12 AI Marketing Trends Shaping The Future As We Know It

12 AI Marketing Trends Shaping The Future As We Know It

Not long ago, marketing tech was extremely limited. Programs could only make practical recommendations based on a set of fixed assumptions; input, output. These programs provided helpful macro insights, but...

Not long ago, marketing tech was extremely limited. Programs could only make practical recommendations based on a set of fixed assumptions; input, output. These programs provided helpful macro insights, but simply could not drill down to the specifics or accurately scale. But the advent of consumer Artificial Intelligence (AI), including software like Albert, Datorama, and Salesforce’s Einstein, has already flipped this paradigm on its head, and we’re beginning to see AI marketing trends take shape.

Aided by machine learning, marketers can now easily extrapolate macro customer insights and deeply understand audiences’ micro-level mannerisms. AI marketing technology is able to collect and process massive volumes of customer data and make deep connections at scale.

AI has already shaped our marketing world. In 2020, it’s estimated that 60% of companies will be using artificial intelligence for driving digital revenue. Marketers can expertly wield AI technology to drive profits, even as the applications of AI continue to mature and unfold. Machine learning as a marketing tool has precipitated shifts in eleven significant ways: Predictive Forecasting; Audience Hyper-Segmentation; Persona Building; Efficient and Personalized Ad Serving; Content Recommendations; Targeted Timing; Lead Qualification and Scoring; Email Marketing Optimization; Chatbots; Visual Search; Dynamic Pricing; and Voice Search.

Here is the list of AI marketing trends shaping 2020 and beyond:

1. Predictive Analytics and Forecasting

Predictive forecasting might not be among the newest AI marketing trends. Still, AI-powered tools continue to advance, and now offer unparalleled computing power, accessible data, and usability to even small and mid-sized businesses. This means the modern marketer can move beyond regression models to hone in on actionable insights about their customer base and more accurately identify future outcomes.

For example, what product would future customers purchase? How will they behave within the sales funnel in five years? Who are tomorrow’s customers, and where do they live? Through Identification Models and Predictive Scoring, marketers can even extrapolate who is most likely to become a lead based on similar profiles to existing customers. AI-powered software also allows marketers to pinpoint the highest value lead from that future group. Predictive modeling is perhaps the most valuable innovation AI brings to the marketing world, as it allows marketers to not only optimize their offerings now but for the foreseeable future.

2. Audience Hyper-Segmentation

Most CMOs would agree that a deep understanding of the target audience is key to a successful campaign. However, in early marketing, much of this was based on assumptions and limited data.

AI programs give marketers an edge by accumulating and effectively processing massive amounts of data – far larger datasets than previously possible. As machine learning algorithms comb through the data, they learn more and more about the audience and are able to make an increasing number of connections between each data point. This enables the creation of hyper-focused, segmented groups based on these similar connections.

3. Persona Research and Psychographics Building

Likewise, audience hyper-segmentation offers a thorough understanding of customer characteristics. AI-powered software can, in turn, build extremely robust personas that go beyond the typical modeled representations of your buyer. With AI, personas aren’t just assumptions on a whiteboard or Google Doc- they are actual, real-time amalgamations of your customer base.

The granular detail made possible by AI presents a wealth of opportunity for creating extremely targeted personas and super targeted audience segments based on these personas. These tools help find commonalities among your audience, from basic demographic info to affinities, which can unlock hidden insights for future strategy. For example, say the data shows your best-performing audience over-indexes as pet lovers. This learning could inspire you to incorporate animals into your next ad creative. Maybe you co-host an event with a local animal shelter, or you develop a product that you know your pet-owning audience will love.

As your audience continues to grow, AI can identify and optimize segments in real-time, as the audience is experiencing the customer journey through your sales funnel.

4. Dynamic Personalized Ad Serving

The ability to understand your audience in real-time means the customers’ ad experience can be optimized with a level of unprecedented personalization at scale. Facebook and Google are experts at this. Rather than bombarding users with generalized content, they serve targeted advertisements to each user. To serve personalized ads, they rely on a collection of data points their machine learning algorithm collects, digests, and extrapolates on a user’s behavior over time. And they do this on a massive scale with extremely high efficiency, projected to exceed $200 billion in combined revenue in 2020.

That’s why you might be presented a Facebook ad for dog chew toys, even though you don’t own a dog (but you were thinking of getting a puppy soon). Contrary to popular belief, Facebook isn’t listening to your conversations and serving you ads based on specific words and phrases they’re able to hear. You were served that ad because AI can predict what product you’ll be looking for, and therefore what type of content you’ll be interested in, by using your various behavioral and demographic data points and connecting the dots.

5. Content Recommendations

With an understanding of how machine learning optimizes the ad serving experience, it follows that AI platforms would also offer a considerable advantage when it comes to content recommendations. Companies like Netflix and YouTube have been using this AI marketing tactic for years to recommend additional content a user might like, driving continual engagement and time spent on their respective platforms.

Simply put, AI software can predict what users are interested in, and what type of content they’ll engage with. It can then offer the customer the exact content they’re looking for through specific recommendations, served in real-time. Marketers no longer have to assume that a customer in group A will enjoy a video ad about soccer cleats. With AI software, marketers can serve their customer Sylvia an ad about women’s soccer cleats, available at a store near her home in Detroit, versus Michael, who is served an advertisement for men’s soccer cleats available near his home in Seattle.

6. Micro-moments & Targeted Timing

Just as machine learning can parse vast amounts of data into hyper-segmented groups, so too can it use data to calculate and predict times of peak engagement. By learning when specific groups have engaged with content in the past, AI programs can predict when they are most likely to be interested in content in the future.

This provides a tremendous edge over traditional advertising, which depended heavily on trial and error. Machine learning removes all the guesswork, allowing marketers to precisely time customers’ ad experience and pivot campaigns in real-time to optimize for engagement.

7. Lead Qualification and Scoring

More specifically, AI-powered campaigns with targeted timing are optimizing lead qualification and scoring process at scale. As we know, successful campaigns come down to delivering the right message to the right individual at the right time. Machine learning tools give marketers the ability to achieve this perfect fit of content and timing.

The resulting interactions then provide another wave of data that allows the marketer to better qualify and score leads. And as real-time data about lead interaction flows back into the machine learning mechanism, it continues to learn about the audience. It can better identify high-value leads with precision. Multiply this process by an audience of a few million, and it is easy to understand why AI programs have become the new standard for lead qualification and scoring.

8. Email Marketing Dynamic Optimization

The same can be said for using AI to optimize email campaigns. Email marketing has long taken advantage of automation platforms and segmented content, so this isn’t exactly the newest among AI marketing trends. Still, machine learning takes this a step further with its hyper-segmented groups and optimized timing.

As email recipients interact with content, the machine learning algorithm learns the behavioral patterns of the audience and can pivot to achieve maximum engagement. From a timing perspective, this means you get email campaigns delivered when each user is most likely to engage and content curated for each user’s individual preferences. AI removes the guesswork of what time to send an email and what images and copy to use. Marketers can now create multiple versions and let AI determine who gets what creative and at what time, continually learning and driving improved results and efficiency.

9. Chatbots

By now, most people have probably talked to a chatbot without even realizing it. Brands have been utilizing services like Facebook Messenger and Slack to communicate with users for a while. However, these services typically require human resources to manage, as well as potentially provide a poor customer experience by making people wait for the next available representative.

Chatbots solve this problem by utilizing AI to automate responses by providing potential buyers with ways to find the right product or service. They also drive efficiency by handling unlimited inquiries at the same time and by being available 24/7. Chatbots also retain data, and thus, are able to learn and improve the results of future conversations based on past inquiries.

10. Visual Search

What if consumers could find your products and where to buy them online, even if they don’t know the name of your brand? Through visual search, this is becoming more and more of a reality. Visual search uses real-world images as the input for online searches, which are then absorbed by AI and machine learning to understand the content and context of these images and return a list of related results.

The benefits of this are clear to anyone in the eCommerce space, particularly for fashion or home decor. Products like Pinterest Lens or Google Lens are able to identify over a billion images each. So say you’re in the market for a new sofa, or see someone wearing a cool shirt that fits your style. Simply taking a photo of these items and searching using that image can bring you directly to a product page on how to buy that item.

Among the AI marketing trends on this list, visual search may have the longest way to go. But as we have seen with voice search, if we can assume that adoption will increase as the technology continues to improve, visual search has the potential to be a powerful tool in 2020 and beyond.

11. Dynamic Pricing

Finally, for most CROs and CMOs, setting prices is always a daunting task. It has historically been determined using algorithms that take into account a variety of market factors: competitors’ prices, cost of production, demand, and so on. Businesses want their products to sell and look for the highest yield possible.

Machine learning disrupts this process by adjusting to fluctuating variables instantaneously, resulting in dynamic pricing. By automatically raising or lowering prices depending on the real-time values of these variables, and continuously adapting to consumer behavior and preferences, AI programs can optimize for maximum profit and inventory. Businesses benefit from less waste, and consumers are guaranteed a more accurate value for their purchase. In fact, it’s likely you’ve already experienced dynamic pricing. Major platforms like American Airlines, Booking.com, and Amazon have already adopted AI-powered dynamic pricing, which is why consumers purchasing the same flight may pay different prices depending on their location.

12. Voice Search

Smart speakers have quickly become a staple in many households, with roughly 25% of US adults over 18 owning at least one device. While we’re still a ways away from critical mass, we’ve clearly reached a point where devices like Amazon Echo and Google Home aren’t just novelties, but necessities.

Google has been at the forefront, not only by creating the very devices, but also by evolving their search algorithm for more natural language processing. This has not only caused disruptions within SEO, but created a huge incentive for reaching the Featured Snippet of the search engine results page (SERP Position Zero). While this has always been a coveted spot, they’re the holy grail for voice search, as they’re the result that your Google Home or Alexa will read from.

However, beyond SEO, businesses have been a bit slow to adopt, so the larger trend here will be seeing how they integrate. Some brands (Purina, Johnnie Walker, Zyrtec) have developed Alexa apps that not only offer consumers value, but keep their brands integrated and at the forefront.

AI Marketing Trends For 2020 and Beyond

Making smart marketing decisions boils down to how well a business knows their target audience. When dealing with thousands or even millions of customer interactions, it’s impossible to get to know each individual on a personal level.

But these growing AI marketing trends now allow us to analyze mass amounts of data and information gathered from campaigns, social interactions, and engagements. As we move into 2020, AI marketing offers the opportunity to get to know every individual user through their unique data profile. And that means accurate personalization at scale – something never before accessible to the average marketer.

Mostafa ElBermawy
As NoGood CEO and Founder, Mostafa is a seasoned growth engineer and venture builder with experience accelerating revenue/user growth, as well as leading and advising growth and product teams for VC-backed startups, venture funds, and Fortune 500 brands.


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