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It might be surprising but your keyword list is one of the most important tools behind an effective PPC advertising campaign -- for a lot of reasons. Among other things, you need to have an arsenal of relevant, high search volume keywords to hone in on and directly target the key sectors of your audience. You also need to constantly identify high-value keywords that your competitors have overlooked to stay ahead of them. Succeed at this and your keyword list can help drive marketing goals while minimizing necessary ad spend and maximizing overall ROI as a result. Thus, smart PPC keyword research is non-negotiable.
That said, PPC advertisers need to think beyond the most basic PPC keyword research strategies. Here are seven tips and tricks to expand your PPC keyword research strategy.
Google’s autocomplete feature is both a valuable and underutilized tool for PPC keyword research. Ostensibly, it’s designed to help searchers by suggesting terms related to their search query - and subsequently people click on Google’s autocomplete suggestions because it displays a correct spelling or it’s relevant to what they’re looking for. Thus, PPC marketers who target these key phrases can capture some of this traffic.
You've likely seen this before when you've conducted Google searches - users type in a root keyword or phrase and Google displays a drop-down menu of ways to complete it. Among other things, Google’s autocomplete phrases can help PPC marketers understand:
Ultimately, Google is trying to connect users with relevant content that’s available on the web, so its drop-down menu of autocomplete suggestions is a reflection of the most popular searches.
But what most people (marketers included) don’t realize is that autocomplete is capable of doing more than just finishing a phrase. For one, you can also use it for PPC keyword research in the beginning or middle of a phrase by inserting an underscore, which provides countless more opportunities to discover key search phrases related to your root keywords for PPC.
You may discover that sometimes the query you enter doesn’t bring up any autocomplete results. This is likely because it has a low search volume - that is, not enough people are typing in similar phrases.
If you’re just starting out with PPC marketing, your competitors can potentially be a great resource for PPC keyword research - by learning what search queries are bringing traffic to your competitor’s website. And you don’t have to pay for any advanced PPC keyword research tools to do it.
Google Ads Keyword Planner is all you need to analyze other websites. First, go to Keyword Planner, and click “Find new keywords.” Then you can type in the URL of one of your competitors, or a high authority site you want to emulate. Click “Get ideas,” then it will bring you a database of keyword terms that people are typing into search engines to arrive at your competitors’ site:
You can sort the results of your PPC keyword research by many relevant factors, like average monthly searches, competition, and bid. What you do with this information depends on your strategy - if you plan to compete with this website directly, then these could be keywords to target. If you want to avoid competing directly with them, then you might avoid these keywords in your PPC strategy. You can also enter multiple websites into Keyword Planner to get an idea of how your competition as a whole attracts traffic through Google search.
Your own website can offer just as much valuable information for PPC as your competitors’ websites. Unless your business is brand new, people are likely already using a variety of search queries to access your website from Google search. So don’t forget to run your own URL through Keyword Planner to see what search queries are already bringing you organic traffic.
One thing you can do is look at these existing traffic sources as an opportunity for PPC targeting. Research has shown that when organic and paid search results appear together, it significantly improves average CTR:
On the other hand, you may not want to target these keywords to avoid cannibalization. If your organic SEO is already attracting significant traffic from these search queries, then you should allocate your PPC budget to target other relevant queries that aren’t ranking well.
But both approaches are relevant, and you can discover which one is right for you by trying and testing the performance and ROI of different keyword strategies.
Another way you can mine your own website for audience search queries is using your site search feature. While most businesses offer a search box at the top of their website or on their blog, they very rarely pay attention to the insights it brings for keyword targeting.
Consider these statistics:
People who visit your website and use the search feature are looking for something specific, and as a result are much more likely to convert than passive site visitors. The search queries people use on your website can be just as relevant when targeting these audiences in search engines as well.
You can start tracking site search queries using Google Analytics. From the side menu, click “Admin.” Then from the Admin View column click “View Settings.” From there, scroll down to the bottom of the page and toggle “Site search Tracking” to on. Now you’ll be able to track site search queries and over time discover popular keywords that you can target for PPC as well.
One of the first things marketers learn about PPC strategy is to avoid targeting broad keywords. Broad keywords are catch-all phrases that bring in huge volumes of traffic. As a result, they’re incredibly expensive and competitive to target.
That said, a common mistake marketers make is ignoring broad, unspecific phrases altogether when researching keywords, instead focusing entirely on long-tail keywords (phrases with three or more terms). But while long-tail keywords are highly relevant, they often have very low search volume, making it difficult to scale your PPC strategy.
The key to building a long list of keywords with the right combination of relevance, search volume, and competition, then, is starting broad and refining. Instead of ignoring generalized, high competition keywords, use them as a root to inspire related long-tail keywords.
There are a variety of free and paid tools available that are designed to help you brainstorm relevant long-tail keywords using broad keywords as a root -- Ubersuggest and Answer the Public are popular examples.
Your existing blog content is a great place to find inspiration for keyword ideas. Most marketers overlook this resource because their post topics are so specific, and targeting the related long-tail keywords wouldn’t be scalable for PPC.
But a great place to find PPC keyword research ideas is by looking at your blog categories and cornerstone content. Scrolling any given category, you can see whether there’s a lot of material about one subject in particular. You can use this as a root keyword to explore more possibilities.
Type something like “machine learning” into a long-tail keyword research tool like Answer the Public, and it returns a variety of relevant terms that might be worthwhile to target for PPC.
Your list of relevant keywords to target for PPC can grow indefinitely if you allow it. But more importantly than capturing all the right keywords is identifying the ones that are most contextually relevant to your target audience.
Consumers today rely on the internet heavily at different points during the path to purchase, which vary greatly by customer demographics, location and industry. If you want to identify the most valuable keywords, you need to map them onto your customer journey.
Most businesses that incorporate customer journey mapping into their PPC strategy do so to evenly distribute their investment across it. For example, they target relevant keywords evenly across the main funnel stages:
But if you dig deep into consumer behavior insights, you can identify the specific points of the customer journey for which the internet plays an important role for your audience. For example, someone in the hotel industry might discover that most of their audience searches for hotels when they’re ready to book. In this case, they would want to invest heavily in targeting bottom-of-the-funnel keywords that suggest their audience has their credit card in hand (e.g. “Book Chicago hotel” or “Chicago hotel prices”).
Think With Google provides all the data and tools businesses need to make these discoveries about their own audience’s customer journey. Their Consumer Barometer is specifically designed to illustrate how the internet impacts the customer journey — from consideration to purchase.
You can use this tool to answer a variety of important questions about the customer journey, such as: In which parts of the purchase process do people use the internet?
Or how do people use the internet to help make their purchase decision?
You can narrow down the results by product category and location, making them more specific to your industry. Say the majority of your audience uses the internet to compare choices during the purchase process. That’s a middle-of-the-funnel activity, so you can focus on relevant keywords like “[your product] vs [competitors]” or “[product niche] price comparison.”
Search engine queries are all about context. If you run a business where seasonality is a factor, this is something you can take advantage of in your keyword targeting strategy. You can probably come up with some relevant seasonal keywords to target, but you can also dig deeper to discover other opportunities you might have missed.
Google Trends is an invaluable tool for this. Say you’re doing PPC advertising for a Las Vegas hotel. You can type in a very generalized keyword into Google Trends, such as “Las Vegas.” You’ll see that interest in Las Vegas remains fairly constant throughout the year:
But if you scroll down to the bottom of the page, you’ll see a list of related topics and queries that are seeing spikes:
Some of these can potentially be relevant PPC keywords to target, or inspire keywords for future trends. The spiked query “gwen stefani las vegas” suggests that people will search for popular performers when they’re scheduled to perform in the city.
Those waist-deep in PPC campaign strategy know that PPC keyword research is a never-ending task. Markets and consumer needs are constantly changing, and in turn, the queries people use to find information will also change. That said, marketers also need to understand exactly how those markets and consumers are changing. What’s more, like your audience, your business is constantly growing and evolving. And as it does, so will your keyword list. Nailing the right keywords for your target audience is perhaps some of the most fundamental - and critical - research you can do as a PPC marketer. Thus, continually expanding and refining your keyword list is an inherent part of any successful PPC strategy.
There are a multitude of ways to discover new and effective keywords that will boost the quality of your keyword lists and enable you to better target your key audiences - while even reaching new ones. Effective PPC marketers will embrace the challenge.
Gone are the days of smoky, whiskey-infused boardroom meetings, a gaggle of middle aged men talking strategy -- among other things -- seated around a table in starched suits, while well-coiffed secretaries take notes and schedule important client lunches.
It’s been almost 60 years since the “Mad Men” era of generations past, and safe to say, advertising has come a long way, baby.
For starters, an actual eight-hour workday is a rarity. You likely can’t smoke within the vicinity of your building, let alone your office. And like the typewriter and the rotary telephone, the three-martini lunch is all but a faded memory.
But while hit TV shows like Matthew Weiner’s “Mad Men” looked at this era through the technicolor lens of nostalgia, the evolution of advertising has unfolded with more positives than negatives, and its trajectory continues to go up and to the right. Women now comprise around half of advertising agency staffs, while almost 20% hold management or executive positions. Ad agency staffs are also becoming increasingly diverse, with minorities now occupying between a fifth to a quarter of employees.
This rising tide of diversity has helped facilitate a more tolerant work environment that embraces different cultures, races, nationalities, sexual orientations, and beliefs, with less tolerance for sexist, racist or otherwise harmful language that once pervaded office life. Not surprisingly, then, that increased diversity in-house is also mirrored in more inclusive ads that feature a broad array of individuals of all colors, shapes, sizes and ethnic and religious backgrounds.
But perhaps the biggest and most significant change is that “Madison Avenue” may, in fact, be making a shift away from Madison Avenue. Here’s why: Like New York City’s Broadway or Wall Street, Madison Avenue is a district inextricably associated with the industry that occupied it -- advertising -- garnering its reputation from the dearth of agencies that emerged and flourished there dating back to the 1920s. Thus, to refer to Madison Avenue was to refer to the advertising industry as a whole. And in many ways, it still does.
But over the last two decades, the heart of the advertising industry began to shift to Silicon Valley, as digital advertising experienced explosive growth on rapidly evolving new platforms.
Technology giants Google and Facebook have upended the advertising industry by strategically leveraging their users’ personal data and purchasing history while also cultivating highly-targeted sponsored ads in response to user queries.
Today, advertising has successfully made the leap to digital, a market that reached $111.14 billion at the end of last year and is projected to account for 55.0% of total media ad spending in 2019, according to eMarketer. And its growth trajectory is only anticipated to accelerate in the near future. That means the advertising industry is now truly bi-coastal, continuing its path on the West Coast -- and more specifically Silicon Valley -- as it progresses.
To illuminate this transition, we’ve released our latest Infographic “American Advertising: From ‘Mad Men’ to Silicon Valley” that chronicles the evolution of advertising, and some of the biggest changes the industry has undergone over the last six decades.
No doubt, advertising in the days of David Ogilvy and Bill Bernbach enjoys it own storied past. But that story is far from over. The narrative continues to unfold as cutting-edge technologies are developed that enable advertisers to reach new audiences and break into new markets, while influencers and leaders from all walks of life innovate in ways that reshape and redefine the industry. At QuanticMind, we’re picking up the torch and continuing to tell the story as well. And we look forward to the chapters that lie ahead.
By now, it’s no secret that many consider the terms “artificial intelligence” and “machine learning” some of the most overused buzzwords in the industry -- and with good reason. News about their capabilities -- and related use cases -- seems to emerge on an almost minute-by-minute basis. Without a doubt, artificial intelligence and machine learning are the driving forces behind a vast amount of innovation today, with applications that span across a wide range of industries. Thus, it can be difficult to keep up with ancillary terms -- like deep learning -- and distinguish the difference or see where exactly these technologies fit in a larger, digital advertising schema.
Adding to that growing list, deep learning is another term that’s on the rise this year. So what does that mean for you and your SEM program? This article provides a clear picture of the differences, and applications that distinguish machine learning and deep learning, and the unique capabilities they each bring to your digital advertising strategy.
While it’s often used in conjunction, and sometimes interchangeably, with the term AI, machine learning is a subset of AI that comes with a slew of extremely valuable, sophisticated capabilities. In the most basic sense of the word, machine learning is exactly what its name implies: a computer with the ability to learn from whatever task it performs.
Artificial intelligence uses preprogrammed software or algorithms to deliver smart responses to queries and tasks, such as with popular voice assistants like Siri and Alexa. But here’s the distinction: machine learning has the ability to learn and improve at whatever task it’s assigned. Conversely, regular AI can never grow beyond what it's preprogrammed to do. Thus, AI doesn’t include machine learning until the computer has the ability to process data inputs, learn from said data, and make changes to its responses based on related insights.
For example, you have a smart coffee maker that will automatically brew coffee when you say “Make coffee.” What if that coffee maker could learn from the things you tell it? Say you always tell it to “Make coffee” at 7 am Monday through Friday and at 9:30 am on the weekends. Thus, the coffee maker would be leveraging machine learning if it could use this information to change its programming and automatically start making coffee at those desired specific desired times of the day.
Perhaps surprisingly, most people don’t realize they interact with machine learning in their everyday lives. Take video streaming services for example. Netflix uses machine learning algorithms to analyze users’ viewing behavior, tailoring its offerings to the types of shows people like, then leveraging that information to recommend shows to similar users. Similarly, music streaming services like Spotify and Amazon’s recommended products are also examples of machine learning technology at work.
Altogether, there are four main types of machine learning possible today:
That said, machine learning can only serve the same functions for which it was designed. A coffee maker that uses machine learning can get better at brewing coffee and anticipating when to brew that coffee. But it can’t spontaneously learn how to do something else, like automatically order your groceries when you run out. A different AI machine must be programmed to do that. From that perspective, the growth potential of basic machine learning computers is still fairly limited.
Now, enter deep learning.
While deep learning may be a fresh technology buzzword, it corresponds to related machine learning and AI technologies. As mentioned, machine learning is a subset of AI. By the same token, deep learning is a subset of machine learning — specifically, a special way of implementing it that entails various unique capabilities.
At its core, deep learning uses a specific subset of machine learning algorithms designed to mirror the performance of a real human brain. Specifically, it leverages a structure of algorithms called an Artificial Neural Network (ANN), which incorporate three main layers -- the Input Layer, the hidden layer and the output layer -- designed to mimic human neural activity.
Thus, because of these capabilities, deep learning can draw correlations between data points and forming data clusters that inform understanding of the task at hand. Instead of making binary decisions based on basic data inputs, its sophisticated algorithms can draw conclusions from the data.
When applied correctly, deep learning has the potential to solve complex problems that would otherwise require human thought. With its ability to draw accurate conclusions, deep learning has the potential to successfully make difficult decisions that many engineers have tried to master with AI for years.
As we previously mentioned, machine learning and deep learning are not mutually exclusive. However, the algorithms and execution of deep learning equip it with features that are distinct from the rest of machine learning applications.
Here are some key differences:
General machine learning requires more human guidance
General machine learning often involves basic algorithmic processing guided by human input. Data provided is often structured and labled, and the processing task preassigned. A classic example of basic machine learning is image recognition. Google Image Search uses machine learning to identify web images and associate them with relevant keywords. Some images on the web already come with descriptive phrases. Basic machine learning uses this labeled data as a base to identify and categorize images with no description (unlabeled data).
Deep learning, on the other hand, is able to work entirely with unlabeled data, making relevant clusters and associations without being specifically preprogrammed to do so. Whereas a machine learning algorithm needs an engineer to make adjustments when it returns inaccurate results, deep learning has the potential to self-correct.
General machine learning requires less processing power
Even some of the most basic computers are capable of handing machine learning processing. Machine learning doesn’t have to be complex, it can use simple algorithms such as decision trees, random forests, and find-S to draw conclusions and achieve a task. The Artificial Neural Networks that make up deep learning algorithms are a different story. Deep learning involves large volumes of matrix multiplication operations, requiring sophisticated machines with lots of processing power for operation. Computers capable of deep learning also require a graphics processing unit (GPU) -- a specialized electronic circuit designed to rapidly alter memory to accelerate the creation of images and results.
Deep learning offers more nuanced results
The layered algorithm structure of deep learning is what makes it possible to elicit nuanced decisions that are similar to those made by a human. What do people do when presented with new information? They refer back to their previous knowledge and experience to make sense of it. The layers of deep learning function in a similar way. The first layer processes a large data set related to a certain topic, drawing correlations and associations between a wide range of data points. It then uses this “past knowledge” to inform interpretation and action on data presented in subsequent layers.
Here's an example: As mentioned, machine learning can look at a database of images (e.g. images of road signs) and use this information to identify other similar images. The layered structure of deep learning makes it possible to look at images of road signs and cluster them by type (e.g. stop signs, yield signs, speed limit signs, etc.). It can then use this back knowledge to identify blurry or partial images of road signs that regular machine learning might not be able to accurately categorize.
And another: Machine learning can analyze current news and categorize it by type or other factors. Deep learning can developing nuanced understanding of the qualities of current news and use this knowledge to accurately identify fake news designed to deceive readers. That’s something a lot of people can’t successfully discern!
Deep learning requires much more data
As mentioned, deep learning requires an initial layer of data analysis in order to deliver nuanced results. But in order to make accurate decisions, it requires more initial data to analyze than machine learning.
Both machine learning and deep learning have the potential to analyze enormous data sets to inform results. But machine learning is able to make sense of small sets of data as well -- although the smaller a dataset, the more likely deep learning will make inaccurate associations and deliver poor results.
At this point, hopefully you have a better understanding of how machine and deep learning relate to each other and how they differ. That said, understanding the nuances of AI is not required to apply and benefit from these technologies. While AI and machine learning are already revolutionizing a wide variety of industries, deep learning touts its own potential to reshape how businesses and societies approach and execute on critical tasks.
Here are a few of the many current applications:
Cybersecurity
Machine learning and deep learning are uniquely suited to help automate and improve various cyber security processes. For example, computers can learn what normal website traffic looks like and effectively identify malicious traffic. Convolutional Neural Networks can also detect and classify malicious code, which can be incorporated as a critical component of a multi-layered strategy to improve the security posture of organizations.
Self-driving cars
Machine learning is a key component for driver-assisted or self-driving cars. By training algorithms using huge data sets, these machines have the ability to react to potential risks in their surroundings or avoid accidents. These computers are then able to replicate driver behavior based on previous “experiences.”
Improved Healthcare
Probably one of the most impressive applications of machine learning technology is in the healthcare industry, which is already leveraging intelligent computers to help doctors accurately diagnose patients. These fast processing capabilities lead to quicker, better medical care for people, while lowering costs for hospitals.
Marketing
Artificial Intelligence makes it possible to analyze large consumer datasets and drive unique audience insights, and businesses are already using it to discover new potential leads and deliver personalized marketing messages. Taking that up a notch, machine learning can cluster consumers based on demographic data, interests, and online behavior to help marketers identify new potential audiences to target.
Advertising
Deep learning is already helping advertisers optimize their marketing spend and increase the relevancy of their ads, among other things. Deep learning algorithms can also be used to predict long-term advertising performance, and make automated adjustments to bidding strategies based on these insights. For businesses, this means they can automatically ensure they’re only bidding as much as they need to meet their advertising goals.
Other applications include:
Understanding the capabilities of machine learning and its subset deep learning is now more important than ever in digital advertising. Both are integral in achieving predictive advertising performance and automating bidding strategies based on these insights. As its name implies, machine learning can learn and adapt to the tasks it regularly performs. Deep learning takes that a step farther, emulating human brain activity that can not only make decisions based on provided data, but draw logical conclusions that inform next steps.
As technologies become more intelligent, the possibilities for their applications infinitely expand. But while marketers can leverage these technologies for myriad functions such as bidding, targeting, and optimization endeavors, so too can the competition. Thus, it’s likely that in the near future, machine and deep learning won’t be a luxury, but a necessity for digital marketers and advertisers to stay competitive and relevant with their audiences -- paving the way for an even more intelligent and sophisticated set of technologies down the road.
Effectively geo-targeting SEM campaigns is one of those arcane questions with an answer that never satisfies. There may be a precedent to go off of, or a thousand of them, but it feels like a leap of faith anyway. That’s because, of the thousands of people who have tried to answer this question before you, not one of them were wrestling with circumstances exactly identical to yours.
Best practices are notoriously unclear when confounded by a swarm of variables that ensure that no two cases are alike. So, even though it’s all been done before, there’s no one best approach. The ever-unsatisfying answer to the question of how best to geo-target your SEM campaigns? It depends.
It depends on your business model and your audience, on the size of your program and its level of automation. It depends on your resource constraints, on your budget constraints, and countless other things.
The goal of this article is to demystify all of those dependencies, to show you that others have indeed tried this before, and ultimately, help you toward a reliably satisfying answer to the question of how best to geo-target your SEM campaigns.
There are two primary schools of thought for geo-targeting SEM campaigns:
National Geo-Targeting (One Extreme):
Campaigns are only grouped by segments other than location (i.e. product line, device type, network, brand, match type, etc). All campaigns target the entire coverage area in which ads are served.
Local Geo-Targeting (The Other Extreme):
Location is one of the segments by which campaigns are grouped. Each campaign only targets a subset of the total coverage area of your business. For this reason, multiple identical campaigns are required to cover that total area, which means lots of duplicative keywords. Campaigns may still be further subdivided by additional segments (i.e. product line, device type, network, brand, etc).
Little Bit o’ Both (Not Extreme):
As with most everything, not only is there always a middle ground, but that middle ground is almost always where the answer lies. Perhaps you want to have separate campaigns for each metro area in the US, but nationally-targeted campaigns abroad? Or perhaps you want to have metro-segmented campaigns everywhere in addition to national campaigns as catch-alls for what Google can’t attribute to a metro? Perhaps you even want special campaigns only for certain critical categories in certain critical business hubs, like raincoats in Seattle or tailors in DC? Blending national and local targeting allows you to form-fit your program to the diverse reality of your business.
The combinations are endless -- which makes the choice between them all the more complicated.
Simple. It’s one of those words that means one thing and could mean the opposite, depending on the context in which it’s used. In that sense, national targeting is simple. Here are some of the ways in which it’s beneficial to your program:
So far, the benefits of national targeting have appealed mostly to the lazy or, as we call ourselves, the opportunity cost-conscious. These last couple benefits are fun for the chronic fiddlers, too:
And lastly, but importantly…
The disadvantages of national geo-targeting - the ways in which its simplicity is a drawback - are best shown by an examination of its alternative.
Local targeting is more complicated. And that complexity has its own pros and cons, that represents an inverse to the simplicity of national targeting. Here are the pros:
Those increased levers, however, must be weighed against these familiar complications of any highly-segmented program:
National and local targeting both are viable strategies, but paramount to which one you choose is understanding why you’re choosing it. If you know your business model and your goals, your constraints and your markets, then you’re 90% of the way to your version of success. Perhaps your business is inextricable from the places in which it operates, and added complexity could lead to richer relationships with your users. Or perhaps a simpler approach is warranted, giving you the high-ground vantage point to better acclimate to those users’ behaviors. In either case, successful geo-targeting is the scaffolding from which you lay these patterns of interaction with your diverse users, and the foundation to a thriving, pumping SEM program.
In the fast-moving, modern-day world of SEM, digital marketers would be forgiven for dedicating their time to enhancing PPC elements rather than optimizing post-click landing pages. Keyword research, ad design, audience targeting, and campaign creation alone are the subject of great industry debate and analysis. Whatever it is that your business offers, though, it is your PPC landing pages that will do the heavy lifting in terms of converting your traffic into leads and paying customers.
So just how do you get the most out of your online traffic and ensure no conversions are being left behind -- and with them, wasted marketing dollars? Conveying product value, design pieces, relevant messaging, call-to-action rules – they all need to be finely tuned and equally balanced in order for your paid search ads to provide their true value and become a vital pillar in your overarching marketing strategy. Here, we take a look at some simple yet effective best practices that can empower you to drive conversion rates to new levels.
You have probably lost count of the number of times you’ve clicked on a site, only to be immediately presented with information that’s irrelevant to your search. Regardless of whether it’s an ad or an organic result, the experience is frustrating and can leave a bad impression that can be difficult to forget. People who construct these types of webpages often do so under the premise that “you just have to keep reading to find what you’re looking for,” but that is a counterproductive and potentially detrimental approach. What good is that to the user? Why would you want to frustrate your potential buyers? The reality is that by creating more obstacles for users, you risk losing them for good.
The first job of good PPC landing pages, then, should be to provide instant and easily digestible value from the moment they load on the user’s browser. The entire point of marketing is to communicate value to prospects -- while you’ll be sprinkling that throughout their time on your site, it’s critical to do so from the very beginning. Your PPC landing pages have to immediately establish why the user should follow through with their click rather than simply navigating away. This means affirming your credibility, showcasing your unique selling point, and leading them toward your call-to-action (CTA). Keep in mind that you don’t have to bombard them with everything upfront, but the value they get from their initial click has to be recognizable right out the gate.
It is also worth noting here the fundamentality of creating dedicated PPC landing pages for every campaign that you launch. Ideally, you will have different ad campaigns set up that target audiences across various stages of the buying process, each with clear and concise messaging that encourages them to convert based on their needs and timing. These focused ads need to come in tandem with equally targeted landing pages that deliver on the promise you make to them.
Put simply, even uninspiring business products and services need a landing page that packs a visual punch, and this begins with the hero section. Full-screen images have been the modern digital practitioner’s design option of choice over the last few years and for good reason - they are streamlined, easy on the eye, and provide a dominant element that works well in conjunction with main headlines, helping to communicate primary messages quickly and efficiently (alluding to the above). Just make sure that they’re responsive on all devices (desktop, laptop, tablet, and mobile) and optimized for speed.
For those of you going the extra mile and placing a video at the top of your page, be sure that loading times are minimal (borderline non-existent) and that the footage isn’t so striking that it detracts attention away from your key message. When used in this context, videos should set the appropriate tone while avoiding any distracting audio, voiceover, or soundtracks. In addition, be sure to keep it simple, no longer than 30 seconds maximum, and set it on an automatic loop. Whatever design approach you do eventually take, be sure to make the most of the hero section and create enough value so that your visitor converts there and then, or at the very least, continues scrolling to learn more.
Within the design piece, there’s also the lead capture form to consider. A great many studies have been conducted around landing page forms and the general consensus is that the more information you require, the lower the conversion rate. Thus, you need to strike the right balance between the information you need and how many fields you require the user to fill. In general, it’s best to ditch the peripheral information and cut straight to the chase. Asking for “First” and “Last” name? Condense that to “Full Name.” Asking for a home telephone number? Forget it - not important. Asking for a website URL and company name? Surely you can find one from the other. In short, less is more.
Webpages may live forever with a hosted domain, but that doesn’t mean that the information they contain should remain static. Numerous sections of your landing pages should continually be updated for any number of reasons -- information becomes obsolete; new, fresher, statistics are released; links break; and images fail to load. If you are directing users to PPC landing pages with any of these issues, you are putting up a serious red flag for professionalism that could (and probably will) affect your ROI.
If you’re utilizing a landing page for a long-term marketing campaign, then checking in on the coding and the content at regular intervals is crucial for maintaining its effectiveness. Test links, ensure images load and are formatted properly, and continually verify the accuracy of any of your claims. Ultimately, it’s the only way to guarantee that every lead who finds your page receives an optimal experience when they click on your ad.
PPC landing pages created for shorter-term campaigns also need to be monitored carefully. Once the campaign has ended, you need to diligently remove any links from AdWords as well as your internal site navigation. Few things will mar the customer journey more quickly than clicking on a broken link or being redirected to a landing page containing an offer that is no longer valid. After all, let’s face it, when you see a 404, it’s likely that your trust in the brand takes a hit.
It’s easy to understand why marketers become obsessed with CTAs; at their best, they’re simple but persuasive, direct but not outrageous. When crafting a CTA that reliably converts for your landing page, it’s best to consider that the other parts of the page already did the majority of the work to convey your unique selling proposition (USP). The CTA is just there to guide your buyer to the finish line.
It is true that you should direct your user’s attention to one main CTA that jumps out at them when they’re ready to convert. Giving them multiple options can create confusion, which is the exact opposite of what you want when they’ve already made the decision to purchase. Remember, they’ve already chosen to follow the path -- your PPC landing pages are simply lighting the way for them.
However, no matter how well-executed your campaigns, you’re never going to convert everyone the first time around. This is precisely why remarketing exists, and why displaying an alternative CTA for users not yet ready to convert can make all the difference. Including a secondary CTA can help salvage lost conversions and jumpstart your personalized email marketing strategy. Just make sure it’s crafted with a specific goal in mind, whether it is to collect email subscriptions, promote a different product, or anything else, and doesn’t compete with the primary action you’re attempting to execute. While a secondary CTA may exist as a last resort, remember it still needs to be focused and direct in order to be effective.
If your landing page is performing poorly, or more specifically, is performing below your expectations, following these simple tips would be a good place to start in rectifying the problem. While the actual practice and nuances of PPC garner more attention in the industry, your landing pages act as your home base for lead generation and search visibility, and as such, they should never be overlooked. To that end, you should be in a constant internal process of testing every single element to isolate the effect. Is your headline focused? Does your image choice speak to your offering? Are you conveying value from the get-go? Is your call-to-action conveniently placed? Is the messaging relevant?
Test, test, and test again to ensure you are getting the biggest bang for your PPC marketing buck. Each and every ad campaign and content marketing asset serves as a great opportunity for you to be found on search engine results pages. Don’t fall into the trap of doing all the hard work and then neglecting the page that brings you all the conversions. Your landing page is the gateway users will cross to become leads or actual customers, and thus, could be the deciding factor in whether they make that final step, or ultimately move on.
By now you know that meeting business goals is essential for the growth of a company and an important way to ensure you are getting the most out of your PPC program. So it also might come as no surprise if you find your boss breathing down your neck, asking “Why has our ROAS dropped from 150% to 130% over the past month?!” After doing a quick cost-benefit analysis to determine whether or not you can make a comfortable living by leaving your job right now -- and realizing you can’t -- you are now tasked with figuring out why Return on Ad Spend (ROAS) dropped so significantly, and ultimately why your PPC performance is suffering.
But where do you even start?
Ultimately, you need to identify the root cause of the issue. Your program might contain hundreds of campaigns, with thousands of ad groups, and tens of thousands of keywords or product groups. With so many avenues to explore, how on earth will you identify the root cause of PPC performance? We’ve provided an easy-to-follow guide.
Starting out, you’ll want to treat your root cause analysis like you would your PPC account structure. First, identify poorly performing accounts, followed by campaigns, ad groups and finally keywords/product groups. By following this structure, you’ll be able to thoroughly identify poor PPC performance impacting almost all aspects of your SEM program.
More often than not, you’ll find your campaign structure follows some variation of the 80/20 rule; 80% of spend will come from 20% of campaigns.
Clearly, it’s not the best use of your time if you find yourself spending countless hours exploring bad PPC performance in campaigns that make up 1% of your total spend. Instead, look at the PPC performance of each campaign, sort them by descending order of spend, and pick out those with the poorest PPC performance. Filtering out these campaigns early on will set you on a path to identify the root cause of poorly performing campaigns that have the most significant impact on your program.
Now that you’ve identified a poorly performing campaign or two, it’s time to look at the ad groups. Like campaigns, ad groups tend to have fragmented spend metrics in the sense that a smaller portion of ad groups are responsible for the majority of spend within that campaign. And as you did with the campaigns, you’ll need to calculate the PPC performance for all the ad groups, sort them in the order of descending cost, and find the highest-volume ad groups that experienced the largest drop in performance.
As you can probably guess at this point, keywords/product groups, like ad groups and campaigns, also tend to have an uneven spend distribution. Because of this, you should be able to pinpoint the keywords/product groups that are responsible for the drop in PPC performance. You can do this by finding the highest-volume keywords/product groups with the worst performance.
You are now at the point where you’ve found what is causing PPC performance to drop. For example, you might have found two or three keywords that were previously driving the majority of revenue, but have recently declined. Or, you might have found high click-volume product groups with diminishing conversion rates. But WHY is the PPC performance so bad from these segments? To determine this, take a few minutes to think about the cause. Perhaps most importantly, have you made any changes recently that might have led to poor performance?
In fact, there are a LOT of changes that can lead to this type of drop. Here are some of the most common:
Landing page changes
Making changes to landing pages is common, and essential for growth of your business. However, the wrong changes can be detrimental to your paid search performance. Questions to ask yourself following landing page changes include:
These types of changes will be evident in the quality score of the keyword, which will drop if the landing page changes aren’t good.
Business model changes
Have you made any changes to your business model recently that could affect the ROAS? For example, a change to item pricing could potentially hurt your performance. If you sell ten shirts one month for $15/shirt, and you spend $10/shirt, you made $150 and spent $100 for 150% ROAS. If the next month, you sold ten shirts but for $13/shirt and still pay $10/shirt, you would make $130 and spent $100, resulting in 130% ROAS.
In theory, conversion rates might have been the same month over month, but if you’ve reduced the price on an item, you could be artificially lowering your ROAS.
Conversely, have you NOT made any changes recently that might have led to poor performance?
In today’s competitive paid search space, you HAVE to constantly make changes to accommodate the evolving bid landscape. For example, a $10 CPC one day might put you on position 2 of the search results page. While the next month, the same CPC might only put you on position 4. You might have outdated CPC limits affecting your keywords/product groups/ad groups, which realistically need to be changed to help you achieve and maintain your goals.
Keeping the same business goals month over month might not be feasible if seasonality plays a big factor. For example, if you are selling Christmas trees, and your goal in December is 200% ROAS, you probably wouldn’t expect to hit that same metric in January, when search interest in Christmas trees is bound to drop off. Make sure you are adjusting your business goals in general to take seasonality into account.
Historical data
If you have enough historical data, you can look at data from past years to get a sense of how relevant seasonality is. Some examples of KPIs to consider include impressions, clicks, spend, conversion rates, conversion volume, revenue per click, revenue per conversion, CPA, and ROAS.
Google search trends
These charts will provide insights of search interest over time, and can help you figure out where seasonality peaks and drops. For the Christmas trees example, you can see a significant drop in search interest between January 2017 and December 2018 for ‘Christmas trees’ across the US:
Now I understand the idea behind finding the root cause. But with so much data, what tools can I use to do this?
With so many resources available, effectively troubleshooting my data in the fastest way possible isn’t a question of what tool can I use, rather it’s a question of which tools should I use?
Small data sets
For data sets with <300,000 rows, Excel or Google Sheets will do the trick. Combining pivot tables along with sorting options will help you quickly identify high-volume, poorly-performing segments of your program.
Large data Sets
Excel isn’t compatible with larger data sets. Because of that, using a scripting language such as R, Python, or SAS will help you quickly identify poorly-performing segments of your program. There are inherent tradeoffs; if you don’t have programming experience, it will be a time investment to learn how to use one of these big data tools. On the bright side, once you learn how to use them, you can scale these checks to quickly identify poor-performing segments of your program in a matter of seconds.
To improve PPC performance you need to understand all parts of your program -- including, and perhaps especially, your weaknesses. Troubleshooting poor performance may seem like a daunting task. However, with the right approach and the right tools, you can effectively identify the root cause, monitor certain segments more closely and start making changes to bring your performance back up to speed.
AI vs. Machine Learning -- it’s likely you’ve heard both of these buzzwords relentlessly over the last few years, and often interchangeably.
While these terms are thrown about ad nauseam, and often without context or definition, in reality, they’re distinctly different. Artificial Intelligence is a broad concept with many applications and machine learning is just one part of it. Each has a distinct role and brings unique value to your SEM and digital marketing efforts. We’ll break down their attributes and how you can leverage them to boost campaign visibility, profits, and ROI.
Ever played chess against a computer on your old desktop? You were interacting with AI without even knowing it.
The term artificial intelligence was first coined in 1956 by American computer scientist John McCarthy, who believed it possible to create machines that could mimic the intelligence and many of the reasoning capabilities of a human brain. In actuality, even the earliest computers possessed intelligence capabilities, with the ability to conduct math, perform logic, and store information. Over time, however, computers were developed with increasingly advanced capabilities that allowed them to perform tasks and follow thought processes that more closely resembled human intelligence. Today, AI can perform a wide variety of advanced tasks, such as detecting and understanding languages, recognizing voices, solving complex problems, and event planning, among countless others.
Weak AI and Strong AI
There are two different kinds of available AI: weak AI and strong AI. The most common is weak AI, applications that we utilize on a daily basis in digital assistants such as Siri, Alexa, and chatbots. And while weak AI is designed to mimic the experience of human interaction, it doesn’t actually deliver the same level of intelligence. Instead, it’s programmed to understand certain interactions and tasks, classify them and then respond accordingly.
Alternatively, strong AI processes information more like a real human brain does when learning from and adapting to new experiences. Specifically, strong AI creates clusters and associations that can then inform a response when presented with new data. Over time, it develops strategies and responses independent of its programming.
Artificial intelligence, to say the least, is a broad umbrella term incorporating a wide array of computing and learning capabilities. Machine learning, on the other hand, is a subset of AI with specific unique functions -- which ultimately makes it a more impressive technology.
Specifically, machine learning, a term first coined in 1959 by American pioneer of computer gaming Arthur Samuel, involves processing large amounts of data related to a task, analyzing it, and using it to inform the computer’s performance at that task. Whereas AI technologies entail predefined programming on how to respond to new data, machine learning makes it possible for computers to learn as they perform a task, essentially creating their own new programming as they go to perform it better.
Additionally, machine learning can also be predictive. Algorithms build a mathematical model of sample data which predicts future performance, which informs the decisions to optimize performance at any given task.
Google’s image analysis capabilities provide one such example. While the vast majority of images published on the web provide no alt tag describing what they are, some some of them do. Google’s Vision API analyzes a series of photos that come with a tag, such as “chinchilla.” Inherently, Google doesn’t know what a chinchilla is, as it was never programmed into the system. But by studying the tags and the associated images, machine learning can accurately predict and identify other images that are likely to also be chinchillas.
Machine learning and AI are not mutually exclusive technologies, and each have distinct applications that make them valuable for different areas of your SEM and digital advertising programs. In our breakdown of AI vs. Machine Learning, we weigh the unique benefits of each and show you where each wins out.
AI vs. Machine Learning: Understanding audiences
Both AI and machine learning can help marketers analyze large amounts of consumer data to better understand their interests, needs, and purchase intent, while also automating and optimizing this process. However, machine learning can also identify new segments of your target audience and their unique traits. Affinio is one such enterprise product that does this, with the ability to analyze billions of interest variables and network connections to create unique audience clusters. This in-depth analysis, in turn, reveals new patterns, relationships, and commonalities about audiences that regular data analysis can’t uncover.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing marketing messages
Better understanding your audience also brings more opportunities to create content that’s relevant to their interests. Automating the testing of different content types to see which resonates best with your audience is one of the places where AI shines.
Google Ads already does this with their ad rotation settings. Setting your ad versions to “Optimize” will automatically optimize your ads for individual auctions using signals like keyword, search term, device, and location. Then as Google Ads collects more data about ad performance, machine learning algorithms determine statistically which ads are likely to have stronger performance.
Winner: AI
AI vs. Machine Learning: Improving Lead Scoring
With machine learning, it’s possible to create predictive models that leverage input data and probability to accurately determine future trends. While this capability is beneficial to a wide variety of digital marketing initiatives, it’s especially relevant to lead scoring. Traditional lead scoring capabilities rely on individual lead behavior to determine their potential to convert, and machine learning algorithms can draw correlations between those lead characteristics to identify valuable prospects before they express strong purchase intent.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing Marketing Spend
Machine learning’s analysis capabilities can also be used to better allocate marketing spend by considering factors such as consumer data, buying signals, quick bidding options and other historical signals to predict future advertising performance. Powerful software can learn from past market data and current micro-changes in performance to make quick bidding decisions, which can both improve advertising performance and reduce wasted ad spend due to over-bidding.
Winner: Machine Learning
AI vs. Machine Learning: Delivering timely messages
Many would argue that there’s still no AI computer that can process decision factors with the same sophistication as a real human brain. While that may be true, AI can make decisions much faster than an entire team of data scientists.
From there, machine learning can help deliver the right marketing message to the right audience in real time. Using search queries, demographics, location, and other factors, the technology can automatically serve relevant ads to the right audience when they need them, enabling marketers to automatically deliver a more relevant message to their audience.
Winners: AI and Machine Learning
AI vs. Machine Learning: Engage dynamically
Probably the most interesting application of AI to marketing is the potential to engage with audiences dynamically. Smart technologies like Alexa and Siri are prime examples of this type of audience engagement. However, there are many other potential use cases, such as Google’s dynamic ads that can change the PPC advertising message based on audience input.
In addition, chatbots that leverage AI and machine learning have a unique ability to both engage with as well as help their audiences based on the information they provide. Far from being automated Q&A systems, many can perform advanced marketing and sales tasks, using natural learning processing to learn more about audience needs as they interact.
Winners: AI and Machine Learning
Neither artificial intelligence nor machine learning are new concepts -- AI enables computers to make decisions based on input data. Machine learning enables computers using data inputs to grow and learn. Both have been around -- at least in theory -- for decades.
What is new, however, are the ways in which these technologies are used to shape the direction of countless industries, including digital marketing and advertising. With the ability to engage audiences, provide new customer and competitor insights, deliver timely messages and optimize ad spend, these technologies have the power to reshape and essentially revolutionize the SEM industry as we know it, while taking your PPC campaigns to new and yet undiscovered heights.
It's no secret that digital marketers are on the cusp of realizing the power of AI and machine learning. Those who step up first to take advantage of all they have offer will position themselves well for a profitable future that’s leagues ahead of the competition.
The holidays are over and 2019 is here in full swing. You’re firing up your computers, opening up the books for a new quarter and sharpening your PPC tools. It’s a new year, and with it comes a host of innovative new SEM trends that will be integral in the direction of your digital marketing and advertising strategy. That means reaching new audiences, achieving new goals and adopting new tools to accomplish all your SEM objectives. Often the new year can mean taking new risks, exploring new markets and targeting new customers. But whatever your direction, it’s likely that 2019 will bring a host of new opportunities—and corresponding rewards.
The year promises to be unprecedented in terms of the number and sheer sophistication of tools and technologies for all your SEM endeavors. Here are six SEM trends to watch in 2019 that are expected to put the gas pedal on all of your campaigns.
Voice search is a red hot technology, and when it comes to SEM trends this year, it only promises to get hotter. Currently, more than 20 percent of mobile searches on Google are voice searches, while Comscore predicts that more than 50 percent of all search will be voice-related by 2020. It’s a trend that doesn’t show any signs of stopping in the years to come. What’s more, these personal assistants are becoming even more sophisticated in their ability to recognize human speech and formulate complex sentences.
That’s not lost on digital advertisers. And as consumers become more accustomed to giving voice commands, organizations are continually retooling their advertising and marketing strategies to accommodate a new medley of spoken word patterns. Going forward, it will be critical for digital marketers to tailor their PPC strategy around the latest voice search techniques in order to stay relevant and target new groups of consumers.
Perhaps not surprisingly, studies have shown that consumers tend to gravitate toward interactive content—any content that requires some kind of user participation—more than traditional static content.
Content like quizzes, polls, interactive interactive infographics and calculators—or anything else that requires consumer feedback—often stands out against the vast majority of static ad copy, providing a unique channel for consumers to feel stimulated and engaged. This is why interactive content will be one of the most significant SEM trends of 2019.
In addition to being a much more effective way to inform users, interactive content goes a long way in regards to cultivating brand awareness and interest, establishing and maintaining brand loyalty, capturing relevant consumer data and reaching new audiences. And while interactive content is still unique, advertisers who leverage its use strategically will likely have a leg up on competitors relying solely on passive content—at least for the time being.
While video still is a major part of digital marketing and advertising efforts, live video—as you might expect—will take it a step further as one of the biggest SEM trends this year.
For digital marketers, this means the ability to provide customers access to live tours and demos, as well as interactive chats with experts and other professionals. At its core, live video offers digital marketers the ability to get creative while letting customers see a side of them and their products that not many get to see. Plus, that content can be leveraged strategically across other content platforms such as blogs, social media, case studies, reports, and more.
And video, in general, is accelerating—according to Cisco, 82% of internet traffic will be through video by 2021. That means, regardless if it’s used as an independent asset or in tandem with other content, live video will most definitely give your product or service a much-needed boost in raising brand awareness and visibility against competitors.
As far as SEM trends go, artificial intelligence (AI) has been firmly ensconced in the corporate vernacular for a quite a while. That said, few understand its true potential as a tool for digital marketing.
Perhaps more than anything, AI possesses immense power to uncover consumer insights, giving marketers a unique ability to better understand, analyze and predict patterns of behavior. Among other things, AI can be leveraged to discover and target new audiences and specific demographics, illuminate correlations between customer behavior and numerous sets of variables, and glean new competitive insights that would otherwise be impossible to achieve manually. And that’s just scratching the surface. AI can also drive customer segmentation, retargeting, click monitoring, and tracking, while helping organizations achieve granular visibility throughout their entire ecosystem.
Safe to say, AI’s potential as a powerful SEM tool is on the cusp of becoming realized by digital advertisers and marketers across all industries. And its use in 2019 will indisputably accelerate as more and more organizations recognize its value. Those who don’t find ways to incorporate AI into their arsenal will undoubtedly risk falling behind the industry curve—and competitors.
The rise and proliferation of smartphone technology has produced new ways to reach audiences, and consequently an increasing number of consumers are relying on their mobile devices to both search and shop. Underscoring these developments, mobile ad spend is expected to more than double that of TV by 2022, comprising $141.36 billion of the total US ad spending, according to eMarketer.
With mobile advertising revenues rapidly increasing, big changes and trends in the mobile advertising landscape are imminent in 2019. And in keeping with these SEM trends, digital marketers will have to adjust their campaigns accordingly to stay competitive and profitable.
2019 will see a dearth of highly sophisticated mobile campaigns, such as high-quality video aimed at mobile users by many of the large platforms, which in turn will increase demand and compel more interactive video advertising.
Other sophisticated mobile campaigns will strategically leverage AI for features like personal assistants, modeling consumer behavior along the buyer journey and augmented reality (AR). Of the numerous use cases for AI, personal assistants will likely be deemed one of the most important, as tools like Siri, Google Assistant and Alexa will further connect users’ mobile devices to their personal shopping experiences.
Amazon now has a net worth of over $1 trillion, which will almost certainly make it a force to be reckoned with on the SEM front in 2019. The e-commerce giant recently took its place right behind Google and Facebook respectively as the third largest advertising platform on the planet. And it will continue to take share from its two major competitors, as ad revenue is expected to grow significantly for the foreseeable future.
The retail giant also experienced its biggest shopping day in history on Cyber Monday, touting the most products ordered worldwide than any other day. That untapped potential will continue to be a powerful driver for growth in its ad business as merchants look to capitalize on Amazon’s copious search traffic. Thus, going forward, it stands to reason that advertisers will rapidly buy up even more advertising space in 2019 to stay competitive, and to prevent losing visibility in the search results to sponsored listings.
At Centro, we know that keeping up with the trade pubs and latest trends can be tough and time-consuming—so we made it easier for you, and compiled all the articles, reports, and other bits of awesomeness you may have missed, but should definitely read. Bundle up, and enjoy the latest list below!
5 Things We Learned About GDPR in 2018 [:05]
One of the hottest topics to hit the digital media industry in 2018—this EU-issued data protection regulation required many brands, advertisers, agencies, and ad tech vendors to scramble and meet this regulatory launch this past May. How much impact did it really have? What are some of the key learnings from the year and what things you should watch out for in 2019? Read on to answer those questions—and more!
Podcast advertising generates up to 4.4x better brand recall than other digital ads [:03]
According to new findings from recent Nielsen effectiveness studies, podcast advertising yields far better brand recall than other widely-used forms of digital advertising. The aggregated findings have also highlighted an average 10% lift in purchase intent among listeners exposed to a podcast ad, with 61% percent saying they would be more likely to buy. (PS: have you listened to Shawn’s awesome AdExchanger podcast yet?)
The Next Evolution Of Programmatic: The Publisher Exchange [:03]
In the late 2000s, we experienced a burst of data-driven buyin—and in 2014, we saw the header bidding gain popularity. What's next? Another technical change is on the horizon that will allow publisher-direct connections or publisher exchanges. Until now, the pipeline has been to go from the advertiser, DSP-to-exchange-to-publisher. By removing the exchanges entirely, publishers can connect directly with DSPs. This does not, however, come without complications—read to learn more.
Media Buyers Will Grow Spending In Private Marketplaces Over Open Exchanges [:03]
Spend will continue to increase in private marketplaces and programmatic deals in 2019, according to Digiday Research. Part of this is due to buyers being less than enthusiastic about the open marketplace and remaining skeptical due to fraud and brand safety issues.
Ads.Txt For Apps Is Finally (Nearly) Ready For Primetime [:03]
The IAB tech lab has released information for the app version of its Ads.txt initiative, conveniently named App-ads.txt. The expectation is that this will ultimately help to reduce counterfeit ad inventory, once it is rolled out after ongoing the open comment period through early February.
Myths of Addressable TV Advertising [:06]
The myths debunked in this Digiday article range from the cost to buy addressable TV versus ‘regular’ television, to the ability to scale, as well as different measurement options, and what the true definition of addressable is.
‘Advertisers Think It’s Just Like Buying Digital’: Myths Of Connected TV Advertising [:05]
When it comes to Connected TV, there is still a lot of confusion around what is possible and what is not. Take a peek at some common myths surrounding Connected TV such as Connected TV inventory, CTV ads (can they be tracked like other digital ads?), and high-quality TV inventory. Debunk away!
The Era of the Camera: Google Lens, One Year In [:07]
By some estimates, 30% of the neurons in the cortex of our brain are for vision, so why shouldn’t we set our sights on augmenting our world through the power of machine learning and computer vision? As Google Lens celebrates its first birthday, a new and improved version is being rolled out that puts us all one step closer to superhuman vision.
7 Things to Think About Voice [:06]
What happens as the voice systems we know and love (hey Alexa) get smarter by offering more automation and proactivity? We adapt to a new interface that gives us greater control. And boom—the future happens, just like that.