Not long ago, marketing tech was extremely limited. Programs could only make functional 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 transformed marketing as we know it.
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 has already shaped our marketing world. 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 nine major ways: Predictive Forecasting; Audience Hyper-Segmentation; Persona Building; Efficient and Personalized Ad Serving; Content Recommendations; Targeted Timing; Lead Qualification and Scoring; Email Marketing Optimization; and Dynamic Pricing.
Predictive Analytics and Forecasting
Predictive forecasting might not be anything new in the marketing world. But, AI powered tools 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.
Most CMOs would agree that a deep understanding of the target audience is key to a successful campaign. 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.
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. AI can identify and optimize segments in real-time, as the audience is experiencing the customer journey through your sales funnel.
Efficient and 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, resulting in over $150 billion in combined revenue annually.
That’s why you might be presented a Facebook ad for dog chew toys, even though you don’t own a dog (but were thinking of getting a puppy soon). Facebook predicts 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.
With an understanding of how machine learning optimizes the ad serving experience, it follows that AI platforms would also offer a huge advantage when it comes to content recommendations.
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.
Just as machine learning can parse huge 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.
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 which 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, and 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.
Email Marketing 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. 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.
Finally, for most CROs and CMOs, setting prices is always a daunting task, and 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 adjusting 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 Airline, Booking.com and Amazon have already adopted AI powered dynamic pricing, which is why consumers purchasing the same flight may pay a different prices depending on their location.
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 artificial intelligence now allows us analyze mass amounts of data and information gathered from campaigns, social interactions, and engagements. It 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.