Ever wonder how Netflix knows what shows to recommend? Or how Google Docs can finish your sentences?
The answer: AI, baby. Specifically, AI analytics.
Both Netflix and Google have placid boatloads of data from the millions of people using their products. Netflix keeps tabs on what you watch and how you rate it, then based on all the information it’s accumulated, it’s worldly-wise to make predictive recommendations (usually darned good ones) on what it thinks you might like. Similarly, Google has watched how humans craft language—what you type and which words tend to go together. (Like “To whom it may concern” or “Actions speak louder than words.”)
Netflix and Google collect this data so they can make predictions well-nigh what you want next. (And to make sure you alimony using their products). This can only be washed-up by analyzing millions upon millions of datasets from previous users.
And that’s the point: AI’s worthiness to process so much data at incredible speeds makes a lot of today’s digital experiences possible. The increasingly AI observes, the increasingly it learns and improves.
The never-ending incubation of AI-driven analytics is revolutionizing how marketers (like you!) engage with their audience, optimize campaigns, and make data-driven decisions. By leveraging the sophisticated pattern recognition and predictive capabilities of AI, marketers can now visualize consumer behaviors, trends, and preferences with unprecedented accuracy. Neat, huh?
We’re gonna explain the who, what, when, where, and why of AI analytics in marketing. There’s a lot to imbricate here, so let’s swoop right in:
- What is AI analytics?
- How is AI used in marketing?
- What are the benefits of AI analytics?
- What are the challenges of AI analytics?
- The weightier tools for AI marketing
- The future of data analytics
What is AI analytics?
AI analytics refers to the merging of strained intelligence and machine learning techniques that unriddle data, pericope insights, and squire marketers in making data-informed decisions.
As marketers, we’re all familiar with the likes of Google Analytics. This traditional web analytics platform uses machine learning to penny-pinch data and present them to the marketer—but they’re nothing increasingly than static visualizations or dashboards we’ve pre-selected, like daily visitors compared year-over-year or month-over-month.
When we talk well-nigh Artificial Intelligence in analytics, we’re talking well-nigh something that is increasingly dynamic—something that can explain the “how” and “why” of our performance to help us make the weightier decisions. AI in analytics is all well-nigh the interpretation of the thing. It’s the scene at the end of Clue when Wadsworth runs virtually all the rooms explaining how and why the murders went down. Without Wadsworth, we’d all still be wondering whodunit? (“It was the CMO, in the boardroom, with the poorly-planned marketing strategy!”)
The weightier part well-nigh AI for analytics is that it never rests. It doesn’t take vacations or long walks on the beach. AI analytics tools constantly unriddle data and evolve their outputs at no-go speeds, while humans are off doing… human-y things. But that doesn’t midpoint marketers don’t play a hair-trigger role in all this. In fact, AI still works weightier when paired with a flesh-n’-blood reviewer who can double-check the work.
AI analytics is a helpful—nay, an essential companion for any marketer that wants to squash the competition by harnessing the power of data to proceeds valuable insights that momentum merchantry growth and innovation.
How is AI data wringer used in marketing?
AI data wringer can be a powerful tool for marketers to proceeds a largest understanding of consumer policies and preferences. There are variegated types of analytics, though: Predictive analytics use historical data to forecast future outcomes, while prescriptive analytics can take it a step remoter and encourage specific deportment based on insights derived from the data.
You can thank predictive analytics for letting you know that cute little woebegone dress sitting in your digital cart would squint wondrous with this thick belt… And look, it’s on sale! Prescriptive analytics help provide recommendations for the weightier hiking boots without you let the ‘Gram know you plan to hike the Appalachian Trail later this year. (Or as marketers like to undeniability it, “retargeting.”)
By analyzing large data sets, Strained Intelligence can identify patterns and trends well-nigh consumer needs, preferences, and plane habits. With this information in hand, marketers can create increasingly targeted and constructive marketing campaigns—like the hiking boots scenario.
Additionally, AI helps track wayfarers performance in real time. (No increasingly waiting virtually for a specialist to interpret data.) Now you can quickly see what’s working and what’s not, then make adjustments to optimize for maximum impact.
In practice, an AI analytics tool can evaluate pages or campaigns and provide insights well-nigh how they’re performing, and then you—the marketer—can use the information to optimize your campaigns (assuming the AI doesn’t automatically optimize for you). Rinse and repeat.
What are the benefits of using AI analytics?
As a marketer, using AI analytics offers a ton of benefits. Most importantly, AI analytics tools can help you get a leg up in merchantry and crush the competition.
Aside from satisfying your competitive side, AI-powered analytics can process vast amounts of data at a speed untellable for humans. And when you finger the need (“the need for speed“), AI analytics is your wingbot.
Let’s dig a little deeper into how Strained Intelligence in data wringer can help you out:
Be increasingly personalized
76% of respondents in a Zendesk report said they expect a unrepealable level of personalization from the brands they interact with. When customers land on your website, they want you to once know what they want.
With machine learning, you can make it happen. The right data can help you tailor messaging, offers, and experiences, resulting in improved targeting, higher engagement rates, and happier customers.
This is where something like Smart Traffic comes into play. The AI identifies trends in your audience’s behavior, then automatically sends each person to the page variant where they’re most likely to convert. (It’s personalization, and it works!)
Be increasingly data-driven
What used to take weeks, months, or years can now be workaday in record time. Thanks to AI analytics, it’s easier to make informed decisions based on real-time data rather than relying solely on intuition or stale numbers. When things transpiration fast, plane three-day-old data can be too old.
By analyzing vast amounts of information and extracting valuable insights, AI helps you identify trends, understand consumer behavior, and optimize strategies therefrom for maximum merchantry success.
Be increasingly efficient
Comparing a machine’s speed and stamina to a human would be like watching a cheetah take on a sloth in a 100-meter dash. Or like that one time I walked up the CN Tower for soft-heartedness and it took me… ah, a loooong time. Meanwhile, the firefighters in full gear—including oxygen tanks—sped right past me, and looked as fresh as they do in the calendars. (*Gulp.*)
Sorry, what were we talking about? Oh yeah. Efficiency of AI in analytics.
So, AI-powered tools can generate reports, perform data analysis, and provide violating insights at speeds most humans can’t plane fathom, improving productivity and freeing up time to focus on higher-value activities.
What are the challenges of using AI analytics?
In the fall of 2022, ChatGPT was a mere whisper—and just a few months later, thousands of marketers were quaking in their home offices, stressing well-nigh losing their jobs to bots. But while AI has enhanced the marketing landscape, it ain’t all sunshine and rainbows. There are some challenges that come with using it.
Impersonality
Put a finger lanugo if you’ve recently yelled at a phone bot for giving you the runaround when you just wanted to speak to a human. This is an example of how AI can help a visitor save money—while simultaneously alienating its customers.
When engaging AI analytics, the consumer wits should unchangingly come first—especially if your goal is to modernize trademark loyalty and increase trust. (Hint: This should be your goal). As an experienced marketer, you know marketing involves towers relationships and fostering emotional connections with customers. The good news is that AI doesn’t (yet) have the cognizant worthiness to do this without the aid of humans.
Algorithmic bias
Because they’re trained on historical data, AI algorithms can unintentionally contain biases and reflect existing societal or cultural prejudices. These biases can result from things like poor training of the machine learning systems or faulty data sets.
Regardless of where they come from, if left unchecked, AI analytics can perpetuate these biases, leading to discriminatory outcomes or unfair targeting.
On data bias, John MacCormick, professor of computer science at Dickinson College (who accidentally built a racially-biased AI algorithm in 1998), said:
…Even in 2023, fairness can still be the victim of competitive pressures in academia and industry. The flawed Bard and Bing chatbots from Google and Microsoft are recent vestige of this grim reality. The commercial necessity of towers market share led to the premature release of these systems.
The systems suffer from exactly the same problems as my 1998 throne tracker. Their training data is biased. They are designed by an unrepresentative group. They squatter the mathematical impossibility of treating all categories equally. They must somehow trade verism for fairness. And their biases are hiding overdue millions of inscrutable numerical parameters.
The marketing lesson here? Be wary of where your AI gets its data. Is it pearly and unbiased, or is it narrow in scope? Humans have a tendency to bring their biases with ’em, plane when constructing machines.
The weightier way to mitigate these bias risks is to find out where and when the AI has placid data. You might be enlightened that ChatGPT 4.0 is only trained on data up to 2021. And, uh, so much has happened since then. As marketers, if you’re going to integrate AI analytics into your plans, remember that not all AI is created equal.
Poor ethics
AI analytics are wicked smaht (said in my weightier Good Will Hunting accent), but they operate on predefined algorithms and training data. This ways AI can unknowingly make unethical and irresponsible decisions—like writing fake reviews or pretending to be people online.
Of course, there’s the treatise that values are difficult to define—but if you don’t want machines making upstanding decisions for your trademark (which, no!), then humans are still required to validate what AI analytics vacated might overlook.
In short, don’t fire your marketing team.
Human expertise and wits are still essential in interpreting the insights generated by AI analytics and making decisions that prioritize the well-being and privacy of customers.
Lack of context
Trends transpiration quickly (like during 2021 to 2023, for instance). One day we’re wearing wide-legged jeans, then skinnies all the way—only to wake up one day to the news that skinnies are out and wide legs are when in. (Just waiting for the tintinnabulate bottoms in my closet to be the hot trend again.)
Although AI never sleeps, it may need help to fully comprehend ramified market dynamics, cultural nuances, or rapidly-changing trends like the fickle fashionistas in tuition of women’s jeans (#biggerpocketsplease).
We still need people to interpret data, add contextual understanding, and requite it that human touch. For example, your AI can tell you there was a waif in MoM site visitors from July 1-5, but it probably won’t recognize that’s the period between Canada Day and Independence Day. As the human eyes, you know it’s probably just folks taking a long weekend—because when you squint at the YoY analytics, it happens every year.
Best tools for AI marketing analytics
Since you’re a marketer and not a data scientist (… unless you are?), we’ve rounded up some of the weightier AI marketing tools to help you crush your goals (and the competition) withal the way.
Dealtale
Dealtale offers users an interface that lets you prompt the platform for the data you want, not unlike ChatGPT. It’s perfect for those of us who aren’t super data-savvy. The inside dashboard lets you view all your metrics from multiple marketing tools in one user-friendly login, and you can track sales and create personalized marketing campaigns without overly leaving the platform. With Dealtale, you don’t need to be tech-savvy—and if you reach any sticky points, their consumer service is ready and waiting to help.
(To hear increasingly from Dealtale, trammels out this episode of the Unprompted podcast where Dealtale’s Throne of Marketing, Molly St. Louis, offers insights and predictions on the future of AI marketing.)
Key features:
- Machine learning and prescriptive analytics to build the ultimate consumer journey with customized communication
- A seated IQ tool transcribes the data in plain language to help you understand what you’re looking at without getting a Ph.D. in geek
Pricing: Custom, but they offer a two-week self-ruling trial
Optimizely
Don’t know a lick of lawmaking but want to run A/B tests and capitalize on web visitor personalization? Optimizely can help you unhook the ultimate user wits by offering website transpiration suggestions backed by data and providing visitors with hyper-personalized product recommendations.
Key features:
- Uses multivariate testing that allows businesses to test multiple variations of variegated elements simultaneously and identify the most constructive combination
- Allows businesses to segment their regulars based on various criteria, such as demographics, browsing behavior, and purchase history, to unhook a targeted consumer experience
- Enhances engagement and conversions with mobile app optimization
Pricing: Custom
Google Analytics
Google Analytics is the most recognizable AI analytics tool, used by millions of businesses virtually the world. The new, beefed-up version of Google Analytics (GA4) promises to offer largest cross-platform tracking and improved reporting to understand consumer behavior.
Key features:
- Traffic and conversion reporting wideness multiple search engines shows where your marketing efforts are most profitable
- Offers in-depth insights well-nigh who your website visitors are, where they come from, and how they interact with your product
- Integrates with Google Ads so you can track consumer engagement from their first interaction to where they dropped off or converted
Pricing: Free
Adobe Analytics
Considered one of the weightier AI data supersensual tools misogynist to marketers, Adobe Analytics offers the most in-depth insights to generate increasingly traffic, provide an unforgettable consumer experience, and increase profits.
Key features:
- Machine learning and a proprietary “segmentation IQ” indulge you to quickly identify the most profitable segments to reach your goals better
- Provides multi-channel integration and data hodgepodge so you never miss an opportunity to gather information well-nigh the consumer journey
- Visually track data hodgepodge in real time from your live-traffic dashboard
Pricing: Custom
The future of data analytics
As AI continues to advance, it will play an increasingly vital role in how marketers tideway their jobs. AI algorithms will get faster and increasingly efficient, and real-time analytics will wilt standard practice in marketing. As with any marketing tool, this can be good or bad, based on how you use it. For example, when washed-up well, email marketing helps increase sales—but when used to spam, it does increasingly harm than good.
However, armed with data-driven insights, businesses can run increasingly targeted and tailored marketing messages, offers, and recommendations at scale.
In an vendible for IBM, Tom Kershaw, Chief Product and Technology Officer of Travelport said:
AI is so important considering it lets us scale the internet. It lets plane a small publisher or a regional app have wangle to the same intelligence, the same creativity as a super large behemoth and that is a hair-trigger function to the way the internet works and the way society works.
Christine Crandell, AI consultant and journalist, suggests that AI analytics is to marketing what Goose is (was) to Maverick. (Yes, that’s flipside Top Gun reference. Did you reservation the first one?) Your Robo-Goose, if you will.
Marketers will need to prepare for AI and siphon forward the lessons learned from predictive analytics and today’s patchwork of martech solutions—automation can only offer recommendations that are as good as the underlying data sources. In the end, the human is still the pilot.
The final verdict on AI analytics in marketing
AI isn’t going away. By leveraging the power of AI data analytics responsibly and ethically, marketers can unlock new opportunities to create increasingly meaningful experiences for their customers.
The marrow line is that the future of AI analytics in marketing is promising for businesses willing to embrace it, use it as a ways to identify performance insights, and protract to place the user wits at the cadre of their marketing strategies.
