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Consumer data is a critical performance factor for digital and search engine marketing. In fact, it’s now practically impossible for businesses to gain visibility in either organic or paid search results without relying on consumer data insights. Yet at the same time, the European Union and other government bodies around the world have continued to introduce stringent regulatory compliance mandates governing how that data can be collected and managed—which, needless to say, has significantly affected the way that organizations treat, store and use their most valuable data.
And while forward-thinking businesses need to make every effort to adhere to these regulatory compliance mandates, these regulatory compliance mandates under a new paradigm universally have had an impact on data practices around the world.
Here are some insights into how you can navigate regulatory compliance in the world of SEM.
In 2016, the European Union passed what's known as the General Data Protection Regulation (GDPR), a stringent and broad reaching law requiring search engines, advertising platforms and individual businesses to do more to protect the data of European citizens—and compelling Google, Bing, and other advertising platforms to place data management and security at the top of their priority list.
Among other things, the GDPR requires any business that uses consumer data to:
While the law only applies to European citizens, its impact is clearly global: any website that collects information from site visitors—even if the business is based outside of Europe—could be subject to these regulatory compliance requirements. And the emergence of GDPR has also inspired similar regulatory compliance mandates elsewhere, including a new state data privacy law in California and several data protection bills gaining traction at the federal level in the US.
So, how does it affect search engine marketing? Simply put, the responsibilities for data protection impact both search engine platforms and the marketers that use them to reach their audience. The extent of your responsibility all depends on who controls the consumer data vs who processes it. If you either control the data and/or process it, then GDPR will govern your regulatory compliance responsibilities.
Consider Google’s in-market audiences, for example. Advertisers use Google’s consumer behavior data to target new audiences with their advertising message. But in-market audience data is both controlled and processed by Google who possess and analyze it for ad targeting—this places the responsibility for GDPR compliance on them.
In another scenario, consider remarketing lists for search ads (RLSA) in which you upload a file of your leads’ email addresses to create a new Customer Match audience. In this case, you are the data controller and Google is the data processor. Thus, both you and Google have compliance responsibilities under GDPR.
Additionally, browsers are also affected by GDPR regulatory compliance mandates. To ensure compliance with this mandate, Apple made significant changes to how cookies are tracked on Safari, now deleting third-party tracking cookies after 24-hours, while making it difficult for marketers to use cookies for different forms of tracking like remarketing. While most advertisers might see this change as a huge limitation in their ability to advertise to their audience, it also helps cut down on low-quality spam ads and encourages marketers to target audiences with a relevant message during the most critical time frames.
While GDPR is working to prioritize the best interests of people, it also creates some new challenges for businesses that market through search engines. But despite the strong potential for inconvenience and challenges, business owners can’t ignore their responsibilities toward user privacy if they want to both effectively reach their audiences and remain within the law.
The GDPR is a complex and extensive piece of legislation governing regulatory compliance. And while this post is aimed at giving search engine marketers an idea of some of their responsibilities under GDPR, it is in no way comprehensive or a means of helping you interpret this document and how it pertains to your business. For any questions regarding your regulatory compliance posture, please enlist the help of legal professionals.
That said, here are some key best practices to follow for regulatory compliance as a search engine marketer:
Because the GDPR is open to almost limitless interpretation, even businesses that attempt to be compliant could end up inadvertently in violation of its terms. This had led to a number of businesses outside of Europe taking steps to avoid being subjected to the law altogether, including more than 1,000 US news sites that chose to block European visitors to their sites instead taking the necessary steps to be compliant or undergoing scrutiny under its regulations.
Don’t be tempted to follow suit. Not only would you be blocking your European audience base, you might inadvertently block US site users when they visit or live abroad. It can also negatively impact SEO if Google’s crawler can’t effectively read your site when you have links pointing at your website from other EU sites.
While blocking access to certain site visitors might seem smart from a financial perspective, it’s really only a temporary solution to a growing global data privacy issue that won’t go away anytime soon. The US has already enacted numerous compliance regulations, such as SOX, HIPAA and PCC DSS, which are all becoming more stringent and financially punitive. Thus, thinking ahead, it’s better to comply to GDPR regulations now with your user experience intact, than wait until you’re forced to make similar changes down the road.
Probably the most significant change search engine marketers need to make is ensuring they obtain proper consent for using tracking cookies on their website. This is a necessity if you use cookies for retargeting in online advertising.
In the past, it was possible to track site visitors with site cookies without asking them to opt-in, usually with an alert that read “By using this site, you accept cookies.” That doesn’t fly with GDPR—these days, visitors must give expressed permission before you can start using their data.
When obtaining consent, you should:
What’s more, there are a variety of tools out there that can help you create GDPR-compliant cookie banners for your site, as well as help you track and manage cookie data:
Another important requirement of GDPR is that businesses should only collect the data they need for marketing. Say an individual reaches one of your landing pages from organic search or a PPC ad. If you serve them with an extensive lead form requesting additional information that doesn’t have a direct marketing need, you could be in violation.
Not surprisingly, your landing page lead forms also need a lot of the same wording as your cookie agreement in order to comply, so use clear language to explain how the data they provide will be used by yourself and third-parties.
You should also design your lead form so users can provide clear consent about what you can do with their data. For example, one good practice is to have seperate boxes for users to agree to the Terms and Conditions vs. signing up for your mailing list. You also need to be clear if you plan to use lead form information to build your Google ads remarketing lists.
What’s more, you can also provide options for what kind of communications they can opt into receiving from you, such as email, phone, or SMS messages.
Essentially, it’s better to err on the side of being transparent on your landing pages about data use. As with your cookie consent, avoid being long-winded on your lead forms by providing prominent options for users to “read more” about how you might use their data for search engine marketing or other purposes. You can then direct visitors to your data security policy.
Optimizing how you collect consumer data for search engine marketing is only the first step in GDPR compliance. An equally important task is implementing and updating a data security policy that details exactly how you will handle the data you collect.
Many businesses simply update their existing privacy policy to comply with GDPR standards, while others create a dedicated data security policy for the regulation. Whatever path you choose, make sure it's thoroughly reviewed by your legal team.
At the very basic level, a GDPR-compliant security policy should include:
This should be a clear, complete explanation of who you collect personal information from (e.g. your site visitors) and what type of data you’re collecting. This includes cookie information, user device and IP address data, what browser they used, search queries they used to reach your website, information about on-site behavior, and geolocation data, among other things. The more detail you can provide about the type of information you can potentially collect, the better. You should also explain how the data is collected, including any third-party tools you might use.
Next, in order to ensure you’re only collecting necessary data about consumers under GDPR, you need to explain the various circumstances in which you might use personal data obtained from your site users. Essentially you need to justify why you’re collecting certain information by explaining your potential use of it. For example, you can use consumer data to define an audience that is most likely to respond to your marketing content, evaluate usage of your website, or market information about your products and services.
Under this stipulation, you should also disclose any third-party vendors or individuals with which you share consumer data, such as affiliates, advertising networks, or cloud service providers.
GDPR mandates prohibit you from retaining consumer personal data indefinitely without reason. Thus, you need to have standards in place that explain how long you will retain data and under what circumstances.
For example, if you collected cookie data from site visitors for the purposes of remarketing, you might retain it for a maximum of 12 months, although the amount of time you hold onto the data really depends on the length of your sales funnel. If you run an ecommerce business that sells leather boots, it makes little sense to retain cookie information for more than a month, as the sales cycle is short. If you collect other kinds of data, such as lead contact information, you can specify another timeframe for data retention.
Under GDPR, data subjects (consumers) have certain rights that businesses must adhere to, including:
This, of course, should be reflected in your data policy that should include details about how people can make these requests, and how long they should reasonably expect to wait before receiving a response.
There’s always the possibility that your consumer data could inadvertently be used in a way outside of your data policy definitions. It could be misused by your own employees, or compromised by hacking, for example. GDPR acknowledges this potential, and requires organizations to have a process in place to notify consumers about data breaches and take efforts to minimize negative impacts. Specifically, you’re required to report necessary information regarding a data breach to all consumers and other relevant bodies within 72 hours of discovery.
GDPR is, to date, the most extensive and comprehensive regulatory legislation addressing data privacy online. But it’s not the first, and it won't be the last. But it anything, this privacy mandate sets a new bar, raising it ever higher, for data security, compliance and consumer data protection.
For many businesses, it has already created countless headaches and compliance woes. And it is sure to create more challenges down the road. But like any challenge, it also entails opportunity. For you as an SEM professional, that means you have the opportunity to get ahead of the game by adhering to its set of data privacy laws. Ultimately, that will mean your consumer data will be more secure, and your customers’ privacy will be protected—all of which go a long way to building trust and loyalty, both within your audience and in new markets.
Ignoring it is not the answer—this reinvigorated focus on consumer privacy isn’t going to go away anytime soon. But with a little time, effort and foresight, you can navigate it successfully and even use it to your advantage.
In the world of SEM, your campaign strategy doesn’t stay the same from quarter to quarter, month to month or even week to week. Like the weather and tide schedules, your paid search campaigns change with the seasons. And as a Paid Search Marketer who has lived through numerous Black Fridays -- and countless promotional SEM campaigns -- you know that some seasons are, well, busier than others.
Safe to say, you might feel like you spend all of your time, bandwidth and energy gearing up for the holidays only to do it all over again with President’s Day promotions or spring sales. To say the least, this can be exhausting, labor-intensive and mentally draining for your Paid Search team. After all, these aren’t just fun-one-time campaigns, but an integral part of your campaign strategy -- as well as the key to higher revenue and conversions. Ignore them or let them slide, and you lose out to competitors more on top of their promotional game than you.
So what’s essential for easing your campaign strategy woes, freeing up bandwidth and ultimately setting you up for success?
We’ll let you in on a little secret: it’s your data. Your data about past promotions and seasonality is the ultimate secret weapon to future campaign success.
In Part II of our Enterprise Paid Search Pain Pain series, “Having Trouble Optimizing Your Seasonal and Promotional SEM Performance?” we talk about just that.
Look, we know that, like many advertisers, you spend a lot of time making these changes manually -- and probably wish that you could easily access centrally located data to make informed and strategic decisions on your next campaign.
We feel you. So in our next installment, we discuss some of the pains from this lack of insight, whether it’s hours of manual work to make necessary adjustments or continually being forced to override your bidding solution.
But, true to form, we don’t present any problem without presenting some kind of corresponding solution. (glass half full, right?) We also explore what non-negotiable features you need to stay competitive, relevant and profitable in today’s market (psst...think strong data integration). And in doing so, we ask you to think hard about what you need, answering questions like:
We know this is not an easy problem to solve. Every organization’s seasonal promotions differ with their unique business objectives, revenue goals and performance metrics.
But on our end, we want your promotion calendar to be armed to the teeth with powerful insights and tools so you can apply the right changes to create a strong, repeatable revenue driver for your business that allows you to take profits and ROI to their full potential.
You can find questions -- and answers -- around this complex challenge here in the latest of our Enterprise Paid Search Pains series: “Having Trouble Optimizing Your Seasonal and Promotional SEM Performance?”
To your success!
As a digital advertiser, you know that PPC advertising is a valuable tool to drive all sorts of marketing goals, such as finding new leads, nurturing prospects, driving conversions, and increasing the lifetime value of your current customers. But in order to be effective and profitable, PPC requires ongoing financial investment, constant analysis and continual adjustments to ensure maximum ROI. As part of that recipe, it’s critical to know how to calculate Cost Per Click -- or your maximum CPC -- and to get it right.
The good news for digital advertisers is that CPC is the one factor over which you have the most control. And CPC has the most significant impact on performance. Maximum cost per click affects:
So it’s a constant balancing act. Set CPC too low and your ads might not even appear or be seen. Set CPC too high and you can end up grossly overspending for ad space.
When you perform keyword research using Google Ads Keyword Planner, it provides an average CPC bidding range that's needed to appear at the top or bottom of the page for certain keywords. PPC advertisers can use it as a guide to what they might spend to reach a certain ad position. But to ensure you have efficient PPC campaigns that maximize ROI, it’s essential to calculate maximum Cost Per Click for yourself.
The reason? You run a unique online business and you need specific profit margins to justify your PPC advertising spend and keep your business growing. Relying on Google’s averages to guess the right CPC leads to mediocre results at best, and negative ROI at worst. You need to evaluate your own market and optimize your PPC strategy to truly meet and exceed your business goals. Read on to learn how to calculate maximum CPC for Google Ads.
In order to accurately calculate Cost Per Click, the first thing you need to know is the value of your customers. How much money do your customers spend with your business on average?
When calculating your CLV (customer lifetime value), don’t forget to first determine the real profit you get from your product. So you need to take into consideration taxes, shipping costs, and the overhead expenses for administrative tasks. Your product can have a list price of $99, but your actual profit is more like $50 after you factor in these internal costs. You also need to consider internal costs when calculating your CLV, or you will end up grossly overspending on ad space.
Depending on what kind of product or service you offer, this can be based off of a single purchase or average of purchases over time. For example:
Because every business has a unique setup and expenses, determining your internal costs is probably the most difficult part of calculating your CLV. But it’s important to include a calculated estimate here, or risk seriously overestimating your CLV. You will use your CLV to help make sure you don’t spend more on advertising than you earn from it.
The next thing you’ll want to figure out is the average conversion rate on your website. Specifically, of the people who visit your website, what percent convert into paying customers? This will help you determine how many clicks are required for a conversion.
For example, if you get 50 sales for every 1000 website visitors your conversion rate is 50/1000 = 5%.
Your conversion rate is a static number. That said, it can change over time. And you can create and optimize targeted landing pages designed to boost your conversion rate, which impacts your CPC.
The best way to calculate your conversion rate is with your existing analytics software. You can use Google Analytics Goal Tracking or eCommerce tracking to see your conversion rate based on current data.
If you already have PPC campaigns up and running, check your conversion rates for specific ads, landing pages, and keywords. The only problem with this strategy is targeting branded keywords: people who type your business name into search are already familiar with your brand and more likely to convert as a result. Including conversions from branded queries in your calculation can artificially inflate your average conversion rate. Avoid including branded keywords or find another method to calculate a more balanced conversion rate.
At this point, you have all the information you need to calculate a CPC that makes you zero profit but incurs zero losses. To be clear, this is not your final CPC. It is a base number you use to decide on the right CPC for your business needs.
Let’s say you calculated your customer lifetime value at $50, and your conversion rate at 1%. Multiplying these numbers together gives you your base CPC: 0.01 x $50 = $0.50
That means you can spend 50 cents per click and spend (on average) the same amount that you earn from sales. Your ROI would be $0.
Of course, this number should not be your maximum CPC. After all, you need to profit! To do this, you’ll want to set your maximum CPC lower than this number, but it needs to be balanced. Set your maximum CPC too low and it limits your ability to drive clicks and conversions from PPC. Because you’re competing for ad space, bidders with a higher CPC will get higher ad positions in search results, which receive significantly more clicks than ads at the bottom of the page. Set your max CPC even lower, and your ads won’t appear in search results at all.
The question for you to answer is how close to your base CPC you should bid to maximize visibility, clicks, conversions and profit. It’s important to remember that your max CPC will never equal your actual CPC. You’re set up to only bid the amount needed to win the auction. So your actual CPC can be lower than your max CPC.
For example, if you set your max CPC as $0.45, your actual CPC could end up being $0.36. With that in mind, you can set your max CPC much closer to your base CPC and still deliver positive results.
The best way to test your market and the bidding landscape is by trying and testing different max CPCs below your base CPC threshold. For example:
Start with one of the percentages in the middle and monitor performance. Then adjust to a higher or lower percentage and see what impact it has (if any) on ad visibility, clicks, and conversions.
If you want to ensure you that maximize ROI from your PPC campaigns, you should regularly recalculate and optimize your CPC. Google uses a variety of factors to determine actual CPC -- improve these areas and you can potentially reduce the costs needed to reach your advertising goals.
Some areas to optimize and improve include:
Google Ads prioritizes relevance and quality over bid amount, determining these factors by using a metric called Quality Score. The higher your quality score, the less you need to bid to rank for a specific keyword. So pay attention to your quality score and the factors that affect it, such as your landing page and ads.
As mentioned before, you can create targeted landing pages designed to maximize on-site conversions. Your conversion rate is a huge factor in determining CPC. Improving your landing page conversion rate from 0.5% to 1% can mean moving your base CPC from $0.42 to $0.85. So create different versions of your landing pages using different headlines, copy, supporting media and calls-to-action, and test and discover which elements are the most effective at driving conversions from your PPC traffic.
Creating more compelling ads can improve your click-through rate and provide more conversion opportunities. This indicates relevance and can improve your quality score. Because you need to ensure your advertising message is sufficiently relevant to the related keyword, using ad extensions and other strategies to make your ad more prominent can also drive more clicks.
Of course, the current state of the market and the bidding landscape can also have a big impact on the max CPC needed. It’s worthwhile and recommended to regularly calculate and adjust your CPC manually. But there’s no way to effectively keep up with the quickly changing markets without relying on automation.
Bid management tools offer a proven strategy to make necessary adjustments to your CPC while reducing wasted ad spend and maximizing ROI. Using data from Google Ads performance and the latest market insights, bid management tools take on the challenging task of ensuring you always set the right CPC. At the end of the day, knowing that you’re on a path to higher ROI frees you up to focus on growing your business, increasing revenue and taking your PPC strategy to new levels.
As a digital marketer, you know by now that not everything goes according to plan the first time around. Sometimes it takes trial and error. Changing it up. Taking a slightly different route. And just doing things differently until you get it right -- or get what you want. That’s the concept behind remarketing.
Essentially remarketing is the art of serving targeted ads to people who have already visited or taken action on your website -- an investment that has the potential to significantly increase your advertising costs before driving ROI -- so you have to be judicious about how it’s executed. But, if implemented correctly, it also has the potential to be an extremely valuable advertising strategy. Consider these statistics:
The value and possibilities of remarketing ads are plentiful. That said, it’s a vastly different advertising strategy to execute than traditional digital advertising. For one, you’re targeting a very specific group of people who are already familiar with your business, and as such, you’ll need to adopt a new set of approaches and tactics to ensure that your efforts effectively reach your audience. Below are seven important tips that can help you launch your remarketing strategy the right way.
Before anything else, you need to prioritize your marketing goals and the performance of your existing campaigns -- rather than launching remarketing campaigns for all of your relevant sales funnels, it’s best to first narrow down the ones that will truly help you achieve your marketing goals. This will help you select the most important areas for remarketing out the gate, while also minimizing your initial expenses and driving quick ROI.
If, for example, your main marketing priority is to drive more sales, then you should select a few of your top-performing sales pages to tag for remarketing. If these products already sell well, then you know the offer is valuable and your sales funnel is effective. From there, you can add on remarketing simply to maximize the potential conversions you get out of one of your top-performing campaigns.
On the other hand, if your most important marketing goal is lead generation, then you should focus on tagging your best-performing lead generation landing pages for your initial remarketing campaigns.
When remarketing for search ads, many advertisers make the mistake of simply duplicating their current keyword lists. Granted, while your site visitors navigate back to Google to continue researching a topic, they can type in a lot of the same keywords while actually searching for different results. But that doesn’t mean you shouldn’t expand your keyword lists!
Remember that when using lists for search ads (RLSA), you’re targeting a very small group of people — your previous site visitors. Subsequently, you’ll want to use every relevant keyword possible to trigger your ads for this audience. So, broaden your keyword lists to include other relevant terms that you excluded for one reason or another in your PPC ad campaigns.
For example, most PPC advertisers avoid targeting broad keywords because they attract a lot of traffic that may or may not be relevant to their business. But these keywords are very relevant for remarketing campaigns! And because you’re only targeting people who already expressed interest in your business, you already have some understanding of their search intent when using broad keywords.
One reason marketers shy away from remarketing is that they believe it will affect their audience’s view of their brand. Consumers notice when they’re being targeted with remarketing ads, and some don’t like the idea that businesses are tracking them across the web.
That’s why effective campaigns always require a careful balance -- deliver too many remarketing ads for too long, and you might end up annoying your leads instead of converting them. Google Ads offers two features that can help you manage this balance: frequency capping and remarketing duration.
Frequency capping is an advanced setting that allows you to control how many times someone can see your remarketing ad in a day, and can be set for campaigns, ad groups, or individual ads.
The type of frequency cap you should implement depends on your business niche and goals. If you’re an eCommerce seller targeting abandoned carts, then you might want a fairly high-frequency cap, which will give you the biggest opportunity to convert a lead who is likely to make a purchase decision in the short term. A higher frequency cap also makes sense if you’re promoting a limited-time sale or webinar. On the other hand, if your main goal is to build brand awareness through remarketing, then you’d want a lower frequency cap to avoid coming on too strong with your audience.
Membership duration is another feature you can use to control how frequently people see your remarketing ads. By default, your remarketing campaigns are set for three months, meaning that someone can potentially see your ads for three months after joining your remarketing list.
Again, the duration you choose will depend on your market, specialty, and goals. A long remarketing duration makes sense for brand awareness campaigns. But it wouldn’t make sense to remarket to abandoned shopping carts three months after the first interaction.
While audience behavior should drive your bidding strategy, it’s just as important for informing the type of remarketing message that you deliver. Use audience behavior to identify their point in the sales funnel, and from there, segment them into separate remarketing lists to deliver unique ad campaigns personalized to their unique point in the customer journey.
For example, someone who just consumed some of your blog content still might not understand your product or its value. Thus, you can leverage remarketing ads to promote content that introduces them to your value proposition.
On the other hand, someone who abandoned their shopping cart has already done their product research and understands its value. Now your remarketing message is all about getting them to convert. Offering a special discount or free trial in your remarketing ad is a great way to do this.
You can also adapt your campaign duration and remarketing frequency based on your customers’ points in the sales funnel. If you’re working on brand awareness with top-of-the-funnel leads, then it makes sense to extend your campaign duration. At the same time if you’re trying to convert a bottom-of-the-funnel lead before they decide on a competing product, then increasing your remarketing frequency cap gives you more opportunities to convince them to convert.
Most PPC and display ads are aimed at introducing people to a company and encouraging them to enter the sales funnel. Remarketing audiences are different. These people are already familiar with your company and its primary value proposition, so there’s no need to target them with generalized ads that reiterate these same points.
Instead, you should create ad copy that’s much more targeted at driving conversions than educating audiences. The reason? Many of the leads you’re targeting with remarketing navigated away from your site because they’re not ready to make a purchase decision. They’re unsure if your business is the right fit for them, or if your products are a good use of their money. To create more targeted and relevant remarketing ads, alleviating these fears and concerns should be the primary focus of your ad copy.
Here are some examples of remarketing display ads that does this well:
In one particular ad, personal injury lawyers were able to infer the kind of injury the lead had suffered through site behavior and/or form fills. They used this information prominently in the ad headline to attract attention, then followed up with the subheadline “100% FREE Case Evaluation” which alleviates fear by showing their audience they’re not required to make a financial investment for them to learn more about a potential case.
Sit down with your sales teams to brainstorm what kind of fears and concerns prevent your audience from converting on-site. Then address these directly in targeted remarketing ads to propel more conversions.
Most marketers that use remarketing ads only focus on driving initial conversions. But they’re missing out on a huge opportunity to upsell and cross-sell to their existing customers through remarketing.
People who have already bought something from your business are much more likely to purchase again than people who have never converted into customers. As such, targeting your previous customers is often low-hanging fruit that can bring big wins for your remarketing campaigns.
Since you’re targeting previous customers, you can use the additional information you have about them to create more relevant, personalized ads -- in fact, you can take it as an opportunity to remind them what you know they already like about your business. Target previous customers by:
You can even create special campaigns that target your most valuable customers. Create special rewards and promotions that you only share with people who already spent a certain amount of money with your business. Then you can increase their customer lifetime value even more with remarketing ads.
When you begin your remarketing efforts, you’ll want to focus on creating remarketing campaigns for the site pages that are most relevant to your main marketing goals. As remarketing proves its ROI over time, you can expand your strategy to tag a wide variety of site pages. But you can still strategically allocate your remarketing budget to ensure you’re maximizing its impact on your bottom line -- and the best way to do this is bidding based on audience behavior.
When your audience visits your website, their on-site behavior is a good indication of how close they are to converting. Compare someone who made it all the way to the checkout page vs. someone who visited your home page for five minutes. Which of these two leads would be more likely to convert through remarketing?
That’s not to say you shouldn’t remarket to the person who only visited your homepage -- just reduce your bid accordingly knowing that this lead is much less likely to convert. Focus the majority of your remarketing budget on targeting leads at the bottom of your sales funnel, which will likely elicit a lot of quick sales.
Also, remember that not every site visitor is worth the remarketing investment. An important metric to consider is time-on-page. Most site visitors stick around for less than 15 seconds. That’s not enough time to develop an interest in your product or service, let alone understand what it is. Save your remarketing budget for leads who have illustrated genuine interest in your business by reading your content, clicking through to other pages and filling out lead forms.
At the end of the day, remarketing is about taking a second look at your audience, re-adjusting, and taking another approach. Remarketing ads are a huge opportunity to nurture leads, drive conversions, and maximize the lifetime value of existing customers -- all you need to get started with remarketing is a little budget allocation and time to optimize your first campaign. Start with the low-hanging fruit then expand as you learn more about how your audience responds to remarketing content. Make incremental adjustments -- broaden keyword lists, segment audiences, create new ad copy. And remember, you don’t have to reinvent the wheel -- that’s already been done. You just have to refine it to make it work even better for you.
Let’s face it, these days, the success of your PPC program -- and your SEM performance and strategy as a whole -- is contingent upon data. Accurate, reliable and applicable data. And nowhere is this more important than in your bidding strategy. In fact, your competitive success and relevancy in the market depend on it.
As you probably know by now, data is the fuel to your SEM engine, enabling you to devise comprehensive PPC strategies, create progressive business goals and drive future marketing decisions. In short, data is critical in driving your business forward toward peak SEM performance. Thus, the best and most efficient strategies will thoughtfully take into account increasingly more -- and higher quality -- data from key sources.
But what if your SEM performance isn’t up to par? Or you’re not meeting critical business goals? Or competitors always seem to be a half-step ahead of you, beating you to the proverbial punch? The other side to this double-edged sword, of course, is that if your SEM performance is suffering, it’s likely sourced to a data problem as well. More specifically, chances are your current PPC solutions are utilizing incomplete or low-fidelity data to power your bidding strategy, which ultimately fails to give you the necessary big-picture of both your customer and competitive landscape. Thus, any bidding strategy you attempt to employ is almost sure to miss the mark or otherwise fall short of expectations.
There are few things more frustrating. By now, you’re likely tearing out your hair, wondering how in the world you’re going to generate required leads, or meet conversion metrics, let alone exceed them this quarter. We feel your pain. That’s why we’re here to help with a new series that takes a hard and thorough look at the numerous pains common to at-scale SEM/PPC programs.
In the first of our Enterprise Paid Search Pains series, we examine some of the reasons Paid Search programs continually suffer from poor data utilization and sub-standard SEM performance. From there, we dive into real-world data issues you have likely encountered with technology solutions in your day-to-day campaign efforts -- everything from solutions that can’t integrate a full set of historical data or have trouble integrating data from third-party sources to solutions that take too long to incorporate bid data.
And of course we provide you some relevant tips for resolving some of your biggest data issues, while directing you to technologies that truly address any one of these data challenges you might be experiencing. We’ll even let you in on a little secret -- finding just the right tool will help you to collect more data, align it to publisher activity, and automate bidding optimization in a way that is sure to result in the best possible performance for your business.
After all, here at QuanticMind, we want to see you reach peak SEM performance in all areas of your SEM program, whether that’s knocking your quarterly PPC metrics out of the park, reaching new customers or achieving higher ROI than ever before.
And we take pride in not only helping others reach peak performance, but taking their PPC programs to new levels. To your success!
[Learn what pains marketers are feeling and overcoming in paid search HERE.]
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.
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.
AI vs. Machine Learning -- it’s likely you’ve heard both of these buzzwords relentlessly over the last few years, and often interchangeably.
While these terms are thrown about ad nauseam, and often without context or definition, in reality, they’re distinctly different. Artificial Intelligence is a broad concept with many applications and machine learning is just one part of it. Each has a distinct role and brings unique value to your SEM and digital marketing efforts. We’ll break down their attributes and how you can leverage them to boost campaign visibility, profits, and ROI.
Ever played chess against a computer on your old desktop? You were interacting with AI without even knowing it.
The term artificial intelligence was first coined in 1956 by American computer scientist John McCarthy, who believed it possible to create machines that could mimic the intelligence and many of the reasoning capabilities of a human brain. In actuality, even the earliest computers possessed intelligence capabilities, with the ability to conduct math, perform logic, and store information. Over time, however, computers were developed with increasingly advanced capabilities that allowed them to perform tasks and follow thought processes that more closely resembled human intelligence. Today, AI can perform a wide variety of advanced tasks, such as detecting and understanding languages, recognizing voices, solving complex problems, and event planning, among countless others.
Weak AI and Strong AI
There are two different kinds of available AI: weak AI and strong AI. The most common is weak AI, applications that we utilize on a daily basis in digital assistants such as Siri, Alexa, and chatbots. And while weak AI is designed to mimic the experience of human interaction, it doesn’t actually deliver the same level of intelligence. Instead, it’s programmed to understand certain interactions and tasks, classify them and then respond accordingly.
Alternatively, strong AI processes information more like a real human brain does when learning from and adapting to new experiences. Specifically, strong AI creates clusters and associations that can then inform a response when presented with new data. Over time, it develops strategies and responses independent of its programming.
Artificial intelligence, to say the least, is a broad umbrella term incorporating a wide array of computing and learning capabilities. Machine learning, on the other hand, is a subset of AI with specific unique functions -- which ultimately makes it a more impressive technology.
Specifically, machine learning, a term first coined in 1959 by American pioneer of computer gaming Arthur Samuel, involves processing large amounts of data related to a task, analyzing it, and using it to inform the computer’s performance at that task. Whereas AI technologies entail predefined programming on how to respond to new data, machine learning makes it possible for computers to learn as they perform a task, essentially creating their own new programming as they go to perform it better.
Additionally, machine learning can also be predictive. Algorithms build a mathematical model of sample data which predicts future performance, which informs the decisions to optimize performance at any given task.
Google’s image analysis capabilities provide one such example. While the vast majority of images published on the web provide no alt tag describing what they are, some some of them do. Google’s Vision API analyzes a series of photos that come with a tag, such as “chinchilla.” Inherently, Google doesn’t know what a chinchilla is, as it was never programmed into the system. But by studying the tags and the associated images, machine learning can accurately predict and identify other images that are likely to also be chinchillas.
Machine learning and AI are not mutually exclusive technologies, and each have distinct applications that make them valuable for different areas of your SEM and digital advertising programs. In our breakdown of AI vs. Machine Learning, we weigh the unique benefits of each and show you where each wins out.
AI vs. Machine Learning: Understanding audiences
Both AI and machine learning can help marketers analyze large amounts of consumer data to better understand their interests, needs, and purchase intent, while also automating and optimizing this process. However, machine learning can also identify new segments of your target audience and their unique traits. Affinio is one such enterprise product that does this, with the ability to analyze billions of interest variables and network connections to create unique audience clusters. This in-depth analysis, in turn, reveals new patterns, relationships, and commonalities about audiences that regular data analysis can’t uncover.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing marketing messages
Better understanding your audience also brings more opportunities to create content that’s relevant to their interests. Automating the testing of different content types to see which resonates best with your audience is one of the places where AI shines.
Google Ads already does this with their ad rotation settings. Setting your ad versions to “Optimize” will automatically optimize your ads for individual auctions using signals like keyword, search term, device, and location. Then as Google Ads collects more data about ad performance, machine learning algorithms determine statistically which ads are likely to have stronger performance.
Winner: AI
AI vs. Machine Learning: Improving Lead Scoring
With machine learning, it’s possible to create predictive models that leverage input data and probability to accurately determine future trends. While this capability is beneficial to a wide variety of digital marketing initiatives, it’s especially relevant to lead scoring. Traditional lead scoring capabilities rely on individual lead behavior to determine their potential to convert, and machine learning algorithms can draw correlations between those lead characteristics to identify valuable prospects before they express strong purchase intent.
Winner: Machine Learning
AI vs. Machine Learning: Optimizing Marketing Spend
Machine learning’s analysis capabilities can also be used to better allocate marketing spend by considering factors such as consumer data, buying signals, quick bidding options and other historical signals to predict future advertising performance. Powerful software can learn from past market data and current micro-changes in performance to make quick bidding decisions, which can both improve advertising performance and reduce wasted ad spend due to over-bidding.
Winner: Machine Learning
AI vs. Machine Learning: Delivering timely messages
Many would argue that there’s still no AI computer that can process decision factors with the same sophistication as a real human brain. While that may be true, AI can make decisions much faster than an entire team of data scientists.
From there, machine learning can help deliver the right marketing message to the right audience in real time. Using search queries, demographics, location, and other factors, the technology can automatically serve relevant ads to the right audience when they need them, enabling marketers to automatically deliver a more relevant message to their audience.
Winners: AI and Machine Learning
AI vs. Machine Learning: Engage dynamically
Probably the most interesting application of AI to marketing is the potential to engage with audiences dynamically. Smart technologies like Alexa and Siri are prime examples of this type of audience engagement. However, there are many other potential use cases, such as Google’s dynamic ads that can change the PPC advertising message based on audience input.
In addition, chatbots that leverage AI and machine learning have a unique ability to both engage with as well as help their audiences based on the information they provide. Far from being automated Q&A systems, many can perform advanced marketing and sales tasks, using natural learning processing to learn more about audience needs as they interact.
Winners: AI and Machine Learning
Neither artificial intelligence nor machine learning are new concepts -- AI enables computers to make decisions based on input data. Machine learning enables computers using data inputs to grow and learn. Both have been around -- at least in theory -- for decades.
What is new, however, are the ways in which these technologies are used to shape the direction of countless industries, including digital marketing and advertising. With the ability to engage audiences, provide new customer and competitor insights, deliver timely messages and optimize ad spend, these technologies have the power to reshape and essentially revolutionize the SEM industry as we know it, while taking your PPC campaigns to new and yet undiscovered heights.
It's no secret that digital marketers are on the cusp of realizing the power of AI and machine learning. Those who step up first to take advantage of all they have offer will position themselves well for a profitable future that’s leagues ahead of the competition.