Marketing is an ever-changing field, where even the pace of change continues to accelerate. Twenty years ago, CRMs were considered “the cutting edge.” Back then, marketers faced tasks like finding the right email vendors and getting any data at all about their leads. In the last ten years alone, the amount of data created has increased 30-fold.
Today, consumers expect personalized, relevant, and timely marketing. At the same time, they’re being bombarded by marketing and ads, which means “ad blindness” is at an all-time high.
Therefore, marketers need to use real-time customer data to optimize the experience for new leads and existing customers.
AI marketing is the solution. AI models can help marketers with both decision-making and marketing automation. Modern digital marketers use data-driven insights to drive the optimal customer experience across different product categories and customer segments. Predictive analytics unlocks advertisers’ ability to target audience engagement and maximize critical metrics.
From content marketing to conversion rates to customer behavior to demographics, digital marketers are increasingly taking advantage of AI. It unlocks the ability to optimize marketing strategies and provide customers with the best possible customer journeys tailored to their needs.
AI marketing is an emerging practice that is helping organizations target leads with the right message, at the right time, and in the right place. Data science is one of the most powerful tools companies have to connect with customers, and it has dramatically changed the way companies market their products.
AI marketing is still in its nascency, but it’s already proving its worth as a powerful tool for driving growth. According to MemSQL research, the majority of businesses see AI as their most significant data initiative. It’s the perfect technology to gain insights at scale and make predictions off of large, complex datasets.
There are many ways that AI can be used for marketing purposes. But, of course, many teams have different end goals, so there’s no single answer here. Instead, the key is finding out what your business wants to accomplish and leveraging AI accordingly.
Let’s dig into some valuable use cases.
Amidst uncertain times, marketers are desperate for accurate sales, and revenue forecasts, which are needed to draw up budgets for advertising spend, marketing tools, hiring costs, and more. Inaccurate forecasts lead to misallocated budgets and ultimately damage the bottom line.
Nonetheless, forecasting is seen as black magic by many, and businesses often rely on intuition instead of data-driven strategies.
Fortunately, no-code AI makes accurate forecasting effortless, enabling marketers at any business to have greater visibility into the future, and create intelligent, data-driven budgets.
Marketing is about more than just generating leads. Marketers shouldn’t be only interested in traffic, as what really matters is conversions.
At the end of the day, “traffic” is a lot less critical than “revenue.” To increase conversions, and therefore the bottom-line, it’s important to score leads based on their likelihood of converting. Doing so will also help marketing teams target the right kinds of leads in the future.
Without lead scoring, sales reps end up wasting their time on cold leads that won’t convert, and marketing teams will fail to generate high-quality leads.
Traditional lead scoring involves a manual, intuition-based approach, which is not only inaccurate but resource-intensive. AI-enabled lead scoring allows you to reach the leads most likely to convert. You can even personalize content and price to maximize their likelihood of purchase. With Akkio’s augmented lead scoring, you can effortlessly score leads based on their probability of conversion.
We’ve established that marketing is about more than just lead generation, as conversion is just as important. Taking it a step further, marketing teams need to attend to existing customers and minimize churn
Preventable churn is often called the ”silent killer” of businesses, as churn can easily outpace growth, which inevitably leads to failure. Unfortunately, by the time you see churn making a dramatic impact, it’s likely too late to save those at-risk customers from leaving.
The problem here is similar to that in scoring leads: There are innumerable potential underlying factors, so how could you ever know what to optimize?
With Akkio’s churn reduction, you can use AI to automatically find the causes of churn and predict which customers will leave next.
Marketing teams put so much effort into lead generation, and 90% of those leads use customer service as a factor in deciding whether or not to do business with a company, which means it’s critical to have top-notch support in place.
But, as anyone who’s done customer support knows, not all customer queries are of equal importance and urgency. Still, non-urgent questions need to be handled, which can come at the cost of a diminished response time to more urgent tickets.
This issue gets compounded with big business wins, like successful ad campaigns, which bring about more users and more support tickets.
Manually deciding which queries to take on first is slow and ineffective. Using text classification, businesses can automatically prioritize the most important and urgent customer tickets.
Moreover, businesses can use natural language processing for sentiment analysis on social media platforms like Twitter. Nowadays, many consumers turn to social media to voice both commendation and criticism against businesses. With text classification, marketing teams can automatically retweet positive tweets and quickly solve the issues underlying negative tweets to save potentially disastrous situations.
To understand the future of AI for marketing, it’s important to consider the current state of AI. AI is not new. The term was coined in 1956 by John McCarthy, and the first work on AI was done in the 1950s. As we’ve explored, AI is already used in many industries, including marketing. But what does this mean for the future?
Currently, AI is being used to automate tasks like lead scoring, text classification, and churn prediction. But these tasks don’t require so-called “big data” or massive supercomputers. For more complicated tasks, such as predicting a user’s emotions or the ability to personalize pricing and maximize value capture, companies use deep learning neural networks that can be trained on terabytes (or more) of data.
One possibility is that as these neural networks become more advanced over time, they will perform tasks that previously required human intelligence and expertise. This will allow them to automate expensive and time-consuming jobs - allowing marketers to focus their time and energy on more creative tasks like branding or strategy.
On the flip side, this could lead to job loss and uprooting of the marketing industry; however, many industry experts believe that AI will create demand for new digital skills instead of replacing jobs outright.
From targeting content marketing at specific customer behavior and demographics to maximizing conversion rates, artificial intelligence is the future of marketing. AI models help marketers grow revenue through real-time data-driven decision-making and digital marketing automation.
The perfect candidate use-case for AI tools is a repetitive task, from text classification of support tickets to scoring leads. Machine learning improves the optimization of marketing campaigns while empowering marketers to work on more creative and strategic projects.