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Your customers’ digital journey is a gauntlet. From the initial search to the ad click and all the way down to payment, each checkpoint whittles through all but a dedicated minority. In SEM, we often encapsulate these customer checkpoints discreetly into what we call the “Conversion Funnel”. Lots come in the top and few go out the bottom.
The Conversion Funnel is a useful conceptual model only in that it serves to maximize what goes out the bottom. But any purpose short of that is misguided - it doesn’t matter how many people are interested in your applesauce if no one buys your applesauce. As with any model, though, we have to operate under some educated assumptions. In SEM, we assume:
These beliefs are obvious (and generalizable to all sorts of fruit sauces, and beyond). And they’re the same ones, of course, that all marketing is founded upon. Nobody is going to give you money for anything if they don’t first have the intention to do so. Thus, marketing aims to create that intent in someone and nurture it until they actually give you money. There are, however, some pitfalls lurking in these assumptions. They can be expressed as questions of measurement.
Where everything is quantifiable, as in SEM, these complications double down. A number measured correctly by the wrong metric is tantamount to a number measured incorrectly. And since how you measure determines how you optimize, you might peddle insistently to someone who prefers mango chutney, while neglecting the person who doesn’t know that your applesauce is the cure for what ails them. Misapplying the Conversion Funnel comes at an enormous opportunity cost.
The first step in effectively applying the Conversion Funnel to your SEM program is understanding the traits of the various points along it. The Upper Funnel is the wide-brimmed top half of the upside-down pyramid where potential customers enter. The Lower Funnel is the half towards the pointed tip at the bottom where your company ultimately monetizes those customers. In terms of their usefulness as points of measurement, the two halves of the funnel have inverse tradeoffs.
Upper Funnel Metrics:
Lower Funnel Metrics:
*Note: “Monetization Rate” is defined as the percentage of the time a potential customer at a particular conversion point eventually monetizes
As you move from the top of the funnel downwards, the number of potential customers tapers while their presumed interest hones. For example, more people are going to fill out a web form to be contacted about applying for a loan than are going to actually apply for loans. But a greater percentage of the latter group will actually end up enrolling in loan programs than the group that has so far only filled out a contact form.
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 100). Similarly, one ‘Loan Application’ averages 20x the predictive power of one ‘Contact Form Fill’.
On the other hand, the absolute volume numbers can be viewed as a barometer of the statistical significance of each conversion point: the bigger the sample size, the more likely that information is to tell us something about the future.
For example, let’s say a keyword has 100 clicks. On average, I expect 10% of clicks to become a ‘Contact Form Fill’ and 0.5% of clicks to mature to ‘Loan Applications’. If we see that said keyword has just 5 ‘Contact Form Fills’, it’s probably a below-average keyword. If, however, we instead see that this keyword has 0 ‘Loan Applications’, can we confidently say the same thing? We can only reasonably expect 1 ‘Loan Application’ for every 200 clicks. So, if we’re using this conversion point to measure the worth of that keyword, we’re going to have to wait until we get more clicks before we draw any conclusions. Responsibly untwining noise from signals like this is especially critical when we start to think about optimization, as significant data volume is a prerequisite to all but the most sophisticated bidding solutions.
Now comes the predicament. Assume the below keywords both benefited from the same amount of ad spend input to achieve their historical results. Which of the below keywords would you invest more money in today? Which is more likely to bring you back more money in the future?
Don’t wrack your brains too much - there isn’t an obvious answer. Doing the simple math of applying their monetization rates would suggest that these two keywords are not terribly different in value. Distinguishing further between these two keywords with any significance would require machine-learning algorithms with access to deeper contextual data.
Now let’s see how our decision-making would change if we were only to look at one metric or the other. Bear in mind that these two keywords have already revealed themselves to be of roughly equal value.
If you were to size up the success of your program using only ‘Contact Form Fills’, you would invest more money in Keyword B. You might even invest twice as much in it, ignorant of the fact that it likely won’t result in any more revenue than Keyword A. When we instead evaluate our program against only ‘Loan Applications’, the story flips.
So, if we were looking only at ‘Contact Form Fills’, we’d divert our entire budget to Keyword B. And if we were only looking at ‘Loan Applications’, we’d drown hefty sums in Keyword A. How do we reconcile that?
Well, it’s not quite the landslide that the ‘Contact Form Fills’ would have us believe. Only when we take both conversion points together do we realize that - even though they might average out to 1% or 20% in aggregate - the Monetization Rates can differ wildly across more granular program segments. Some keywords are naturally more conducive to generating higher-funnel conversions but nothing more, while other keywords might drive better advancement deeper into the funnel. In the end, the ‘Contact Form Fills’ and ‘Loan Applications’ tell a story of two similarly lucrative keywords.
To illustrate these keyword-level disparities, let’s return once more to our table, this time adding the bottom-funnel ‘Program Enrolls’ back into the picture. Remember that these are the point of monetization in this example. And since we’ve already established that these two keywords have similar chances at revenue, we shouldn’t be too surprised to see that manifest in their ‘Program Enrolls’ totals.
While the average monetization rates for the entire program are 1% and 20% for ‘Contact Form Fills’ and ‘Loan Applications’ respectively, these keywords are just two of many that go into that average. In actuality, Keyword B is great at getting potential customers just over the doorjamb while Keyword A caters to a more committed bunch.
While this may seem an extreme example, it’s hardly far-fetched when you consider a program with thousands or even millions of keywords. In the most simplistic case, consider two keywords with the same number of ‘Contact Form Fills’. Should we treat them equally? Does our answer change if we happen to know that one has a few more ‘Loan Applications’ than the other?
Each and every keyword is unique: 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. The goal of any optimization is to react to that.
Learn how to create Hybrid Conversions for a more revenue-oriented optimization in “Hybrid Conversions, Part 2: How You Can Utilize the Whole Conversion Funnel”.
Data is every marketer's best friend. There was a time, not long ago, when "advertising" simply meant utilizing the creative side of one's brain to come up with interesting spots that would pique audiences' attention. That's certainly no longer the case. Today's advertising is driven by data, analytics, and everything marketing experts can use to truly get to know their consumers.
This is why programmatic advertising has taken the lead with successful companies that know how to get their brands in front of people who will take notice.
Programmatic advertising employs the use of software to determine optimal ways marketers can utilize their digital advertising real estate. Essentially, machines buy ad space because they understand buyer behavior, so they're able to put ads in front of the people who are most likely to pay attention to them.
The machines that drive programmatic advertising understand where your customers came from, how they found you, and which keywords or ads were deemed most relevant. It's all about embracing the power of data science to learn how to better target your campaigns. Sure, humans are pretty good at managing data analytics, but when you put machines in the driver's seat, there's an entire world of digital behavioral data that will allow you to personalize messages in completely new ways.
For starters, programmatic advertising is effective. You don't need humans monitoring your campaigns because the robots do the dirty work for you. Efficient ad buys are more cost-effective, which means you're not only making money — you're simultaneously saving money, too. Machines don't get tired; they don't take vacation days or need time off on the weekends. They're always "on," which means you can enjoy the benefits of real-time ad bidding, no matter when the optimal time occurs.
Programmatic advertising enables you to answer your customers' questions before they even ask them. Thanks to the machines that are constantly scanning your audience's behaviors, you're now able to place ads directly before consumers who are looking for the exact solutions your company offers in real time. In return, you'll receive a virtually limitless set data that helps you home in on various segments of your audience with more personalized messaging.
Real-time behavioral data is a beautiful thing. The more data you have, the more individualized you can make your campaigns. Programmatic advertising enables you to reach a wider audience with more personalized messages.
Let's say a woman was browsing your site for designer shoes. Thanks to programmatic advertising, you'll know the price range she considered reasonable based on the shoes she viewed. You can also derive other essential information, such as:
Now, you have a lot of information that can be used to build a detailed buyer persona. The women who fit this persona are more likely to respond to certain targeted messaging than a retired male who's shopping for shoes to give his daughter for Christmas.
Since programmatic advertising takes into account the entirety of consumers' behaviors, it would be able to distinguish these two subsets of shoppers based on the way they interact with your site (and others).
Despite programmatic advertising's dramatic impact on the marketing world, there remains a major disconnect between media buyers and marketers. Media buyers have built the greatest understanding of this technology, while many marketers don't even know such a tool exists. This disconnect causes certain obvious obstacles, which can be overcome with the right strategies in place.
Twenty-nine percent of organizations claim they're not using programmatic advertising because they don't have a staff in place with the skills to use it properly. Obviously, since only 22 percent of marketers are using programmatic advertising, there's a huge opportunity for education to bridge this gap. Increasing awareness of this option means more people could begin to gain the skills necessary to execute successful campaigns.
Often, it's not that organizations don't have money available to allocate to programmatic advertising. Instead, they don't see the value in this tool when budgets are being created, so they allocate those funds elsewhere. The initial costs can seem intimidating to decision makers. Yet the increased efficiency and improved ROI, resulting from better-targeted campaigns, have early adopters of this technology experiencing strong competitive advantages in their markets.
Media buyers and sellers in the digital space have traditionally maintained human-to-human relationships. Some companies fear the integration of programmatic advertising would harm the existing trust between these two entities. To overcome issues related to transparency, both sides of the buying universe will have to work together.
Centro is where outstanding programmatic advertising begins. If you want to enhance your audience's experience while targeting the people who are most likely to pay attention to your brand, learn more about Programmatic Advertising with Centro.
PPC bidding optimization has come a long way over the years. The bid calculation process for at-scale programs has many moving parts and can be intimidating for data scientists, let alone the day-to-day users, managers, and stakeholders in paid search programs.
Bidding calculation in the modern era has many flavors, but we’re interested in the best and most optimized version; the type of process that is designed to drive peak performance. Our new Guide, Machine Learning Powered PPC Optimization, walks through both the prerequisites of a thorough bidding process and the bid calculation stages that modern SEM optimization tools are utilizing to unlock the most from large PPC programs. Its focus is on machine learning PPC.
The infrastructural foundation to the best possible bid calculation starts with the data architecture to capture brand interactions as a series of events happening in real-time. The system must be flexible enough to ingest all of the data sources that track, measure, or influence the customer journey.
Once that infrastructure is in place, that data can be utilized for execution; for action; for the modeling and calculation that turns a data set into dollars.
Our new guide to modern bidding, using QuanticMind’s calculation stages as a model and exemplar, explains the concepts and process of fully optimizing PPC bid management. The optimal bidding platform leverages the latest advances in Data Science, including machine learning algorithms, Bayesian modeling, predictive performance methodology, and natural language processing, to optimize SEM performance toward specific business goals.
There are six high-level steps.
One: Understand and Estimate Keyword Value
Stage one looks at the modeling that estimates the dollar value of each individual keyword. This process involves ingesting revenue data from any conceivable source and applying that back to the keywords in such a way as to estimate a dollar-value for each. This involves powerful machine learning models generating revenue-per-click graphs, and results in a dollar value that can be used in later steps to calculate optimal PPC bids.
When keywords lack sufficient data to make a meaningful model of the potential value, deep learning text recognition models are used to map semantically similar data-rich keywords to data-poor keywords. As a result, even low-click or low-conversion keywords still get the most accurate possible value assigned.
Two: Understand Click and Cost Elasticity
Stage two aims to understand click and cost responses to CPC changes, with the goal of generating a map of costs and the expected volume. This is where Bid Landscape Data from Google is highly useful, and applied at scale to an optimization practice.
Three: Calculate a CPC that Promotes Your Goals
Stage three combines the estimated dollar value determined from stage one’s artificial intelligence-powered PPC calculations and the cost ecosystem analyzed in stage two. The decision engine applies the advertisers targets, bidding strategies, and goals and then runs through and selects the best bids to maximize performance, given the data, calculations, and goals. Often, a Portfolio approach is used to bid against a target while maintaining an efficiency metric. This is the modern approach to PPC bid optimization that most bid management tools utilize - if they’re designed for medium to large SEM programs. QuanticMind differs in some ways from legacy tools, discussed further in the Guide.
Four: Calculate Bid Adjustments
Stage four repeats nearly the same process completed in the first three steps, but on a different set of data and with a different purpose: calculating and automatically applying bid adjustments. QuanticMind’s model shines at this point, using machine learning to optimize bid adjustments at scale. Device Bid Modifiers, Geo Location Bid Modifiers, and Audience Bid Modifiers can all be automatically calculated and applied, based on their relative successes in the SEM program. The data science algorithms used here are another advantage when attempting to calculate optimized bids at scale.
Five: Anomaly Detection
Stage five moves into the often understated - but highly important - anomaly detection. This is one of several areas where the infrastructure discussed at the top can “flex” its strength. When designed for effective capturing, cleaning, and piping of data from any source, the system provides better data for better execution. However, the opposite has negative effects: when data is missing or seems different than forecasts would suggest is reasonable, the performance can take a hit. Fully optimized bidding platforms prevent these problems by using multiple anomaly detection and issue-prevention steps, ensuring bids aren’t pushed based on bad data.
Six: Bid Push
Stage six is the execution! Push the bids and bid modifiers through the publisher and go live. Data collection is ongoing and fed back into the system. Other uses for more variable aspects of a program, like inventory management or a “maximum capacity of leads” per day or location, can be applied and fed to make decisions even quicker. Ultimately the process is repeated to create a virtuous cycle of optimized PPC bidding.
This guide will walk you through the modern solution to optimized bidding automation. It is a powerful tool to learn the leading process in paid search bid calculation and optimization. It helps paint the picture for how machine learning PPC is actually applied, how well-integrated data feeds the model, and how bid management decision engines step through the process of calculating and pushing bids. It helps answer the question: how can I optimize PPC bids?
‘Ask the Expert’ is a blog series that breaks down the complicated tools, tech, and trends you’ve been hearing about in the trade pubs and around the office. We reach out to our in-house experts to ask the tough questions and turn them into bite-sized Q&As for your reading pleasure.
This month’s topic? 5G. We brought in Centro’s senior director of media innovations and technology, Noor Naseer, to give us the breakdown.
What is 5G?
5G refers to the network that mobile data passes through. There is no set definition on what it is, in terms of standards and tech specifications. However, the general consensus is that it means data uploads and downloads on mobile devices whole lot faster - The type of speeds that consumers are really going to notice. Think: downloading a whole movie via your wireless network in seconds. With 5G, users could reach 10Gbps speeds. Most U.S. users are using wireless services that are on 4G networks, but major carriers have been busy testing and touting faster technologies.
What does this mean for digital advertising?
Similar to how broadband Internet fueled digital media consumption on desktops, and a subsequent rise in digital advertising, even greater advertising opportunities will be seen in apps, media and advertising on mobile devices. I see the most potential in improved ad quality and delivery. One aspect of this is the enhancement of location capabilities. Higher data speeds, better sensors, plus continued improvements in the geo-capabilities space, will give marketers very precise views of the user. The rollout of 5G will enable seamless sourcing and delivery of location-based data to millions of devices simultaneously. Another exciting potential is high-resolution video ads. Expanded capabilities under 5G include 4K video, with dynamic and personalized messaging, served in real time. More complex video ads will play with little to no latency issues.
Does 5G affect programmatic advertising?
The most significant foreseeable impact will be the serving of video ads on mobile devices through programmatic buying. Opening up the opportunities to serve video ads that render fully, are viewable, and are measureable, will drive marketers to spend even more significantly across this channel. As the demand increases, publishers will utilize more of these types of ad units, and DSPs will see more and more impressions from their supply-side partners. The bidding mechanism behind programmatic channels wouldn’t see significant change, as most major DSPs are already evaluating opportunities and churning through real-time bids at a rate of billions per second.
Beyond paid media, how could marketers capitalize on 5G?
A benefit with evaluating 5G at this point is that the possibilities are vast. The opportunities are whatever creative minds can think up. And it would rejuvenate ideas that were challenging to implement previously. We all remember the widely popular, mobile-driven, Pokemon GO that utilized augmented reality. I see 5G emboldening marketers to think and develop more of those types of VR, AR and XR (extended reality) experiences for their customers. 5G makes it more seamless to offer greater immersion and engagement to users.
What am I not thinking of when it comes to 5G?
It comes down to cost for the user. Broadband Internet on desktops took off because the costs were manageable, or it was bundled into other services (enabling the ease of adoption). Initially, 5G is looking like a premium upgrade, where it is an add-on cost to an already premium mobile plan, or extra equipment is needed. There will be early adopters, but there may not be scalable audiences for marketers to reach. Worth noting is that broadband started as a premium service. Millions of consumers at the onset of broadband had lower cost options (dial-up, anybody?). So, history shows that so-called “premium services” in the present could morph into widely-popular essentials for the normal consumer, especially as more companies enter the market and release competitive offerings. Chances seem likely that 2020 will be the debut year of many exciting opportunities with 5G.
Adwords bidding strategies have changed a lot in recent years. PPC advertising has always been keyword-focused, but audience targeting features are now more advanced when it comes to improving campaign performance. Google themselves are even taking the focus away from keyword targeting, rebranding from Google Adwords to Google Ads in 2018. This shift towards audience targeting is great news for advertisers looking to optimize and grow their efforts, especially if they utilize Google’s audience bid adjustments. Here’s everything you need to know about the role audience bid modifiers play in Adwords bidding strategies.
Bid adjustments are a Google Ads feature that can help you optimize performance by displaying ads more or less frequently based on where, when, and how people search. Through performance analytics and audience insights, you may discover that clicks are more profitable when they come from a certain device, location, audience, or at a certain time of day. Bid adjustments let you take advantage of these insights to target the most valuable clicks for your business.
Types of Google Audience Bid Adjustments
Bid adjustments (also known as bid modifiers) make it possible to increase or decrease your bids based on certain circumstances you choose. With Google Search Ads, there are several types of bid adjustments you can use:
Device - Show your ad more or less frequently for searches on computers, tablets, or mobile devices.
Location - Show your ad more or less frequently to customers in certain countries, cities, or other geographic areas.
Ad scheduling - Increase or decrease your bids for campaigns that show only on certain days or during certain hours.
Remarketing lists for search ads (RLSA) - Set bid adjustments to show ads to people on your RLSA lists. (Example: Increase your bid by 25% for people who viewed your website in the last 30 days.)
Interactions - Increase your bid for mobile devices to show call interaction ads more frequently for mobile phone users.
Demographics - Adjust your bid to target potential customers based on gender and/or in certain ranges of age and income.
How Bid Adjustments Work
Bid adjustments are set by percentages. Say you set a max CPC bid of $1 for a campaign, but you discover your ads perform better on desktop than mobile devices. You can increase your bid by 20% for searches on desktop, resulting in a final bid amount of $1.20.
Bid adjustments can be set at the campaign or ad group level. If you set more than one bid adjustment in a single campaign, they are usually multiplied together to calculate an overall bid increase or decrease. Combined bid adjustments can’t exceed a 900% bid increase.
To target audiences within your search campaigns, Google allows you to use several major audience types:
In-Market
In-Market audiences include people who are actively researching products or services similar to yours. These audiences are great to target if your main focus is to drive conversions. Google has a long list of relevant in-market categories to choose from, examples of which include:
Remarketing
Remarketing audiences include people who have engaged with your company in the past by visiting your website, downloading your mobile app, or watching your videos, etc. You can build remarketing lists using snippets of code on your website or app. You can also set rules for which site visitors should be added to the list, and how long they should stay on it.
Affinity
Affinity audiences are built for businesses running TV ads who also want to expand campaign reach online. Affinity audience targeting is a good choice if you want to build brand awareness in the digital space. Google provides a list of curated Affinity categories for advertisers to choose from. Some ideas:
Custom Affinity
Custom affinity audiences are audience categories that are tailored to your brand. Custom affinity audiences are created using a number of factors, including:
Custom affinity audiences are much more specific and relevant than general TV audiences. To illustrate, a running shoe company could target Sports Fans as an affinity audience, or they could target Avid Marathon Runners as a custom affinity audience.
All bid adjustments are valuable to advertisers to improve targeting, performance, and ROI from Google Ads. Audience bid modifiers, though, have arguably the greatest potential when talking about improved performance, especially when used in combination with other bid modifiers such as location and ad scheduling.
Here are some of the main performance opportunities:
They help you target very specific groups of people
Google’s audience targeting options allow you to show ads to very specific groups of people. You can target based on interests, online behavior, what they’re actively researching, where they are located, how they’ve interacted with your business in the past, and more. Targeting audiences with the right combination of characteristics makes it easier to reach the right customers with your advertising message.
They’re highly customizable
There are a number of ways to define target audiences based on specific behaviors and characteristics of your target market. You can use audience targeting to create, exclude or combine different lists in a way that’s infinitely customizable. The only requirement is that the number of people in a list reaches 1000 for 30 days.
They help you prioritize different buyer personas
Most businesses today have several target audiences who would be interested in their products and services. But some audiences are more likely to convert and drive revenue than others. Audience bid modifiers can help you target multiple audiences simultaneously, while also prioritizing the more valuable ones.
For example, say a software company offers a product that is relevant to both freelance sole proprietors and start-up businesses. Their analytics show that when a startup CEO clicks on their ad, they’re more likely to purchase the premium product version. The company can increase bids for this audience to drive more conversions and revenue, while still targeting all relevant audiences overall.
They help you promote niche products at scale
If your ad groups reflect your product categories, then it’s possible to use audience bid modifiers to effectively promote niche products at scale. For example, say a video game company wants to promote a series of sports video games. They want to target gamers as well as sports enthusiasts. It’s possible to use bid modifiers to prioritize promoting these products to gamers who also like sports.
Advertisers who take full advantage of these performance opportunities will find that audience bid modifiers can:
There are many ways to benefit from audience bid adjustments to improve campaign performance, but doing so manually comes with some issues. Most advertisers begin by adding certain audiences to their campaigns at a 0% bid modifier, then monitoring performance. If the audience is more responsive to their ads, then they start increasing audience bids to improve targeting. This strategy is problematic for several reasons:
Improved Performance ≠ Maximum Performance
Say you identify an in-market audience that is more likely to click and convert. You increase bids for that audience by 15%, and drive more revenue as a result. You might think that your optimization work is done, but you really don’t know if that bid adjustment was the most efficient and effective strategy to improve performance. Maybe a 10% bid adjustment was all you needed to drive the same results. Or maybe 20% would drive exponential growth. The only way to find out is to change and test your bids adjustments to discover the perfect strategy.
Markets Change
Audience bid modifiers aren’t something you can research, set, and then forget about. Audiences, competition, and the overall market landscape are constantly changing. It’s possible for an effective bid modifier to start performing poorly over time. Like with most optimization initiatives, you have to systematically monitor the performance of bid adjustments and make necessary changes.
Challenging to Scale
The challenges mentioned above are enough to hinder even the most modest advertising programs from using audience bid modifiers at scale. Now imagine if you’re running a huge account with hundreds or thousands of campaigns. Applying and optimizing audience bid modifiers across campaigns becomes impossible to scale.
Like with most optimization strategies today, the only way to fully benefit from audience bid modifiers is through automation. Right now there are a few different options to automate audience bid adjustments:
Google Smart Bidding
Google’s Smart Bidding is a subset of automated bid strategies that use machine learning to optimize for a set marketing goal. Smart Bidding strategies include Target CPA, Target ROAS, Maximize Conversions, and Enhanced CPC.
Smart Bidding considers audience signals when optimizing campaign performance. It’s possible to add audiences to a campaign or ad group that uses Smart Bidding. This indicates the audience is important for reaching campaign goals.
Here’s how to set up automated bid strategies with remarketing lists:
Google’s Smart Bidding is a great way to apply audience bid modifiers to your campaigns at scale. But the downside is you don’t have any control over how Google uses your audiences to automate audience bid adjustments.
Google Ads Scripts
Another more customizable option to automate audience bid adjustments is Google Ads Scripts. Using simple JavaScript, scripts are a way to programmatically control Google Ads data. There are many scripts available out there, and some can help you automate audience bidding.
Just keep in mind that if you want to make changes to your audience bidding strategy, you’ll have to edit the script.
Automated Bidding Technology
With automated bidding technology, it’s possible to create bid adjustments for all audience types: Remarketing, Custom Affinity, Affinity, and In-Market. The system uses all your relevant business data, statistical models, and machine learning to dynamically adjust bids and maintain optimum performance in real-time. These granular performance improvements maximize the value of all bid adjustments to drive business goals and revenue.
Automating audience bid adjustments is just one step in a complex series of calculations all aimed at minimizing necessary ad spend and maximizing performance:
Automated bidding technology solves problems PPC managers face with precision, adjusting to market changes, and effectively scaling their programs with the help of audience bid modifiers.
Most PPC managers today understand the value of audience targeting as part of Adwords bidding strategies. But if you want to maximize the value of audiences to improve campaign performance, you need to use Google audience bid adjustments to prioritize the most valuable audiences.
Manually optimizing audience bid modifiers can drive performance, but is limited in scalability. Selecting a strategy to automate audience bid adjustments maximizes the benefits while freeing up more budget and manpower to pursue new growth initiatives.
Digital audio should be part of your media mix now, or at the very least, be part of the media mix consideration.
But don’t take my word for it. Follow the data and these points should convince any reasonable ad buyer:
People are listening to digital formats. This shouldn’t be a surprise because people have always been listening to something – music, live games, talk shows, etc. Audio offers a way to let users consume media either actively or passively. You can drive and listen at the same time. Try reading a web article or watching puppy videos while merging on a highway.
Digital audio formats offer marketers engaged listeners and high share of voice because the users are served only one ad at a time. Furthermore, mobile devices have personalized audio experiences even more. So the signals consumers give while listening to digital audio enables marketers to precisely target the advertising in this format based on their listening habits.
So let’s talk about what you can do.
On our Basis platform, marketers can activate digital audio by buying ads direct from any major vendor or by buying audiences via programmatic channels. It converges digital audio into a marketing team’s overall ad strategy. Users automate direct buying workflow (RFPs, negotiation and IOs) with any vendor for audio AND other ad formats such as video, native, and more.
This is important because:
When media teams are using digital audio ad buying tactics with their overall digital strategy, they are maximizing a full arsenal of tools to operate well-functioning and high-performing campaigns.
Learn more about Audio Advertising with Basis.
If you’ve been running ad campaigns online you’ve likely heard of Portfolio Bidding, and maybe you’re thinking of implementing it across your program. What you may not yet know are some of the intrinsics of Portfolio Bidding, and how this bidding strategy differs from the traditional Keyword-based approach.
This article explores the strategies and motivation behind adopting a Portfolio Bidding approach, why it can be a good idea to set up on your account, and some potential issues that need to be considered when setting up in this way.
Portfolio bidding is a change in the way bid strategies approach hitting the target goals you set. In a traditional keyword bidding strategy, each individual keyword is bid on in such a way to ensure that each keyword hits the target goal. In essence, each keyword is isolated from the performance of other keywords in the same campaigns or account.
For some automated bidding platforms, this can be a limitation. We all know different keywords have different performance, and as such an automated bidding platform may act inefficiently when presented with a goal that is much too aggressive, or lenient for a given keyword. For example, if we are bidding to a Target CPA, a goal that is set too low for that keyword will likely result in disappointingly low volume.
Instead of manually changing targets at a keyword level, or restructuring a campaign in such a way that similarly performing keywords are grouped together, we can instead use a Portfolio Bidding strategy. With Portfolio bidding, the keywords execute in a manner that ensures aggregate performance across all keywords in a portfolio (typically a Campaign or Bid Policy) hits the target goal.
Put simply, Portfolio Bidding gives the bidding algorithm of your choice more options to optimize. To explain this in more detail, we’ll introduce the concept of the volume/efficiency curve. For each keyword in your account, it’s possible to draw a curve representing the relationship between cost, and the expected volume of your target metric. This curve will ‘taper off’ as spend increases, resulting in a lower marginal return for each additional dollar spent on that keyword. In economics, this is called the Law of Diminishing Returns. A given keyword will have a unique curve, which could be subtly or dramatically different when compared to other keywords in the portfolio. Below is a hypothetical example of a keyword’s volume/efficiency curve.
We can see that an increase in spend will correlate with an increase in expected conversions, but each additional dollar spent at higher costs will have less marginal utility than a dollar spent at a lower cost. Portfolio Bidding can promote an increase in volume where one set of keywords drives volume, and another set will provide a much lower CPA to drive the efficiency target. Take a look at the following simple example to help visualize this.
A given portfolio has a single campaign containing two keywords, and the target goal is to drive volume at a Target CPA of $10. The keyword’s volume/efficiency curves are as follows:
As we can see above, Keyword 1 is more efficient at lower costs but tapers off early. Keyword 2 does not have the ‘low hanging fruit’ available at a lower spend but instead has a lower drop off at higher spend levels.
A keyword-level bid policy will likely take this approach to bidding:
When optimizing to the target we can only bid in such a way that finds the highest spend level achieving the target CPA for each keyword. This is inefficient, as the marginal spend on Keyword 1 would have been better used on Keyword 2. As a keyword level bid strategy does not have insight into the performance of other keywords, this is the only option available. This example shows a spend of ~$650 total would expect to see around 65 conversions.
On the other hand, the portfolio bid policy will work in this fashion:
With portfolio bidding we can find the optimal spend distribution across all keywords, to hit our target goal with the highest volume possible. This example shows a spend of ~$600 total would expect to see around 67 conversions. We can see in this example that we can end up with a higher volume of conversions at a lower cost, resulting in improved account performance.
Logically it follows that where possible, our bidding strategies should consolidate on the smallest number of Portfolios. However, we should keep in mind that Portfolio bidding is not a silver bullet for improving the performance of campaigns. Some of the situations in which discretion is advised when potentially switching over to portfolio bidding are:
For most accounts using automated bidding platforms, switching over to a Portfolio-Based Bidding Strategy makes sense. There is a good chance you will see improvements in volume at your target goal across campaigns, as a Portfolio-Based Strategy can greater leverage the characteristics of all the keywords in your campaign, and not just optimize on a keyword by keyword basis.
Most e-commerce advertisers failed to pay attention to Bing Shopping in the past, but things are quickly changing. Bing has made major alterations to their features and user interface to make it more appealing and worthwhile to businesses. Rather than trying to be different from Google Ads, Bing now embraces similarities. They make it easy for advertisers to import their campaigns from one platform to the other.
As an e-commerce business, you have a prime opportunity to reach new audiences with Bing Shopping. If you’re ready to expand your digital marketing strategy, here are nine tips for success with Bing Shopping ad optimization.
Product attributes are an important factor in Bing Shopping. There are a number of them that must be included for your ad to qualify for the Bing Network:
There are also many optional attributes you can include. Some of these optional attributes have a direct impact on how Bing categorizes and displays your products. It’s good practice to optimize your Bing Shopping campaigns by including as many product attributes as possible. You should also consider including:
Implementing all the relevant product attributes can be time-consuming, but it’s advantageous to do so to give Bing the most pertinent information to categorize your products. For example, a shoe retailer that includes optional size and gender attributes - “women’s size 7 US boots” - can make their products much more relevant for queries.
E-commerce advertisers promote a variety of products that drive revenue for their business. Marketers prioritize promoting high-margin products, and this should be reflected in Bing Shopping campaigns as well. Bing offers an opportunity to do this by allowing you to set priority levels for each campaign:
These settings override campaign bid settings. Utilize them the right way, and you can promote all of your products while still prioritizing those that are most valuable. Setting campaign priorities can also help you achieve other goals, such as clearing out excess stock. To maximize the benefits for your business, be sure to set campaign priorities strategically.
Bing makes it easy to set up new shopping campaigns by importing your existing Google Shopping campaigns onto the platform. E-commerce advertisers spend a lot of time optimizing their target keywords in Google Ads while failing to realize that Bing Shopping is a completely separate landscape. You can import your campaigns, but then you must adjust targeting and negative keyword lists to optimize for a different audience.
As a best practice, you should regularly review your search terms in Bing. It can help you to identify important growth areas such as:
Creating your keyword lists and ad groups are not simply tasks for the initial campaign setup. Engage in a constant process of auditing your search terms reports on Google and Bing to reveal opportunities to optimize in the long run.
Generally speaking, you want your ad groups to only include closely related products. Product groups allow you to select products from your Bing Merchant Center catalog to include in specific ad groups. You can organize products into groups using attributes from your catalog feed, namely:
You can also assign multiple attributes to narrow down your group even further:
For example, a children’s toy retailer could create a product group based on brand (Tailspin toys) and the custom label of age (1-5). Using product groups makes it possible to quickly create ad groups and optimize bids for products with certain characteristics.
Your product images are arguably the most important aspect of Bing shopping optimization. Quality images of your product can help your Shopping ads stand out from the competition, improving clicks and conversions.
In April of last year, Bing made it possible for advertisers to add additional images to their product offer feeds. Instead of choosing one key image for your product, you can add up to 11 total images. Take advantage of this feature by showing your product from different angles or with different staging elements. Select one to serve as the featured image for the product and the rest will appear as thumbnails. Images must be bmp, gif, exif, jpg, png or tiff; the recommended minimum size is 220 pixels by 220 pixels.
The Automatic Item Updates feature ensures the availability and price information on your Bing Shopping campaigns always reflects your website. Online e-commerce stores can quickly run out of stock on an item, but still end up serving ads for it. This results in wasted clicks and ad spend.
Automatic Item Updates sync your website’s price and availability microdata with your Bing Merchant Center feed. This way, it automatically updates your inventory data throughout a business day without requiring action from your advertising manager. Employing an automation feature like this can improve the efficiency and effectiveness of your advertising campaigns. Bing will never show ads for a product that is out of stock, and listed prices will always reflect real demand. Based on your needs and goals, you can set Automatic Item Updates to update price only, availability only, or both metrics.
One of the most underused and valuable assets for Bing Shopping ad optimization is custom labels. Labels are attributes that let you organize campaigns, ad groups, ads, and keywords into groups that are important to you.
Custom labels have no effect on how Bing categorizes your products, which is why many advertisers ignore them. Custom labels, though, can help you gain insights and improve targeting in many ways:
Using Bing’s shared labels library, it’s possible to create a single label and add it to different keywords, ads, ad groups, or campaigns. This allows you to create groups that are significant to you across assets, and easily access them using Advanced Search. For example, you could create a shared label called “Holiday promotion” and add it to campaigns relevant to seasonal promotions. Then, to increase the budget for all holiday promotion campaigns, you can filter products based on the label and make the changes.
When you create custom labels based on attributes it’s easy to derive performance insights from them. Say you labeled two different holiday campaigns “Holiday 2017” and “Holiday 2018”. You can run a report to compare the performance of the different campaigns or ad groups/ads associated with them. You can also use labels to tag your keywords as brand name vs generic, or other attributes, then run similar performance reports.
It’s also possible to create automated rules based on your custom labels. If, for instance, you discover generic keywords perform better than brand name keywords from your analytics report, you can create an automated rule to change bids on keywords labeled “generic.” When used the right way, custom labels are a valuable tool to improve targeting and optimize your Bing Shopping campaigns.
Optimizing ads on Bing is an art, especially when it comes to bidding. There are a number of minor changes you can make to your budget allocation that have a big impact on campaign performance. You can use bid adjustments to target specific audiences with your ads and prioritize certain keywords or demographics. Your options for bid adjustments include:
Based on performance insights, you should experiment with different bidding strategies and see which help you reach your audience with relevant Shopping ads at the optimal time.
If you’re advertising in the United States, then you can also take advantage of Merchant Promotions to create more effective Bing Shopping ads. Merchant Promotions are a way for advertisers to highlight special offers in their product ads. These ads have a “Special Offer” extension that display a promotional code.
Merchant Promotions can significantly improve your Bing Shopping optimization by creating a sense of urgency for people to act on the promotion. Special offers help your ads stand out from the competition.
E-commerce advertisers today have a big opportunity with Bing Shopping. If you create and optimize Bing Shopping ad campaigns, it’s possible to reach wealthy shoppers in a marketplace that is much less crowded. Optimizing ads on Bing can be a challenge, though, if you don't approach it strategically. Take full advantage of the strategies and technologies mentioned in this post to get the most out of your Bing campaigns.
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!
Apple Reveals a News Service and Shows Its Ambivalence to Ads [:03]
The announcement of Apple News Plus may have sounded like another win for subscription models and a loss for ad-supported publications. However, the big publishers will not only receive a portion of the subscription fees and increased subscriber numbers—they’ll also be able to run digital versions of their print ads.
Walmart partners with Google on voice-enabled grocery shopping [:03]
In an effort to go head-to-head with Amazon, Google and Walmart are continuing their collaboration to make it even easier for people to add items to a digital cart and order groceries for pick-up or delivery. Powered by Google Assistant, which runs across all Google devices, the new service has been dubbed Walmart Voice Order. Fear not - there is still an opportunity to explore the in-store experience from home!
Kantar Data Reveals Behavior Patterns Of Amazon, Google Smart Speaker Owners [:02]
With continued anticipation in the marketing community for all the advertising possibilities to come from voice search, Kantar conducted a study highlighting how people are commonly using some of the most popular smart speakers, identifying some of the most common uses for Google Home and Amazon Echo, and offering behavioral insights worth noting for future ad strategies.
Domino's Adds In-Car Ordering via Touchscreen [:03]
Domino’s continues its path of pizza innovation—this time, with a connected car app. Connected cars offer advertisers an abundance of opportunity when it comes to location data and brands like Domino's are wading into the space.
Worldwide Streaming Subscribers Surpass Cable Subscribers For First Time [:01]
A new study by the MPAA (Motion Picture Association of America) based on 2018 data found that for the first time ever, more people are subscribing to streaming services online, than traditional cable TV services worldwide. Unsurprisingly, the study also found that transactional home entertainment (DVD and other digital copy sales) are down 5% over last year and subscription services have risen nearly 30% YOY.
Agency ad buyers say there isn’t enough addressable TV inventory [:03]
Despite massive improvements from a technological perspective with buying and targeting households, addressable TV advertising continues to be challenged, due to issues with scale. Only one-eighth of the 65 million U.S. households that could be served addressable ads are actually able to be served. Despite numerous media partners looking to expand offerings including AT&T’s Xandr and NCC Media, they all continue to struggle with the limitation of the two-minute inventory allotment available for addressable ads.
Sizmek Files For Bankruptcy And Faces An Uncertain Fate [:02]
Integrating companies is hard, but integrating them into an ad tech stack seems to be even harder. Last week, Sizmek filed for bankruptcy protection as it looks to work through debt repayment and company restructuring.