Why We’re Working From Home for 60 days

Today, March 13th 2020, Working Planet initiated a 60-day mandatory work from home policy. We think this is the right response for companies capable of managing it. Here’s why.

The data so far on the SARS-CoV-2 virus is that that it spreads easily and without any symptoms necessarily being shown by the carrier. Containment of a virus with these characteristics is close to impossible, and that has proven to be the case.

So, we are not working from home to avoid catching COVID-19. Being exposed will happen to virtually everyone short of a level of permanent self-isolation that is unrealistic. So why are we creating social distance through an aggressive work-from-home policy?

To slow the spread.

While there has been a lot of focus on the number of cases and death rate, the KPI we as a society should be looking at is the Total Simultaneous Serious Cases. This is the number that will determine whether our healthcare system can handle the 20% of confirmed cases that will need medical treatment. When we greatly exceed the capacity of our healthcare system the mortality rate will rise as people who could have survived don’t make it because of a lack of healthcare.

The good news is that slowing the spread makes a huge difference in the number of Total Simultaneous Serious Cases. Some models show that creating social distance even one day earlier at the early stages of contagion can reduce peak TSSC by as much as 40%. That’s enough to save lives. Potentially many lives.

This is something we can do, so we’re doing it. And we’ll keep doing it for a period of time that we think will get us to where more people are getting well than are getting sick, which is when the healthcare system is able to begin recovering.  It is a bit of a dart throw, but we think that is about 60 days from now here in Rhode Island.

We hope we’re being way too conservative, but the data tells us we’re not.

We encourage all companies that can do the same to do so. It is the only action we can take that will make a real difference as the Coronavirus takes hold in our communities. We also hope that as many people as possible choose to limit the number of face-to-face interactions they have in the next two months. It is our personal choice to take action as a group of citizens that can make a difference.

How Our Clients Grew in 2019

2019 was a stellar year. All the clients that we worked with for the year achieved solid, and in some cases spectacular (200%+) growth. Given many of our clients have been with us for years, how we are achieving continual growth is as important to us as the growth itself.

Here are three major factors in the growth we were able to drive in 2019:

1. Using business metrics, not marketing metrics

While many, if not most, companies focus on marketing metrics to assess the performance of their marketing program the truth is that there isn’t a single marketing metric that consistently correlates to profit. Not one.  Marketing metrics, whether unique visitors, cost-per-click, impression share, followers, or even the number of sales-qualified leads are not good or bad in their own right. They all need context to know whether they are good or bad when judged against profitable outcomes. We call these “local optima”, and they should not be ignored but rather handled based on the bigger context of profitability and never as a proxy for profit.

2. Using deep financial and customer data

So many companies own valuable data, but somehow only wind up using it for reporting. Such a waste! Customer data on value, profit, retention and more can be invaluable for modeling and decision making at the ad placement and media buying stage. In creating predictive models based on the financial results of advertising, we’re able to use this deep financial knowledge for decision-making every day.  This results in highly efficient and predictable advertising, as it relates to the only thing that matters for a viable business: financial success.

3. Better targets with better execution creates a competitive advantage

We spend a lot of time (more than anyone we know) talking about target cost of acquisition. New clients are sometimes surprised when we push back on their CAC target or other acquisition metric, but we know that one of the worst things any company can do is have a flat, one-size-fits-all, average target for the cost of acquiring customers. You customers aren’t all worth the same amount, so why pay the same amount to acquire them?  Companies often short-change the process of good target definitions to their detriment. Targets need to be based on financial data and tailored to the business model, otherwise they are not only inefficient, but potentially dangerous to the health of the company.

Bonus factor: Not being a slave to data

I’ll add one more factor that sometimes seems odd coming from a highly data-driven and analytical company. That is to not be a slave to the data.  One of the most interesting questions we ask is, “What is the data not surfacing?” Having a holistic view of our clients’ financial picture and not being tied strictly to imperfect in-channel tracking has allowed us to identify gaps in tracking caused by technical issues, multiple devices, and user behavior. This has surfaced so many opportunities over the years that thinking of Out-Of-Channel effects is an everyday discussion here, and that always has a big impact.

As an Agile company, we focus on continual improvement and hopefully this article has provided a glimpse into that mindset. We have high expectations and hopes for ourselves and our clients in 2020. We hope you have a great year, too, and are always happy to chat about digital marketing. Happy 2020!

“If It Ain’t Broke Don’t Fix It” – Dangerous Words

You are probably familiar with the phrase “If it ain’t broke, don’t fix it”. On the surface this folksy phrase seems to encompass wisdom gained by experience. One can envision the wise elder in her rocker holding forth to the young’uns on how to live an effective life.

The problem is, this is wrong. And dangerous.

In a world where achieving a steady state of survival is the goal, this homily works . I am sure it held up well in our agrarian past.  Focus your attention on those things that could cause disaster, the wisdom goes, and leave what is working well enough alone.

But in today’s digital marketing world this approach doesn’t work. Following this advice might even cause you to fail.  Here’s why.

There are three common trends in today’s digital marketing world that make this approach dangerous. The first is that if you are in a real company that is acquiring customers, it will usually be easier and faster to get better at what you are doing well rather than fix something that is struggling.

The second is that in an auction-based media world volume is the reward for the absolute best performance (as measured in customer value and conversion rate). Therefore getting to be really good at converting an audience can be more powerful than getting off the ground with something quasi-effective.

The last is that digital is in constant motion.  What works today is not always going to work in the future. Being complacent about the things that work today might sneak up and bite you down the road.  Complacency is a killer of digital campaigns, so not paying attention to what is working is dangerous.

Marketing is moving too quickly to “build and forget”. Instead of trying to fix what is broken, one needs a structure that surfaces opportunities. That involves both the improvement of what is working and testing entirely new options. This structure needs to accept and support the changing landscape of digital while not allowing for complacency.

The alternative: Continual Improvement

One reason we’re an Agile shop is that the Agile framework allows us to embed innovation into our daily practices while forcing us to revisit both prioritization and improvement of our work processes. There is nothing like daily and weekly prioritization to force conversations on how to increase performance. The Retrospective (my favorite Agile meeting!) embeds a discussion on how to improve in all areas of performance into daily life.

Retrospectives are scheduled meetings that ritually ask three questions: What is working? What can be improved? What are we going to do differently? Because these questions are measured against hard goals (profit, volume, engagement, etc.) it is hard to ignore ways to improve an existing process, campaign, or channel vs. trying something new or addressing something that is struggling.  The “Opportunity vs. Investment in Time” equation becomes clear and central to the discussion.

Life in the modern digital marketing world is anything but a steady state. Moving from complacency and set best practices to a framework of continual improvement is both powerful and necessary. One must accommodate change and fully take advantage of auction-based opportunities to succeed.

Maybe we need to change the phrase to “If it Ain’t Broke, Make it Better”.

Why CFOs Love Data-Driven Marketing

CFOs and Marketers have not always seen eye-to-eye.  Traditional marketing and advertising costs have always been viewed with suspicion. With no visible connection between financial investment and the return it creates ad spend can only be viewed as a cost.

All that has changed.

The explosive growth of digital and data-driven marketing has created the ability to show the relationship between the investment in advertising and the return in hard dollars. This connect-the-dots ability between ads and profits is new, and is evolving rapidly. It has never existed in this way before.

And CFOs love it.

The CFO’s role is to maintain the financial health of the company.  CFOs are far from being the bean-counter many marketers envision. They are driven by the same financial results as good data-driven marketers.  My favorite moment with a client CFO was seeing him exhort to us to “Spend more money! Spend more money!” He fully understood that optimizing to financial data had moved marketing from the “cost” column to the “revenue generation” column. When you prove the dollars in = more dollars out relationship, CFOs will be the first to push more budget to marketing

Here are three simple rules to help you get the financial backing of your CFO:

1. Speak Finance

Marketing metrics drive CFOs nuts because they rarely mean anything related to financial performance. Clicks, views, impressions, unique visits, cost-per-click, likes, and any of a thousand other marketing metrics don’t correlate well to profit. Our recommendation is to use profit as your marketing KPI.  If you need to talk about another metric, put it in the context of financial results. Maybe (and yes, this might be marketing heresy) don’t use traditional marketing metrics as KPIs at all. Your CFO will love and reward your focus on meaningful financial outcomes.

2. Work Backwards From Revenue

Advertising is an investment that creates a financial outcome at some later time. Marketers often focus on “time” in the order of the user journey. However, the CFO is interested in the financial outcome, and interested in both its magnitude and the time it takes to get there. It is very different to the CFO if the same Cost-per-Customer-Acquisition (CAC) is recouped in one month versus twelve months; which is something a marketer may not differentiate. Showing both the investment-to-return in time and dollars by creating financial projections will align you with the CFO view of financial control. This makes clear the marketing program’s financial success.

3. Involve the CFO in the Conversation

Marketing is likely a significant chunk of your company’s expenses.  CFOs need to know this is being spent wisely, but can’t be expected to know the details of the complex and technical field of digital marketing.  What they do understand is the relative impact of investment, the balance of expense versus cash flow, and keeping the company financially healthy. Engaging your CFO in a discussion where the investment and return is clear in both magnitude and time frame will be eye-opening. They will likely look for opportunities to help you grow your company’s success through your marketing efforts. Most CFOs will be very willing to promote ad spend and growth if it is predictably profitable. Their insight on what affordability means or what constraints on investment exist will be extremely helpful in the marketing team’s prioritization of how to use limited dollars and resources.

We’ve talked to plenty of marketers over the years who disparage over getting the CFO involved, saying things like “they just don’t get it”. Our experience has been the opposite.  Your CFO is your secret weapon if you can tell your story in financial terms and optimize to financial results.  Try talking to your CFO. You might be surprised.

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

This image has an empty alt attribute; its file name is onlineform-2-1030x687.jpeg

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.

Innovate

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.