What’s the difference between search marketing automation and machine learning? Are they interchangeable?
What do search marketers fear most about these topics? How can they fully embrace them?
These are just a few common questions our Lisa Little, a 2020 Search Engine Land Awards Search Marketer of the Year, gets asked all the time, so read on as she answers them.
What’s the Difference Between Search Marketing Automation and Machine Learning?
Automation: This is a wide range of technologies that reduce human intervention in processes.
Search automation examples are scripts, bidding technology, auto-applied recommendations, automated audits, budget-pacing tools, optimization score recommendations and management or reporting platforms.
The tools to complete automated actions are done by a machine instead of a human. Automated aspects act or run behind the scenes on cruise control without someone in the driver’s seat pushing buttons. (Note: The driver tells the car what speed to maintain and sets cruise control before the automation takes over.)
Machine Learning: This is the study of computer algorithms that automatically improve through experience, and using data is seen as part of artificial intelligence.
Today, machine learning comes into play with responsive search ads, real-time auctions, quality scores, smart campaigns, smart bidding or time decay and with data-driven attribution models, ad extensions, a customized SERP experience and more.
Machine learning supports the search account aspects that require so much real-time data that human minds can’t comprehend at the sheer speed required to compute and translate data. Machine learning is constantly used without advertisers’ control, approval or understanding. Search marketers choose to leverage and act upon data in a scalable and agile way through efficient automations. Embrace machine learning along with automation, and you’ll produce amazing results.
What Are Marketers’ Fears About Search Marketing Automation and Machine Learning?
Search marketers are hesitant to use automation and machine learning because of the lack of control and transparency; brand advertisers are typically slow to adopt, or don’t approve; they’re unsure about the automation types available and are concerned automation will replace jobs; and they’re confused about how to learn about them (or are unwilling to do so).
Though, the reality is if you’re in search marketing, automation and machine learning are happening around you so you should explore them, not ignore them! Brands that don’t evolve with customer needs, expectations and behaviors quickly fail and ultimately become extinct.
How Should Marketers Embrace Their Fears?
Use machine learning to keep up with demands as it allows you to adapt quickly and seamlessly based on the data and feedback you’re receiving. Simply put, automation and machine learning are required to keep up an accelerated search pace, particularly with the unexpected, epic and volatile behavior shifts over the past year.
Look fear in the face and trudge ahead through the challenges. Think of this comparison: Search machine learning is like a robot vacuum cleaner. It cleans your floors for you. But first it must learn your floor plan and understand your performance expectations. It requires regular charging, cleaning filters and other maintenance. The best thing about a robot vacuum is you can spend that hour you used to vacuum your house in a more meaningful way. Now you have time to spend with your family, work out or cook dinner – and the chore gets done. The robot vacuum cleaner doesn’t replace you but allows for more productive and efficient floor cleaning so you can focus your time elsewhere. Of course, you still have to check up on the robot vacuum cleaner and troubleshoot and maintain it, but you can use it to your advantage.
Even with a robot vacuum, you still have to do some of the floor work yourself, whether that’s cleaning up a spill or cleaning those tight spaces a robot vacuum cleaner simply can’t reach. The concept is the same with search machine learning. Find ways to leverage automation to complement your overall program and results. At times you will need to do manual upkeep or repair, but that should work in tandem with all aspects of the account.
What Are Marketers’ Search Automation and Machine Learning Do’s and Don’ts? (Handy Checklist)
Take it one step at a time. Dip your toe in with an automated bidding strategy to start. Most smart bidding strategies are generally straightforward, and selection depends on what goals you’re trying to achieve. To start, go with the simplest goal: Maximize clicks.
Check in daily and learn to love data. While we can’t physically see the data going into the automation or making the machine work, we can still know and appreciate the value of what we can’t see. Take a test, tweak and learn approach to analyzing data results. Stay curious!
Own your research and customize how you use automation. Google or Microsoft may say certain tools or automation are better, but that doesn’t mean that’s what’s best for your business or campaign goals every single time. For example, automated ad copy creation might not be the best because it’s harder for machines to understand the best value propositions. However, leveraging responsive search ads automation is strong, outperforms other ad formats and is required to keep up with all the consumer buying signals. Ultimately, you decide how automation will work in a brand’s favor.
Set baselines and frequently check performance and volume against them. Make sure you’re looking at search performance and volume or impact in your marketing efforts beyond paid search. The performance story should not be siloed to any one specific channel but instead is most effective when looking at things like conversions, revenue, sentiment, consumer behavior and messaging across the full marketing mix.
Rely on automation 100%. Automation needs human input, checkups, troubleshooting, maintenance and continual monitoring. Don’t turn on the machine and walk away from it. In the post-setup phase, inspect results and troubleshoot. Once you have statistically significant volume and understand the results, you can begin optimizing and course correcting with campaign adjustments.
Bite off more than you can chew. Because you need to regularly check, maintain, troubleshoot, analyze and explain what the automation is doing for your campaigns, don’t overwhelm the system by having too many things (especially conflicting automations in multitiered brands) going at one time. Troubleshooting can get complicated with too many added variables.
Approach automation as one size fits all. Different campaigns or groups will require different approaches to automation. Structure by goal so you can apply the best rule or setting to a group. For example, setting cost per action (CPA) or return on ad spend (ROAS) target goals may work for certain groups, whereas click or position-based rules may work better for other campaigns. As a general rule of thumb, Max Conversions (with a set target CPA) should be used when the value of the conversion is more static. Max Conversion Value (MCV), a set target ROAS, should be used whenever the conversion value is variable – if the value of a lead or sale can vary from conversion to conversion.
Forget to watch cost per clicks (CPCs). Automated strategies increase cost per clicks (CPCs) rapidly, but conversions don’t always follow. Actions fluctuate with search behavior, search volume, auction competition and so on.
As you embrace search marketing automation and machine learning, understand what they are, how they work together, when to leverage each type and how to measure their impact and success. Stay open, be willing to explore, innovate, impact and adjust automation and machine learning along the way to create the best combination for your business.
P.S. Want more great digital marketing advice and tips to move you and your business forward? Peruse our complete library of free and helpful advertising resources, read our blog, sign up for our newsletter, follow us on social media or reach out to us today.
Cookies get to live another day – almost two more years, in fact, according to Google’s latest announcement.
Though Google’s original plan was to deprecate the third-party cookie by Q2 2022, in late June, the company decided to push back the timeline. The proposed alternatives aren’t ready yet, and Google’s recent agreement with the U.K. Competition and Markets Authority (CMA), a British antitrust agency, must include a six-month evaluation period when any changes to third-party cookies are made.
According to Google’s blog, the revised timeline will roll out in two stages:
Stage 1 will start in late 2022, pending the completion of privacy sandbox testing and the successful launch of APIs and will allow the industry time to migrate its targeting and measurement approaches away from cookies.
Stage 2 is dependent on the success of stage 1 but is currently slated for mid-2023, which will prompt a three-month period to phase out support of third-party cookies.
However, this is tentative timing and depends on how successful the privacy sandbox proposals are and if they’re accepted by the industry and regulators. Chrome is currently testing 4 of 30 proposals now, with FLEDGE trials delayed until later this year, if not early next.
Replace Third-Party Cookies With Cookieless Identity Alternatives
Though third-party cookies don’t provide ad tech a firm foundation to rely on, build on or scale, the industry is still finding it hard to say goodbye.
Parting is hard simply because these cookies underpin much of all audience targeting in ad tech, and no sole cookieless identity alternative can replace them.
How should you best proceed? Assess your goals, strategies and key performance indicators (KPIs) and determine the right set of cookieless identity alternatives available now (or soon) to try:
Authenticated and Shared IDs are consumer-provided consented identifiers provided via publishers who have established a trusted relationship with their users.
Clean Rooms overlap first-party data with secure, privacy-compliant impression-level data aggregated from the open internet and walled gardens like Google.
Publisher-Driven Data/Cohorts group and target by a common characteristic to analyze behavior and performance.
Federated Learning (FLoC) lets you see clusters of people with common interests and browsing activity and target ads to them while not intruding on their privacy.
Contextual Targeting matches ads to relevant environments across the web using identifiers such as keywords, topics, images, videos and more.
Want all the details? Learn all about these cookieless identity alternatives – their benefits and challenges (even get examples!) – in our free download, Preparing for a Cookieless Future: How to Approach Testing Identity Alternatives POV.
Reach and Build Audiences in a Cookieless World in 8 Steps
Today third-party cookies only exist in Chrome-based browser experiences so brand and agencies already find themselves reaching consumers more often in cookieless environments rather than cookie ones. So how you reach and engage customers and prospects in the coming cookieless world has to change if you want to succeed. Read on for our best advice – shared here in these eight steps (or get the visual, download our free cookieless targeting infographic!):
Embrace the cookieless opportunity: Change your mindset. Think of third-party cookies going away as an opportunity to see data and measurement in new ways, which can help boost return on ad spending (ROAS) and long-term customer value.
Evaluate audience targeting and measurement: Categorize your first- and third-party data and other data sources to see where you currently stand. No matter what will change, one thing will stay the same: the need to have and continually build up your first-party data.
Identify, measure and understand your potential data gaps or risks: Identify where you’re using third-party or cookie-based targeting the most.
Research alternative sources and how effective they are: Experiment with the cookieless alternatives mentioned above and gauge which ones are an effective fit for your brand.
Strengthen and add to your first-party data: Consider using mobile wallet coupons, opt in to SMS short codes or email capture on your own properties to get ahead.
Lean on your agency: Gather ideas, gain new insights and create a plan to enhance your existing data and fill future gaps.
Allocate budgets to test alternative targeting: Set aside an incremental cookieless targeting test budget if you can or take 20%-30% of the budget you currently spend on cookie-based tactics to test cookieless identity alternative approaches, your creative and additional channels.
Take the lead: Focus on only those scalable cookieless alternatives that fit your specific needs.
Discover the Way Forward
Google’s cookie delay is a reprieve – not a time to rest. That’s why Goodway isn’t slowing down and will continue identity- or privacy-focused efforts, including innovating within clean room environments, first-party data strategies, offline data integrations and measurement solutions.
These approaches will not only provide alternatives to third-party cookies but also offer solutions surrounding deterministic measurement, cross-platform analysis and more sophisticated modeling for both targeting and measurement.
Ultimately, the goal of the industry is to sustain a methodology that respects consumer privacy while delivering relevant messaging as brands seek to communicate with customers.
Want to keep your own momentum going? Find out if your cookieless approach is the right one or how to improve it. Contact us now and receive a thorough, no-cost, no hassle personalized cookieless targeting recommendation. Or let us be the expert partner by your side. We can help you strengthen your first-party data and your overall data management, increase your knowledge and build market share and customer share so you’re fully ready to take on your bright cookieless future when it arrives.