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Memorial Day has passed, schools are out for summer, and (in Chicago) we’re finally breaking free from the rain and cold. Summer vacations are on the minds of employees everywhere.
However, for those lucky enough to have a flexible or unlimited time off policy, the question often isn’t “where should I go?” but “is it OK for me to go?” For some employees, the flexibility of these policies creates uncertainty that results in the policy not being utilized to its full extent.
What can companies do to ensure that their flexible time off (FTO) policy produces the return on investment (ROI) it was intended to provide? We believe that communication at every level is the key to success. Read on for four ways company members at all levels can optimize the ROI of FTO within a company.
Take the time that’s offered to you. Ask yourself what’s stopping you from taking advantage of FTO, and have an open conversation with your manager to address any concerns.
Avoid added stress by communicating and checking in with your team regularly.
HR can get a bad reputation for being the “Policy Police,”—but if the shoe fits, wear it! Make sure you’re educating the company to ensure that FTO is being used in the way it’s intended.
Are you taking time off? Or are you burning the midnight oil seven days a week? You are in a leadership role because you’ve been identified as someone the rest of the organization looks to for direction. The words you speak are not the only thing individuals are looking at—they’re also watching your behavior.
Learn more about Centro's employee benefits by checking out our Culture page here.
In Part 1 of this series, we harped on the importance of measuring value by a metric that accurately represents it. “Each and every keyword is unique,” we concluded, “they will all defy the average. The goal of any measurement is to understand those differences, using all the signals available and discarding all the noise.”
And the goal of any optimization is to react to those differences. Part 1 demonstrates how the way we optimize is inextricably linked to the way we measure - we can only optimize as well as we measure, and no better.
For a summary example, say we have a set of 100 keywords and our task is to rank them in order of value. But we have two metrics, both of which purport to at least partially denote value. If we rank the keywords by one metric we’ll have a very different list than if we rank by the other. Maybe we wise up and put the two metrics together, but that only results in a third and different list entirely. How do we know which one to use to inform our budgetary direction?
There is no greater service you can do to your SEM optimization than understanding your business. In fact, this is the only way to answer non-arbitrarily the question of “which list has the right rankings?” It matters very little whether you calculate thrust, fuel requirements, and trajectory if you don’t know which planet you’re aiming for. The scientific approach is pointless if you don’t first know your goals.
To start, here are some questions to think about before voyaging for the perfect metric…
1. Does my website have one funnel or multiple? How many unique flows are there?
2. What are all my tracked conversions within these funnels? How do they relate to one another? Which are in parallel and which are in series?
3. What are the relative “Conversion Rates” (conversions / clicks) and “Monetization Rates” (instances of monetization / conversions) for each conversion point?
4. What are the relative frequencies (i.e. Volume) of each conversion?
5. Are any two conversion points mutually exclusive? Redundant?
6. What are your goals for your SEM program? What are you trying to maximize? What are your constraints?
These questions are just to get you thinking - you don’t need to know exactly the conversion rate of every point along your funnel. But it is helpful to have an idea of how that knowledge might change the way that you prioritize your optimization inputs.
By now, you have a pretty good understanding of your conversion funnel. You know the various points along it and how they relate to each other. You recognize that they all tell limited parts of a grander story: the truth but not the whole truth. Now, how do we turn these insights into something useful?
The perfect Hybrid Conversion balances two criteria:
1. Frequency
2. Accuracy
To illustrate these, let’s return to our simplified example from Part 1. Suppose your conversion funnel is comprised of ‘Contact Form Fills’, ‘Loan Applications’, and ‘Program Enrolls’, in that order.
Click --> Contact Form Fill --> Loan Applications --> Program Enrolls
Let’s then take a snapshot of 1000 clicks and look at the sample data that results.
In terms of our criteria, “Conversion Rate” is a measure of Frequency whereas “Monetization Rate” is a measure of Accuracy. Notice that they’re inversely proportional (but we’re used to such tradeoffs in SEM, where the only maxim seems to be “what goes up… must cause something else to inconveniently go down”.) The better the lead of any leading indicator (like an upper-funnel proxy conversion), the worse the indication. For this reason, the operative word is “balance”.
Armed with this knowledge, we can now concoct the perfect Hybrid Conversion. But first! A detour into the importance of Frequency.
The long-tail is a much talked about topic in SEM. But how long exactly is it? Where does it end and the body begin? And where does the body meet the head terms?
Well, where you draw the line depends entirely on which metric you’re looking at. In the upper funnel, there's more volume so fewer keywords are long-tail. In the lower-funnel, the inverse is true. Generally, the deeper down into the funnel you go, the longer the tail gets.
When it comes to optimization, the long-tail is notoriously difficult for all but the smartest data sharing techniques. But what we’ve shown above is that you can finagle your metrics to squeeze or shrink the tail to your advantage. This is the very reason we use upper-funnel proxy metrics in the first place: they give us information where we might not otherwise have any. And where there’s more data, there’s more potential for action.
“Statistical Significance” is the point at which we have enough information to act. What is that point for your program? There’s no surefire answer, but the below formula offers a simple rule-of-thumb. Plug in the Conversion Rate for a high-level segment and out pops the click threshold at which the conversion data of particular objects within that segment is actually meaningful.
Statistically Significant Click Volume = ln (0.005) / ln (1 – Conversion Rate)
Let’s try it out! We’ll calculate click thresholds for the conversion points on either end of our example funnel.
Contact Form Fill | ln (0.005) / ln (1 – .10) = 50 clicks
Program Enroll | ln (0.005) / ln (1 – .01) = 527 clicks
So, on average, it takes over ten times as long to reach significance with the lower-funnel ‘Program Enroll’ than it does with the upper-funnel ‘Contact Form Fill’. Hence the volume edge of the upper funnel. For an intuitive explanation of this concept, see the section headed “The Volume & Monetization Rate Tradeoff” from Part 1.
The above calculator can also be extended to roughly quantify the percentage of your program that classifies as long-tail. The first step is as above: to calculate the “statistically significant click volume” for your program at large (or some subsets of it). Next, for the program (or those subsets individually), count the number of keywords with historical clicks above and below that threshold. Divide by the total number of keywords and there you have a good enough estimate of long-tail proportion for most practical purposes.
From Part 1 of this series (with a few edits to fit our sample data)…
“Think of the Average Monetization Rate as a measure of predictive power towards future revenue: the higher the rate, the more power packed. One ‘Program Enroll’, then, holds a lot more weight in this department than does one ‘Contact Form Fill’ (by, on average, a factor of [10] Similarly, one ‘Loan Application’ averages [20x] the predictive power of one ‘Contact Form Fill’.”
The more likely something is to turn into revenue, the more we want to preference it. Nice and simple. Time to put it all together.
In pursuit of the perfect Hybrid Metric, let’s start simple and then layer in complexity based on what we’ve learned.
Attempt 1
What if we just added the three conversions together? How would that sway the value rankings of our keywords?
Hybrid Conversion = Contact Form Fills + Loan Applications + Program Enrolls
Per 1000 clicks = 100 + 50 + 10 = 160 Hybrid Conversions
Here we must introduce the idea of an effective weighting. Doing a simple sum like this means that we’re weighting each conversion point equally (i.e. 33%). The volumes of each conversion, however, make the effective weighting very different: ‘Contact Form Fills’ account for 63% of the total whereas ‘Program Enrolls’ only account for 6.3%.
Effective Weighting = Individual Conversion Volume / Total Hybrid Conversion Volume
Well, given that ‘Program Enrolls’ are a lot more accurate to revenue than ‘Contact Form Fills’, we probably want them to factor in more heavily. As is, keywords that get us a ton of upper-funnel but not much beyond that are still ranking too high.
Attempt 2
If the Monetization Rate is a direct measure of accuracy to revenue, shouldn’t we incorporate that? If a ‘Program Enroll’ gets us ten times more money on average than a ‘Contact Form Fill’, then shouldn’t it be weighted ten times more?
Hybrid Conversion = (1.0 * Contact Form Fills) + (2 * Loan Applications) + (10 * Program Enrolls)
Per 1000 clicks = 100 + 100 + 100 = 300 Hybrid Conversions
All we’ve done here is directly applied our relative Monetization Rates as coefficients: 10%, 20%, and 100% become 1, 2, and 10. But remember, what’s important is the effective weighting. That comes out to 100 / 300 for each, or 33%. So now, we’ve inadvertently created a Hybrid Conversion whereby all three conversion points are effectively weighted evenly!
Attempt 3
We want our Monetization Rates to align with our effective weighting, so that – if a ‘Program Enroll’ is worth 10x a ‘Contact Form Fill’ – we’re treating them accordingly.
Hybrid Conversion = (0.25 * Contact Form Fills) + (1.0 * Loan Applications) + (25 * Program Enrolls)
Per 1000 clicks = 25 + 50 + 250 = 325 Hybrid Conversions
Sure, you can use coefficients less than 1. And scale them up or down all you want, as long as do so for all of them together – the relative amounts are all that matter. This time when we calculate our effective weightings we get:
25/325 = 7.7%
50/325 = 15%
250/325 = 77%
Notice: 77% is ten times 7.7% and about five times 15%, while 15% is twice 7.7%. Or, exactly our Monetization Rate differences!
Attempt 4
The final check here is to balance it out with our Frequency considerations. Is our new Hybrid making the long-tail too long? How long will it take to reach statistical significance? Our new metric might be a near-perfect representation of value, but is it too bottom-heavy to be useful?
We can gauge this by calculating the effective conversion rate of our new Hybrid Conversion, and then plugging that into our Statistical Significance Calculator.
Effective Conversion Rate = Sum of (effective weightings * conversion rates) = (.077 * 10%) + (.15 * 5%) + (.77 * 1%) = 2.3%
Statistically Significant Click Volume = ln(0.005) / ln(1 – .023) = 228 clicks
So, by our new Hybrid Conversion, it will take 228 clicks on a given segment before we know anything useful about the performance of that segment. Is that too many for tolerance? You’ll have to assess that in the context of your own program. If you want to slide the click threshold down, simply weight the upper-funnel conversion points a dash more relative to the lower-funnel ones.
Maybe you decide that looks like this.
Hybrid Conversion = (0.5 * Contact Form Fills) + (1.0 * Loan Applications) + (20 * Program Enrolls)
Perfect!
The only trouble in all this is that there’s no such thing as a perfect Hybrid Conversion anymore than there exists a perfectly predictable funnel. In lieu of precisely knowing the Conversion Rates and Monetization Rates of every keyword in our program, we speak in averages. We can say how much of our program is long-tail, but we can’t say if that’s too much or too little. And all those questions about your business? Those can get a tad mushy… For these reasons, Hybrid Conversions will always be a bit of an exercise in try, check, and revise.
In Part 1, we learned that Hybrid Conversions allow us to smartly differentiate between the values of program segments. Here in Part 2, we learned how to try them, how to check them, and how to revise them, so that you can start putting your SEM spend where it will make you more money.
After almost 10 years in the marketing and tech industries, I’m familiar with the struggles that digital marketers face day-to-day.
For instance, I know that based on all the tasks I have to get through each day, I don’t have time to switch between ten different platforms to do my job. My team needs the ability to plan, buy, analyze, and optimize everything in one place.
I also understand the frustration of working in tools that fail to evolve at the same pace as the industry. My team wants to spend their time planning and implementing strategies, not creating workarounds for tools that can’t keep up!
Ultimately, marketers are looking for a holistic platform that increases productivity, keeps up with industry trends, and executes better performing campaigns across all formats and channels. In short, we want a tool that allows us to work smarter, not harder.
But don’t take my word for it—check out the latest ranking updates from G2, a site that aggregates hundreds of thousands of unbiased user reviews to rank software and services.
For the past two years, users have ranked Basis as the #1 DSP on the market. As of Spring 2019, Basis also ranked as a Leader in the Cross-Channel Ad Software and Video Ad Software categories.
What qualifies a piece of software to rank in these G2 categories? Read more details below:
Best Demand Side Platform (DSP)
Best Video Advertising Software:
Best Cross-Channel Advertising Software:
Centro’s mission has always been to provide software and services that improve the lives of the people working in digital media—and Basis’ Spring 2019 rankings are a great reminder of why we do what we do!
It goes by many names, but you're paying money for it and it’s driving a lot of value to your business. Pay-per-click (PPC), search engine marketing (SEM), paid search, search advertising, Google Ads, etc… it’s the marketing channel that has the biggest opportunity to truly optimize, and do so based on hard statistics. Why? Because the big data it generates enables very accurate statistical analysis and computation.
Paid search optimization. It’s the practice of making the most effective use of advertising budget towards your SEM goals. More specifically, it’s identifying trends and opportunities based on past performance data in your search engine marketing campaigns, and applying those learnings to drive better results. Whether by eliminating waste and cutting costs where they aren’t leading to revenue or conversions, or (of course) investing more on the keywords, segments, and topics that have better results.
But you’re not here for that high-level overview, and neither are we. PPC bid optimization is a rigorous and technical field, and the goal of this article is to discuss the approach to automation and optimization of SEM campaigns with big data.
You and your business are likely using Google Ads (et al) in a very sophisticated way. Large SEM programs not only pay a lot of dollars to search engines and gather a lot of dollars from ad-conversions, but they are generating massive amounts of data as a byproduct.
For example, every search query that leads to a click and subsequent conversions has numerous elements along that entire journey that are being tracked inherently by Google… and then by Google Analytics, tag managers, “3rd party” pixels and analytics tools, and conversion tracking tools… and then deeper funnel data points about latent conversions, offline sales, and lifetime value that may live in a CRM or data warehouse. Plus you can collect browsing behavior and seemingly unrelated data on the sidelines that ultimately may correlate to some future value.
The point here is that when your organization uses PPC as a primary channel to generate revenue, you are (or should be) collecting a very, very large–and very powerful–dataset.
Alright, so what?
I'm glad you asked!
Big Data may be a buzzword that gets thrown around a lot lately, but let’s take a look at it in two manners:
1. The definition from Wikipedia: "Big data" is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.
2. I like to simplify it into a simple concept: When nearly everything is tracked, you do get a lot more noise, but you also end up with a much more complete picture of associations and correlations. With big data, correlations trump causation.
Big Data removes the necessity of understanding cause and effect (the "Why?”) and turns the problem into a game of correlations. You see that when “A” happens, then “B” happens very frequently. To make that information actionable, we needn't make a claim about cause nor reason. There is a correlation. And that correlation has power.
Nowhere in marketing does this type of correlation have more easily accessible power and immediate actionable application than large paid search programs. SEM is the “rocket science” of marketing. Bold claim? Yes. Accurate? Probably.
Your data, much of which is simply a natural byproduct of running your program and having tracking set up in a healthy way, has enormous potential to optimize and automate results in your SEM campaigns. Of course when you add in the other tools, tracking and analytics providers, and your offline data (CRM, data warehouse, historical results, etc), then you've got yourself a gold mine of data.
But you need to activate it. You need to turn that potential and make it kinetic. (Thanks, Physics.)
How do you take that fuel, that tinder, that coal, and turn it into a humming machine driving optimal results and performance? It’s similar to how a car does it: you need an engine.
Manually parsing, sorting, and analyzing data to make decisions is out of the question. You don't have to be an Excel pro to realize that hundreds of thousands of clicks on your copious keywords and all the accompanying data from publisher attributes, conversions and revenue data, and analytics data won’t be easy to draw insights from in a spreadsheet.
Maybe you’ve been there too, with 20 tabs in Excel, trying to stitch, pivot, and analyze. Hitting “Save” is precarious by itself.. you might waste 15 minutes waiting! Besides the speed and infrastructure constraints of spreadsheets, statistical models need to be run on all that data, and preferably on a very frequent basis to make sure that the insights drawn from all that data can be applied to your program via bidding decisions.
The point is, you need something more robust if you’re going to use that data for a meaningful approach at optimizing your paid search campaigns.
At this point, it’s noteworthy to mention that an SEM Agency could be the next step. Give the agency access, and ask for results. Here’s the issue: unless that Agency is employing some additional technical solution, the optimization efforts are still manual and simply can’t hit optimal performance.
Big data, as noted by the Wikipedia definition, requires different methods and tools because you’re dealing with “data sets that are too large or complex to be dealt with by traditional data-processing”.
So, what are your options?
All jokes aside, hopefully you’re here reading to learn about how to truly optimize PPC programs with big data, automating this revenue-driving channel for your business. (So, let’s count out the cyborg option.)
Building a script is taking the responsibility into your hands to figure out that relationship between all your data and the results you desire. It’s a big undertaking, but can be very fulfilling.
Frankly, building any script for any job feels good, and makes you proud. In fact, if you’ve been involved in building anything (from a coding or programming or even process-driven perspective), let’s take a minute to realize you’re driving innovation in the small and immediate ways that help to make humans more efficient; the virtuous cycle of ever evolving and improving! (Pause and smile!)
Okay, back to action. Scripts are definitely a good first step if you’ve mastered the programming methodology, bidding methodology, and the syntax and logic. Most often you’ll have to hire the resources to build and maintain that solution.
The benefits? You have total control. Your engineering resource will know all the inner workings, the inputs and outputs, and logic for making bidding decisions based on the data. At a minimum, you’ll need someone with a very strong data analytics background, or better yet a data science background. Needless to say, it’s a lot of work. It’s the classic control versus effort challenge.
There’s another option we noted: purchase a bidding optimization tool.
PPC bidding software automates and optimizes your SEM program through a few steps, starting by integrating all of that big data: online and offline data sources. It models the relationship between the data on keyword performance, predictive data from publishers, audience and attribute segments, and revenue and conversion outcomes. It uses those machine learning-based models and projects the impacts of bids versus revenue. The big data comes into play heavily when estimating the keyword value from a Revenue-per-Click standpoint. Throughout the bid calculation process, PPC optimization tools will crunch your big data sets into accurate, actionable bid decisions.
Taking a step further than just the keyword-level bids, the front-runner PPC tools are able to use a slightly different set of your big data to calculate and automate bid adjustments for attributes like device, audience, or geography. The correlations seen between audience segments + the keywords they clicked and converted on in your data sets provide this.
I’ll paint the picture with a bit more color. Your revenue funnel can be sliced and diced into incredibly specific niches based on your big data. The performance outcome of each slice informs the bid that should be set. Layer in all the keyword data with your contextual data, and audience segments, time-of-day, day-of-week, geography, device, seasonality, etc etc etc: the venn diagram overlap of each of those various attributes will each have a different “worth” to your business. Processing that data with a data science based approach in an SEM optimization tool will figure out which of those segments have value (or not) and adjust bids based on the expected return of each.
Further, tools like these can provide other functionality that is built off of your data–such as forecasting ROAS or ROI. The more data there is to ingest, the better.
There are some tools and vendors designed for smaller programs without as much data, while some designed for bigger programs with the types of data and scale we’re discussing here. These must have the infrastructural underpinnings that enable clean integration, fast processing, and actionable outputs.
Ad management platforms that “sit on top of” Google Ads (Adwords), Microsoft Advertising (Bing Ads), and Yahoo Ad Manager have a few flavors. Some focus on campaign management and easing up workflow. Some help to cater to multiple channels beyond just paid search. And others are in-line with our current discussion: focused on big data ingestion and activation for peak performance. All of them should do a variation of what we’re talking about here: using performance data to make bidding decisions. However, there are multiple depths to traverse.
Let’s introduce another word: predictive. Predictive bidding optimization tools approach the problem from a slightly different angle. While fundamentally the same, there are nuances that stand out:
With big data and optimizing PPC performance, the underlying problem is one of scale. The amount of data we’re talking about isn’t easy to process. There are two solutions that the best SEM bidding optimization software will employ: infrastructure and lower space machine learning.
From an infrastructure perspective, distributed cloud servers with in-memory processing and anomaly detection help to manage the load and manage the scale with speed. Reporting on millions of rows can take a couple of minutes instead of the better part of an hour. These infrastructural elements also prevent problematic data from interfering with bids through bias correction and anomaly detection.
Machine learning strikes again to manage the scale. Specific algorithms can allow the interactions between millions of interactions to be simplified in a different “lower space” to remove the load issue, and reduce the problem into one that’s more practical. This class of algorithms is similar to those used by companies like Netflix, who is also dealing a scale problem: millions of users interacting with thousands of shows and movies.
Big data is difficult to deal with and make work in your favor without the right tools. However, with the right methodology, technology, and incentives (ROI!), all that data your collecting about your customer journey is the perfect weapon for optimizing PPC performance.
Bid optimization is very serious and technical business. It gets deeper and deeper the more you look at it. It’s not easy to manage with only our human brains (until we become cyborgs), but the modern suite of automated bidding software for paid search is doing a great job at extracting peak performance from programs with lots of data.
When you’re losing potential revenue from campaigns that aren’t performing at expectations, it’s painful. Some of us work just to live our lives on nights and weekends. However, others are looking to really get ambitious about how to add more value at work, and turn our profession and career into just that: something we are professionals at!
At Centro, we know that keeping up with the trade pubs and latest trends can be tough and time-consuming. To make that easier, we’ve compiled all the articles, reports, and other bits of awesomeness you may have missed, but should definitely read. Enjoy our latest list below!
Brands Are In-Housing And Agencies Are Adapting [:02]
The media in-housing trend is real, but agencies aren’t getting kicked to the curb. It’s a common misconception that in-housing is an all or nothing proposition. More brands are embracing a hybrid model in which they gain more control and also discover economic efficiencies.
Read Marc Pritchard's landmark speech on creating a 'new media supply chain' [:15]
In a speech to the Association of National Advertisers, Procter & Gamble's chief brand officer, Marc Pritchard, challenged advertisers to support a digital ecosystem that prioritizes quality, civility, transparency, privacy, and control.
LUMA’s State of Digital Media 2019 [:10]
Leading AdTech investment firm, LUMA Partners, has put together their annual State of Digital Media, which covers their views on the market, bigger industry trends and the future of the ecosystem—it's always worth checking out!
One year into the paywall, Wired is testing letting advertisers unlock access for readers [:03]
To paywall or not to paywall? That is the question. After a year of A/B testing and experimentation with digital subscribers, emerging tech publication, Wired, is testing the lines between paywalls and added access to content via sponsorship. The piece describes how Wired seeks to discover the balance between gaining and maintaining paid subscribers while simultaneously creating unique and appealing ad opportunities for advertisers.
Musings On Why LinkedIn Bought Drawbridge [:04]
This week the trades dropped surprising news that LinkedIn plans to buy cross-device data company Drawbridge. The announcement begs the question of what the popular B2B social platform looks to gain from a probabilistic device graph. Columnist Allison Schiff shares thoughts on what LinkedIn may have to gain from the unexpected acquisition.
WTF is bid shading? [:05]
First price auction models have led to increased revenues for publishers, but because advertisers have to pay the price they bid (versus slightly more than the second highest bid in second price auctions) they end up spending more than they may have to. Enter bid shading which enables advertisers to predict the highest bid, in hopes of not overbidding
Anti-Fraud Measure Ads.txt Is Coming To Mobile Apps But Very Slowly [:01]
Roughly 4% of the top mobile apps have adopted app-ads.txt. App-ads.txt adoption among mobile apps has been scant because there is not much incentive for app developers to make an effort to adopt it.
YouTube Reintroduces Third-Party Ad Serving In Europe [:03]
YouTube is reintroducing third-party ad serving on reserved buys in Europe after scrapping it last year in anticipation of GDPR. Google says the new third-party ad serving integration is GDPR complaint since it runs through an API-based framework that serves partners’ creatives via Google’s systems.
Snap Makes A Comeback After The Release Of Its Rebuilt Android App [:02]
Snap is heading in the right direction again. While their updated numbers are still 1 million people short of their peak user base since it went public in 2017, they are showing signs of making a comeback. In the latest rebuild of the Android app release, it has been designed to be faster and less buggy. Snap also held its first partner summit recently where they debuted that Snapchat stories would now be coming to Tinder and Houseparty.
Google’s Next Big Money Maker Could Be The Maps On Your Phone [:03]
Google maps has been a big opportunity for Google to monetize on that has been slow playing. While the search business is still profitable but with companies like Amazon rising up in the ranks, Google is looking for new territories to start making a profit. If executed properly, this will be a huge opportunity for local businesses.
Are smart speakers making audio advertising more enticing? [:04]
With global smart speaker usage forecast to grow 82.4% in 2019, rising from 114 million units in 2018 to 207.9 million this year, more brands are looking to leverage the power of audio (and voice) to break through the ad saturated world and effectively deliver messages in moments when users are actively tuned in. But the voice assistant platforms have only just recently opened the door to paid audio ads.
Hundreds of NYC Taxis Are About to Go Programmatic [:03]
Welcome to the next big thing in programmatic, taxi cabs! Urban OOH platform, Firefly, is expanding their footprint of adding digital smart screens on taxis, Ubers, and Lyfts to new cities. Their screens run targeted, geofenced ads based on driver routes, area demographics, and traffic patterns.
Opening day for Major League Baseball came early this year, and similarly the 2020 election cycle has kicked off earlier than ever before. So now is the perfect time for political operatives to hone their skills for the next big game by reviewing the hits and strikes of last season – and when it comes to digital media in the 2018 elections, programmatic advertising is the MVP (Most Valuable Performer).
This is just one of Centro’s takeaways from this past November’s elections, where our software, Basis, was used to manage political digital ad buying for 300+ state and local races throughout the country across display, video, native, search and social media.
The trends we observe point to competitive election strategies – as 73% of the races Centro was part of had winning outcomes. Now that the next election cycle has already begun, it’s imperative for candidates and advocacy groups to plan with these factors in mind order to maximize impact in 2020. Political marketers can study the winning playbooks, project how to use it for the next cycle, and make educated, yet bold, conjectures about how to differentiate campaigns and go the distance.
Let’s unpack what worked to get campaigns across home plate last season.
Programmatic Makes the Big Leagues
Programmatic ad budgets accounted for 60% of overall digital ad spend from political clients.
This was a 14% increase over 2016 allocations from Centro’s clients and is a stark contrast to the diminishing amount marketers buy directly from publishers. Even as recently as 2012, direct buying on key local and national digital publishers was a significant part of the overall game plan for proliferating a message among voters in specific parts of America. Now, only 16% of overall digital budgets are being funneled to this tactic.
Programmatic buying offers the speed and agility to launch campaigns, optimize on the fly, and still reach precisely-targeted audiences wherever they consume media. This was heavily utilized during the election cycle, especially as audience attention fragments across multiple digital channels and platforms. It’s a tactic that plays well for politics, where the people who operate in the space want the ability to score runs by moving ad money fast and diverting it quickly to the highest performing media.
Digital Video Remains an All-Star
Video ads made up 56% of digital media political spend.
Political campaigns have long loved the emotional impact of video. As a result, not much has changed in how the elections generate massive levels of TV ad spending. What is changing is how campaigns are complementing and extending TV buys by reaching voters with video ads on digital devices. Hulu and YouTube, which garnered sizable political ad budgets, illustrates how digital video ads can be utilized in powerful ways.
Furthermore, programmatic is a tactic often used to extend targeted digital video impressions across the board. While programmatic hasn’t opened the floodgates of TV budget shifting to digital, it has demonstrated how candidates gain the upper hand by delivering the emotional impact of video with precision targeting and cost-efficient pricing.
Another factor making an impact on digital video is the increasing availability of high-quality video through programmatic channels, including connected TV inventory. The potential win-win of CTV is undeniably appealing: the big-screen impact of television, in a non-skippable “forced viewing” environment.
Hyperlocal Hits a Home Run
Marketers have leveled-up the local advertising strategy.
“Hyperlocal” programmatic tactics, which utilize location pattern data to target residents of a district, or to geo-fence events and/or polling locations – were used by more than 55% of candidate and ballot campaigns using Centro’s technology.
In previous election cycles direct buys on local media sites played a much more prominent role for Centro’s clients. In 2012, 57% of political spend in our system went to local site buys, but that dropped to 30% in 2014, and then lower to 17% in 2016. In 2018, local site direct buying represented just 10% of political spend. This shift in the campaign playbook is largely due to programmatic technology and the available of high-quality local targeting at scale.
Leveraging Utility Players
As digital devices continue to multiply, so do the options for campaigns to reach audiences across platforms.
Political marketers, now more than ever before, have comprehensive options to create multiple touchpoints with a voter. This is due to a myriad of factors coming together, namely the explosive growth in programmatic advertising, improvements in device maps, and the availability of cross-device targeting. While serving ads on desktops and phones still dominate in programmatic ad spend, we experienced significant overall spend shift in connected TVs and tablets, which were virtually nonexistent in programmatic channels during previous election cycles.
One data point to watch is the impression-to-spend ratio, where the dollar multiple on connected TV ads is much higher than other devices, because of premium CPMs in this format. As CTV scale continues to grow, we’re watching to see if costs decline—or if increased demand keeps them relatively high.
Get Your Scorecard; Know the Players
Despite the rapid ascension of programmatic as a priority in political marketing strategy, site-direct buying still garners a significant amount of digital ad dollars.
Top non-programmatic vendors for Basis political campaigns in 2018
Among ad vendors, Facebook, YouTube, Hulu and Pandora were standout players. This was partly due to their limited inventory availability via the open exchanges, and the specialized targeting offered by some. But another significant factor is the sheer national reach they offer in every voting market and desire for premium reserved inventory.
The revenue share drops off a bit after the top tier, but it is made up of the most visible brands in news and media, such as Fox News, Washington Post, Spotify and CNN. Honorable mentions go to local newspaper publishers such as The McClatchy Company, tronc, Gannett, The New York Times and Lee Enterprises, that are closely chasing the top national media organizations on this list. In the middle of those newspapers is a bit of an outlier – ESPN.
If media players want steady site-direct sales, they should enhance data-centric audience targeting and improve speed to market. A compromise may be to shift to more private marketplace products or increase use of second-party data.
Leave it All on the Field
More than half of the advertising dollars spent in the 2018 elections were used during October and the November days leading up to the election.
On Centro’s Basis platform, we saw that 53% of political dollars in 2018 were delivered in the last five weeks before Election Day, and an astounding 21% of ad dollars spent in the last 10 days. It is common for political campaigns to spend more on the home stretch for many reasons—some strategic, such as GOTV (Get Out The Vote) efforts, and some practical, like late fundraising.
Digital media—and particularly programmatic buying – is uniquely suited to handle the need for speed and scale that is affected by rapid budget fluctuations. This activity isn’t caused by last minute discounts. Quite the opposite, the flurry of ad volume in digital media creates higher demand and more competition for the buyers. The upside is that supply is meeting the down-to-the-wire demands of the market.
Bring in the Closer!
Among the 300+ campaigns that utilized Centro’s Basis platform for the 2018 U.S. elections, 73% had winning outcomes.
Centro’s mix of technology and services provides our clients with a digital media home-field advantage in driving voters to take action. Centro’s experience has been honed by more than a decade of work with political and advocacy groups in races throughout the nation, encompassing candidates at all levels and local ballot measures. Our activity during the 2018 election season gives us a wide breadth of knowledge and a diversified view on successful campaigns using digital media.
Post-game analysis
How the game may change in the next two years.
Here are our predictions for 2020 campaigns:
But nothing stays stagnant in politics or in digital media, and an X-factor could still emerge that impacts the next elections. Keep your head in the game.
About Centro
Centro is a global enterprise-class software provider for digital advertisers. Our technology, Basis, is the industry’s most comprehensive and automated digital media management platform. Through a single user interface, Basis converges the entire advertising workflow, enabling marketers to plan, buy, analyze and streamline campaigns for programmatic, direct, search and social. By unifying all major aspects of digital media into one platform, Basis breaks down silos, improves performance, and helps businesses grow profitably.
For more than a decade, Centro’s technology and services have been trusted by agencies and consultants in politics, public affairs, and advocacy. Throughout the years, Centro’s Candidates + Causes group has collectively worked with 700+ political campaigns and independent expenditure committees, and 800+ issue advocacy advertisers. Our proficiency for driving perception in government, in the public sphere, or among specific audiences is a differentiated and valuable asset in this field.
Centro is headquartered in Chicago with 40 offices across North America, including a Washington, DC, hub for its Candidates + Causes team.
This infographic breaks down portfolio bidding as a PPC optimization strategy. This is the modern approach to SEM optimization that looks at shared data across a group to make decisions that benefit the whole portfolio, rather than any individual keyword within.
Google Ads documentation provides a high-level view, but may not answer the questions you have about the value of this type of bidding methodology.
Take your paid search campaigns to the next level with this optimization strategy. Learn more in our other blog posts on portfolio vs keyword bidding, or in our new eBook: SEM Optimization Techniques: Are You Overpaying Google?
To download this infographic, click here. Alternatively, if you'd like to request a demo with us and discuss all the ways we could drive improved SEM performance for you, get in touch here.
Search engine marketing is constantly changing. Adwords went through a major overhaul last year, rebranding as Google Ads in the process. As Google introduces new tools and features, advertisers must change their PPC strategies and adapt. That makes it hard to keep up with new ways of approaching SEM every year.
One big shift has been an increased interest in PPC automation technology. But PPC automation tools aren’t just the latest trend. They’re a solution that can help advertisers follow the future of paid search without having to reinvent their strategy time after time. More businesses are investing in PPC automation technology than ever before. We take a deep dive into why it will soon be essential for all search engine advertisers.
Most advertisers today know that it’s possible to automate certain SEM tasks using Google Ads or third party tools. But few realize the true scope of possibilities with PPC management tools. Here are some of the applications of PPC automation technology today:
Bid management is probably the most valuable feature of PPC automation technology. Setting the right bids for individual keywords is a huge undertaking that requires constant adjustments. Your ideal cost-per-click (CPC) can change based on a number of factors, including business goals, demand, competition, and other market changes. Managing bids manually is basically impossible when dealing with larger accounts.
Google Ads offers automated bidding features for search and shopping advertisers. Choose Smart Bidding for your campaigns and Google will automatically calculate bids for you at the keyword, ad group, or campaign level.
They offer 5 different automated bid strategies to choose from based on your specific business goals:
Google PPC automation technology can analyze important data in real-time, including device, language, operating system, time-of-day, and other factors. It then uses machine learning technology to set the right bids to maximize your goals.
Third party PPC automation tools are another option to make bid management even more precise. For example, QuanticMind can factor all important business data into bid calculations, such as internal metrics, LTV data, and historical performance. By fully utilizing your relevant data to calculate CPC, it ensures you only spend the minimum necessary budget to reach your business goals.
Creating targeted ad copy is one aspect of PPC management that needs human input. In order to drive clicks and conversions, you need to come up with a unique headline and subheadline that speaks to your target audience. But manually creating ad copy becomes a challenge for businesses with large or quickly changing inventory. Automating the process can save time and help you identify the most effective ad copy for your products.
IF functions allow you to insert or change an ad message when certain conditions are met. You can use this Google Ads feature to automatically tailor your ads to be more relevant to your audience. For example, when showing ads to people who’ve abandoned their shopping cart, you can use an IF modifier to increase a discount, encouraging them to finally convert. Here’s what it would look like:
Google AdWords scripts are an option to make automatic changes to your account using JavaScript code. There are lots of ways to use scripts for bidding, reporting, alerts, and more. But you can also use scripts to automatically create ads from product information stored in Google Sheets.
Dynamic Search Ads are a special Google Ads type that can automatically create ads for you based on your website content. With this option, Google crawls your website, then matches your landing pages to closely related search terms. When someone’s search query is relevant to your product or service, Google Ads will dynamically create an ad with a relevant headline. Google now allows you to expand your dynamic search ads to provide deeper messaging that focuses on what consumers care about most:
Advertisers can further rely on automation to optimize their ad creative as well as create it. Google Ads’ option to automatically rotate different ad versions and identify which is most effective is a good example of this.
It’s also possible to use artificial intelligence (AI) and machine learning to generate effective ads. Google’s AI PPC management solution automatically suggests ways to improve ad copy based on prior campaign performance. Ad suggestions are made based on headlines, descriptions, extensions, and information found on landing pages:
Identifying performance issues and quickly fixing them is one of the most important aspects of a PPC manager’s job. But it’s impossible to monitor accounts 24/7. Some issues are bound to pop up that you can’t address quickly enough to avoid some campaign performance issues.
Automation can help with this by detecting issues and pausing problem campaigns or ad groups when necessary. It’s possible to do this using AdWords scripts. You can create a script to regularly analyze an account’s performance then email the manager if it differs from expectations by a set percentage.
PPC automation technology like QuanticMind offers an even more sophisticated solution for this. The technology infrastructure looks for data issues by comparing key metrics to forecasted performance, such as cost, revenue, clicks and CPC. If necessary, it will automatically pause bidding until the issue is resolved or corrected.
Accurate PPC forecasting is essential for securing necessary budget for your campaigns. And knowing how potential strategy changes can impact performance allows you to better allocate budget spend. Creating accurate forecasts is no easy task, though. You could do it manually using spreadsheet data, but it’s time-consuming and gets outdated quickly. Accurate forecasting requires you to constantly update your projections with the latest market and competitive data.
PPC management tools can help with this. Google Ads can help you automatically create performance forecasts using Keyword Planner. All you have to do is upload your keyword terms and it generates a detailed report of potential clicks, impressions, cost, and more.
Bid automation software can also help you automatically create accurate performance forecasts. QuanticMind, for example, creates reports predicting performance up to 100 days in the future. This automatically incorporates all relevant information, including historical performance, seasonality, and bid landscape data. Using PPC automation technology to create forecasting reports helps ensure they’re always up-to-date. Then you can make informed bidding decisions based on the latest data insights.
Most new PPC technologies are designed to address a few problems with your advertising strategy. PPC automation technology is different because it does more than address a pain point. It reinvents how we approach search engine advertising today.
The benefits of PPC automation tools are many, but ultimately it boils down to how it helps you achieve business goals. Here’s why automation is the future of paid search:
There’s no denying that all worthwhile marketing automation tools help you save time. PPC automation technology can save time on both mundane tasks and complicated ones. Anomaly detection and alerts help you spend less time monitoring performance. Bid automation helps you identify performance-enhancing opportunities and act on them. Ad creation technology saves you time creating and testing ad copy. The list goes on.
PPC automation tools are the future of paid search, but not at the expense of marketing managers and their teams. Automation serves as a strong supplement to their efforts, freeing up more time and manpower to focus on new growth and optimization opportunities.
Automation helps you make more precise and accurate campaign decisions based on your unique business goals. And when you use machine learning technology to analyze your business data, it can uncover novel connections to improve audience targeting and performance.
Automated forecasts is another example of how automation can drive insights to improve campaign performance. Most advertisers must rely on experimentation and monitoring results to know how their targeting and bid decisions will perform. Real-time, data-driven forecasting can help you project how various strategy changes might impact performance down the road. This ensures you always make smart decisions to improve campaign efficacy overall.
Anomaly detection can help you stop problems in their tracks before they have the chance to hurt your campaign performance. This ensures you’re never spending budget when you have issues with campaigns, ad groups, keywords or targeting.
More efficient bidding can also help you reduce wasted ad spend immensely. Advanced bid optimization technology uses algorithms that consider the value of each keyword in meeting your set advertising goals. This ensures you only spend the minimum necessary amount bidding on keywords to meet business goals overall.
A combination of saving time, improving campaign performance, and reducing wasted ad spend provide unique opportunities to pursue new growth initiatives. Instead of focusing your energy on maintaining campaigns, PPC automation technology allows you to spend more time and money exploring new audience targeting, ad types, or marketing platforms.
As more and more advertisers start using PPC management tools, it will become necessary to invest in them to keep up with the competition. It doesn’t matter even if you have a whole team of data scientists working to find PPC performance opportunities. Artificial intelligence and machine learning technologies have more computing power to derive quick insights and improve performance. Taking advantage of AI PPC management tools will soon be necessary to stay competitive and scale your strategy overall.
Technologies go in and out of style, even in the world of PPC. Advertisers are already using automated rules and scripts less and less thanks to the features of AI PPC management and automation.
That doesn’t necessarily mean that five years from now everyone will be using dynamic search ads and Smart Campaigns in Adwords. AI PPC management tools are also changing fast, so advertisers can expect many new and better ways to use automation in the near future. What remains true is that automation is the future of paid search, no matter how it evolves in the long run.
New technology has made the consumer journey anything but linear. From finding a prospective car buyer in the market for that new SUV to the middle-aged parents looking to refinance their home, the keyword and geotargeting tactics that once worked are no longer enough to drive conversions. While advertisers continue to adapt across digital channels, paid search may be the most primed for innovative tactics powered by machine learning that drive both efficiency and efficacy.