Keeping up with People
Marketing has a challenge: It needs to keep pace with technological developments as they unfold. This is no easy feat—there are billions of interactions online every single day, and millions of users visit the Internet looking for a range of products from different companies. Brands need to pivot with rapidly shifting consumer technology to ensure their products and services stand out, or risk getting lost, overlooked, or going out of business altogether.
It’s ironic that in our chaotic online world, one of the best ways to reach people is by not using people at all. Artificial intelligence is often conflated with robots, but the truth is far more subtle (and far more complicated). Software can now run autonomously and market to internet users directly, in real time, without human interference, with much higher relevancy. Analytics from these interactions and online experiences are converted into data sets that continuously train and retrain the software to take this data into account when setting up its next interactions, or events, or deciding how to serve you the most relevant ads possible.
AI is frequently a buzz word in marketing circles, but it’s important to break down the buzz terms and get to the root of what it all means and how it’s really used, day to day.
It’s also important to understand that technology has driven down the cost of using AI in marketing to the point that even small-medium size businesses can afford to play in the Martech sandbox.
What Are Some Examples of AI in Marketing?
Some of the biggest uses for AI in marketing include:
- Programmatic Advertising
- Retargeting
- First-Party Data Lead Nurturing
- Databases
- Analytics and Business Intelligence
- Customer Communication and Process Automation
Programmatic advertising is software-driven ad delivery. Most people are unaware that 92% of ads served on the internet are delivered “programmatically,” as opposed to direct placement on a specific site. This means ads that are served to customers are placed by software that have determined the most desirable data sets to serve the ad campaign to, analyzed price and placement. Advertisers, who buy ad space, and publishers, who sell ad space, can use AI to efficiently organize and exchange on this market for the most optimized results, and the most competitive pricing.
YouTube famously uses programmatic ads when it places messages in preroll, banner, and in-video ads before (almost) anything you watch on the platform. When an advertiser submits an ad to be placed on YouTube, they set certain criteria (including budget and reach) and let the automated ad placement software deliver the ads in the specified form to whichever audience best suits the desired targets. Doing these one by one, as in the old days, would be entirely cumbersome and have far less effectiveness. Spectrum uses YouTube ad placements to reach customers in different parts of the country, with messages specific to each geographic location.
Ads delivered via Google can also be enormously effective. BigIron Realty, for example, uses PPC and programmatic ad placement on Google and its networks for real estate promotion and farm equipment advertisements to reach more people in rural areas. For any programmatic campaign, it’s vital to reach customers where they are, when they are, with messages that appeal to them individually—all enabled by AI placements.
After a user has left a website, their actions on the page might indicate they’re a good potential retarget. Retargeting is the ability to re-serve display or video assets tailored to those who’ve already left a site or a page. Often, a site visitor isn’t ready to make a purchase or any click commitment. But if the ad is shown again, in a different context or a different day, the odds of clicking might have increased to the point of viable conversion. Again, this is a process AI has made possible that was previously unimaginable.
The two main types of retargeting are:
- List-based, which requires a built contact list, such as email. The people on this list have likely visited with you or your site and would make a great choice for follow-up.
- Pixel-based, which is a cookie-based retargeting method that uses pieces of tracking software on specific browsers.
The process of AI data collection and use can help cultivate a first-party data strategy. First-party data is data collected directly from a site or company’s audiences, including site users, social media followers, attendees of events or webinars, customers, and any potential leads. First-party data is entirely collected between a single company and its audiences, without third parties involved.
For example, if a user were to visit the website for the University of Northern Iowa, they might fill out a form for more information about the school. In the form, they’ll hand over their name, age, location, etc., a trove of actionable data points. This action cements them as an active lead. The school, if it has artificial intelligence software for its marketing, could use this data to infer more about them and initiate an automated email sequence or text with them, to continue the dialogue and nurture the lead into an eventual conversion. If the user stayed on the site a certain amount of time, the AI software might well begin a chat with them and find out more information than what could be volunteered from a simple webform:
- What are they looking for in a school?
- What interests them?
- What majors/minors are they seeking?
- Does their family attend?
- What are their goals in life, and how can the school help fulfill them beyond just academia?
When this happens often enough, the school can generate a user database of all their leads. This database can provide valuable demographic information about the user base that can helpfully inform the school’s marketing going forward. If they want to serve ads to this group to induce them to attend the school, they can place programmatic ads on YouTube or Google and retarget these users based on their attributes. The school could then use analytics and AI business intelligence to make smarter decisions about their budgets and messaging for reaching key audiences.
And, if there’s too much for them to handle on their end (always a challenge in the digital era), they might seek to hire a firm that specializes in marketing technology and data science.
Design for the Future
If a company or institution like the University of Northern Iowa were to use a service to manage their lead capture and first-party data usage, they might employ an analytics platform like HighLevel, which can automate the process even further and ensure a smooth customer journey from the top of the funnel all the way to conversion. First-party lead software can help gather information from sources like:
- Surveys and webforms
- Capture forms
- Appointments and scheduling
- SMS and texts
- Emails
Larger data-based companies can also help smaller or local companies find opportunities using AI that might not have been discovered before. Walmart Connect Marketplace, which uses data-driven recommendations to help sellers increase profits, identified potential areas of improvement for local ecommerce powerhouse Spreetail, improving their sales and boosting KPIs higher than otherwise attainable.
While AI is still not perfect and has plenty of room for improvement, it’s become clearer than ever that the future of the online world is finding ways to better negotiate the use of data and customer relations than a simple ad-serve as in years past. Software still requires intelligence to best reach desired prospects, and no amount of money can make up for an inferior product or poor experience.
And things are always changing.
For example, as Google transitions from a cookie-based advertising world to an event-based architecture on Google Analytics 4, it’ll be more important than ever to carefully cultivate leads and customer lists from first-party, clean sources and natural human interaction that comes from organically growing a user base. If one is using Google and YouTube for programmatic ads, this will be an important evolution to take into consideration.
In the end, branding — especially trustworthy branding, has never been more important than it is now, and in a highly automated AI marketing world, the one thing that will make it all stand out is that irreplaceable human touch.
