In the Era of Privacy go Deep on First-Party Data

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We’re in the Era of Privacy where the protection and security of personal information is in the forefront of the news. Privacy issues and concerns about the protection and use of personal information, particularly in marketing, are at an all-time high. At the center of these concerns are personal data collected by companies that are then sold to advertisers directly or through platforms like Facebook. This “Third-Party” data collection and sharing is rarely transparent to the end-user whose data is being shared, and third-party data is being steadily removed or limited from use.

 Take these examples of topics affecting privacy in the last year: 1) Facebook being hit by high-profile disconnects between their privacy policies and actions has erupted concerns at every level and has already resulted in both heavy fines to Facebook and a loss of audience targeting options in the Facebook ad platform.

2) European GDPR policies upping the ante for getting public opt-in on the use of cookie-based tracking, as well as regulation of the storage, protection and individual rights regarding personal information acquired by companies.

3) California, Canada and others are following suit by restricting the use of personal data for audience targeting.

4) The exposure of personal information through the hacking of Experian and other data aggregators has helped create the public perception that personal data is not well-protected by corporations and marketers.

The good news is that there are real alternatives to aggregated third-party data and in most cases the alternatives may be better for the optimizing of digital campaigns. These lie in a deeper investment in first-party data.

First, some definitions: 

First-Party Data – This is behavior, action, or interest data that you gather from visitors to your web sites or apps. This includes data provided to you by your customers that lives in your CRM or other customer database and can include personal, subscription, survey, or social information measured directly by you or given to you by your customer.

Third-Party Data – This is data that you acquire from an outside source that was not gathered by you our given to you directly from the consumer. Many companies gather information specifically for sale to others (Experian for personal financial data is an example); while others may sell customer information they already have as an additional revenue source (many credit card companies do this). This is the type of data that is increasingly under scrutiny for the reason that the consumer has no control over the spread or use of their information.

At Working Planet we have never purchased third-party data directly. We have taken advantage of it indirectly through ad networks, such as Facebook, that combine third-party data with their own first-party data to extend targeting options (eg. financial, demographic, and other information based on third party data known to Facebook and not the advertiser It is only through the reduction of targeting options in Facebook following the Cambridge Analytica scandal that we have insight into the extensive use of third-party data at Facebook.

One of the reasons we rely less on third-party data is that first-party data holds greater value. What individuals do on a website is extremely valuable in optimizing to financial outcomes, and this first-party data gives you the control on this optimization while allowing you to uphold privacy and security commitments to your customers and site visitors. Some examples of valuable first-party data and uses: Multi-touchpoint tracking: A detailed understanding of the history of your customer interaction with your site gives you the ability to use that data to understand audience value.  In our world, this gives us the levers to segment audiences and understand what to pay for them in the digital auctions.

Pre-sale email collection: Almost any company, regardless of business model, can benefit from capturing email addresses whether through gated forms, newsletter signups, demo requests, or webinars. Nurturing contacts through email newsletters and other outreach, as well as using email addresses to create AI-driven similar audiences, are standard uses of this valuable first-party data.

Customer financial data: While advertising often focuses on the top of the funnel, don’t ignore your customer data as a valuable source of insight for optimizing marketing campaigns. In addition to profitability of sales and deep financial data, customer data can also be used to create customer persona profiles, audience segment descriptions, as well as other segmentation valuable for optimization of digital campaigns.

Caveats on data storage and privacy, particularly for GDPR:

In this new era of privacy, there are commitments to consumers we must honor in order to be reputable brands in the modern world.  Advertisers need to make sure that their privacy, cookie, and data use policies are up to date and in compliance with GDPR-level laws. Theoretically, European GDPR laws cover European consumers, not web sites, and while this has not been legally upheld, non-European web sites serving European customers are intended to abide by these standards.

If you sell consumer data or share it with others, what and how data is being shared needs to be disclosed to consumers. The laws surrounding this compliance are evolving quickly, so it is also necessary to be aware of these changes and respond accordingly. Our view is that the loss of third-party data is in no way the end of the world. The opportunities to improve performance using first-party data outweigh the loss of third-party data. There has been a lot of hand-wringing by advertisers about the loss or restriction on third-party data, but we think a world where users can control their personal data and where advertisers can target and optimize efficiently is possible.

Channels that are not Channels: Taking advantage of “The Unknown Bucket” of Brand Search and Direct

Marketing managers and the C-Suite love their marketing channels. Wrapping up channel performance in tidy columns displayed on the wall in a digital dashboard feels very measured and certain. But there are two columns on the digital channel chart that require a deeper dive because they are not channels at all. These two “channels” make up what we call “The Unknown Bucket”.

These are Brand Search and Direct.

Brand Search and Direct are not channels, because they cannot live in isolation. Both Brand Search and Direct reflect the end of a conversation that started somewhere else, but because of limitations on tracking or understanding the part of the user journey that exists inside the customer’s head, we don’t know where it began. We just know where it ends.

Brand Search and Direct usually look amazing, and there is no surprise there. You are seeing audiences at the final stages of engagement, disassociated from the evaluation or learning phase. However, as amazing as they look in terms of low cost and high conversion rate, you have no direct way to scale these up. Marketers laugh at the joke about the CEO who looks at his channel performance chart and says “This “Brand” channel looks great! Let’s buy more of that!”, but the truth is that more Brand Search and Direct volume is not something you can go out and buy.

But you can create more of it.

While there is no way to directly buy more Brand Search and Direct traffic (assuming you are already getting 100% of your brand search traffic), you can create more by identifying the drivers of Brand Search and Direct traffic. Luckily, user behavior dictates that Brand Search and Direct traffic have similar motivation therefore driving one will invariably drive the other as well.

How do you identify the drivers of The Unknown Bucket?

Luckily, identifying cause and effect is not that hard once you decide to try. Regression analysis, pattern identification, controlled geographic or pulse testing can all reveal the relationship between known channels and The Unknown Bucket. For example, we recently wanted to understand the effect of mobile Facebook and Instagram advertising on brand search and direct traffic for one of our clients. We did a controlled geographic test, pushing ad delivery in these channels in one geographic area while using another as a control. The gains were easy to identify vs the control, and a replication test confirmed the results. With that knowledge, we were able to understand the level at which we were undervaluing Facebook and Instagram based on lift not tied to that channel.

Because The Unknown Bucket can be a sizable piece of overall leads or sales (normal ranges can be from 20-70% of all sales), identifying the drivers of Brand Search and Direct gives the data-driven marketer even more opportunity to find financial success with their marketing program. It astonishes us how often this is ignored as an opportunity, simply because establishing the relationship between known channels and the Unknown Bucket feels daunting. Do you know how large your Unknown Bucket of leads or sales is? Do you know the drivers? Hopefully investigating this will lead you to an understanding of how to make use of this massive yet under-investigated area.

Marketing, Meet AI

Artificial Intelligence is often discussed as a future technology. The reality is that AI is here, and is deeply embedded in digital marketing. Existing ad networks like Google and Facebook are betting heavily on AI as growth drivers (think Google SmartBidding or Facebook lookalike audiences), and new networks have cropped up offering custom machine learning algorithms to help zero in on the perfect audience for any company.

Although it has been a growing presence in digital marketing for years, AI is relatively new to digital marketers from a direct management perspective. Networks, old and new, are offering a cure-all for finding your target audience, and getting them to convert at your target cost-of-acquisition, but we’ve found that it’s not the magic wand the network sales reps make it out to be. In this article we identify four problem scenarios created by the use of AI and potential solutions.

Problem Scenarios:

The Black Box

When networks talk about custom, proprietary algorithms, they may not just be concerned about their intellectual property. By nature, machine learning algorithms are more organic than synthetic, and there is often no way to describe the decisions the algorithms are making. We can speculate about the specific types of machine learning being used by advertising networks, but features such as lookalike audiences (now offered by most big programmatic, social, and display networks), can be created using a neural network, an algorithm that actually learns as it gets more data to make better predictions. These decisions are often complex, and likely cannot be distilled down to the tidy audience descriptions that marketers are used to, such as “ males, age 35-44, married, no children, associate’s degree”. Not knowing all of the variables that are in use, and how they are weighted and applied in the algorithm means that marketers have less control or understanding of the causes of failure or success.

Incremental Value

Is this adding value? How much? These two questions are vitally important to any digital marketing program, but can be trickier to measure with AI campaigns. Programmatic campaigns are designed to get ads in front of a relevant audience, and particularly individuals who are showing indications of being interested in converting. We have found evidence that the algorithms may be too good in some cases. In these instances, we can see the network in question reporting huge numbers of channel conversions (seriously, ridiculously huge numbers of conversions), but no corresponding increase in total conversions. This poses a problem that is not prevalent in any other type of digital marketing (with perhaps the exception of remarketing): are we paying for conversions that we would have garnered anyway? Are the algorithms so good at identifying people who are likely to convert, that they’re serving ads to people who would already be converting?

Metrics in Isolation/Top of Funnel Bias

We have had the same conversation with at least a dozen different clients over the past few years where we say “If that’s your priority, we can impact {some metric} in isolation, but we don’t recommend it.” Once a specific KPI has become the topic of conversation, it can be hard to see the forest through the trees – or the profit through the interim metrics (see our blog post on the dangers of Local Optima here). Networks are now offering products that can optimize to a target for you. You can maximize your conversions, set a target cost-of-acquisition, or just let ‘er rip and set everything to enhanced cpc. Just like with any other campaign, optimizing to a single metric in isolation is probably not going to get you where you want to go. For example, a 50% decrease in cost-of-acquisition sounds great, but can actually be terrible if we decreased overall acquisition numbers by 50% to get there (think: you saved 75% of your ad dollars, but your net profit fell by 50%). The algorithms in use by these networks are focusing on specific top-of-funnel metrics, in isolation. This means that your bottom line isn’t safe if you take a set-it-and-forget-it approach to your AI testing – at least not until these networks figure out how to incorporate down-funnel value into their optimizations.

Limited Data

In instances where you have a very limited testing budget, AI may be problematic. The issue we’ve run into is that the machine learning algorithms need plenty of data in order to actually learn. If you’re trying to run a smart shopping test with a $500 all-in budget, you will almost definitely see worse results than in your standard shopping campaign, since the campaigns are not designed to perform well initially, they are designed to gather as much data as possible. Many networks have actual thresholds for the amount of data you need in order to even launch a test, but those thresholds don’t necessarily indicate that you will be achieving optimal volume for the campaigns to properly optimize.

The Solutions:

Methodical Testing

In instances where data is scarce, you will need to employ some patience in watching spend slowly trickle through a campaign that doesn’t seem to be doing much of anything for days and weeks on end. Be prepared to battle with the temptation to pause your tests until they have left the learning phase and start showing you the kind of performance you can expect in the future. Some networks are nice enough to provide a “learning phase” status update, so you have a sense of how long you have to wait.

If you’re not so sure that there is value being added from your AI campaigns, set up a split test or pulse test, double-check that your data is squeaky clean (and includes all the information you will need to make a good decision), and let it run. The clean and complete data will also help you assess the down-funnel impact of the AI campaigns, so you can adjust based on financial outcomes, and not just the top-of-funnel results the ad networks will report to you.


It can feel like its Google’s world, and we’re just living in it. The reality is that each of the challenges presented by the transition to AI bidding, targeting, and optimization, are also opportunities. We continue to identify unanticipated side effects of automated optimization in networks like Facebook and Google, and turn them into opportunities to use algorithm biases to our advantage and help our clients reach their goals. It’s easy to imagine digital marketing as an increasingly automated field. In actuality, the shift to more AI in digital marketing requires a savvier and steadier hand on the wheel than ever before.

Learn & Iterate

One of the exciting benefits of the ad networks processing so much data is that they can share some insights with us. Look for opportunities to learn more about the audience that’s engaging with your ads and site by mining the data available from the ad networks and the data you collect yourself. Then, use this information to prioritize targeted landing pages, update copy, and adjust your targeting.

AI is here to stay and will only become deeper ingrained in digital execution. While the use of AI requires continued thoughtful testing and potential revision of best practices from account structuring to “targeting” to budgeting, AI does present massive opportunity for profit-driven campaigns.

Why Working Planet Manages Programmatic In-House

Over the last two years we have increasingly moved to in-house management of programmatic platforms and are firmly biased in favor of DSPs (programmatic Demand-Side Platforms) that we can run ourselves.

There has been a lot of debate over outsourced programmatic vs. in-house and I want to be clear on the difference. Yes, we are an agency, so from our client’s perspective this seems like an outsourced solution, but this is different than “managed” programmatic run by the programmatic firms themselves.

Here are the reasons we choose to become experts on the programmatic platforms we manage rather than to rely on the expertise of the DSP/Ad Networks themselves.

1. Financial Control

I am talking about the financial control of the outcomes of marketing, not spending the budget. Ad Networks and DSPs are very good at spending ad dollars, but not very good about prioritizing strong financial outcomes as it might mean not spending the whole budget. We are fine underspending a budget if we feel it doesn’t add value to our clients’ bottom line.

2. We Have a Better Financial Lens

Because we manage to financial KPIs, we have the deep financial and holistic view that allows for better decision-making in all ad networks. The DSPs/ad networks simply don’t have the decision-driving financial information for making informed choices. It is just not possible for them to know how to do what is right and when to do it.

Case in point: One of our clients had Amazon run an Amazon DSP campaign for them.  We then ran the same test managing the choices in the DSP ourselves.  Yes, we spent less than they did, but we made far more money at the end of the day, moving from a total loss at Amazon’s hands to significant profit in ours. You simply can’t make money in digital if you don’t measure against financial KPIs.

3. We can manage risk in testing

With our holistic view we often don’t have to put the same size budget into testing a programmatic audience compared to when that is managed by the DSP/ad network. This is because we can set up tests to look for Out-Of-Channel effects in brand search and direct visits as part of the test protocol. Most of the time we cannot do these lift-based analyses inside the DSPs themselves.

The second part of risk management is in knowing explicitly what choices are being made in audience selection. It is quite common for DSPs being managed internally to not be transparent about audience choices. In addition the managed service staff may have no insight into audience targeting performance in other networks in order to make informed decisions.

4. Data quality

While it should be apples-to-apples on the data side in terms of what you get out of a DSP when managing it in-house vs. through a managed service, it is often quite different. We have seen multiple times that the data availability from a managed service campaign is far, far less than what is available when managed in-house. Things we would consider basic, such as audience breakouts may not be available.

We’re always going to be biased in favor of better data, as that makes for better decisions.

5. Coordination and Timing

Working with any managed service can sometimes feel like a game of Telephone. Even when the correct action items are taken, there is often a delay that is more than that expected. Whether timing is based on correcting for performance or coordinating messaging with other programs, you want as much control as possible for seamless execution.  You also need the accountability be able to know what happened when. While this might seem to be driven by client reporting, it is actually most important for the accurate interpretation of performance data.  Again, we’ll err on the side of data cleanliness, thank you.

6. Results

In the end, we get better results when we can control the decisions in the programmatic exchanges. Since our results are stated in financial terms to our clients, there is no arguing something working or not working. The control, trust, clarity, and transparency we get by operating programmatic in-house is a clear win for us and a clear road map for our future.

Will Voice Search Resurrect Broad Match?

Is Voice Search Resurrecting Broad Match?

For Search Marketers, unmodified broad match has long been the lowest “don’t go there” option in targeted, profitable search campaigns.  For those unfamiliar with keyword match types, broad match means that the designated keyword can appear anywhere in any user search string, or maybe not at all if Google or Bing’s algorithms deem that there is a “match”. Broad match can be “modified” by insisting that one or more words are included, but unmodified broad match is something of a dumping ground for random, long, or even untargeted queries.

We used to joke that Google threw everything but the kitchen sink into unmodified broad match traffic, so you can imagine our laughter (and tears) the day we found the query “kitchen sink” in a query report on an enterprise software keyword. As data-driven marketers it is this type of example that has trained us to hate, fear, and loathe unmodified broad match.

Until lately…

Before unmodified broad match became a dumping ground for unloved (or unsellable) traffic, it had a real role to play in search campaigns. It was a match type that could be used in “harvest mode” to understand query behavior and mine both new queries to move to phrase and exact match as well as negative keywords to block from campaigns.  In the heyday of short searches and high volume keywords, broad match helped in sculpting keyword mixes for effective search campaigns.  But when broad match modifiers were released, less risky options became available and unmodified broad match fell by the wayside. It made sense to abandon unmodified broad match since modified broad captured most of the relevant queries in most campaigns. Unmodified broad match wasn’t particularly useful any more.

But then voice search came along.

Broad match wasn’t as useful prior to voice search because typed search queries enforced a more limited range of query behaviors.  Voice search changed that.  Natural language is richer than typed search queries and therefore more nuanced and more varied. Add in Google’s developments in contextual understanding of threaded voice queries and suddenly the query range isn’t just long tail, it’s hyper-long tail. Tools are needed to capture this incredibly varied source of targeted searchers. And it turns out there is just such a tool: unmodified broad match.

Hopefully, the ad networks will carve voice search into a unique addressable audience where we can target and optimize separately, but for now old tricks might be the best for a new dog.

Why Agile Works for Us

Working Planet is an Agile shop. This doesn’t mean we’re sprightly and nimble, although we generally are those things too. It means we adhere to strict Agile methodologies in how we prioritize and execute our work. We are very routinely told by clients who have worked with multiple agencies that we’re the best they have ever worked with, and we credit Agile with helping us to be the secret weapon our clients want in a digital media buying partner.

So I am often astounded when I hear marketers say Agile doesn’t work.

“Oh we tried that Agile thing” some say. “We gave it a shot, it didn’t really work for us” say others. “I think it’s a fad” you can hear whispered on social media, if whispering on social media were a thing, which it isn’t.

Agile is so critical to our work, I thought I would take a moment to try to dissect why it works so well for us and hypothesize why it might not for others.

1. Agile doesn’t work without clear goals

We optimize digital campaigns for financial results. The financial KPIs are unambiguous goals for us to pursue. As a measure of business success, profit is unequaled and unarguable, which makes it an excellent KPI. Agile, whether you are in the Scrum or Kanban camps (we’re proud Scrumban fence-sitters) works for three different reasons, one of which is the continual prioritization of what you are working on in the moment. This implies you have something meaningful to prioritize against. Our financial lens and using profit as a KPI gives us clarity for prioritization in a way other agencies might lack.

2. Agile works best when marketing is a team sport

Many agencies are set up as specialists working independently on work for the same client.  It is not at all unusual for agencies to have a unique expert for Facebook, Twitter, or data analytics.  Or they might have all work for one account done by a single person. Accomplishing any one thing when it can only be done by one person can absolutely benefit from Agile (one of my favorite books on this is Jim Benson’s Personal Kanban) but Agile really kicks into high gear for effectiveness when you have a cross-functional team. Team-based Agile Marketing is better for two reasons. The first is that teams lend strength and flexibility to client work, making deadlines attainable and vacations possible even if they happen to coincide. The second is that a team of smart people who all have intimate knowledge of the workings of the client’s campaign and finances are unmatched in effective prioritization.

The second and third reasons Agile works are through visualization of the work and the limiting of context shifting. The multiples of effectiveness in these gains at the team level cannot be overstated.

3. Agile rituals can’t be done ritualistically

Agile, and particularly Scrum, is full of rituals. The fact that they are commonly called “rituals” instead of, say, “building blocks” or “improvement checkpoints” makes them too easily bucketed into the “I have to do this because it is in my calendar” zone of things we hate but are forced to do at work. This is a shame, as Agile rituals exist for a purpose. At Working Planet we don’t stand for our daily standups (gasp) and they are more often held at the end of the day than the start (more gasp), but we do make sure the daily meeting involves a hard look at current priorities and whether reality has shifted since those priorities were made. Our Retrospectives are always held, and are always about how we continually improve as an organization. If a Retrospective is just a recap of what you did or a social chat, it doesn’t move you forward towards your goals.

Often when I read about Agile experimentation it feels like the Agile rituals are done ritualistically, by which I mean without purpose. I am guessing this might not doom an Agile migration to failure, but it won’t help anything improve quickly, or rapidly show the power of Agile processes.

4. Agile isn’t something you “try”.

As Yoda famously exhorted, “No! Try not! Do or do not. There is no try. I know of no better place to apply this Jedi maxim than with Agile. When we first started Agile seven years ago, it was hard. It was different, it felt weird. It wasn’t always easy to voice to a team how you as an individual prioritize tasks. We wanted to just get on with the work. It feels easier to just go do something than talk about it. Like most firms starting Agile, we started with Scrum and all those meetings! It was only with time that we realized how much control it gave us. How the frequent fires became a lot less frequent. How it was easier to know what to do when everyone agreed on why to do it in the first place.

You have to see it through for a longer period of time than you might think before it becomes habit, and you will likely need everyone to do it all at once. If everyone is not doing it, it will be very hard to protect the Sprint or Kanban or process of those who are doing it. It also has to be viewed as permanent or no one will commit to it, because they know they don’t need to. Lastly, Agile also needs a champion, and (at least in the beginning) that person needs to have the power of Yoda to repeatedly hammer home that there is only “Do”, and the seniority to apply the Do Hammer to everyone.

Early last summer we switched from Scrum to Scrumban, although we call it Kanban internally to highlight the differences from our old practices. Our learning curve was much easier and within a few weeks our teams were hitting a new stride. We found it worked well, and I believe Scrumban might be the superior flavor of Agile for those delivering ongoing services vs. creating a product. With its focus on finishing over starting and softly enforced collaboration, it is a good fit for us. Our teams credit it for their being able to easily achieve some seemingly hard goals with more ease than was expected. We’re an even better agency for migrating to Scrumban. Once again, we can’t understand not doing it when the benefits of doing it are so clear.

When you have something that works, it is hard to let it go. And I think that might also be the power of Agile as a methodology of continuous improvement. Before we moved to Agile we had embedded best practices that worked for us as all agencies have practices that work for them. But Agile was better. And then the Scrumban version of Agile was better than that. Now we are testing layering on personal Kanbans, incorporating DevOps concepts, and other experiments. With Agile we test new things all the time. And maybe this is the most powerful reason I wouldn’t let Agile go. Agile won’t let you be complacent. If you embrace the purpose of Agile, embrace continuous improvement, don’t fall into ruts, and prioritize towards goals that are lofty and measurable then you simply cannot be complacent. And maybe that is Agile’s hidden strength.

Dogs in the Office: Delightful or Distracting?

Dogs are said to be (wo)man’s best friend, but are they best for the workplace?

What could be better than having a loving, little furry friend run up to greet you every morning when you step foot in the office? Receiving such unconditional love is guaranteed to start your day off on the right foot, kick starting your motivation.

Personally, I love being able to bring my dog (featured above) to the office. I think that it creates a more welcoming environment and can even be enticing to potential new hires. It also saves me from having to worry about what he’s doing home alone all day, as well as saves me money from having him go to doggy day care or with a dog walker.

Being around dogs has shown to lower the levels of stress in your life and increase your levels of serotonin, so if you are having a bad day just call that adorable pup over to your desk and take a few minutes to pet him/her and enjoy the love that they radiate.

Having a dog in the office that wanders from desk to desk increases the possibility of employees who don’t normally connect to interact with one another on a more consistent basis. This can get ideas flowing and improve employee relationships.

As much as I would love to believe that there are only pros to having a dog around while you work, there are definitely a few cons to be on the lookout for.

Dogs can sometimes become unnecessary distractions; between any barking and whining that occurs, having to be walked periodically during the day and distracting employees from their work. The dog’s owner should be able to control their dog and make sure that he/she is acting like a mini four legged employee should. Dogs playing with each other can be extremely entertaining, but can also disrupt the flow of work.

Some dogs resort to chewing and destroying items when left alone or feel anxious in new situations, causing damage to office equipment. Accidents do happen (and need to be taken care of immediately and thoroughly by the owner), but some people prefer to just avoid the possibility of it occurring altogether.

Making sure that the dog is up to date on all vaccines, house trained and well behaved are just a few requirements for owners before allow their dogs to enter the office on a regular basis. Some companies may want to include some clauses in their pet policies, such as some consequences the dog would face if it acted out in an a, b, or c manner. There could be legal and insurance worries that would come along with having a dog in the office. In the case that someone is injured by the animal, the company’s owners should have their bases covered.

Here at Working Planet, we have found that the pros more than outweigh the cons, and happily share our office with our four-footed friends. If you have dogs in the office, be clear with owners about the expectations for dog behavior in the office. Address the fact that dogs are in the office with potential employees to surface allergy (or even fear) issues. In the end, it’s up to you whether you believe that the positives outweigh the negatives, which can be a ruff decision (Ba-Dum Pa).

Top Five Digital Marketing Trends to Look For in 2019

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

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

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 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 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.