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As a digital advertiser, you know by now that SEM is a fast-paced, rapidly evolving industry—and that the amount of change that happens in one year can easily equal more than a decade of development in another industry. To that end, it's not surprising that 2018 saw significant changes across the entire advertising landscape, with everything from the emergence of new data and privacy legislation, to new ways to target audiences and acquire insights—and looking ahead, it's safe to assume that none of these things will experience a slow down anytime soon.
If you’re wondering what new digital advertising opportunities to watch in the New Year, here are our Top 9 PPC predictions for 2019.
Back when Bing first started developing PPC advertising capabilities, the company created a platform completely unique from that of Google’s. The strategy didn’t work, however, and most advertisers ignored Bing as an advertising option.
Now Bing has revamped its strategy yet again, making it as easy as possible for advertisers to import their campaigns to its platform directly from Google Ads, while continuing to make improvements, and launching new features like dynamic search ads, automated bidding, and in-market audience targeting.
While Google still holds the lion’s share of the search market, Bing’s user base has some unique characteristics that makes it attractive for advertisers. For instance, Bing users tend to be wealthier, and spend 32% more shopping online than average internet searchers. Since Bing minimized the hassle of importing Google Ads campaigns and maximized the potential to reach target audiences, we can expect that more advertisers will launch Bing PPC campaigns in 2019.
Advertisers have been aware of the importance of cross-channel marketing for a long time. But it’s been very difficult to deliver the same marketing message across channels and accurately measure the impact of these efforts—that is, until now. And it’s getting a lot easier. Google’s automated bidding strategies, for example, make it possible to attribute across devices and platforms. And Google’s multi-channel funnels can include paid and organic search, referral sites, affiliates, social networks and email newsletters.
Google is just now waking up to answer this long-awaited need. And there are also a variety of third-party tools that advertisers can leverage to manage cross-channel advertising initiatives. As more options make it easier to launch, track and attribute multi-channel campaigns, businesses will be more inclined to pursue a comprehensive cross-channel strategy for advertising.
Chatbots have been around for several years now, but most still see them as a futuristic marketing tool that only the most competitive digital marketers develop, deploy, and use at scale.
In 2019, more marketers will realize that this couldn’t be further from the truth. Creating a chatbot for your brand is easy and relatively inexpensive. And the opportunities to integrate them into your marketing strategy are abundant.
Chatbots can interact with members of your audience just like a real customer service representative in many ways, such as:
Consequently, chatbots represent a huge opportunity for PPC advertisers to optimize their campaign performance and drive conversions. It’s possible to include a chatbot as a widget on your landing page as an optional element. Or you can go all out and turn your entire landing page into an active chatbot. But undoubtedly, including them as part of the landing pages for your ads offers your audience a customized experience that no other content can deliver.
Automation isn’t a new trend in 2019, but it’s a PPC standard that’s here to stay. And to this end, Google Ads have rolled out a variety of new features that encourage advertisers to take advantage of artificial intelligence (AI) and machine learning to optimize their campaigns.
So instead of “adopting automation”—which just about every PPC advertiser has already done—automation will become more integrated as an an integral component of strategy for PPC managers. While initially many PPC managers saw automation as a tool that made their role redundant, it’s now being looked at as an opportunity to supplement human intelligence—and making PPC managers much better at their jobs in the process.
Among other things, automation can take over tedious manual campaign management tasks, freeing up managers to focus on new growth initiatives. It minimizes busy work and helps uncover new opportunities for marketing managers to pursue. Thus, instead of seeing it as an intrusive technology that threatens employee value, marketing managers will soon embrace automation as the powerful tool that it has come to be.
2018 saw a growth of new ad types as platforms like Google and Facebook continued to develop new ways for advertisers to strategically target their audiences. Here are just a few examples of the many new ad types that came out in 2018:
While it might seem like advertising platforms would likely run out of new ad types, that eventuality never actually occurs. Instead, more ad types emerge every year that make it easier for advertisers to target and capture their audiences’ attention. And 2019 will be no different. (Plus, even with traditional ad types, Google, Facebook and other platforms are constantly adding new features that change the way they work.)
Historically, keyword targeting was the one best way to reach specific audiences. Search queries reflected user intent, and can tell advertisers a lot about the person behind the keyword. Now, however, PPC targeting has become more advanced, making audience targeting a more critical part of the strategy.
Google’s in-market audiences, for example, draw on user search history and sites they’ve visited around the web to identify individuals who are researching to buy in certain niches. And targeting audiences that have already shown purchase intent is more valuable for PPC advertisers than traditional keyword targeting.
Up until now most advertisers have relied on audience targeting as a way to refine their keyword strategy. But in 2019, more advertisers will start to realize that it should be the other way around—audience targeting will be an opportunity to reach the right customers as well as make ads more relevant and personalized for users' interests. Keyword targeting is still important, but it doesn’t offer the same advertising potential as audience targeting leveraging online behavior insights.
Video is one of the most powerful types of content on the web—and businesses who invest in video marketing are positioned to reach new, broad and diverse audiences, especially on mobile devices. In fact, by Cisco’s estimates, 80% of all internet traffic will be video content by 2019 and global video traffic will increase threefold between 2016 to 2021.
It stands to reason then that advertising platforms are following these trends and continuing to develop their video capabilities as a result. Google Ads, for example, recently released the option to create vertical video ads for TrueView and Universal App campaigns on YouTube.
Looking ahead in the near future, advanced video advertisers are sure to take advantage of these nuanced campaign optimization opportunities in 2019. Meanwhile, those that were slow to enter the video advertising arena in the past few years are bound to finally start.
As a digital marketer, you know that Google Ads is consistently pushing AI and machine learning as a standard part of modern day advertising, and many marketers are already using these advanced technologies to act on data insights and optimize their campaigns.
But as more marketers adopt AI and automation, competitive advertisers will start looking for new strategies to stay ahead of the competition, giving rise to significant growth of predictive advertising in 2019.
Like most automated advertising technologies, predictive advertising tools leverage AI and machine learning to drive nuanced performance insights, as well as vast amounts of behavioral data from various web platforms. From there, advanced statistical models rely on real-time data and historical performance insights to effectively predict future ad targeting opportunities. Instead of reacting to big data insights, advertisers can subsequently make proactive campaign adjustments based on future market changes.
In the past few years, advertisers have focused so much on AI and the current opportunities it creates, that the value of predictive statistical modeling has stayed a well-kept secret. But as AI and automation become status quo, advertisers who take advantage of predictive advertising will have a huge opportunity to gain a competitive edge and carve their own space in the market.
Most marketers who use search engine PPC do so as a way to supplement their organic SEO efforts, as it’s well established that creating a comprehensive SEM strategy is the best way to maximize visibility in search engines, and drive more clicks, conversions and sales.
That said, Google is constantly coming out with new ad types and features to help advertisers expand their PPC efforts. In August 2018, Google launched Expanded Text Ads, an ad type that enables advertisers to specify three headlines and two descriptions to convey more information in their ad, ultimately giving them more real estate for their marketing message.
Meanwhile, it’s well known that Google prioritizes paid advertising over organic search results, and the latest addition of Expanded Text Ads goes to show the extent of its plan for PPC ad growth. Forward-thinking advertisers recognize that Expanded Text Ads will likely become the PPC norm before long—so expect to see many more advertisers using the feature and other new ad extensions in 2019.
Many PPC advertisers focus the bulk of their efforts on optimizing campaigns for search ads—and with good reason. It’s profitable, reliable and a known entity on which they can comfortably and (somewhat) predictably reach their core target audience. But it’s certainly not the only path for revenue.
Location-based ads—and in particular Google Maps—increasingly offer a variety of new features that advertisers can leverage to better target and convert local audiences. By adding its features to your digital advertising toolkit, you're sure to give conversions and ROI a significant lift.
The following is everything you need to know about how to advertise on Google Maps to supercharge your online advertising strategy.
When people search for businesses through the Google Maps app, they automatically have access to search ads that feature business locations. Similarly, when users search Google for nearby businesses, location-based ads appear in local at the top of search results from the Google Maps mobile app. (This also works when conducting searches using Google or Google Maps on desktop.)
Compared to regular Google PPC ads, it might seem like Google Maps ads provide little opportunity to rank, simply because the Google Maps app only displays one ad above organic results and Google Maps desktop shows two ads before organic results. And as such, your targeting strategy needs to be competitive enough to land your ads in those top one or two positions.
That said, there are numerous ways you can optimize your listings and leverage Google Maps to improve online advertising—it just requires you to execute precision with your targeting strategies and optimize your ads to attract more in-store visits.
E-commerce has grown astronomically in the past 10 years. It’s no secret that people love to shop online, using the web to buy everything from shoes, to cars, to ordering groceries for home delivery. This development has left many advertisers wondering if allocating their budgets to drive in-store visits is really worth the investment.
Yet despite the growth of ecommerce, in-store shopping is still going strong. It is propelled, in part, by the wealth of location-based shopping information available on mobile phones. Consider this:
With more than a billion users, Google Maps is the most popular mapping app for acquiring location-based information. So incorporating local search ads into your advertising strategy carries a myriad of benefits, that include:
In short, mobile phones make it quick and easy for people to find relevant local information when they’re out running errands or planning a trip to local businesses. People who use their smartphones to get local business information don’t want to endlessly search to find what they need—they want to access it quickly and easily, underscoring that location-targeted advertising is increasingly essential for any business with a physical location. And advertising with Google Maps is key to reaching these users.
Google Maps has implemented many changes recently that give businesses even more opportunities to improve advertising performance on its platform. In fact, during Google’s most recent Performance Summit, they announced four key new features:
Let's take a quick look at each.
A new and important advertising feature that helps your business get noticed in Google Maps are Promoted Pins. Promoted Pins are the array of red pins on the map highlighting businesses. But, perhaps surprisingly, not every business automatically gets a pin. By strategically leveraging Promoted Pins, it’s possible to display a special branded pin to help your business stand out to people who are nearby your business and looking at Google Maps.
These pins are purple instead of the traditional red, indicating that it’s an ad. Promoted Pins can also include your business logo right in Google Maps. For example, if someone searched for “Contact lenses near me,” it could trigger a branded Walgreens Pin. If they click on the pin they’ll see a targeted ad as well.
The ads that correspond with Promoted Pins display important business information such as location, distance, and Google Reviews rating. But they’re also an opportunity to highlight special in-store promotions to convince people to come visit your business.
Along with these ads, it’s also possible to promote coupons related to the person’s search. So if someone searches for “Contact lenses near me,” Walgreens could display a coupon for $3 off contact lens solutions to entice the person to visit.
When people click on your local search ad, they’re taken to your Google My Business page. This is a local business resource you can customize and optimize to increase in-store visits and conversions. At a fundamental level, your business page needs to include important information like your location, business hours, phone number and website.
Also, these days many customers only make location store visits only if they’re sure it stocks the items they want. To that end, you can also use your local pages to display a searchable database of that store’s local inventory. By sharing your local inventory along with your Google Maps ads, you can increase in-store visits and, thus, conversions.
To display this information on your local pages, you need to have a local product inventory feed, which requires you to submit information daily about all of your inventory. The incremental local product inventory feed allows you to make regular updates to item prices and quantities available throughout the day.
To use local inventory search, you must provide the store code, quantity and price of each item in stock. But you can also include optional inventory details like sales price and sales price effective date. Google can then use this information to recommend special promotions to members of your audience.
In-store promotions can appear alongside your promoted pins, directly on the map below the logo. It’s also possible to display coupons, specials or sales right from your business page. If you use the incremental local product inventory feed, you can offer special sales a specific times of the day. This helps your local audience find what they’re looking for while also offering relevance and opportunity to convince them to choose your store over another.
Here’s a basic, three-step run through of how to set up location-based ads to show on Google Maps:
To show ads for Google Maps, you first need a Google Ads account and a Google My Business Page. A Google My Business page is critical for all aspects of search engine marketing. This free profile lists important information (location, hours, photos, and reviews) and appears in local organic search results. It can also appear with your local search ads or on Google Maps.
You likely already have a Google My Business page and have optimized it to make your business look appealing. (Quick tips: list correct information, upload appealing business photos, and promote positive Google reviews for your business.) From there, the next step is to set up a location extension.
One important way to optimize Google Maps ads is to target people who are near your business location. Do this by setting a custom radius for how close someone needs to be for your ads to appear. Radius targets quick conversions from people who are out and can visit your local business based on proximity and convenience.
Location targeting allows you to target entire countries, areas within a country or a radius around a location. Choosing a radius around a location may result in less reach for your ads overall. But it helps target specific audiences that are likely to convert. This is particularly important for location-based businesses that deliver services within a specific radius, or a local business whose customer base is within a certain radius.
Say, for example, you run a supermarket chain with three locations in town. The targeting radius for one store should not overlap another location when people are physically closer to a different store. In the same vein, a towing company wouldn’t want to target ads to a radius outside of their service area.
It’s also possible to adjust your bids based on audience location. For example, you can increase your bid for someone who’s within a one mile radius of your business.
Radius targeting is only one way to optimize your location-based ads. If someone searches for “[niche keyword] near me” and they’re within your set radius, then they could see your ads on Google Maps.
However many potential consumers have location-based search intent even when they’re not within a specific location radius. For example, someone traveling to Minneapolis next week could search for “Minneapolis hotels” while they’re still physically in Seattle. So you’d need to target location-based keywords if you want your ads to appear for these queries.
To build a location-based keywords list, use your search queries report, Keyword Planner and other third-party keyword research tools. The biggest difference between regular keyword targeting is that location-based keywords include modifiers that imply local intent.
A Seattle hotel, for example, could target keywords like:
There are a variety of modifiers relevant to your business location, such as nearby employers, event venues or other colloquiums used to describe different parts of town. The keywords you target can also inform the promotions that display along with your Google Maps ads. A pharmacy like Walgreens wouldn’t just target keywords like “pharmacy near me,” or “pharmacy [city name]." They could target keywords like “contact lenses [city name]” then display a special discount on related products as part of their Google Maps ad.
While sometimes overlooked, Google Maps offers a host of features aimed at helping businesses attract local traffic and drive conversions. And targeting keywords with local intent and radiuses around your business location are just the beginning. Used strategically, Google Maps can help you harness relevant inventory information, limited-time promotions, and help your location stand out on the map using special promoted pins. In short, it broadens the horizons of your strategy with exponentially more possibilities for targeting specific demographics. Like a premium fuel, or a shot of vitamin B12, learning how to advertise on Google Maps will supercharge your digital advertising strategy.
The advertising industry has made huge strides in attempting to understand the mind of the consumer and potential buyer. But it hasn't been able to predict the future - that is, until now. With the emergence and proliferation of predictive advertising technologies that predict consumer behavior before it actually occurs, that future may well be here.
It's well established that predictive analytics has dramatically changed the digital marketing landscape in recent years, and it’s only continuing to grow and evolve. According to a recent report, the global predictive analytics market is set to reach $10.95 billion by 2022. Predictive analytics has a variety of applications in marketing. But for digital marketers, the most valuable application will be predictive advertising.
As a subset of predictive analytics, predictive advertising is leveraged by businesses to analyze data from consumer behavior, which then uses these insights to predict future trends and optimize marketing and sales strategies.
Perhaps most importantly, predictive advertising can help you identify members of your audience that are likely to convert while automatically prioritizing leads, made possible by leveraging advanced statistical modeling and machine learning. Specifically, machine learning looks at historical data patterns and derives insights from these patterns in order to make informed changes to advertising strategies. And there are a number of benefits to using machine learning, including:
What’s more, machine learning makes it possible to achieve these goals with more speed and accuracy than an entire team of data scientists.
Artificial intelligence is a powerful technology that makes predictive advertising possible. But another key element is the wealth of consumer data available today. The growth of Internet of Things (IoT) devices is presenting a myriad of opportunities for data collection and analysis. And it’s no secret that the average consumer today leaves a massive digital footprint, which is only continuing to grow. Thus, predictive advertising strategically leverages this data to drive a plethora of consumer insights, including:
Without a doubt, the wealth of data available today enables marketers to better understand the intentions, preferences, and buying patterns of individual members of their audiences, while also making it possible to better track the nuances of the market they’re advertising in. That said, this data is only valuable if advertisers find ways to successfully unlock it. Conversely, the abundance of data can easily overwhelm marketing teams with too many relevant touchpoints to analyze, creating significant gaps and a host of missed opportunities.
Now enter predictive advertising, which can use your internal audience data as well as third-party behavioral data to identify new potential customers to target and enable you to broaden your audience base further than you would if you simply relied on regular targeting tactics.
Predictive advertising can draw correlations between demographic and behavioral factors that regular marketing analytics simply wouldn’t be able to make. Leveraged in tools like Google’s Lookalike Audience and Facebook Similar Audiences these technologies provide the ability to draw correlations between different demographic and behavioral data to identify new relevant audiences to target. For example, say predictive analysis helps you discover that members of your current customer base are likely to read a certain online publication. From there, you could then start targeting other people who also read that publication.
Most marketers today who use data to drive targeting insights rely on their own first-party behavioral data: metrics like site pages visited, lead magnets downloaded, and abandoned carts. But first-party data only tells you so much about your audience’s needs and intent to purchase. When you add in behavioral touchpoints from other sites, you get a much richer picture of a lead’s intent, which in turn makes it possible to reach leads with relevant marketing messages deep in the sales funnel, as well as improve retargeting capabilities for display ad campaigns.
The better businesses understand their audiences, the easier it is for them to create relevant, targeted ad copy that converts. Combining first and third-party audience data allows you to build more robust buyer personas to inform your marketing collateral. But can these creative changes be automated and implemented at scale? The answer is yes, but with the help of machine learning. Machine learning can drive predictive insights and in turn help you make smart choices to improve advertising copy and ad design. While machines aren’t replacing real creatives, they can help optimize marketing collateral to become more effective and relevant to target audiences.
Predictive advertising already allows you to automate ad personalization based on a variety of behavioral, demographic, and situational factors such as device ID, domain, location, purchase history, or interests, and trigger that data to reach relevant audience members. Predictive advertising insights can also inform the right kind of copy to include in dynamic PPC ads. And we’re only just beginning to realize the possibilities for its potential to unlock new, unique and valuable customer demographic insights.
According to data from Nielsen Digital Ad Ratings service, billions of online marketing dollars are being wasted in advertising -- largely because ads are reaching the wrong audiences, reaching the right audiences at the wrong time, or businesses are simply spending more than necessary to achieve their marketing goals.
On the other hand, businesses that focus on minimizing wasted ad spend get the double benefit of being able to channel their saved budget into other marketing initiatives. Predictive advertising can help with this by executing nuanced targeting and bid adjustments in real-time to drive more ROI. A good example is Google’s automated bidding platform, which allows advertisers to choose a goal (site visits, visibility, conversions, etc.) and then leverages audience data from its advertising network as well as competitor performance data to automatically adjust bids in real time.
But targeting capabilities become even more nuanced and effective when using third-party predictive advertising technologies. Because now you’re free to use any kind of relevant third-party data you want, so you can rely on site visits, social media engagements, your PPC campaign data and other variables to drive even deeper insights. And instead of relying on the one-dimensional goal options of Google’s automated bidding, you can customize the key performance indicators (KPIs) you want to target.
Thanks to smartphones and on-demand information, it’s become increasingly difficult for advertisers to target audiences at some of the most critical and relevant times. For many purchase decisions, it’s possible for people to show intent and convert into a buyer mere minutes later. By the time they see advertisements for the product they’re looking for, it’s already too late.
Google calls these micro-moments: An intent-rich moment when a person turns to a device to act on a need — to know, go, do, or buy. There are four key micro-moments that advertisers can target to help their audiences convert:
Not surprisingly, targeting ads to consumer micro-moments is incredibly valuable for businesses because consumers are determined to find what they’re looking for, and will be interested in brands that help them achieve that goal.
But successfully targeting ads to micro-moments is essentially impossible without using some kind of automation. While PPC advertisers can use Google’s in-market audiences to determine who is searching for certain categories of products and automatically target them with relevant advertising, predictive advertising can take this goal to the next level by helping you anticipate customer needs before they start showing strong signs of purchase intent. Using historical data of consumer online behavior and demographic information, predictive advertising can anticipate what kind of people could be interested in your products and services before they start the buying journey. In essence, it’s possible to start targeting these micro-moments before they even occur.
Of course, there are some kinds of micro-moments advertisers can’t anticipate in advance. Someone’s car breaks down on the side of the road and now they’re looking for auto repair services. Or someone’s relative passed away and now they need funeral home services. How can advertisers target these new “interests” when they come up so suddenly?
Predictive advertising technologies can handle the challenge because they analyze massive amounts of data in real time. Machine learning can infer intent from search queries, location, and other factors, then automatically serve relevant ads. What’s more, this is all done in the background, giving marketing managers the freedom to pursue other media-buying opportunities.
Predictive advertising is a relatively new application of predictive analytics. For most marketers, it might sound like a luxury only the most competitive enterprise businesses can afford to fully utilize. And that likely won’t change as long as adoption of the technology remains low. In the future, however, it will likely be essential for a wide range of businesses. Consider:
The competition
Over time more businesses will adopt automation and predictive advertising to optimize their campaigns. Taking advantage of the same technologies to improve campaign performance will become the only way to keep up with the competition, let alone stay ahead.
The savings
Arguing that predictive advertising is “too expensive” to fit budgets will be difficult as the technology continues to demonstrate value for businesses. Predictive advertising can identify minute bidding inefficiencies that can add up to a significant reduction in unnecessary ad spend. Not taking advantage of the technology can end up costing you money instead of saving it.
Budget reallocation opportunities
Predictive advertising helps advertisers ensure they only spend the minimum amount necessary to reach their marketing goals. That means more of your advertising budget can be redistributed to other initiatives that maximize ROAS. This is particularly important considering that for every $1 spent on Google Ads, businesses can earn $2 of revenue. Predictive advertising helps make sure each dollar of your budget is well spent.
Efficiency
Predictive advertising can automate a wide range of campaign optimization tasks that would otherwise need to be done by advertising managers and their teams. Rather than replacing their jobs, AI and machine learning can help free team members to focus on other marketing initiatives, such as discovering new media buying opportunities to drive growth.
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Google Ads and other major advertising options are already making big changes to incorporate AI and machine learning into their platforms. And a wide variety of external tools, with more advanced targeting, data analysis, and optimization features, are also available. Given the enormous benefits, value and competitive edge predictive advertising brings, it’s simply a matter of time before all advertising becomes predictive. And tools like machine learning paired with advanced statistical analytics can help businesses of every size improve the relevancy, efficiency and ROI of their advertising campaigns.
The future is being able to predict the future. And with predictive advertising, we’re one step closer to that reality.
You might often hear marketers tell you to take the time to carefully execute PPC campaign management for optimal performance, then you can simply "set it and forget it." While it may be a popular philosophy, it’s also a common misconception among amateur advertisers.
Without a doubt, setting up well-targeted campaigns lays a strong foundation that gets you off to a great start. But experienced marketing managers know that PPC campaign management and optimization aren’t static disciplines. Both competitors and the market rapidly change, and thus, so do the needs of your audience and their search queries. And if you don’t keep up and make necessary changes, your competition will soon outperform you by doing so first.
As with a well-tended garden, in order to get your campaigns to grow and thrive, you need to continuously keep them managed and maintained. Reviewing how your audience reacts to and interacts with your PPC ads can also provide you with new insights that make your campaigns even more effective.
Smart advertisers conduct routine PPC campaign management tasks regularly -- sometimes even daily -- to implement important changes like:
If you choose, you can let your PPC campaigns run themselves in the background. But if you want to maximize ROI from your ad spend, ongoing PPC campaign management and maintenance is a must. Here are eight strategies you can leverage to maximize ROI from your PPC campaign management efforts.
PPC advertising can help drive numerous marketing and business goals. But targeted, successful campaigns don’t attempt to achieve everything at once. Subsequently, an important part of PPC campaign management is defining a clear goal and appropriate metrics that can be measured against key performance indicators (KPIs).
Before starting a campaign, you should be able to answer questions such as:
Define your goals before you start because it will ultimately impact what bidding strategy you choose, what keywords you target, and what kind of ads you serve.
Carefully structuring your account from the beginning is an important part of PPC campaign management, making your campaigns easier to manage, and reducing the chances of making big targeting mistakes, such as:
When you’re starting out with just one or two campaigns, account setup might seem easy. But if you plan to expand in the long term, it’s important to consider multiple factors when initially laying a foundation for your account structure.
Setting up your account the smart way means:
When setting up your campaigns and ad groups, consider what kind of PPC campaign management tasks you’ll be performing down the road, and make choices that will simplify this for you. For example, don’t put too many ads in an ad group, and use detailed names to describe your campaigns and ad groups. Don’t create multiple campaigns for the same product category, or target multiple products/services with a single ad group.
When you first create your keyword lists, you’re often taking a guess at what keywords are relevant to your audience without any hard data. But as your campaigns get underway and evolve, you can then start to gain some perspective regarding which queries perform better than others.
A great strategy advertisers can apply when setting up new campaigns is to start using modified broad match keywords, which provide a list of search queries that trigger one of your ads. You can then duplicate your campaign and add these same keywords as phrase match keywords. Search terms that actually generate clicks on your ads can then be added as exact match keywords. Known as segmenting keywords by relevance, this strategy enables you to bid more on high converting queries. Also focusing on more relevant keywords will improve your Quality Score, making ad relevance an important factor.
It’s equally essential to remove keywords that are causing problems with your campaigns. Take the time to review the information under the “Search terms” tab and remove keywords that:
Removing problem keywords from your campaigns will help free up your budget so you can focus on high-quality keywords, and thus, channel your efforts around achieving the highest ROI.
Even if you do a great job establishing your negative keyword lists in the beginning, you can’t possibly know every potential irrelevant keyword associated with your ads -- this can only be determined by monitoring campaign performance.
Some experts will actually tell you that maintaining your negative keyword lists is even more important than managing your normal keyword lists, simply because there’s a significant negative impact every time your ad shows for one of these irrelevant keywords. As previously mentioned, relevance is an important factor in determining your Quality Score for a search query. Low relevance means low Quality Scores, worse ad position, and poor performance.
Making regular changes to negative keyword lists is especially important for advertisers in niches related to news or pop culture topics. Beyond mining your search terms report, you can also set up Google Alerts for important brand keywords, which will allow you to discover new and trending news topics that could cause irrelevant search queries to trigger your ads.
Some advertisers put so much energy into keyword targeting and bid strategies that they forget about their advertising collateral altogether. But the actual advertisements you display and their associated landing pages are of equal importance for PPC campaign management.
Delivering ads for the most relevant search queries doesn’t matter if your ad copy and call-to-action aren’t optimized and attract prospective targets. By the same token, you won’t also be able to drive on-site conversions from your efforts if your landing pages offer poor user experience.
So if you’re experiencing unexpectedly low Quality Scores within your campaigns, your advertising collateral could be the culprit. Consequently, you’ll need to monitor performance and take the necessary steps to continually improve it.
Test your ad copy
Often, even the smallest changes to your ad copy can significantly impact PPC performance. A more relevant headline or call-to-action, for example, can mean the difference between a person ignoring your ad or clicking through and converting. Similarly, including relevant ad extensions can also have a big impact -- an auto repair business, for example, could significantly improve ad performance by including call extensions with their ads.
In short, smart advertisers simply don’t guess at the ad copy that is most relevant to their audience -- they set up different ad versions and test performance. One way to do this is by leveraging advanced settings in Google Ads to rotate different versions of your ads indefinitely. You can also rotate and automatically optimize to display the best performing ad over a set amount of time.
Improve landing page user experience
Equally important to monitoring how audiences interact with your ads is how they interact with your website content once they click through. A slow loading page will naturally result in a higher bounce rate. Meanwhile, other landing page elements could lead to user experience issues that frustrate visitors.
It’s important to also ensure the content on your landing page matches the content of the associated ad. A specific, targeted ad that leads users back to a generalized landing page will most definitely cause confusion or make visitors wonder if they accidentally clicked on the wrong ad. Consequently, you’ll need to be sure to use language that helps reassure visitors that they’ve landed in the right place.
Google wants to help its advertisers succeed in their PPC efforts. And PPC campaign management is a huge part of this, so they have a big incentive to help with that as well. One of the ways Google does this is by sending email alerts with important information to help you improve your accounts and campaigns.
Google can send you email notifications for:
Smart advertisers can and should take full advantage of these notification services so they can react quickly to the problems and opportunities Google identifies. That said, you’re not automatically signed up to receive all of these alerts -- some require an opt-in. To ensure you’re receiving the most important alerts you need for campaign maintenance, check your email notification settings under “Setup” then “Preferences” from your Google Ads account.
Set up Google’s email alerts the way you want them. With Reports, for example, you can choose to receive all available reports as an email alert, or select the most critical reports that you customize yourself. This way Google can bring your attention back to your PPC accounts whenever they need critical maintenance.
Automated rules are another important tool Google offers to help you manage your PPC campaigns, allowing Google to make changes to your account for you based on rules you create. And by taking full advantage of these automated rules, you’ll save time and improve the performance of your campaigns without needing to make laborious manual changes.
Here are some of the many ways you can leverage automated rules for PPC campaign management:
To leverage these tools to their full potential, consider the kind of regular campaign management tasks you perform manually and see if they can be automated via Google. From there, you can then start creating your own custom rules to help you save time and improve campaign performance.
It’s no secret that in recent years, Google Ads has rolled out a variety of important campaign management features. For example, by using machine learning technology, the platform provides recommendations that can help you improve campaign performance and the strength of your accounts.
When you’re first starting out, it’s highly valuable to take advantage of these features because effective campaign management isn’t something you can do all on your own. The market, competition, and your audience are constantly changing. As a result, comprehensive PPC campaign management is a full-time job for even small business marketers. For larger businesses or those with aggressive growth strategies, keeping up with campaign maintenance manually is practically impossible.
And while Google’s internal automation features are a great place to start, they offer limited use of powerful technology. Machine learning and artificial intelligence can not only help you discover new insights and opportunities, it can act on them for you.
However, while the data that Google leverages comes solely from your PPC ad performance and that of your competitors, no business solely exists within the bubble of Google PPC advertising. There’s a wealth of data beyond Google that can drive even more relevant insights such as audience data from your CRM platform and third-party intent data from other sites around the web -- none of which Google’s AI platform even considers.
So if you want to get the most relevant help to improve PPC campaign performance, you’ll need to use a technology that draws on all these data sources to gain a full picture of your audience and market. By using the same powerful machine learning and AI technologies as Google, you can make significant changes to your bid strategy, keyword targeting, and other campaign factors aimed to dramatically boost conversion rates, lift ROI and improve the overall performance for even your most underperforming campaigns.
Creating and executing successful PPC campaign management is a critical building block for an effective SEM strategy and imperative for maximizing your return on ad spend (ROAS). That said, it’s no secret that the myriad of maintenance tasks associated with a high bar of success are an expense that can quickly add up to take you over budget. In short, you have to spend a lot of time, personnel and resources to consistently and continuously maintain your accounts while making the necessary changes needed in order to keep them optimized. And while necessary for PPC growth, it’s a precarious balance to maintain.
That’s where artificial intelligence and automation come in, which, among other things, make it possible to better optimize your campaigns while simultaneously reducing the amount of hands-on work needed from marketing managers.
Any marketer worth his or her salt knows that there is no recipe for instant campaign success. A successful PPC campaign needs to be well-tended, carefully monitored, and constantly updated to accommodate the ever-changing market tides. Like anything worthwhile, it requires patience, care, and a little TLC. But maintenance and management can also be time-consuming, laborious, and eat into profits when it becomes too big for your organization to handle or exceeds your resources. Finding the right tools that leverage automation to establish accounts, refine keywords, define and uphold campaign goals and enforce automated rules can go a long way to ensure that crucial functions are done swiftly and accurately. And by keeping the busy work off of your plate, you’ll be able to focus on what matters most -- designing even better, stronger, and more creative campaigns that take ROI -- and resulting profits -- to new levels.
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Interested in how Centro's automated technology can help you unlock improved PPC performance? Connect with our digital experts today.
As a digital marketer, you know that in order to truly succeed in digital marketing, you need to understand the needs of your audience, target them at the precise moment of purchase intent, and deliver a relevant message that helps them convert -- easier said than done. Historically marketers have relied solely on traditional business intelligence to strike the right balance between this complex mix of elements in order to achieve their campaign and revenue goals -- a feat nearly impossible in today’s fast-paced digital world. But looking ahead, that's where smartphone AI will come in.
These days more than ever, consumers rely on their mobile devices as a tool to fulfill their shopping and product search needs. Thus, as technology has continued to evolve and adapt to consumers' changing needs and behaviors, smartphone AI has emerged as a viable tool for businesses, helping them drive insights to deliver the right marketing message to the right audience at the right time, while unlocking new potential targets, buyer intent and other relevant opportunities that promise a major win for ROI.
Safe to say, business Intelligence (BI) has grown exponentially in sophistication over recent years -- a development driven by increasing amounts of available consumer data. What’s more, today’s business intelligence capabilities are incredibly advanced, with the potential to drive more actionable insights than ever before.
The most common type of business intelligence marketers use today is descriptive analytics. Leveraging first party data directly from your business, descriptive analytics essentially summarizes the performance of a business in the digital market. Data sources include on-site behavior, your brand’s social media mentions, performance of your internal sales teams, advertising market share, which, when combined, can drive data insights to help make better business decisions.
The next step in business intelligence capabilities is predictive analytics. By strategically utilizing a combination of advanced statistical algorithms, first party data and external third party intent data, predictive analytics can project future performance of your marketing and business efforts.
As such, most marketers who adopt predictive analytics do so to forecast the trajectory of their campaigns and other marketing efforts based on their advertising investment and other strategic factors -- and its applications are numerous. For example, in addition to basic annual and quarterly forecasts, predictive analytics can perform sentiment analysis based on data from social media conversations and online forums. It can also make generalizations about how people feel about your brand by performing text analysis.
Yet even in light of these advanced capabilities provided by regular business intelligence, Artificial Intelligence (AI) can still, in many ways, surpass these insights. And when combined, both technologies complement each other to offer unique advantages and unprecedented lift to your PPC campaigns. And the businesses that take full advantage of it are primed to increase business growth and stay ahead of the competition.
Contrary to popular belief, AI doesn’t refer to one specific technology, but an array of potential digital capabilities that mimic cognitive human functions. Thus, any computer with the ability to adapt and learn from surrounding conditions and solve problems has artificial intelligence.
As you might already know, mobile devices today include internal AI engines that leverage a user’s personal data, as well as information from mobile device sensors, to analyze and make decisions on the device itself. Here are a few of the many applications of smartphone AI today:
Perhaps not surprisingly, smartphone AI not only offers a myriad of benefits for consumers, it’s changing the way they interact with their mobile devices. The combination of advanced natural language processing and intent prediction now makes it possible for mobile assistants to become intelligent chatbot interfaces that can both understand the relevant information that people need when they ask for it, and also infer intent through natural conversational language. In many ways, they have the potential to become as helpful as a knowledgeable friend -- with the added benefit of search engine access -- setting the stage for conversational commerce that holds the potential to be the next big game changer in app marketing.
Most importantly, AI is changing the mobile advertising market. As the majority of marketers know, mobile devices aren’t primarily used for making phone calls -- they’re the main purchasing tool for many eCommerce shoppers. And, thanks to AI, they can potentially be used by consumers for buying recommendations for an even greater, more personalized shopping experience.
Similar to keys or a wallet, mobile devices have become everyday tools for consumers, who regularly use their phones to communicate, navigate, research potential purchase decisions, and shop. As a result, mobile devices provide an extensive amount of relevant, actionable consumer data that organizations can use as part of their business intelligence efforts -- data that only increases in value as more people rely on their mobile devices to make everyday life decisions.
That said, the wealth of new available consumer data also presents a problem. Businesses simply can’t process it quickly enough for it to continually drive marketing insights and create additional value. That’s where AI comes in.
AI can help drive advertising strategies by:
It goes without saying that using AI technologies makes it possible and easier to analyze large amounts of consumer data. It can also help predict which kinds of consumers to target in order to reach your marketing goals. By making advertising campaigns more efficient and effective, AI can simultaneously reduce your necessary advertising budget while improving ad performance. It makes sense, then, that its capabilities be incorporated in the tools that consumers most use -- their mobile devices.
But while there are already a variety of tools and technologies on the market designed to help businesses improve mobile advertising with smartphone AI, adoption has only penetrated a small group of competitive marketers -- largely because the rate of AI adoption has been generally slow. Currently, most businesses are often too focused on gaining insights from their regular business intelligence capabilities -- which have significantly grown in value in the recent year -- to explore and consider more sophisticated options.
The truth is that business intelligence and smartphone AI aren’t mutually exclusive advertising growth strategies. While, AI can mimic the cognitive functions of real human decision-making, ultimately it’s your unique business and marketing priorities that drive the choices you need it to make.
Business intelligence capabilities such as descriptive and predictive analytics can tell you a lot about current and future performance potential. And businesses that combine these BI capabilities with smartphone AI can often create sophisticated, efficient and effective advertising strategies that can often outpace competitors solely relying on just one of these technologies.
In 2016, mobile internet usage surpassed desktop for the first time, and the gap only continues to become ever wider.
Since the future of internet will be mobile, businesses are starting to make it a priority. Google itself has made several major algorithm changes to prioritize mobile-first indexing, as well as launching its Accelerated Mobile Pages (AMP) project to improve the speed at which sites load on mobile devices.
Mobile prioritization affects all areas of search engine marketing (SEM), including SEO and PPC advertising. The landing pages you create for your PPC ads need to offer a good mobile user experience, as this affects your Quality Score and other advertising outcomes. Users are also much more likely to bounce after clicking on your advertisement if they find landing pages difficult to navigate, further underscoring that mobile will need to be a priority for a successful, long-term SEM strategy.
Most businesses today build their buyer personas based on demographic factors and what they imagine are the needs and pain points of their target audience. But with smartphone AI, it’s possible to gain an even deeper understanding of what people really want and why by conducting intent-based marketing -- nearly impossible to do properly without using advanced data insights. Among other things, smartphone AI has the potential to process phone and chat conversations, as well as draw on location-based data to specifically understand what a person has the intention of doing or buying.
And businesses that use smartphone AI insights in combination with their own business intelligence tools can optimize their mobile sites to deliver the kind of content audiences really want -- because they have the power to really know. As more and more businesses adopt this advanced targeting strategy, it will becomes increasingly challenging for the competition to keep up.
Targeting your audience with the right message during these micro-moments is essential for business success. And Smartphone AI makes it possible to better understand a user’s intent so you can deliver a targeted ad or marketing message at the time and place they can benefit from it the most. Google calls these micro-moments, intent-driven moments of decision-making and preference-shaping that occur through the entire consumer journey. Of online consumers, 69% agree that the quality, timing, or relevance of a company's message influences their perception of a brand.
In addition, the user experience will also have to be prioritized as smartphone AI becomes more prominent. Marketers will need to create concise, targeted landing page copy that easily enables users to make quick purchase decisions with the click of a button. It will also become important to prioritize mobile ease-of-use, while also reassuring users on your page copy that they’re making the right decision.
When you make marketing decisions and advertising campaign adjustments based on business intelligence insights, it’s always a process of trial and error -- in order to stay optimized you need to constantly measure results, adjust for market fluctuations and retarget your strategy consistently.
Machine learning provides a solution to help simplify this process for advertisers. Among other things, machine learning can analyze consumer behavior, see how audiences interact with your ads and marketing message, then help you make necessary decisions to improve your targeting -- all of which boost smartphone AI's capabilities that provide deep insights into customer intent. Machine learning also has the ability to analyze large amounts of consumer data and recognize complex patterns that regular data scientists wouldn’t be able to discover.
Managing reams of business intelligence data is daunting enough as it is and ostensibly, adding smartphone AI into the mix could be perceived as yet another obstacle for your marketing teams to overcome. But in reality, with the increasing reliance on mobile devices as a tool for shopping and searching, leveraging smartphone AI is an investment that not only makes sense but promises sustainable ROI over the long term.
As a digital marketer, you know that the industry doesn’t stand still for a moment. Competitors have are always finding new and creative uses for business intelligence capabilities in an effort to gain an edge in the market.
That’s your opportunity to leverage a tool that your customers are already using. Marrying smartphone AI to your existing arsenal of business intelligence capabilities will only work in your favor -- whether it's unlocking buyer intent, applying new predictive behavior technologies, or creating a better shopping experience -- that will give you the leg up you need to leave your competitors behind.
If you’ve advertised on platforms like Google Ads or Bing Ads, you know by now that they try their hardest to make the SEM account maintenance process as easy as possible. Thanks to automation, you can easily set and forget your PPC campaigns, letting Google Ads do the heavy lifting for everything from optimization to simplifying campaigns to targeting the right prospects. But your work doesn’t stop there.
In fact, even the most experienced search engine marketers understand that their accounts often require additional maintenance—a little TLC and a personal touch—to garner the highest possible value and ROI for their advertising dollars, while ensuring their PPC campaigns remain profitable and efficient.
However, as your PPC campaign initiatives expand and evolve, so do the corresponding maintenance obligations. The more time, money and resources your marketing teams invest into SEM account maintenance, the more difficult it is to create enough efficiency and momentum to steadily increase ROI. At the same time, marketers who fail to put time and resources into campaign optimization risk falling behind competitors who do.
This post discusses the challenges around SEM account maintenance and efficiency at various points throughout your campaigns, and what PPC managers can do to strike the right balance between maintenance and momentum for their business.
It likely goes without saying that your bidding strategy is perhaps one of the biggest opportunities to improve campaign performance. Google Ads offers several main options to bid for your ads, and the one you choose is contingent upon your campaign goals and overall account strategy:
Clicks and conversions are the relevant bidding options for SEM. Focusing on clicks means you’ll use cost-per-click (CPC) bidding, enabling you to only pay for an ad when someone clicks on it. With CPC, you have the option to bid manually or use automated bidding. If you choose to bid manually, you have full control over the bids you set at group level, or for individual keywords or ad placements. If you choose automatic bidding, Google makes the bidding decisions for you.
If you focus on conversions, you’ll leverage a cost-per-acquisition (CPA) model, and you’ll need to set up conversion tracking so you can define the kind of on-site actions you want visitors to perform. While focusing on conversions can help you nurture leads and realize higher revenue from your PPC efforts, you also relinquish control of how your bids are set.
Without a doubt, Google Ads automatic bidding features make SEM account maintenance and campaign optimization a lot easier—especially for less experienced advertisers who lack the expertise or resources to make well-informed decisions based on their own insights. To garner the most relevant and strategic insights possible, Google’s automatic bidding platform leverages a machine learning algorithm in combination with behavioral and demographic data, such as location, day, time, browser and device, among other things.
Most SEM managers concerned with SEM account efficiency opt for automated bidding, in which Google adjusts CPC bids in order to maximize clicks. However, while perhaps more labor intensive out the gate, the ability to control your own bidding strategy can potentially give you the opportunity to factor in external data insights that can improve the effectiveness and efficiency of your campaigns and give you a bit of an unanticipated leg up over competitors.
The reason? You can gain key insights on consumer behavior from external data that Google Ads just can’t provide or factor in, thus giving you an edge over competitors not leveraging those same insights. And when marketers make bidding choices based on an understanding of their full unique funnel, they in turn can improve conversion rates, lower their minimum bid, and improve their Quality Score.
While it may be a well-kept secret, third-party bidding tools combine both the efficiency of automation and the accuracy and personalization of manual bidding, truly optimizing your entire campaign strategy.
Here’s why: Third-party bidding solutions automate your bidding strategy based on a broader range of factors, allowing you to create your own unique bid policy settings to increase the performance and value of your advertising efforts based on specific factors and campaign goals. For example, third-party platforms leverage data science and machine learning technology to maximize a KPIs like conversions, profit or revenue. But you can also set them to do so within a monthly spend amount and specify minimum margins in order to maximize your revenue-based KPI within a framework that fits your broader business goals.
On the other hand, in a purely automated scenario, Google Ads assumes advertisers want to bid with a single marketing goal in mind (i.e. clicks, conversions, etc.). In reality, advertisers working toward complex goals like performance and growth likely have several relevant key performance indicators (KPIs) that determine the success of their campaigns. Google’s automatic bidding technology simply doesn’t have the sophistication, options or information to make these kinds of strategic, highly personalized decisions. And savvy advertisers who use Google automatic bidding in an effort to improve efficiency can end up sacrificing optimal effectiveness as a result.
In today’s PPC advertising world, bid adjustments are just as important as your campaign structure and initial bidding strategy. Google now offers a variety of bid adjustment options, and they’re a standard strategy for advertisers wishing to better allocate their ad spend and improve campaign effectiveness.
You can adjust bids for the following factors:
Some advertisers use bid adjustments as a general targeting tool—doing a -100% desktop bid adjustment, for example, will prevent your ads from appearing on desktop. That said, the real value of bid adjustments is helping you target your bids more efficiently to improve performance.
Some common bid adjustment strategies include:
While bid adjustments can help improve SEM account maintenance and campaign performance, they also increase the complexity and maintenance needs of your campaigns. Some bid adjustments are only optimal during specific market conditions, so leaving these adjustments in place unnecessarily can negatively impact campaign efficiency. Thus, marketers should be prepared to make necessary and timely adjustments to their bids, or risk hurting their campaigns. And the best way to address this issue to to strategically leverage a technology designed to automate bid adjustments in a way that automatically calculates location and device for Google or Bing based on trends and changes relevant to your unique business.
Many advertisers focused on maintaining the efficiency and effectiveness of their SEM account maintenance and initiatives fail to realize the critical and fundamental role of account structure. In fact, how you first establish your campaigns is one of the most significant factors impacting potential optimization down the road.
When an account is well organized, it’s also easier to systematically identify key areas, then adjust and optimize to improve efficiency. It also ensures that the search queries you’re targeting are relevant to your target audience. This in turn improves your Quality Score, which ultimately makes it easier to target your ads at lower prices.
One aspect of your account structure that you can continuously change is ad grouping. As you continue to understand and refine the keywords and market information that can improve your account’s relevance, it’s worthwhile to follow keyword grouping best practices. Among other things, you can:
Create broad, top level ad groups - Select generalized keywords that relate to the products or services you want to advertise. A clothing retailer, for example, could group keywords based on product types (e.g. “women’s boots”).
Segment your groups into targeted subgroups - Next you’ll create sub groups using keyword modifiers. These will provide more detail about user intent, whether it be transactional, investigative or instructional.
Clean up and optimize your groups - Lastly you’re ready to optimize your keyword groups by adding in synonyms and variations on your base keywords, and organize duplicate keywords into your desired groups.
Once you create your ad groups and organize your keywords within them, it’s important to take the next steps to improve your structure as your PPC campaign matures. Setting and leaving your ad groups make it impossible to keep up with competitors over the long-term, as your ad performance can degrade as competitors make small adjustments that adapt to new market trends leaving your business behind the curve.
Instead, getting ahead of the curve, so to speak, will be reliant upon how often you revisit and refine your ad groups to optimize performance and efficiency, a strategy that you will constantly hone as your business grows and as markets change.
Luckily, keyword grouping is another area of SEM account maintenance that you can automate. And by leveraging predictive analysis and machine learning, it’s also possible to improve keyword grouping beyond what PPC managers and standard automated processes provide.
Some marketers incorporate negative keywords into their PPC strategy almost as an afterthought, selecting the most obvious ones and deciding that’s all they need to truly optimize their campaigns. In reality, failing to discover and use all your relevant negative keywords can result in significant wasted ad spend.
Negative keywords can come from a variety of sources that are either irrelevant to your business or irrelevant to your PPC advertising goals. For example, keywords such as “[brand keyword] login” or “[brand keyword] order number” suggest a user has already converted into a paying customer. If your main PPC advertising goal is to drive conversions, then these are important negative keywords to add to your list.
It’s worthwhile to put extra time and effort into compiling your negative search terms list initially when setting up your campaigns. But you should also put processes in place so you continuously discover new negative search terms. For example, it’s good practice to set up Google Alerts for your branded keywords and others relevant to your industry so you can track what news and content searches could trigger your ads to appear.
That said, constantly reviewing your keyword lists for negative keywords can create monumental challenges—at the very least, this requires you to sift through thousands of keywords and make important distinctions between exact words, phrase or broad match negative. But for businesses that invest heavily in PPC campaigns, its a challenge that can’t be overlooked. And if complete, updated negative keyword lists are continually maintained, it’s possible to save up to 20% or more of your PPC budget.
The key to success is to continuously assess Google Ads performance to identify new keyword opportunities, then group keywords in a way that’s easy to asses. From there, use your search terms report, negative keyword tools, competitive research, and other sources of data insights to continuously add to and refine your negative keyword lists.
Performance analytics is also arguably the most important aspect of SEM account maintenance, simply because it’s essential to optimizing every other aspect of your PPC campaigns. If you’re not able to fully attribute your PPC performance metrics to optimization efforts to, you’ll have no key insights for future improvements.
Accurate and full-funnel performance attribution can help with your SEM efficiency and efficacy efforts by:
Google Ads offers a variety of internal features that can help you gain insights to make these important optimization decisions—for example, the Keyword Planner tool can help you identify growth opportunities based on your budget allocation. But these are generalized estimates that don’t reflect the unique market and position of your business.
Meanwhile, the same predictive analysis and bidding automation tools you use to improve efficiency can also paint a better picture of campaign performance—serious marketers will often use Google’s internal reporting features along with third-party technologies that provide deeper campaign insights. But the best advertising technologies provide flexibility so that you can make key decisions to customize your bids and budgets or automate changes to focus on efficiency. It’s never one or the other.
Despite the abundance of optimization and automation features at your disposal, creating efficient campaigns that maximise your return on ad spend (ROAS) still represents a major hurdle for marketers. Compounding this challenges is that many advertisers underestimate the range of opportunities available for them to improve their campaign structure, ads and landing pages, keywords and bidding strategy.
Those who focus on all of these unique areas are in the best position to be able to reduce wasted ad spend and maximize campaign performance. But doing so requires PPC managers invest extensively in SEM account maintenance. Only the largest, best equipped PPC management teams can attempt to keep up with the task long term. And even then, new data and market insights will always complicate the data and introduce new challenges. In short, SEM account maintenance presents a daunting and formidable task for even largest and best-supported teams—that is, unless they leverage the right tools.
Google Ads’ internal automation and optimization features are a great place to start when managing the complexity of SEM account maintenance. But it’s far from the only option. External technologies can factor in optimization points specific to your unique business and market, while giving you the ability to act on data insights from a much broader understanding of consumer behavior across the web.
At the end of the day, true SEM account maintenance is learning to strike the right balance between insights, automation and hands-on optimization. It’s about finding the desired combination of efficiency and effectiveness, personalization and profits. And then finding the right technology to get the job done.
You’ve done your keyword research. You’ve compiled a monster keyword list. You’ve run plenty of campaigns on that keyword list. You’ve worked hard trying to optimize your bids. You have analyzed and tweaked and done your best to make sense of the results.
But you can’t. Your ads don’t seem to produce the profits you seek, your keyword list isn't getting play and your superiors are wondering if you actually majored in marketing.
Nevertheless, this story is a familiar one to internet advertisers of all stripes. Keywords, while their harvesting and list-building is pretty straightforward, are actually monumentally difficult to make pay … at least until you know what you’re doing.
Luckily, there are some answers. In this article, we’ll discuss a wide variety of relatively simple tricks to help you boost your ads’ effectiveness. This includes understanding the difference between keywords and search terms, discarding irrelevant keywords, removing errors and extra characters in your lists, and tightening up your words and phrases.
It also will entail making ad groups more profitable by winnowing out underperforming keywords on your keyword list and organizing them more effectively, understanding the other uses of each keyword, using negative keywords to avoid bidding on them, and using your keywords and search terms on your site.
You likely know how to search for keywords, most likely using a free tool like the one provided in Google Ads, as well as how to segment your keyword list somewhat proficiently. Given that, we’ll provide you with tips to make them perform better and more efficiently during your campaigns.
Lastly, you probably already have outlined and executed a PPC strategy, even if it’s not yet paying off in the way that you want. If you haven’t, feel free to read and file these tips, although you might want to go back to basics first. Whatever stage you’re at amid your strategic PPC journey, you can benefit from these smart, easy steps. Let’s get started.
First and foremost, it’s critical to understand the difference between keywords you add to a keyword list and search terms. This is somewhat fundamental, but don’t feel offended: Many marketers still don’t understand the function of each. Simply put:
This matters because a variety of search terms will funnel toward the same single keyword. For instance, if you sell prepackaged muffin mix, the following search queries might trigger any number of keywords. Once you run a campaign, you can look at the data to see which search queries triggered your keyword. This tells you two things:
You can immediately add the pertinent keywords to your list. We’ll talk about what to do with the keywords that don’t work in Steps 5, 8, and 9.
Understanding what your ideal customer is looking for and reflecting that in your keyword list is critical. First, that starts with researching queries rather than keywords on your keyword list. Keywords serve you, but only if you first make sure you’re serving prospects. And that comes from knowing their queries.
Again, you can perform this research in your own data. You can also use tools such as Google Trends, which will tell you what searchers are looking for. Once you have those queries, you can plug them into a keyword tool and see what you get.
The bottom line here is, what do you actually sell, and what are your ideal customers looking for? The more granular you can become with regards to defining intent, the further you go toward the long tail and low competition. It also nails the prospect’s experience when they hop online to do the research that will bring them to you.
Micro-moments are a fascinating new-ish idea stemming from Google’s research that “people are more loyal to their need in the moment, rather than any particular brand (as Tourism eSchool reports, 65% of smartphone users agree that when conducting a search on their smartphones, they look for the most relevant information regardless of the company providing the information)
What are your micro-moments? What do people come to search engines looking for in the moment, and how can you snag them when they need what you sell the most? For muffin mix companies, it might be school bake sales or holiday parties, Fourth of July picnics, or even a funeral, where no one has time to cook. It’s not your brand that will win them over, it’s your ability to take care of them in these situations.
So: What’s their time of need? How can you serve them in it? Now use the answers you come up with to run keyword searches, test them via campaigns and use the rest of these steps to keep the most valuable ones.
While older PPC platforms used to reward variations of keywords, that’s no longer the case. Nowadays, search algorithms tend to weed them out using a rough version of the following rules:
What does this tell you? That you’re not getting much out of your ad spend on words that are likely closely matched keywords, but are not in fact those words. So just cut them out entirely and stop the competition/wasted money.
Remember when we talked about irrelevant keywords from your keyword list in Step 1? Well, there’s a good chance some of them are still on your list. How do we know? Because if you search “muffin mix” in Google Ads, you will pull up keywords that contain words such as “recipe,” “homemade” and “how to.”
These all tell you that searchers are looking to do something at home, probably right now (an irrelevant micro-moment). While a small subset of them might purchase your mix if it pops up, most will quickly look elsewhere – which means you’re ultimately wasting your ad spend.
You’ll also likely pull up terms relating to products that you, the “dedicated muffin mix maker,” just don’t carry. This begs the question -- do these keywords even have a place in your keyword list? Some marketers run their campaigns on the notion that any impression is a good impression. But is it? Are those impressions worth much if they don’t convert and steal spend from keywords that do?
The short answer is no, they do not have a place on your keyword list, and may in fact only be there because your marketing department has run lots of keyword searches, thrown everything into an Excel spreadsheet, and then failed to properly vet the list.
If you’re guilty of hoarding a long keyword list while also completely disinterested in combing through hundreds or thousands of keywords, we understand. Yet if you want your ads to perform, you must get those irrelevant keywords out of there.
You can do further work to tighten up those words on your keyword list by weeding out misspellings and errors. Pure sloppiness is a waste of money, and because the system no longer rewards variations, the use of misspellings is low (if the system lets you input them at all).
Luckily, Google Ads is pretty good about returning clean keywords, as are most other research tools these days. (With the exception of some of the Amazon keyword-mining tools, which haven’t caught up.) Still, some misspellings do sneak in, as do duplicate spellings from other dialects.
In the case of a muffin mix vendor, the misspelling of “recipe” may not have been valid anyway, but the spelling of “savoury” has to go. So does anything along the lines of “mufin” or “bluberry” or any other obvious misspelling that’s just plain wrong.
Once you’ve removed errors and tightened up the words on your list, you will want to segment them appropriately to avoid keyword cannibalization. Perhaps you have ad groups dedicated to different flavors, how many ingredients they have to add, holidays, and school functions, among other things.
This step is simple, but it’s not necessarily easy. In order to know which ads are under-performing, you need better tools to help you see which terms deliver results and which don’t, and to help you distinguish which is which. Machine learning and data science are probably your best bet because they create a wealth of usable data and deliver metrics you might never arrive at on your own. Plus, platforms that utilize such cutting-edge technologies do a better job of helping you maximize your ad spend and optimize the words from your keyword list that do work, so you can turn your attention to other aspects of your marketing, such as ad creative or search query research.
Keywords can mean many things to many people. As we’ve seen, the keyword “muffin mix” can mean prepackaged or homemade.
According to the site Keyword.com, "I was looking through an Adwords account from another Adwords agency. One of the most used keywords was “Danish Swedish.” It was in the top 10 with most impressions, clicks, and cost. With that keyword people were searching for the search term “Danish-Swedish Farmdog” more than 80% of the time. It is all right if you are a dog breeder, but this client is a translation agency looking for big companies (It was not difficult to win that client after I show them this example).
Here’s where those terms from Step 1 – “homemade blueberry muffin mix” and “blueberry muffin mix recipe,” both of which may trigger your ad’s keyword “muffin mix” – come in again. To avoid these kinds of scenarios, make a chart of common search terms that might trigger your ad but aren’t related. Obviously, you’ll want to remove them from your keyword lists so you’re not bidding on them.
That said, you can use them in other ways
Once again, we turn to the lesson in Step 1, that many search terms will trigger keywords even when the searcher isn’t actually interested in your product. If you want to save as much money and see the greatest results, you need to ensure irrelevant user searches can’t trigger your keywords.
This might seem similar to discarding irrelevant words, but it’s subtly different. That step means taking out keywords that don’t match your intentions, while this means actively blocking the keywords you do use from triggering search terms that don’t match your business and brand.
Let’s return to our muffin mix search. We’ve already crossed out the misspellings, because we don’t need those. Now we’ll focus on terms that could trigger your keyword “muffin mix,” but not for the right reasons.
First and most obviously, you want to avoid “muffin top,” because you’re not a fitness site, nor do you sell empire-waist shirts to hide your beltline. Most people who are searching for ways to make a streusel or something for the top of their muffins will search more specifically, and it’s likely that they still will be looking for a recipe. This is most obvious as a keyword (leave it off the keyword list!), but there’s still value in excluding it as a search term. While not that many searches for “muffin top” may trigger “muffin mix,” any that do represent waste.
Similarly, you might want to avoid the phrase “healthy muffin mix,” because someone who comes to your site and sees high-calorie treats geared toward holidays or birthdays will most definitely not be a customer.
When you do find those terms – healthy, recipe, muffin top, homemade, etc. – you can add them to a negative keywords list. That tells the system not to trigger your ads when someone searches for those words or phrases. That way, you’re not wasting your ad spend paying for people to see ads they don’t care about.
Ranking higher in organic search has value of its own, which is why it’s important to use good SEO practices. However, using a lot of those terms on your site will also help you achieve higher ad rank and provide more legitimacy for the human eyes that land on your pages. Incorporating an intelligent SEO campaign into your marketing efforts is critical if you want to see the most from your PPC endeavors, plain and simple.
As with most things, putting in a little time up front to understand often subtle nuances in what makes keywords ideal for your campaign and what makes them irrelevant will quickly pay off both in the short and long term for your campaigns. While it might seem like common sense, taking the time for best practices such as removing errors and tightening up words and phrases, understanding exactly what your customer is looking for, researching other keyword uses, and removing under-performing keywords will make your campaigns more efficient and highly targeted -- which in turn, will lead to higher ROI and conversions. Ultimately, keywords define you, your brand, and your offerings to help you target the right customers in need of your products and services. Putting in the care to find the best keywords will give those customers a better path to find you as well.
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To learn more about how you can build more effective, higher-performing keyword lists, connect with our digital media experts today.
It’s no secret that words like artificial intelligence (AI) and machine learning (ML) carry a lot of weight these days. No doubt, these technologies have changed industries for the better with the ability to process high volumes of data rapidly and accurately, providing analysis and actionable insights that can be leveraged in myriad strategic business decisions.
Perhaps not surprisingly, ML is especially relevant to digital advertising. With its ability to not only compile consumer data, but analyze and predict future consumer behavior, the technology has significantly transformed the space and will continue to open up new doors for the industry going forward. Yet few people really understanding its specific role and its vast potential for advertisers.
First, we’ll distinguish between AI and ML.
Artificial Intelligence (AI) is essentially a computer system that can perform tasks that would normally require human intelligence, such as making decisions or understanding speech language. AI has applications to a wide variety of industries, including advertising.
Machine Learning (ML) is a type of AI that uses statistical techniques to learn from data. When an AI computer system is set to perform a specific task, machine learning allows it to learn from new information as it comes in. It can then make changes to improve performance at a task without being programmed to do so.
In advertising, AI and ML elevate the ability to make buying decisions that emulate human decision making. However, with the right AI technologies and data insights, it’s actually possible to significantly improve decision-making processes beyond human capabilities.
The digital footprint consumers leave is huge, and it’s only continuing to grow. On Google alone, 40,000 search queries are performed per second. To put that in perspective, that's 3.46 million searches per day.
In order to create the most relevant advertising message and deliver it at the right time to the right audience, marketers need as many data insights as they can get. But, in light of the enormous amount of consumer data generated on a daily basis, advertisers are struggling to analyze and act on the information available to them. By the time they manage to derive insights and adjust their campaign strategy accordingly, a new, more relevant data set emerges. Only the largest and most competitive global corporations could hope to build an advertising team with enough data specialists to effectively tackle this kind of undertaking.
While many advertisers don’t yet realize it, ML is the only viable solution to this growing problem. Among other things, the technology makes it possible to analyze and gain insights from vastly more data than a human ever could ever process in their lifetimes. What’s more, it also automates advertising decisions based on these insights, making ads more efficient and effective, and driving business revenue in the process.
Among other things, ML has the capacity to draw on data from a wide variety of sources, including search engines, social media, and other third-party platforms. It can then draw connections and make sense of it in a way that a normal human brain isn’t equipped to do.
Consider social media, for example, where individuals discuss their interests and pain points, follow topics that interest them, and check in at various locations. In what’s known as cognitive intelligence, ML can use this information to build complex personality profiles. In addition to better understanding customer demographic information, marketers can then leverage these comprehensive profiles for insights into users’ thinking, behavior and even their next probable move.
Thus, building better audience profiles makes it possible to create more relevant and targeted ads for potential customers -- a capability that has become increasingly critical in recent years as ads have become more irrelevant to, and thus ignored by, consumers. Having a deeper understanding of the needs, pain points, and even your audience’s train of thought makes it possible to develop truly meaningful and relevant ads aimed at addressing their toughest challenges.
The same processes that make it possible to build better audience profiles can also help advertisers discover new audiences that they never before thought to target. ML can help uncover unlikely but valuable correlations between consumer demographics, interests, and online behavior that reveal new potential target audiences.
For example, an AI system has the ability to analyze a large set of Facebook data and discover that older women who are interested in green living and like horror movies are more likely to buy a certain category of digital products. The data might not explain why this unlikely correlation exists, but advertisers can test out the opportunity by showing ads to them and accurately gauging their response.
AI also makes it possible to develop a template of target audience characteristics that you can search for, identify and cross-reference across the web. As with Google’s similar audiences or Facebook’s lookalike audiences, you can discover new leads to target across the entirety of the internet, as opposed to just one platform. Using vast amounts of third-party intent data, ML can process this consumer information and make sense of it in new ways that lend themselves to better and more strategic decisions for your business.
Historically, most advertisers have relied on spreadsheets and basic analytics software to track their data and generate insights to optimize their campaigns. All too often, this becomes a tedious, error-prone, and laborious strategy that generates only marginally valuable insights.
Artificial intelligence and ML, on the other hand, can automate this process, freeing advertisers to focus on discovery, growth, and other initiatives to improve the media-buying process. Because ML does this more rapidly and accurately than humans, it also results in more valuable insights. Depending on what kind of AI technology is being leveraged, it’s possible to improve insights even more by incorporating your own previous ad performance, as well as that of other advertisers, into your analysis.
ML is also a worthwhile investment because it vastly broadens your dataset and analysis capabilities beyond that of advertisers. What’s more, it continues to grow in value over time as it learns from the performance of its own choices and uses this information to improve future campaigns. In short, the longer you use machine learning technologies, the smarter it becomes, thus increasingly emerging an essential tool for obtaining more ambitious advertising goals and metrics.
Employees tend to get nervous when their employer starts implementing AI and ML technologies -- largely because they assume AI will supplant their jobs and minimize their value to the business.
But in reality, the best way to use AI and ML is by allowing them to support internal teams, not replace them. In fact, using ML effectively can make advertising teams more competitive and relevant in the industry, which can actually increase their overall value to the organization. Also, because ML has the ability to predict the outcomes of campaigns, media buyers can leverage the technology as a check to help them avoid mistakes before they occur. If, for example, they accidentally clicked the wrong campaign setting or forgot to adjust their budget to match seasonal bid costs, these outcomes could easily be displayed in the predicted results, enabling the business to course-correct before a bad decision is officially executed.
As previously mentioned, ML frees media buyers of tedious tasks and saves them time, providing more opportunities to focus on strategy and development. Applying ML to routine, administrative tasks means that advertising teams spend less time doing busy work, and more time working on creative, innovative projects within their department.
While some businesses might envision a future in which advertising is 100 percent automated by machines, it’s more likely that ML will become an essential tool that advertisers will regularly rely on to support their internal teams and streamline workflows.
ML can derive and help businesses act on new data insights faster than a human, or even a team of humans. At the same time, it can use advanced statistical algorithms to identify trends and predict future outcomes. That means it can help advertisers see around corners and make necessary bid adjustments to maximize their return on ad spend.
Its ability to make changes quickly also helps businesses cut costs and reduce wasted ad spend, as it can identify unnecessary spending quickly that will enable you to lower your bids accordingly. Leveraging ML for more accurate and timely insights mean better ad targeting, making it possible to reach advertising goals with a significantly lower budget.
The right advertising software will utilize an advanced decision engine to predict the best ad investments. By using important buying signals, data, and quick bidding options, these solutions can ensure you always effectively leverage your advertising budget to increase ROI.
ML is transforming the digital advertising industry on a daily basis, and as such, has the ability to dramatically improve the direction -- and ROI -- of your campaigns. Currently, AI has the ability to discover new audiences, build comprehensive user profiles, save time, reduces ad spend, and supports advertising teams in countless ways.
That said, it’s only effective as the solution in which it's being used. Advertising software can pay for itself and then some by improving the efficiency and effectiveness of your campaigns. And looking ahead, ML will become an increasingly necessary tool to add to the advertising arsenal, further accelerating the evolution of the digital advertising industry, as businesses find new and innovative ways to realize its true potential.
You’ve probably seen the headlines calling ad blockers the “death of online advertising," and anyone who works in PPC advertising knows a thing or two about them.
But while ad blockers gained popularity back in 2016, three years later, advertising revenue is as strong as ever. Still, some worry that the rise of ad blockers in popularity and adoption will continue to grow, affecting long-term advertising growth potential.
It’s true, ad blockers can have an impact on your PPC growth potential, but only if you allow them to. In fact, pragmatic advertisers see ad blocking as an opportunity more than a hindrance. And once you understand the real scope and impact of ad blocking features, you’ll also gain clarity on your path of growth.
The first step to overcoming the challenge of ad blockers is understanding why people use them. After all, at least some portion of your target market is using ad blockers.
As of 2017, roughly 40% of laptops and 15% of mobile devices in the US used ad blockers. Globally, more than 600 million devices were using ad blocking software in 2016 -- and all industry predictions expect they will grow.
More than any generation, millennials are the driving force behind ad blocker use, as they’re the most disenchanted with intrusive online ads. This represents a significant and accelerating problem for advertisers, as millennials have already overtaken Baby Boomers as the largest generation. And perhaps not surprisingly, their buying power is also expected to more than double over the next four years. Perhaps ironically, the generation with the greatest potential to drive ad revenue is also the generation paying the least attention to ads.
That said, millenials (and others adopters) actually have very good reasons for why they use ad blockers. According to PageFair’s recent ad blocker report, the number one and number two motivations behind ad blocker usage represent about 60% : (1) Exposure to viruses and malware, (2) Interruption. The biggest takeaway? Nobody’s saying they hate advertising. They simply don’t want to be exposed to malicious ads or be exposed to ads that detract from their web experience.
As a PPC marketer, you know that not all ads on the web are spammy, intrusive, or irrelevant. But, unfortunately, a high percentage of them are.
While it seems ad blocker adoption is an attack on low-quality advertising, the ad blockers themselves are indiscriminate. While you might have a highly targeted, relevant PPC campaign that helps your audience convert, it won’t reach anyone who uses ad blockers.
Ad blockers may seem like bad news for advertisers, but really they’re a response to a problem. Even if your ads are unintrusive and highly relevant to your audience, would you want them to appear alongside spam ads and clickbait? Low-quality advertisements give the whole industry a bad name, discrediting honest, customer-centric PPC advertisers.
If nothing else, ad blockers are a way for consumers to fight back against spam adverts. And they’re more prevalent than you might think. According to a recent AdGuard survey: 57% of web users were attacked by scammers through online ads representing a major intrusion in the advertising market -- a fact not lost on the advertisers.
Even Google, which makes 90% of its money from advertising revenue, created an ad blocker people can use with Chrome in response to research released by the Coalition for Better Ads, detailing the types of ads that make it difficult for people to browse and use websites.
While its ad blocker doesn’t impact Google PPC or social media ads, it does block ads that create a poor browsing experience. The tech giant launched their own ad blocker iAd blockers that may help cut down on bad ads and spam ads. But there’s no getting around the fact that they can negatively impact ad visibility and returns for quality marketers in the process. So what’s the solution?
The short answer is: a change in strategy.
Instead of viewing ad blockers as a hindrance to growth, it’s better for PPC advertisers to perceive them as an opportunity.
There’s a certain group of people who are always going to ignore advertisements, ad blocker or not. So why bother serving ads to people who are only going to ignore them? You shouldn’t. Instead, it makes more sense to invest your time and advertising spend reaching people who are receptive to your marketing message -- people who are actually paying attention.
Also, the idea that one day in the future nearly every consumer will use an ad blocker (like nearly every person with a computer now using antivirus software) is unlikely. Advertising isn’t inherently malicious (like malware), and consumers understand this.
According to Hubspot research:
Consumers are open to the idea of viewing advertisements as long as they’re quality ads that don’t take away from their user experience on the web. So why not make efforts to meet this need with your advertising strategy? If you create quality, relevant, unintrusive ads, you'll show your audience there’s no need to use an ad blocker.
Of course there’s the issue that even if advertisers make these changes, the people using ad blockers are never going to know about it. But that’s not necessarily true either. Ad blocking software is predominantly used on desktops and laptops. In fact, only 22% of people who use ad blockers use them on their mobile devices.
So the same people who have tuned out advertisements on their computers can still be reached with targeted ads on their phones. There’s still an opportunity to reach them with targeted PPC ads, which can be even more relevant when you use advanced mobile targeting features such as location-based targeting.
Marketers who actually understand and appreciate the reasons people use ad blockers are much better positioned to develop an advertising strategy that doesn’t annoy them. Not only will this make them more receptive to viewing ads, but they’ll also be much more likely to convert.
That said, even if advertisers can’t recapture the attention of people who use ad blockers, there’s still everyone else. Ad blocker penetration is still extremely low. At the same time, the number of new internet users is always growing. If the advertising market can eliminate intrusive, spammy ads while providing a more relevant message, new users will be much more willing to see ads than old-time internet users. And the more advertisers can do to prevent their audience from considering using ad blockers, the better.
The truth is, the rapid growth in desktop ad blocking is nothing more than a tough lesson from which advertisers can learn. There are still plenty of opportunities to create better marketing messages for your target audience that address the problem -- particularly for PPC search advertisers. Hubspot’s survey also asked which mobile ad formats people find most valuable or useful, and search ads were the top result.
Consumers have multifaceted needs when it comes to online advertising, but there’s a clear path to success. Focus on the right key areas and it’s possible to grow your PPC revenue despite the rise of ad blockers.
Start with consumer data
If you want to deliver relevant, helpful ads to your target audience, your most important tool is data. The more you understand the interests, needs, and intentions of consumers, the better you’ll be able to target them.
There are many different sources of behavioral data advertisers can mine to gain insights:
First-Party Behavioral Data
This is information your business collects about your audience through tracking cookies and site form fills. Most marketers collect this using their website analytics tools and CRM platforms. First-party data is the most common kind of data marketers use and is often considered the most valuable. But there are other important sources that can broaden your understanding of your target audience and their needs.
Second-Party Behavioral Data
Second-party data is behavioral information collected by other businesses. Form a partnership with another business that targets a similar audience to yours, then you can share behavioral data and insights to get a deeper understanding of the interests and needs of your audience.
Third-Party Behavior Data
Third-party behavioral data comes from publishing networks and data aggregators. They collect information about people’s behavior on other sites around the web. Third-party data is a great way to learn more about consumer demographics and what kind of information they’re consuming on the web. You can use this to optimize your marketing message. Third-party intent data can also help you uncover qualified leads who haven’t yet interacted with your business directly.
You can use these sources of information to flesh out your buyer personas and inform major marketing strategy decisions. Understanding buyer intent can help you better target warm leads, increasing your conversion rates in the process.
Businesses that are customer-centric consider the needs and desires of their audience over their own agenda: getting clicks, driving revenue, etc. And when you really do prioritize your audience, it will help drive your advertising goals in the process.
Consider these top reasons people click on advertisements, according to HubSpot: Two of the top reasons are negative: (1) It was a mistake, (2) The ad tricked me into clicking. Using deceptive strategies to get people to click doesn’t prioritize the needs of the user. And while it may be a great way to improve click-through rates, it likely does little to convince these leads to become paying customers.
What’s the main reason someone would click on an ad by mistake? Likely because it’s intruding into the content they’re consuming. People don’t visit a web page to look at ads, they want to consume the content without interruption.
Advertising networks have made some efforts to make ads less intrusive. Google, for example, now penalizes sites that display interstitial ads on mobile. But unfortunately, this issue is still very common (You’ve probably seen ads that don’t fully block the page but still prevent you from reading the content.)
Serving ads that block or distract from the content experience isn’t a customer-centric marketing strategy. Making your ads less intrusive to maintain a good user experience is. And it’s the job of both advertisers and the platforms serving these ads to ensure audience needs are the main priority. Then the only reason people will click on an ad is because they want to.
Remember, most consumers are open to seeing ads that are relevant to them. To achieve this, it’s not just about targeting people with products or services that interest them. You need to also serve these ads at the right point in the consideration process.
Take for example a woman who purchases a pair of leather boots online. Then the next day she visits another webpage and sees an ad for leather boots. Since she’s already made the purchase, the ad is completely irrelevant. Even if she likes the style and color of the boots in the ad, it’s unlikely she will buy two pairs of boots in two days.
The goal here is to get your ads to appear in that small window of opportunity when someone’s suggested they have purchase intent but haven’t yet made a purchase. Your intent data can help you identify this key timeframe.
In the PPC world, the best way to tackle this challenge is to optimize your reach and frequency. It’s possible to use intent data to set frequency caps, make bid adjustments and optimize your audience targeting to ensure your ads show up when they’re most helpful to searchers.
Most advertisers don’t even attempt to do this because the sheer volume of actionable intent data out there is too large to begin to derive actionable insights. By the time they’ve made the necessary adjustments, the window of opportunity has passed.
The solution that will help you deliver timely, relevant ads to your audience is technology. Use a bid adjustment tool to derive key insights and automate changes in your PPC accounts. Without some amount of automation, it’s virtually impossible to serve the right ads at the right time to the right people.
There are also key advantages when you engage in predictive advertising. Use a technology that incorporates AI and machine learning to understand major market trends before they happen. This way, you can be proactive in your advertising strategy, making necessary changes to gain future ad visibility during these key points of relevance. Because the more relevant and timely your PPC ads, the more your audience will appreciate and respond to your advertising message.
Ad blockers aren’t just an impediment to be overcome by advertisers -- they’re indications of a greater problem with digital advertising. If people are going so far as to install software to prevent seeing ads, then you know there’s something is likely wrong with the digital advertising strategy and approach.
Customer-centric businesses want to serve ads that inform, help and ultimately convert their audience into customers. And customers often appreciate these ads. The reason? They provide relevant products and services when they need them the most, providing a crucial stepping stone in their own buying journey. The good news is that the public is ready to see these ads whenever marketers are ready to serve them.
At the end of the day, it’s about making their customers want to click. Advertisers will start succeeding in their battle against ad blockers when they get to the root of the problem, embracing it not as an obstacle, but as a new opportunity to help meet their customers’ needs.