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Marketers know they need to be active on social media, but what exactly does that mean? Sure, it's important to engage with your audiences and comment when discussions arise, but there are customer conversion opportunities you may be missing if you're not utilizing the power of social media ads. You can't just slap an ad on the Internet and call it ‘good.’ There are best (and worst) practices that can bolster sales or ruin reputation.
The best Facebook ad examples incorporate relevant messaging, visual stimulation, and enticing value propositions.
Here are effective social media ad writing practices:
Use Your Brand Voice
You've likely designed a particular persona for your brand. Be authentic when talking to potential customers. If your brand is edgy and quirky, the voice used in your ads needs to speak in the same tone. If your brand voice is authoritative and fact-based, that needs to come across in the advertisements you display to the public. Utilizing a narrative that's true to your brand will attract the customers you want.
Simplify Ad Copy
Keep it short. Really short. Social feeds are cluttered with ads and distractions. Provide simple ad copy that hold audiences’ attention in as few words as possible. Use photos or videos to captivate people so they're engaged with the message you're trying to send them.
Remove Anything That Doesn't Have Immediate Value
Get rid of fluff, extra words, and anything that doesn't instantly communicate value and the benefits your brand offers people.
Make Benefits Immediately Apparent
Why should anyone buy your product or visit your site anyway? Your audience needs to understand the benefits you're bringing to the table from the beginning.
Start With a Question
What works for social ads? Questions are a great way to pique the interest of people who may otherwise just scroll by your ad. Start with a question relevant to what your target market needs or wants. Then, immediately answer the question with a solution you're able to provide.
Use ‘Revealing’ Words
It's all about creating buzz and making people feel like they're getting insider information nobody else has. Use evocative words that step outside the box and get readers' attention.
Speak Directly to the Reader
You're talking directly to your audience, so they should feel connected to the words you're saying. Use "you" to address your audience as much as possible. Your ad is really a conversation between two people.
Use Brackets
People are more likely to click on a title when it includes brackets. For example, your title might be, “The Power of Increasing Social Media Ad Sales [Webinar].” In this instance, your audience already knows the benefits you're going to provide, and the bracketed information gives them greater insight, thus enhancing overall interest.
Use Strategic (Not Spammy) Capitalization
There's a fine line between proper grammar and going overboard when it comes to online ad space. Make sure you're using proper capitalization, and add strategic caps when warranted, but be sure to avoid ultra-spammy capitalization. That can immediately be a turn off to readers.
Utilize Ellipses
Ellipses (the three dots that typically add suspense to a statement) can really draw readers' attention to your ad. What are you going to write next? This arouses intrigue without using important ad space.
Experiment with Ad Lengths
Everything digital is an experiment. It's a continually evolving process, which means you will constantly adjust your approach to help ensure you hit the right position. The definition of "appropriate lengths" varies from platform to platform. On Twitter, you have up to 280 characters, but the recommended length is closer to 120. Some of the most successful ads on Instagram only feature two or three words. On Facebook, the recommended text space is 125 characters.
Test Calls to Action
Steer clear of using the same call to action on every post. Get creative with your approach. "Learn more" tends to be consistently effective on Facebook. Look for ways to touch your audience and incite them to take action. Instead of "Contact us" try "Reach out to us." Replace "Call us" with "Send us a line." Mix it up.
Test Display URLs
URLs should be short, clear, and concise. Test different display URLs until you achieved optimal visibility. Effective URLs are not only great for customers who have already found you; they'll enhance your online visibility by making your site more SEO-friendly. This is a win-win that'll also get you "in" with the search engines.
Keep these tips in mind as you launch your next social ad campaign.
Speech recognition. Virtual assistants. Self-driving cars. If you’re a Sci-Fi fan, or if you’ve ever seen a classic apocalyptic movie, you likely know that the concept of intelligent machines is hardly new. Historically it’s been the fodder for books, movies and imagination for the past few generations. But the last several years have shown us that Artificial Intelligence is not only here to stay, but is not-so-gradually disrupting industries and changing our lives.
That said, contrary to popular perception, AI likely won’t supplant digital marketing jobs anytime soon -- but rather help strengthen digital marketing strategies and make the jobs of digital advertisers and marketers more productive, efficient and easier in general. In the following, we’ll provide a brief history of AI, explore the different types and use cases of the technology, and discuss how this technology is becoming a critical and necessary tool in an increasingly sophisticated digital marketing arsenal.
So what exactly is Artificial Intelligence? At its core, Artificial Intelligence (AI) is an advanced software-based technology that combines sophisticated computer programming with elements of human intelligence in various combinations to complete a wide range of functions previously thought only possible by humans.
The concept of AI has its origins in the the development of stored-program electronic computers. Computer scientist John McCarthy first coined the term “Artificial Intelligence” at a Dartmouth College conference in 1956. But while the study of AI started to gain momentum, interest waned in the 1970s and eventually the government withdrew funding for the technology's development. Although bringing AI to real-world scenarios was never quite realized over the next few decades, the concept of AI, or intelligent machines, surfaced erratically in popular movies such as Blade Runner, Terminator, and War Games. Then IBM’s computer touting early-stage AI capabilities beat a Russian Grandmaster chess champion in 1997, turning the heads of the scientific community along with the rest of the world, and rejuvenating interest in potential real-world applications of the technology.
Flash forward a few decades, and Artificial Intelligence is becoming less of a luxury and more of a necessity for numerous industries around the world. A 2017 IDC Future Scapes report noted that 75% of developer teams planned to actively implement some type of AI in at least one service or application in the next three years. What’s more, global analyst firm Gartner predicts that by 2020, 85% of customer interactions will be managed with no human involved. The implication of course, is that AI is well on its path to upending and revolutionizing numerous industries -- including digital marketing.
At a high level, there are three types of AI systems: the first one is designed and programmed to perform specific tasks or adhere to certain requests. The second is designed to greatly outpace human cognitive performance. And the last one combines AI technology with elements of the human brain.
Intelligence from Data Algorithms
If you’ve ever told Siri to find the nearest taqueria or told Google to play your favorite indie rock tunes, you know that these systems can understand and process a human command, and then respond intelligently -- at least most of the time.
Perhaps the most common, recognizable, and ubiquitous form of Artificial Intelligence can be found in virtual assistants such as Apple’s Siri or Amazon’s Alexa, which leverage copious data inputs to emulate certain human behaviors with the aim of achieving very specific tasks. The learning process includes natural language processing (NLP) capabilities that enable these systems to understand and communicate human language. This capability in turn enables these systems to acquire information, along with a limited ability to reason and self-correct when they’ve made errors.
Artificial Super Intelligence
Think Artificial Intelligence but on steroids. As its name implies Artificial Super Intelligence is a technology with capabilities that greatly surpass normal human intelligence and cognitive thought. In short, it can easily exceed almost all human activity. In addition to being able to solve complex problems, this advanced technology also outpaces humans in numerous other areas as well, such as creativity, social interaction, and wisdom.
While still being developed, these highly sophisticated intelligence capabilities are achieved through digital emulation of a human brain by replicating the brain’s neural network and linking to a computer interface, putting us one step closer to true man-made human intuition that thus far, has been the stuff of futuristic movies.
Artificial Intelligence and the Human Brain
The blurred lines between humans and machines might be closer than we think. One tech start-up, backed by Tesla’s Elon Musk, is attempting to actually blend human brain activity with the enormous computing power of AI-driven intelligence in an effort to enable people to keep up with AI-enabled devices in terms of problem-solving and other processing capabilities.
So far, we’ve discussed Artificial Intelligence based on data algorithms, Artificial Intelligence that's more intelligent than people and Artificial Intelligence technologies that integrate with human brains. So how does that impact digital advertising?
Well, in a lot of ways. In fact, Artificial Intelligence is becoming an increasingly essential tool in the digital marketers’ toolkit, with more advanced capabilities that help marketers identify and refine their targets, improve marketing strategy and give a big boost to ROI.
Personalized Email Campaigns
Of its numerous benefits to marketers, Artificial Intelligence is critical for helping scale email marketing efforts, particularly when it comes to analyzing audience and segmentation data. Specifically, AI is the driving force that allows you to capture relevant data about campaign activity, such as the number of targets, who opened emails, who and how many people clicked the CTAs, visited the landing pages, or interacted with the website - all with the aim of better understanding your audience.
To that end, AI-driven marketing platforms can also help automatically generate uniquely personal content in the body of the email specific to individual users, with subject lines tailored to the person’s interests, profession, hobbies, and buying patterns. A hospitality company, for example, might send users personalized content that offers room deals and specials that suit their needs based on their demographic (e.g. families with small children, couples, senior citizens, etc..), as well as activities and sites that might be of interest to them if they stay. These kinds of highly personalized insights, in turn, allow you to create more targeted and relevant campaigns that will likely give a big boost to open rates.
In addition to personalizing content, marketers can now use AI-driven tools to both assess and improve the quality and effectiveness of their marketing campaigns, optimizing every step of the email campaign process at granular level, all the way down to the ideal time of day for sending an email to potential leads. What’s more, Artificial Intelligence gives marketers the ability to immediately access campaign results in real-time. This means that labor-intensive A/B tests with weeks-long analysis will slowly fade into a distant memory for marketers, who will instead be able to access relevant campaign data at the drop of a hat anytime they want.
Improved Ad Copy and Content Creation
Okay, so Artificial Intelligence can be used to analyze data, optimize campaigns and otherwise perform repetitive tasks, but can it actually be used in a creative capacity for ad copy and other content creation? The short answer: Yes.
While it still has its limitations, it can also be used for producing creative marketing content based on a plethora of inputs that include raw consumer information and specific targeting and segmentation data. AI-based tools, such as Articoolo and Quill, are already are being used to quickly and efficiently generate high-quality marketing content - including ad, landing page, and email copy. Among other things, AI can be used to create content that gauges audience relevance, produces engaging and compelling storytelling, or triggers an action or response. For marketers, this means they now have an enhanced ability to generate different types of content for a multitude of campaigns without adding resources to their creative team or investing in an external agency.
Whether informative blog posts, customer testimonial videos, or recorded webinars, in recent years, digital marketers are constantly generating new content in an effort to better engage and reach their target audience. Thus, as these solutions become more refined, it’s likely that marketers will increasingly rely on Artificial Intelligence to fuel content creation more quickly and even more creatively than ever before.
Reduced Ad Spend Waste
It’s no secret that Artificial Intelligence uncovers a dearth of new efficiencies that enhance productivity and boost ROI. And as such, one of the biggest benefits will be to significantly reduce ad spend waste.
The most obvious, of course, is its ability to optimize your campaigns and PPC strategy as a whole. AI-based tools can breathe new life into your PPC campaigns by providing:
But perhaps less known is that AI can also be crucial in identifying malicious bots and other types of ad fraud activities, helping marketers identify and avoid costly attacks before they occur while ensuring that their advertising dollars are only spent on real customers and genuine clicks and views.
Increased Conversions
It’s no secret that AI-enabled platforms have the ability to learn, analyze, and adapt -- all good news for marketers attempting to get a finger on the pulse of their audience. Now becoming a staple to digital marketers and advertisers, AI is routinely being used to uncover relevant product and brand recommendations, as well as offering tips to consumers at just the right time in their buyers’ journey. For brands, that means countless new avenues to create customer loyalty and build trust within their consumer base.
In addition to creating a better overall customer experience, AI-driven tools can even be the conversion funnel itself. Global beauty giant Olay’s skin care analysis tool, for example, leverages AI technologies to analyze customers’ skin, and then makes recommendations for ideal products that they can purchase based on their unique needs and individual tastes. From making recommendations about clothes to cars to the latest lawn care tools, the possibilities are just about endless.
Artificial Intelligence is already making waves in the digital marketing industry. While still in nascent phases, it’s already a critical component in a plethora of tools designed to boost PPC efficiency, ROI, and even creativity.
Safe to say, these are exciting times for marketers. As Artificial Intelligence progresses in its sophistication and acceptance in the industry, marketers will be uniquely positioned to access new ways of reaching and connecting with their audience. The scope of AI’s innovation is only going to expand in the months and years ahead -- and for marketers, now is the perfect time to find ways to leverage its vast potential to make their marketing efforts faster, more efficient, and more profitable than ever before.
Your paid and organic marketing efforts are doing the business -- click-through rates, or CTRs, have never been higher and record numbers of visitors are finding their way to your website. Time to put your feet up and stick the kettle on? Not so fast. If that traffic isn’t converting, all of your hard work has been for nothing. Whether you are just starting up and running the show solo or if you are a seasoned marketing director who has signed up to some pretty ambitious KPIs, optimizing your webpages to increase conversions is plainly a must.
Naturally, every online marketer will measure successful conversions differently based upon their business model and specific goals. Across all verticals, conversions are expressed simply as visitors who perform a desired action: for an online publisher this would be the submission of a form on a subscription page; for a digital retailer this would be the addition of products to a cart. However, whatever industry you’re working in, a 2% conversion rate should be the baseline goal for your website: reaching this number and beyond is the very foundation of high sales volume. In this article, we outline four tried and tested tactics that, if put into practice effectively, will be sure to get you the conversion percentages you covet.
On search engine real estate it’s all about gaining prime location for your ads. When it comes to optimizing conversions, it’s all about continuous testing. The nature of experimentation by definition dictates that some tests will succeed and some will fail: either way, both outcomes represent great learning opportunities. It is the best way to mitigate the risks that come with decision-making while simultaneously affording your creative teams room to do what they do best - design, discover, innovate, explore.
The first step of any meaningful test is to study the statistics and data available to you to isolate an element that has the potential to improve customer engagement. Speaking generally, some components on a webpage tend to have higher effects on conversion rate and numbers than others, so begin by focusing on these things:
The headline
Are you getting your message across instantly? Is it providing immediate and easily digestible value?
Page design
Are you packing a visual punch? Are your images streamlined, easy on the eye, and independently adding value?
The offer
Take a step back and consider what the customer is getting for their money. How is it presented? Is the information on the page still relevant? Is the message conveyed clearly?
Call to Action, or CTA
Does it guide your visitor to the metaphorical finish line? Is it concise and synthetic?
Testing should be a never-ending process. As soon as you’ve optimized one aspect of your webpage, shift attention and build out variations of something else. The more you integrate A/B and multivariate testing into your core marketing strategy, the more your decisions can be influenced by real-time data rather than ideas and opinions. If you’re leaving things to chance, you’re leaving precious dollars on the table.
A common mistake among digital marketing practitioners today is that they do not provide a distinct and compelling value proposition up front. If your homepage or landing pages are simply dominated by your logo or ineffective “welcome” messages, you are missing out. The importance of nailing your core messaging cannot be overstated in the environment of saturated online marketplaces -- while you are likely to match your competitors in a number of ways, you need to outperform them in certain key areas and sell that to your prospective customers.
Of course, crafting and refining your value proposition is a facet of your business that goes far beyond the pages of your website -- it is the very essence of what you stand for and what you do. It requires reflection and a constantly open dialogue both internally and externally. But once it has been defined, it must be communicated efficaciously in order to achieve optimal results across your entire organization -- from marketing to sales and everything in between. Ideally, your value proposition should be articulated in a single, credible sentence that leaves no space for ambiguity. However well-defined and eloquently expressed you think it may be, though, to understand its true resonance, you must be repeatedly measuring how it performs (scroll back to point number 1). While many marketers first look to things like font size, button shapes, and other elements to tackle lagging conversions, the first step has to be analyzing the value you are communicating.
For anyone to part ways with their sensitive data by filling out a form, or their money through a purchase of a product or service, they need to feel confident in your brand and secure in the transaction. One of the most straightforward ways to boost conversions is to make it easy for the visitor to verify the accuracy of your content through articles in renowned publications, citations, reviews and case studies. We’ve all seen the traditional before-and-after type testimonials -- when done well they are an extremely effective marketing tool, triggering emotional responses in viewers and providing tangible examples of just how consumers can benefit from your offering. The effects of such endorsements have been the source of great industry debate, with some reports suggesting that the inclusion of videos on landing pages will increase conversions by a massive 80%. That is a compelling number and certainly justifies giving it a go if you’re not already.
While the above would undoubtedly be part of your long-term strategy -- video testimonials are not cheap or easy -- there are some quick fixes around your website that you can implement to build credibility. Start by listing a physical address, showcasing photographs of your offices, listing your affiliations with respected organizations, displaying biographies of your leadership team, and making it easy to get in touch. Then take into consideration the navigation and ease-of-use -- nothing kills trust quicker than an outdated, amateur-looking website. When constructing your webpages, be sure to update all of your visible content, remove any peripheral distractions (pop-ups, blinking banners, etc), stay consistent, and avoid errors of any kind. You’d be surprised just how many seemingly minor things can hurt your credibility.
Now this sounds like a given, right? But again, you’d be amazed at the number of companies who make doing business difficult. In short, your visitors should not have to figure out how to sign up, buy, or where to click -- the desired action should be apparent. On every page that contains a CTA, everything should be geared around guiding the user toward the action you want them to take. If it’s a sign-up, ask them to fill in as few fields as possible -- don’t ask for any information that you don’t need to know to complete the process. If it’s a product order, don’t bombard them with similar offerings that can detract attention and do not force guests to sign-up before checking out.
You should also keep in mind that regardless of the quality of your offering or your webpage, there are always going to be visitors who are not quite ready to make that all-important click. To keep these visitors engaged, consider providing a second CTA. In addition to saving those potentially lost conversions, they’re great for progressing leads through the funnel and supporting other company goals that could involve social sharing or signing up for company updates.
Every visitor comes to your website for a reason - most likely because they clicked on an ad or an organic search result that they liked or was appealing. Each of them arrives with wants, needs, and expectations. Displaying value out of the gate, building up trust and encouraging conversions through simple navigation are the basic strategies you can employ that will ultimately compel your audience to take action. Getting traffic to your website is one thing, but getting that traffic to convert is another thing entirely. And understanding the difference is the key.
A growing number of agencies are radically transforming their businesses by equipping their media buying teams with a robust digital media platform. Centro has guided hundreds of digital marketing leaders and media teams on what to look for in a Demand Side Platform (DSP), and the first thing to note? You need more than just a DSP.
In this January's webinar, we helped to solve the puzzle of selecting a digital media platform by breaking it down into 10-steps and sharing key considerations and questions for potential vendors.
No two businesses have identical goals and targets, and as such, choosing what we want to measure should be done with care. If we incorrectly set our business goals and track the wrong metrics, we are essentially playing a high-stakes game with the wrong rules.
With automated bidding platforms, selecting a metric is even more paramount. An automated platform will happily optimize toward the metric chosen for it, regardless of how appropriate it is to the company’s business goals.
To ensure we choose the right metrics going forward, we will go through some of the common pitfalls, discuss how to determine the goals for an online advertising campaign, and consider a hypothetical case study that applies these new rules.
Some of the most common mistakes when defining metrics include choosing vanity metrics, measuring what is convenient, and measuring the wrong part of the sales funnel. We’ll go into each.
Vanity Metrics
Vanity metrics, as you could probably can tell from the name, are the kind of metrics that are easy to measure and increase, but don’t tell you anything about the underlying process for the business goals you are trying to measure and improve. These kinds of metrics have been discussed quite extensively elsewhere -- entrepreneur Tim Ferris describes them as “the easiest to measure and they tend to make you feel good about yourself.” Another entrepreneur, Neil Patel, defines vanity metrics as “all those data points that make us feel good if they go up but don’t help us make decisions.” In short, vanity metrics are easily gamed, but changes to them will likely not result in noticeable changes to the underlying business goal.
Here’s an example -- imagine a soccer team that only measured success by the number of passes they completed in a game. Sure, there is probably some relationship between the amount of time they had the ball and how many goals that team scored, but in reality, optimizing toward passes would likely lead to a lot of wasted time with the ball, racking up a large number of passes with no intention of putting the ball in the back of the net -- which is ultimately the underlying team goal.
Similarly, an example of a commonly used vanity metric when optimizing online advertising campaigns is click-through rate. For example, if a company’s main business goal is to sell as much product as possible, and the percentage of clicked ads increases, then we should see more sales, right? Not so fast. Click-through rate can be changed by many factors - a decrease in Ad Views would result in an increase in click-through rate, but for a reason that does not necessarily affect the number of people on the site who end up buying a product.
Despite these drawbacks, metrics like these still have their uses -- often as a supplementary measure to help explain changes in the main metrics you are optimizing.
Measuring What is Convenient
As with vanity metrics mentioned above, there is also often a bias to choosing goals based on metrics that are easily available to us. Often the conversion data currently being recorded in an analytics platform is sufficient to measure our business goals, but occasionally additional integration will be required to pull data from another source.
For example, you may include leads and conversion metrics in your analytics software, but revenue data may be ‘stuck’ in your CRM or accounting software. If you were attempting to maximize profit, the ‘convenience metric’ would be conversions.
Measuring the Wrong Part of the Sales Funnel
One area where metrics are often poorly used is when targeting the wrong section of the sales funnel. Making this mistake typically comes from an error in understanding the relationship between each section of the funnel, and the characteristics of visitors to your website or online store. There are often underlying characteristics of the populations in each section of the sales funnel that we can’t see or that aren’t immediately obvious -- and optimizing toward higher sections in the funnel do not necessarily increase the number of leads in lower sections of the funnel, which is typically where revenue is recorded.
To help understand the process of determining the metrics that require optimization, we will go through a simple hypothetical situation, and explore how we would choose the appropriate metrics to track.
Example: Online Florist
Imagine an online florist that offers two types of plants - a cheap bouquet of flowers, and an expensive potted plant. The owner cares about one thing, maximizing their total revenue. The store currently records Ad Views, Ad Clicks, Site Views, Orders, and Cost for each bidding keyword in their analytics program. The store’s accounting software records Revenue data for each sale, but the team has put off integrating that data with the analytics program.
How would we go about selecting a metric to optimize toward the owner’s business goal?
First, let’s identify some potential metrics that would come under the common pitfalls discussed earlier. Ad Views, Clicks, and Site Views -- while they are all a good idea, increasing these will not directly help us with our business goal of maximizing Revenue.
It seems as though Revenue is the best metric to optimize toward, but it is not available to us without some integration work. However, the Orders metric is easily available to us, and is similar to Revenue - is there any harm in using it instead?
Actually, there is. With this simple example, Orders conversions come from one of two populations - either the low-revenue bouquet customers, and the high-revenue potted plant customers. If we only try and maximize the number of Orders that occur in the online store, we lose sight of our true goal of maximizing Revenue, as each Order has a different Revenue value attached to it, and our analytics platform has no way of knowing that value.
With an automated bidding platform, this distinction between Orders and Revenue is even more important. If we were to let an automated platform optimize toward increasing the number of Sales, a likely result is that bids for keywords such as “cheap bouquet” will increase, and bids for keywords such as “fancy potted plant” would decrease. This is because the algorithm does not know the ‘value’ of a given Order, as the Revenue data is hidden from the algorithm’s view. As such, this would likely result in an increase in total Orders, but possibly a decrease in total Revenue, as potential customers looking for cheap flowers are less expensive to target compared to those wanting to spend a lot more money on a fancier plant.
As such, the recommendation here would be to integrate this Revenue data into the store’s analytics program, and optimize toward the Revenue metric instead.
The florist case study was a very simple (and somewhat contrived) example, but the process in understanding what metrics to choose does hold when applied to real-world business goals.
Each business is different, but being conscious of the pitfalls that might occur when optimizing toward vanity metrics, convenient metrics, and metrics that are targeted at the wrong level of the sales funnel will go a long way toward improving your online campaigns. Doing so ensures that you are measuring what matters, and that you can be confident that hitting targets with your chosen metric will actually result in improved business performance.
It might be surprising but your keyword list is one of the most important tools behind an effective PPC advertising campaign -- for a lot of reasons. Among other things, you need to have an arsenal of relevant, high search volume keywords to hone in on and directly target the key sectors of your audience. You also need to constantly identify high-value keywords that your competitors have overlooked to stay ahead of them. Succeed at this and your keyword list can help drive marketing goals while minimizing necessary ad spend and maximizing overall ROI as a result. Thus, smart PPC keyword research is non-negotiable.
That said, PPC advertisers need to think beyond the most basic PPC keyword research strategies. Here are seven tips and tricks to expand your PPC keyword research strategy.
Google’s autocomplete feature is both a valuable and underutilized tool for PPC keyword research. Ostensibly, it’s designed to help searchers by suggesting terms related to their search query - and subsequently people click on Google’s autocomplete suggestions because it displays a correct spelling or it’s relevant to what they’re looking for. Thus, PPC marketers who target these key phrases can capture some of this traffic.
You've likely seen this before when you've conducted Google searches - users type in a root keyword or phrase and Google displays a drop-down menu of ways to complete it. Among other things, Google’s autocomplete phrases can help PPC marketers understand:
Ultimately, Google is trying to connect users with relevant content that’s available on the web, so its drop-down menu of autocomplete suggestions is a reflection of the most popular searches.
But what most people (marketers included) don’t realize is that autocomplete is capable of doing more than just finishing a phrase. For one, you can also use it for PPC keyword research in the beginning or middle of a phrase by inserting an underscore, which provides countless more opportunities to discover key search phrases related to your root keywords for PPC.
You may discover that sometimes the query you enter doesn’t bring up any autocomplete results. This is likely because it has a low search volume - that is, not enough people are typing in similar phrases.
If you’re just starting out with PPC marketing, your competitors can potentially be a great resource for PPC keyword research - by learning what search queries are bringing traffic to your competitor’s website. And you don’t have to pay for any advanced PPC keyword research tools to do it.
Google Ads Keyword Planner is all you need to analyze other websites. First, go to Keyword Planner, and click “Find new keywords.” Then you can type in the URL of one of your competitors, or a high authority site you want to emulate. Click “Get ideas,” then it will bring you a database of keyword terms that people are typing into search engines to arrive at your competitors’ site:
You can sort the results of your PPC keyword research by many relevant factors, like average monthly searches, competition, and bid. What you do with this information depends on your strategy - if you plan to compete with this website directly, then these could be keywords to target. If you want to avoid competing directly with them, then you might avoid these keywords in your PPC strategy. You can also enter multiple websites into Keyword Planner to get an idea of how your competition as a whole attracts traffic through Google search.
Your own website can offer just as much valuable information for PPC as your competitors’ websites. Unless your business is brand new, people are likely already using a variety of search queries to access your website from Google search. So don’t forget to run your own URL through Keyword Planner to see what search queries are already bringing you organic traffic.
One thing you can do is look at these existing traffic sources as an opportunity for PPC targeting. Research has shown that when organic and paid search results appear together, it significantly improves average CTR:
On the other hand, you may not want to target these keywords to avoid cannibalization. If your organic SEO is already attracting significant traffic from these search queries, then you should allocate your PPC budget to target other relevant queries that aren’t ranking well.
But both approaches are relevant, and you can discover which one is right for you by trying and testing the performance and ROI of different keyword strategies.
Another way you can mine your own website for audience search queries is using your site search feature. While most businesses offer a search box at the top of their website or on their blog, they very rarely pay attention to the insights it brings for keyword targeting.
Consider these statistics:
People who visit your website and use the search feature are looking for something specific, and as a result are much more likely to convert than passive site visitors. The search queries people use on your website can be just as relevant when targeting these audiences in search engines as well.
You can start tracking site search queries using Google Analytics. From the side menu, click “Admin.” Then from the Admin View column click “View Settings.” From there, scroll down to the bottom of the page and toggle “Site search Tracking” to on. Now you’ll be able to track site search queries and over time discover popular keywords that you can target for PPC as well.
One of the first things marketers learn about PPC strategy is to avoid targeting broad keywords. Broad keywords are catch-all phrases that bring in huge volumes of traffic. As a result, they’re incredibly expensive and competitive to target.
That said, a common mistake marketers make is ignoring broad, unspecific phrases altogether when researching keywords, instead focusing entirely on long-tail keywords (phrases with three or more terms). But while long-tail keywords are highly relevant, they often have very low search volume, making it difficult to scale your PPC strategy.
The key to building a long list of keywords with the right combination of relevance, search volume, and competition, then, is starting broad and refining. Instead of ignoring generalized, high competition keywords, use them as a root to inspire related long-tail keywords.
There are a variety of free and paid tools available that are designed to help you brainstorm relevant long-tail keywords using broad keywords as a root -- Ubersuggest and Answer the Public are popular examples.
Your existing blog content is a great place to find inspiration for keyword ideas. Most marketers overlook this resource because their post topics are so specific, and targeting the related long-tail keywords wouldn’t be scalable for PPC.
But a great place to find PPC keyword research ideas is by looking at your blog categories and cornerstone content. Scrolling any given category, you can see whether there’s a lot of material about one subject in particular. You can use this as a root keyword to explore more possibilities.
Type something like “machine learning” into a long-tail keyword research tool like Answer the Public, and it returns a variety of relevant terms that might be worthwhile to target for PPC.
Your list of relevant keywords to target for PPC can grow indefinitely if you allow it. But more importantly than capturing all the right keywords is identifying the ones that are most contextually relevant to your target audience.
Consumers today rely on the internet heavily at different points during the path to purchase, which vary greatly by customer demographics, location and industry. If you want to identify the most valuable keywords, you need to map them onto your customer journey.
Most businesses that incorporate customer journey mapping into their PPC strategy do so to evenly distribute their investment across it. For example, they target relevant keywords evenly across the main funnel stages:
But if you dig deep into consumer behavior insights, you can identify the specific points of the customer journey for which the internet plays an important role for your audience. For example, someone in the hotel industry might discover that most of their audience searches for hotels when they’re ready to book. In this case, they would want to invest heavily in targeting bottom-of-the-funnel keywords that suggest their audience has their credit card in hand (e.g. “Book Chicago hotel” or “Chicago hotel prices”).
Think With Google provides all the data and tools businesses need to make these discoveries about their own audience’s customer journey. Their Consumer Barometer is specifically designed to illustrate how the internet impacts the customer journey — from consideration to purchase.
You can use this tool to answer a variety of important questions about the customer journey, such as: In which parts of the purchase process do people use the internet?
Or how do people use the internet to help make their purchase decision?
You can narrow down the results by product category and location, making them more specific to your industry. Say the majority of your audience uses the internet to compare choices during the purchase process. That’s a middle-of-the-funnel activity, so you can focus on relevant keywords like “[your product] vs [competitors]” or “[product niche] price comparison.”
Search engine queries are all about context. If you run a business where seasonality is a factor, this is something you can take advantage of in your keyword targeting strategy. You can probably come up with some relevant seasonal keywords to target, but you can also dig deeper to discover other opportunities you might have missed.
Google Trends is an invaluable tool for this. Say you’re doing PPC advertising for a Las Vegas hotel. You can type in a very generalized keyword into Google Trends, such as “Las Vegas.” You’ll see that interest in Las Vegas remains fairly constant throughout the year:
But if you scroll down to the bottom of the page, you’ll see a list of related topics and queries that are seeing spikes:
Some of these can potentially be relevant PPC keywords to target, or inspire keywords for future trends. The spiked query “gwen stefani las vegas” suggests that people will search for popular performers when they’re scheduled to perform in the city.
Those waist-deep in PPC campaign strategy know that PPC keyword research is a never-ending task. Markets and consumer needs are constantly changing, and in turn, the queries people use to find information will also change. That said, marketers also need to understand exactly how those markets and consumers are changing. What’s more, like your audience, your business is constantly growing and evolving. And as it does, so will your keyword list. Nailing the right keywords for your target audience is perhaps some of the most fundamental - and critical - research you can do as a PPC marketer. Thus, continually expanding and refining your keyword list is an inherent part of any successful PPC strategy.
There are a multitude of ways to discover new and effective keywords that will boost the quality of your keyword lists and enable you to better target your key audiences - while even reaching new ones. Effective PPC marketers will embrace the challenge.
Gone are the days of smoky, whiskey-infused boardroom meetings, a gaggle of middle aged men talking strategy -- among other things -- seated around a table in starched suits, while well-coiffed secretaries take notes and schedule important client lunches.
It’s been almost 60 years since the “Mad Men” era of generations past, and safe to say, advertising has come a long way, baby.
For starters, an actual eight-hour workday is a rarity. You likely can’t smoke within the vicinity of your building, let alone your office. And like the typewriter and the rotary telephone, the three-martini lunch is all but a faded memory.
But while hit TV shows like Matthew Weiner’s “Mad Men” looked at this era through the technicolor lens of nostalgia, the evolution of advertising has unfolded with more positives than negatives, and its trajectory continues to go up and to the right. Women now comprise around half of advertising agency staffs, while almost 20% hold management or executive positions. Ad agency staffs are also becoming increasingly diverse, with minorities now occupying between a fifth to a quarter of employees.
This rising tide of diversity has helped facilitate a more tolerant work environment that embraces different cultures, races, nationalities, sexual orientations, and beliefs, with less tolerance for sexist, racist or otherwise harmful language that once pervaded office life. Not surprisingly, then, that increased diversity in-house is also mirrored in more inclusive ads that feature a broad array of individuals of all colors, shapes, sizes and ethnic and religious backgrounds.
But perhaps the biggest and most significant change is that “Madison Avenue” may, in fact, be making a shift away from Madison Avenue. Here’s why: Like New York City’s Broadway or Wall Street, Madison Avenue is a district inextricably associated with the industry that occupied it -- advertising -- garnering its reputation from the dearth of agencies that emerged and flourished there dating back to the 1920s. Thus, to refer to Madison Avenue was to refer to the advertising industry as a whole. And in many ways, it still does.
But over the last two decades, the heart of the advertising industry began to shift to Silicon Valley, as digital advertising experienced explosive growth on rapidly evolving new platforms.
Technology giants Google and Facebook have upended the advertising industry by strategically leveraging their users’ personal data and purchasing history while also cultivating highly-targeted sponsored ads in response to user queries.
Today, advertising has successfully made the leap to digital, a market that reached $111.14 billion at the end of last year and is projected to account for 55.0% of total media ad spending in 2019, according to eMarketer. And its growth trajectory is only anticipated to accelerate in the near future. That means the advertising industry is now truly bi-coastal, continuing its path on the West Coast -- and more specifically Silicon Valley -- as it progresses.
To illuminate this transition, we’ve released our latest Infographic “American Advertising: From ‘Mad Men’ to Silicon Valley” that chronicles the evolution of advertising, and some of the biggest changes the industry has undergone over the last six decades.
No doubt, advertising in the days of David Ogilvy and Bill Bernbach enjoys it own storied past. But that story is far from over. The narrative continues to unfold as cutting-edge technologies are developed that enable advertisers to reach new audiences and break into new markets, while influencers and leaders from all walks of life innovate in ways that reshape and redefine the industry. At QuanticMind, we’re picking up the torch and continuing to tell the story as well. And we look forward to the chapters that lie ahead.
By now, it’s no secret that many consider the terms “artificial intelligence” and “machine learning” some of the most overused buzzwords in the industry -- and with good reason. News about their capabilities -- and related use cases -- seems to emerge on an almost minute-by-minute basis. Without a doubt, artificial intelligence and machine learning are the driving forces behind a vast amount of innovation today, with applications that span across a wide range of industries. Thus, it can be difficult to keep up with ancillary terms -- like deep learning -- and distinguish the difference or see where exactly these technologies fit in a larger, digital advertising schema.
Adding to that growing list, deep learning is another term that’s on the rise this year. So what does that mean for you and your SEM program? This article provides a clear picture of the differences, and applications that distinguish machine learning and deep learning, and the unique capabilities they each bring to your digital advertising strategy.
While it’s often used in conjunction, and sometimes interchangeably, with the term AI, machine learning is a subset of AI that comes with a slew of extremely valuable, sophisticated capabilities. In the most basic sense of the word, machine learning is exactly what its name implies: a computer with the ability to learn from whatever task it performs.
Artificial intelligence uses preprogrammed software or algorithms to deliver smart responses to queries and tasks, such as with popular voice assistants like Siri and Alexa. But here’s the distinction: machine learning has the ability to learn and improve at whatever task it’s assigned. Conversely, regular AI can never grow beyond what it's preprogrammed to do. Thus, AI doesn’t include machine learning until the computer has the ability to process data inputs, learn from said data, and make changes to its responses based on related insights.
For example, you have a smart coffee maker that will automatically brew coffee when you say “Make coffee.” What if that coffee maker could learn from the things you tell it? Say you always tell it to “Make coffee” at 7 am Monday through Friday and at 9:30 am on the weekends. Thus, the coffee maker would be leveraging machine learning if it could use this information to change its programming and automatically start making coffee at those desired specific desired times of the day.
Perhaps surprisingly, most people don’t realize they interact with machine learning in their everyday lives. Take video streaming services for example. Netflix uses machine learning algorithms to analyze users’ viewing behavior, tailoring its offerings to the types of shows people like, then leveraging that information to recommend shows to similar users. Similarly, music streaming services like Spotify and Amazon’s recommended products are also examples of machine learning technology at work.
Altogether, there are four main types of machine learning possible today:
That said, machine learning can only serve the same functions for which it was designed. A coffee maker that uses machine learning can get better at brewing coffee and anticipating when to brew that coffee. But it can’t spontaneously learn how to do something else, like automatically order your groceries when you run out. A different AI machine must be programmed to do that. From that perspective, the growth potential of basic machine learning computers is still fairly limited.
Now, enter deep learning.
While deep learning may be a fresh technology buzzword, it corresponds to related machine learning and AI technologies. As mentioned, machine learning is a subset of AI. By the same token, deep learning is a subset of machine learning — specifically, a special way of implementing it that entails various unique capabilities.
At its core, deep learning uses a specific subset of machine learning algorithms designed to mirror the performance of a real human brain. Specifically, it leverages a structure of algorithms called an Artificial Neural Network (ANN), which incorporate three main layers -- the Input Layer, the hidden layer and the output layer -- designed to mimic human neural activity.
Thus, because of these capabilities, deep learning can draw correlations between data points and forming data clusters that inform understanding of the task at hand. Instead of making binary decisions based on basic data inputs, its sophisticated algorithms can draw conclusions from the data.
When applied correctly, deep learning has the potential to solve complex problems that would otherwise require human thought. With its ability to draw accurate conclusions, deep learning has the potential to successfully make difficult decisions that many engineers have tried to master with AI for years.
As we previously mentioned, machine learning and deep learning are not mutually exclusive. However, the algorithms and execution of deep learning equip it with features that are distinct from the rest of machine learning applications.
Here are some key differences:
General machine learning requires more human guidance
General machine learning often involves basic algorithmic processing guided by human input. Data provided is often structured and labled, and the processing task preassigned. A classic example of basic machine learning is image recognition. Google Image Search uses machine learning to identify web images and associate them with relevant keywords. Some images on the web already come with descriptive phrases. Basic machine learning uses this labeled data as a base to identify and categorize images with no description (unlabeled data).
Deep learning, on the other hand, is able to work entirely with unlabeled data, making relevant clusters and associations without being specifically preprogrammed to do so. Whereas a machine learning algorithm needs an engineer to make adjustments when it returns inaccurate results, deep learning has the potential to self-correct.
General machine learning requires less processing power
Even some of the most basic computers are capable of handing machine learning processing. Machine learning doesn’t have to be complex, it can use simple algorithms such as decision trees, random forests, and find-S to draw conclusions and achieve a task. The Artificial Neural Networks that make up deep learning algorithms are a different story. Deep learning involves large volumes of matrix multiplication operations, requiring sophisticated machines with lots of processing power for operation. Computers capable of deep learning also require a graphics processing unit (GPU) -- a specialized electronic circuit designed to rapidly alter memory to accelerate the creation of images and results.
Deep learning offers more nuanced results
The layered algorithm structure of deep learning is what makes it possible to elicit nuanced decisions that are similar to those made by a human. What do people do when presented with new information? They refer back to their previous knowledge and experience to make sense of it. The layers of deep learning function in a similar way. The first layer processes a large data set related to a certain topic, drawing correlations and associations between a wide range of data points. It then uses this “past knowledge” to inform interpretation and action on data presented in subsequent layers.
Here's an example: As mentioned, machine learning can look at a database of images (e.g. images of road signs) and use this information to identify other similar images. The layered structure of deep learning makes it possible to look at images of road signs and cluster them by type (e.g. stop signs, yield signs, speed limit signs, etc.). It can then use this back knowledge to identify blurry or partial images of road signs that regular machine learning might not be able to accurately categorize.
And another: Machine learning can analyze current news and categorize it by type or other factors. Deep learning can developing nuanced understanding of the qualities of current news and use this knowledge to accurately identify fake news designed to deceive readers. That’s something a lot of people can’t successfully discern!
Deep learning requires much more data
As mentioned, deep learning requires an initial layer of data analysis in order to deliver nuanced results. But in order to make accurate decisions, it requires more initial data to analyze than machine learning.
Both machine learning and deep learning have the potential to analyze enormous data sets to inform results. But machine learning is able to make sense of small sets of data as well -- although the smaller a dataset, the more likely deep learning will make inaccurate associations and deliver poor results.
At this point, hopefully you have a better understanding of how machine and deep learning relate to each other and how they differ. That said, understanding the nuances of AI is not required to apply and benefit from these technologies. While AI and machine learning are already revolutionizing a wide variety of industries, deep learning touts its own potential to reshape how businesses and societies approach and execute on critical tasks.
Here are a few of the many current applications:
Cybersecurity
Machine learning and deep learning are uniquely suited to help automate and improve various cyber security processes. For example, computers can learn what normal website traffic looks like and effectively identify malicious traffic. Convolutional Neural Networks can also detect and classify malicious code, which can be incorporated as a critical component of a multi-layered strategy to improve the security posture of organizations.
Self-driving cars
Machine learning is a key component for driver-assisted or self-driving cars. By training algorithms using huge data sets, these machines have the ability to react to potential risks in their surroundings or avoid accidents. These computers are then able to replicate driver behavior based on previous “experiences.”
Improved Healthcare
Probably one of the most impressive applications of machine learning technology is in the healthcare industry, which is already leveraging intelligent computers to help doctors accurately diagnose patients. These fast processing capabilities lead to quicker, better medical care for people, while lowering costs for hospitals.
Marketing
Artificial Intelligence makes it possible to analyze large consumer datasets and drive unique audience insights, and businesses are already using it to discover new potential leads and deliver personalized marketing messages. Taking that up a notch, machine learning can cluster consumers based on demographic data, interests, and online behavior to help marketers identify new potential audiences to target.
Advertising
Deep learning is already helping advertisers optimize their marketing spend and increase the relevancy of their ads, among other things. Deep learning algorithms can also be used to predict long-term advertising performance, and make automated adjustments to bidding strategies based on these insights. For businesses, this means they can automatically ensure they’re only bidding as much as they need to meet their advertising goals.
Other applications include:
Understanding the capabilities of machine learning and its subset deep learning is now more important than ever in digital advertising. Both are integral in achieving predictive advertising performance and automating bidding strategies based on these insights. As its name implies, machine learning can learn and adapt to the tasks it regularly performs. Deep learning takes that a step farther, emulating human brain activity that can not only make decisions based on provided data, but draw logical conclusions that inform next steps.
As technologies become more intelligent, the possibilities for their applications infinitely expand. But while marketers can leverage these technologies for myriad functions such as bidding, targeting, and optimization endeavors, so too can the competition. Thus, it’s likely that in the near future, machine and deep learning won’t be a luxury, but a necessity for digital marketers and advertisers to stay competitive and relevant with their audiences -- paving the way for an even more intelligent and sophisticated set of technologies down the road.
Effectively geo-targeting SEM campaigns is one of those arcane questions with an answer that never satisfies. There may be a precedent to go off of, or a thousand of them, but it feels like a leap of faith anyway. That’s because, of the thousands of people who have tried to answer this question before you, not one of them were wrestling with circumstances exactly identical to yours.
Best practices are notoriously unclear when confounded by a swarm of variables that ensure that no two cases are alike. So, even though it’s all been done before, there’s no one best approach. The ever-unsatisfying answer to the question of how best to geo-target your SEM campaigns? It depends.
It depends on your business model and your audience, on the size of your program and its level of automation. It depends on your resource constraints, on your budget constraints, and countless other things.
The goal of this article is to demystify all of those dependencies, to show you that others have indeed tried this before, and ultimately, help you toward a reliably satisfying answer to the question of how best to geo-target your SEM campaigns.
There are two primary schools of thought for geo-targeting SEM campaigns:
National Geo-Targeting (One Extreme):
Campaigns are only grouped by segments other than location (i.e. product line, device type, network, brand, match type, etc). All campaigns target the entire coverage area in which ads are served.
Local Geo-Targeting (The Other Extreme):
Location is one of the segments by which campaigns are grouped. Each campaign only targets a subset of the total coverage area of your business. For this reason, multiple identical campaigns are required to cover that total area, which means lots of duplicative keywords. Campaigns may still be further subdivided by additional segments (i.e. product line, device type, network, brand, etc).
Little Bit o’ Both (Not Extreme):
As with most everything, not only is there always a middle ground, but that middle ground is almost always where the answer lies. Perhaps you want to have separate campaigns for each metro area in the US, but nationally-targeted campaigns abroad? Or perhaps you want to have metro-segmented campaigns everywhere in addition to national campaigns as catch-alls for what Google can’t attribute to a metro? Perhaps you even want special campaigns only for certain critical categories in certain critical business hubs, like raincoats in Seattle or tailors in DC? Blending national and local targeting allows you to form-fit your program to the diverse reality of your business.
The combinations are endless -- which makes the choice between them all the more complicated.
Simple. It’s one of those words that means one thing and could mean the opposite, depending on the context in which it’s used. In that sense, national targeting is simple. Here are some of the ways in which it’s beneficial to your program:
So far, the benefits of national targeting have appealed mostly to the lazy or, as we call ourselves, the opportunity cost-conscious. These last couple benefits are fun for the chronic fiddlers, too:
And lastly, but importantly…
The disadvantages of national geo-targeting - the ways in which its simplicity is a drawback - are best shown by an examination of its alternative.
Local targeting is more complicated. And that complexity has its own pros and cons, that represents an inverse to the simplicity of national targeting. Here are the pros:
Those increased levers, however, must be weighed against these familiar complications of any highly-segmented program:
National and local targeting both are viable strategies, but paramount to which one you choose is understanding why you’re choosing it. If you know your business model and your goals, your constraints and your markets, then you’re 90% of the way to your version of success. Perhaps your business is inextricable from the places in which it operates, and added complexity could lead to richer relationships with your users. Or perhaps a simpler approach is warranted, giving you the high-ground vantage point to better acclimate to those users’ behaviors. In either case, successful geo-targeting is the scaffolding from which you lay these patterns of interaction with your diverse users, and the foundation to a thriving, pumping SEM program.