AI Marketing Trends Shaping the Future As We Know It

Not long ago, marketing tech used to be very limited. Programs could only make practical recommendations according to a set of fixed assumptions; input, output. These programs gave helpful macro insights, but simply could not drill down to the specifics or accurately scale. However, the advent of consumer artificial intelligence (AI) has already flipped this paradigm on its head, and we are starting to see AI marketing trends take shape.?

Helped by machine learning (ML), marketers can now easily extrapolate macro customer insights and deeply comprehend audiences’ micro-level mannerisms. AI marketing technology can gather and process huge volumes of customer data and form deep connections at scale.?

AI has already impacted our marketing world. It was estimated that 60% of companies in 2020 would employ AI to drive digital revenue. Marketers can skillfully wield AI technology to drive profits, even as the applications of AI continue to unfold and mature. ML as a marketing tool has brought about shifts in 11 notable ways: Predictive Forecasting; Audience Hyper-Segmentation; Dynamic Personalized Ad Serving; Targeted Timing; Email Marketing Optimization; Chatbots; and Dynamic Pricing.?

Below is a list of AI marketing trends that would shape 2022 and beyond:?

1. Predictive Analytics and Forecasting

Predictive forecasting might not be among the latest AI marketing trends. However, AI-powered tools are advancing, and now offer unparalleled computing power, accessible data, and usability to even small and mid-sized businesses. This means that present day marketers can move beyond regression models to hone in on actionable insights about their customer base and more accurately identify future outcomes.?

For instance, what product future customers buy? How will they behave within the sales funnel in 5 years? Who are the customers of tomorrow, and where do they reside? Through Identification models and Predictive scoring, marketers can even extrapolate who is most probable to become a lead according to the similar profiles of current customers. AI-powered software also enables marketers to pinpoint the highest value lead from that future group. Predictive modeling is perhaps the most valuable innovation AI offers to the marketing world, as it enables marketers to not only optimize their offerings now but for the foreseeable future.?

2. Audience Hyper-Segmentation

Most CMOS would agree that a clear understanding of your target audience is key to a successful campaign. However, in the past, much of this was based on assumptions and limited data.?

AI programs offer marketers an edge by accumulating and effectively processing huge amounts of data – a lot larger datasets than previously possible. As ML algorithms comb through data, they increasingly learn about the audience and can make an increasing number of connections between each data point.? This allows the formation of hyper-focused, segmented groups based on these similar connections.?

3. Dynamic Personalized Ad Serving

The ability to comprehend your audience in real-time means the customers’ ad experience can be optimized with a level of unprecedented personalization at scale. Google and Facebook are experts at this. Instead of bombarding users with generalized content, they serve targeted ads to each user. To send personalized ads, they depend on a collection of data points they ML algorithm gathers, digests, and extrapolates on a user’s behavior over time. And they do this on a massive scale with very high efficiency.?

That’s the reason you might see a Facebook ad for dog chew toys, even though you are not a dog owner – but you were planning to get a puppy soon. Contrary to popular belief, Facebook does not listen to your conversations to serve you ads based on particular words and phrases they can hear. You were shown that ad because AI predicted what product you would be looking for, and therefore what type of content will interest you, by employing your various behavioral and demographic data points and connecting the dots.?

4. Micro-moments & Targeted Timing

Just as ML can parse huge amounts of data into hyper-segmented groups, so too can it employ data to compute and predict time of peak engagement. By learning when particular groups have engaged with content in the past, AI programs can predict when they are most probable to be interested in content in the future.?

This offers a tremendous edge over traditional advertising, which relied heavily on trial and error. ML eliminates all the guesswork, enabling marketing to precisely time customers’ ad experience and pivot campaigns in real-time to optimize for engagement.?

5. Email Marketing Dynamic Optimization

Email marketing has long leveraged automation platforms and segmented content, so this is not exactly the latest among AI marketing trends. Nevertheless, ML takes this a step further with its hyper-segmented groups and optimized timing.?

As email recipients interact with content, the ML algorithm learns the audiences’ behavioral patterns and can pivot to accomplish maximum engagement. From a timing perspective, this means you deliver email campaigns when each user is most probable to engage and curate content for each user’s individual preferences. Ai eliminates the guesswork of what time to send an email and what pictures and copy to use. Marketers can now set up several versions and let AI determine who gets what creative and at what time, continually learning and driving enhanced results and efficiency.?

6. Chatbots

By now, most individuals have perhaps talked to a chatbot without even realizing it. Brands have been employing services like Facebook Messenger and Slack to interact with users for a while. However, these services typically need human resources to manage, besides potentially offering a poor customer experience by making people wait for the next available rep.?

Chatbots resolve this issue by employing AI to automate responses by offering potential buyers ways to discover the right product or service. They also boost efficiency by handling unlimited inquiries at the same time and by being available 24/7. Chatbots also retain data, and therefore, can learn and enhance the results of future conversations based on past inquiries.?

7. Dynamic Pricing

Finally, for most CMOs and CROs, setting prices is always a frustrating task. It has historically been determined with the use of algorithms that take into account several different market factors: cost of production, demand, competitors’ prices, and so on. Companies want their offerings to be sold and look for the highest yield possible.?

ML disrupts this process because it adjusts to fluctuating variables instantaneously, and this results in dynamic pricing. By automatically increasing or decreasing prices according to the real-time values of these variables, and continuously adapting to consumer behavior and preferences, AI programs can optimize for maximum inventory and profit. Companies benefit from reduced waste, and consumers are guaranteed a more accurate value for their purchase. In fact, it is probable that you’ve already experienced dynamic pricing. Well-known platforms like Booking.com, American Airlines, and Amazon already employ dynamic pricing, in which consumers buying the same flight may pay different prices based on their location.?

Conclusion?

If you are a business owner who is looking for a way to start with the use of AI and ML for predictive analytics and targeted marketing, you should consider Inqline’s Data Science Autopilot. It is a combination of 3 essential products: Attract, Engage, and Retain. It enables you to leverage ML to segment your market, predict their lifetime value, and their likelihood of churning.?