ryherd & Vida Jakabhazy July 2018

Profit: The Only Marketing Metric that Matters

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I recently wrote about the limitations of one of the most widely-held marketing KPIs: ROAS (Return on Ad Spend). The main limitations with ROAS are that it is a measure of efficiency and not magnitude and that the efficiency it measures is revenue generation, not profit generation.

Yet creating revenues without profit, or efficiency without magnitude is not creating success, and we want KPIs that are tightly aligned with success. In fact, that is the sole purpose of a KPI.

So if ROAS sucks at being an indicator of the magnitude of profit, what KPI does work?

I’m going to go out on a limb here and say all marketing metrics are poorly aligned with business success. Which is why your marketing KPI shouldn’t, in fact, be a marketing KPI at all.

It should be a business KPI.

In fact, it should be a really specific business KPI and that is a form of profit measurement called “Contribution Margin”. Contribution Margin is pretty simple to calculate, it is just Revenue – Variable Costs. In this respect it’s a close relative of a metric we like a lot: Gross Profit (Revenue – Cost of Goods). But the key to a good Contribution Margin calculation is what’s included in the variable costs. For us, the biggest variable cost is Ad Spend. But in actuality, we also add in cost of goods and any other per-unit costs if they exist.

If it sounds complicated, you can probably simplify it. If you want to call it Post-Marketing Gross Profit that works, and in many cases may be close to the same number as Contribution Margin, depending on what other Variable Costs exist. Frankly, we find the hardest part is to get this information from clients, as it’s unusual for Marketing to know these numbers and Finance may need an explanation on why it’s important to share them.

So why is Contribution Margin a good KPI? Well, first, it doesn’t just align with the magnitude of profit, it IS the magnitude of profit. Second, if you’re looking at actual profit, a false read on efficiency can’t cause you to make bad decisions in the name of efficiency (this is called the local optima effect and I’ll post about this in the future). If you use Contribution Margin as a KPI, you actually see the drop in profit. And lastly, it lets you do two very exciting things in growing a business, and those are that the investment in marketing becomes a profit center and not a cost, AND it allows you to have a business metric that transcends channel complexity, but how that works is a topic for another time.

Why ROAS Sucks As a KPI

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ROAS (Return on Ad Spend) is the industry standard for assessing and reporting the performance of paid digital campaigns, but it really sucks as a KPI.

To be totally fair, ROAS has a few strengths. It is easy to calculate (even easier than ROI): Revenue / Ad Spend. It is a relatively clear measure of efficiency. It is a metric that can be used across channels and media. But that’s about it, and when you start digging in, ROAS has a lot of limitations, including one huge killer that makes it, well, dangerous:

You can go out of business optimizing to ROAS.

And that is where ROAS really comes into its own as a sucky KPI. Frankly, I want KPIs that align with the financial health of my business and I certainly want that alignment in KPIs used for assessing campaigns we run for clients. ROAS doesn’t do that. ROAS is an efficiency metric. It has very little to do with how much money you make. This is because (like ROI) it is only a good metric when comparing exactly the same media spend. The problem is, nobody does that.

When you look at marketing reports across the industry, you will almost always see ROAS as a standard KPI, even when marketing spend changes.  And this is where the danger part comes in. ROAS works as a comparison of efficiency, and not magnitude.  An ROAS of 6 is more efficient than an ROAS of 5, but that doesn’t mean you made more money (or any money, but I will get to that). An ROAS of 6 on $10 did not make you more money than an ROAS of 5 on $10,000,000. That is a fairly obvious example, but marketers often treat ROAS as a KPI in isolation, which means you can easily reduce the magnitude of returns in the search for higher efficiency. Google even allows campaigns to use ROAS as a target for bid optimization, regardless of the effects on magnitude. Ponder that for a moment. And while it seems crazy, we have seen marketers make the decision to optimize for ROAS, opting for efficient returns even when the magnitude wasn’t enough to make payroll and keep the lights on.

Which brings us to the second danger with ROAS which is that it is based on top-line revenue, and not profit. This is why you can have positive (>1) ROAS and still lose money. This can happen when the efficiency of the return isn’t enough to cover costs. Unfortunately, many marketers aren’t given deep enough financial information to know whether campaigns are profitable or not. They just have this one metric which will only ever tell them if campaigns are so grossly inefficient that they aren’t even covering revenue. Again, I want metrics that alert us when we are even slightly less profitable, and not just when the bus is so far off the road that it is lying at the bottom of the canyon.

So what’s a good metric? Total post-marketing profit (aka Contribution Margin), which I will cover in an upcoming post.

5 Marketing Data Mistakes Most Companies Make

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Even the most data-savvy of marketing teams can make mistakes in thinking about the use of data in optimizing campaigns for financial success. Let’s face it, marketing data is rife with issues and is never perfect, and it is easy to put on blinders based on the data you have available, the systems you use, the marketing channels you work with, or even the directives of senior executives. Despite that, everyone wants to be able to connect the dots from ad impression to profit. Here are a few common data traps we see even smart companies easily fall into. How do you rank on this list?

1. Using Average Value CPA Targets

Cost-Per-Acqusition targets are fertile ground for data issues. For example, are your targets even based on customer value, or are they merely a “seems reasonable” guess? Smart data-centric companies will base target CPAs on actual customer value to ensure that their marketing programs don’t risk over-paying for customer acquisition. However, even smart companies can fall into using a single average CPA target for all their customers. In doing so, they underpay for audiences that provide higher-than-average value customers and overpay for low-value customers. Companies can avoid this by using targets tied to segmented customer values, not averages.

2. Using Last-Click Attribution

The attribution question has long been mired in a false discussion of “who gets credit?” when there is more than one user touch leading to a sale or lead. Some companies still use last-click attribution, often in a mistaken belief that this is somehow a “truer” view of acquisition, or that it just allows them to avoid thinking about attribution at all. Google hasn’t made things easier by offering multiple views of attribution with little guidance on when and where the different options should be used. Here is our take: Avoid last-click attribution at all costs unless you are evaluating retargeting assists. Last-click attribution will severely over-inflate your brand and direct numbers and cloud your ability to see high-value first-mover channels.

3. Not Considering Out-Of-Channel Effects

Everyone looks at their channel-specific numbers, but an astonishingly few companies continually examine their direct and brand channels, looking for influence from other areas. While everyone logically understands that users did not wake up with magical knowledge of a company’s brand, it often feels like that’s the assumption of marketing teams who put on “channel blinders” when evaluating their programs. Smart companies view their data holistically, looking for out-of-channel trends that increase or decrease direct and brand engagement. While only 5% of users in a typical search campaign are likely to bounce over to direct or brand, it is not unusual for a whopping 50%-90% of sales from social media or display campaigns to come through direct or brand traffic.

4. Over-Valuing Metrics Not Tied to Revenue

What is the value of a Like? Most data-driven marketers have moved on from directly equating social media engagement as revenue-related events, but many metrics that don’t correlate well to revenue are still held as sacred cows. Any metric used for campaign optimization should be well understood in how it relates to revenue before it becomes a key KPI. Data-driven marketers with their eyes on the profit prize quickly realize that Time-On-Site, Impression Share, Cost-Per-Click, or other common metrics are not as tightly aligned with profit as they might think when other factors such as volume, customer value, out-of-channel influence, or profit margin are taken into account. Easy rule of thumb: Use post-marketing profit as your marketing KPI.

5. Assuming Traffic Equals Sales

We’re two decades into the digital revolution, and it’s still incredibly common for people to assume that eyeballs equal profit. Back in the days of traditional media the best shot you could make in media buying was the most eyeballs for the lowest cost. That approach doesn’t work in digital because of the competitive auctions, and yet “Let’s get more traffic to this page/product/site” is not at all an uncommon marching order, particularly from executives who don’t understand the auction effects in digital media buying. Smart data-driven marketers know that their job is as much about when NOT to buy traffic as it is in finding the areas of success, and continually evaluating how to increase the quality of an audience by peeling away the “eyeballs” that are not their target audience. This allows them to compete more aggressively in the auctions while protecting the bottom line. Sometimes less really is more.

These are samples of marketing data traps that are very easy to find in almost any campaign. Most of these issues can be avoided through three core practices: 1) By adhering to a holistic financial lens in optimizing the entire marketing program against financial targets; 2) By working backwards through the path that led from advertising to revenue and; 3) By not ignoring revenue that falls outside of the “channel buckets”.

The Seductive Danger of Impression Share

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Advertising is all about targeting, right? And once you’ve found a targeted audience, you want to get your ads in front of all of that audience. This is the thought process behind Impression Share, that often elusive, seemingly golden metric that Google dangles before us in AdWords reporting.

And why wouldn’t Impression Share be important? You certainly don’t want to cede valuable targeted eyeballs to your competitors, do you? Why, that would be leaving money on the table, so the higher Impression Share the better!

Not so fast.

As we continue our decade-and-a-half long transition from negotiated media buying to auction-based media, it’s worth taking a second look at Impression Share, as nothing may be quite so seductive, or quite so dangerous in our everyday marketing metrics.

Impression Share is an easily understood metric. What percentage of the available audience were you able to put your ads in front of? Impression Share measures exposure as the percentage you successfully marketed to. Simple.

The problem is that, unlike negotiated media where delivered impressions are specifically negotiated (with a make-good period typical when there is a shortfall), in the auctions it usually takes higher bids to capture that elusive missing percentage of Impression Share. That incremental cost may not be worth it. In fact, going after higher Impression Share may cause you to take a profitable campaign into unprofitability.

This is because successful auction-based advertising is performed by knowing how much to pay for advertising relative to the value of the revenue generated by that investment. Unlike the old school media buying paradigm of the most-eyeballs-for-the-lowest-cost, auction-based media is hyper-targeted, competitive, and dynamic. One of the best results of knowing what to pay for advertising is knowing what not to pay.

Which brings us back to Impression Share. If you are getting 100% Impression Share on a wide variety of keywords, chances are you are paying too much. It’s rare for a company to be able to monetize traffic so well that ad positions that garner 100% Impression Share are profitable across the board.

So, what good is Impression Share? If you are optimizing a campaign using value-based segmentation and value-based bidding (as you should be), Impression Share is a great metric for understanding the untapped opportunity available, if conditions change to allow for better monetization. This can happen through focused conversion improvement testing, or speaking to higher-than-average value customers. However you improve the math of conversion, the untapped Impression Share gives a sense of the size of the additional audience that may be out there.

If Impression Share is so dangerous, why does Google promote it? The answer is probably obvious. The only one who always wins the Google auctions is Google. Impression Share promotes tactics that get Google more advertising dollars. But used correctly, Impression Share can be a helpful metric to gauge an additional available audience, but one that can only be tapped into if you can get the math of cost -> conversion -> value to work.