Your goal as a marketer is to find consumers who are the most likely to be receptive to your product, service, or brand, and turn them into paying users. Artificial intelligence marketing, or AI marketing for short, is the use of intelligent machines for marketing tasks like lead scoring, sales forecasting, or churn prediction.
You can see AI marketing in your day-to-day life, such as the personalized ads you get on Google, individualized email offers in your inbox, or even the specific prices you pay for some digital products.
In this article, we’ll explore the major drivers of AI in marketing and how it will have a tremendous impact on the marketing industry. We’ll also examine a few key things to consider when incorporating AI into your business. Finally, you'll learn how to implement AI in your own marketing efforts - it's easier than you may think.
The use of data in marketing is not new, but nowadays, there’s an extraordinary amount of it - “big data” - and it only continues to grow exponentially.
Marketing data encompasses all the customer information that a company collects through its various channels, such as social media, ecommerce, or email programs.
With data science, marketers can predict behavior and preferences in real-time with a higher degree of accuracy than ever before, developing actionable insights for implementing more effective marketing campaigns.
This information helps businesses improve their digital footprint by better understanding their customers' needs and desires so that they can provide them with an individualized customer experience; for instance, by sending them more relevant email marketing or suggesting products based on previous purchases.
With tools like Akkio, data both big and small can be used to fuel machine learning models.
Machine learning has the potential to transform how we think about marketing in terms of strategy, process, and implementation.
Traditional marketing often involves the use of data to guide business actions. Machine learning takes the next step by using this data to automatically make decisions, rather than simply provide insight into what's happening on a macro level. In other words, machines are now starting to teach themselves how to optimize outcomes without the need for manual human work.
Machine learning processes data by using AI algorithms to group, sort, and sift through the large quantities of information collected by typical marketing platforms. It then looks for patterns in the customer journey data to make predictions.
For example, machine learning can analyze historical lead data to find patterns of high-conversion customers and then score new leads according to their probability of conversion.
Natural language processing (NLP) is a branch of artificial intelligence that recognizes the context and meaning of human language to derive insights or predictions.
Marketing data analysis has never been more complicated, now that billions of potential customers are active across social media platforms and customer relationships span far more channels than previously.
Understanding customer sentiment on social media can be done with natural language processing, such as by classifying tweets about your business as “positive” or “negative” and responding accordingly.
Additionally, customer support bots can use natural language processing to classify customer queries, for example, as “urgent” or “not urgent,” in order to prioritize the task queue and understand who to focus on for the overall best customer experience.
Predictive modeling, in the context of marketing, helps companies predict future customer behavior, sales figures, lead conversion, or any other metric in your data.
By looking at historical data and trends, predictive models can forecast future outcomes and make recommendations. Predictive modeling can be seen as a "crystal ball" for your company's marketing teams, but it’s really just modern statistics, leveraging a lot of data, computational power, and deep learning.
There are generally two broad types of predictive models: Classification and regression.
Classification is used to make predictive models with limited outcomes or discrete “classes” like a “Yes” or “No” conversion estimation. Models can also return a “likelihood” estimate of its predictions to better differentiate between high confidence and low confidence predictions.
Regression, on the other hand, is used to make predictive models with a range of outcomes, such as Customer Lifetime Value prediction or sales forecasting.
Platform solutions are an increasingly important part of the marketing landscape. It’s not enough for a marketer to know how to measure, optimize, and iterate upon the campaigns they’re running; they need a platform with the functionality to integrate with their existing marketing tools and make AI technology accessible.
Akkio provides marketers with access to AI at all levels. No coding or specialist skills are required for this type of solution, which also integrates seamlessly into existing systems.
AI is the future of marketing. It’s time to get on board with it.
From email campaigns to content marketing to chatbots, AI is being used more and more by businesses across the globe to execute their marketing strategies and grow their business. It’s no surprise that most major tech companies are heavily investing in AI, some even investing billions of dollars into it as they compete for dominance in the industry.
Now, if you want to stay competitive and grow your business without getting left behind by big tech companies, it’s time for you to start exploring how AI can help you achieve your goals.
There are many reasons why you should start using AI for your own marketing strategy - saving time and resources creating new content, improving customer service interactions, increasing customer engagement, uncovering new target audiences or marketing messages, and increasing conversion rates.
According to recent research, 68% of US businesses increased spending on AI during the COVID-19 pandemic. The power of AI marketing automation shouldn’t just lie in the hands of Google, Netflix, and Amazon. All marketers and retailers need to be taking advantage of its benefits.
There are countless benefits of AI marketing. Let’s narrow in on a few: Personalization, automation, marketing measurement and increased ROI, faster decision making, and reduced errors.
Personalized marketing is a hot topic in digital marketing. It’s also one of the most effective ways to market to customers. The benefits of personalized marketing are clear: it’s more efficient, more effective, and builds customer trust. Marketers across the board agree that personalized marketing works better than non-personalized marketing.
But with so many options on how to personalize your campaign, which option should you choose?
AI-powered personalization is becoming an increasingly popular choice for marketers because of its ability to take all data available and use it for optimal targeting purposes.
Previously you would have to manually set up rules-based outreach lists based on simple demographics, but AI can generate customer profiles for you and uncover trends outside traditional demographic data which can be leveraged in a personalized campaign.
AI allows you to know when, where, and how to communicate with users based on their behavior across all channels.
AI is quickly gaining ground in the workplace, and it's no wonder. The technology has proven to be able to complete tasks that would have taken weeks for a person to do manually, in just minutes.
The benefits of automation in AI are twofold: there's the obvious benefit of saving companies money on hiring and paying additional employees. But there's also an often-overlooked benefit of freeing up employees' time from mundane or tedious tasks so they can work on more creative projects.
Akkio is one tool that helps organizations implement solutions in mere minutes that previously would have taken weeks to run manually, all without programming expertise.
There are many benefits to marketing with AI, but one of the most important is that it is cost-effective. In addition to saving money, you can also save time by using automation.
For example, with sales funnel optimization, marketing teams can focus only on what works best for their specific needs instead of trying everything at once, saving time and money.
AI in marketing has a significant effect on decision making.
In particular, AI marketing is highly adept at automating the boring, repetitive tasks that would typically take up all too much time and throw a wrench in business processes.
For instance, businesses could implement AI to build an appointment booking chatbot that automatically predicts cancellations, giving both the customer and business advanced notice. Sales teams could also use AI marketing technology to predict whether a given customer will convert, letting them know which leads to prioritize and spend time on. It lets companies react faster, speeding up growth.
Marketing can be fraught with difficulties, including the challenge of keeping up with fluctuating demand and figuring out how to market a product or service that has yet to prove its worth.
This is where artificial intelligence can help by reducing errors with a consistent, algorithmic approach. The result is better efficiency for marketers who no longer have to account for human error or guess what customers want.
There are a number of important considerations to keep in mind when it comes to AI marketing. Let’s take a look at consent and privacy, data quantity and quality, and goals and technical considerations.
In recent years, machine learning has become more and more sophisticated with the help of big data. In marketing, machine learning algorithms are being used to personalize ad content, make product recommendations, decide which leads to prioritize, and more.
But not everyone is happy about this development. Privacy advocates worry that the use of AI can erode privacy in fundamental ways when companies take actions from data about what consumers do on the internet. And past patterns of bias against groups of people are sometimes carried forward into AI-driven systems.
Going forward, consumers’ trust of a brand will only become more and more important. Consumers will increasingly do business with companies that use their data and AI responsibly and avoid those that don’t. Companies that seek consent from users and ensure that their data remains private and secure will thrive while those that don’t will flame out.
AI will change marketing as we know it by providing marketers with the ability to make better, more strategic decisions. Today, the most valuable resource for marketers is quality data. Data quantity is not enough; firms need quality data to train their machine learning models and predict patterns that will help them outperform their competitors.
The best way to collect high-quality data is through a targeted strategy that collects the necessary information required for a machine learning model to produce accurate predictions.
But how do you know what information you need? As long as the data contains the pattern you’re hoping to identify and predict, and records are correctly labeled or tagged, then you can train a model.
Any industry where predictive models are used in decision-making should take care that they're built on high-quality datasets that are reflective of the real world.
If you want to use artificial intelligence in your marketing, you need to have specific goals in mind before starting. AI can help marketers answer questions about data, but it needs to be clear what question the marketer is asking.
The best use of AI is when the company has a really clear and articulated goal. Start by looking at your current marketing strategy and asking yourself what aspects and KPIs might be best handled by machine learning models. We’d recommend starting with one thing, such as lead scoring, rather than trying to implement a huge AI strategy all at once.
Traditional machine learning relies heavily on statistical models, programming, and complex computing paradigms related to data engineering and pipelines.
This means that professionals who aren’t software engineers won’t be able to use AI the traditional way.
With Akkio, even non-technical teams can easily build and deploy AI models without worrying about the complexity of the underlying technology.
AI is used across the entire spectrum of digital marketing, including in content creation, sales funnels, forecasting, lead scoring, and more. Let’s dive deeper into these various use-cases to see how they can be useful for your business.
Content creators are constantly asking themselves what type of content will generate the most success in their industry.
With AI, models can learn from an organization's previously published articles, as well as data like tags, keywords, and the resulting engagement, in order to more accurately predict which articles are likely to be successful. With that, creators can drive a more informed SEO and content strategy rather than relying on guesswork from past performance.
In the past, if a company wanted to analyze sales data, they needed to manually go through spreadsheets and reports - a time-consuming process that often led to analysis paralysis. Nowadays, AI can do the heavy lifting.
The use of no-code AI in this context has many benefits: it's cost-effective, can be scaled easily, and doesn't require any technical experience. Marketing and sales teams can quickly build and deploy models themselves to optimize their funnels and boost conversion rates.
Sales forecasting is a process that predicts how the market will turn out and focuses business accordingly.
Traditionally, forecasting was often done with a qualitative approach that included personal opinions, past experience, and assumptions about future conditions. This can have serious drawbacks, such as being unable to anticipate seasonal trends or changes in demand for products or services.
AI has the ability to analyze much more data than people could ever hope to manually analyze, which allows for greater reliability when it comes time to make predictions about the future.
Churn can seem like a black box mystery, but AI can change that.
AI is able to improve churn prediction by examining data from past customers who left for various reasons. This allows companies to find patterns in their customer journey and take proactive steps before problems occur.
With predictive analytics, companies are also able to offer personalized experiences that meet customer needs and keep them happy with their service provider, reducing the probability of churn.
In order for marketers to use data effectively, they need a way of knowing which leads are more likely to convert than others; this is where lead scoring comes in handy.
When businesses use AI to assign scores based on a leads’ likelihood of converting, it becomes much easier for them to allocate their resources better so that they’re not wasting time on less-likely prospects while neglecting those who have a higher chance of converting.
If you're not using AI to power your personalization and audience segmentation, you're not reaching your potential.
AI can be used to inform and curate the content that's being sent out in order to ensure that customers are always being shown the right message.
It also allows businesses to use customer data to predict what they might like to purchase next or deploy more personalized messaging for better campaign performance.
This can also help with understanding how users interact with emails or content and deploying marketing accordingly.
AI marketing is the wave of the future. Marketing teams at the biggest companies are already using it and most others are now scrambling to understand how they can use this new technology. But many are concerned about getting on board because it seems too complex.
With Akkio, marketing departments and small businesses can make use of AI with no coding skills required.
Akkio is a no-code platform that uses artificial intelligence to help companies make sense of data without the expensive and time-consuming process of hiring engineers and writing code.
Previously, AI projects required technical engineers that demanded sky-high salaries, but Akkio makes it possible for non-technical people to build models in minutes. Now teams can accomplish things in minutes that used to take months.
Moreover, traditional platform costs are prohibitively high and come with additional fees for everything from model training to data storage.
With Akkio, there’s no cost to train models, and there’s no risk, as businesses can sign up for a free trial. Now, small businesses can do what previously only big companies could. You can train your own machine learning models with your business data - just point Akkio at your data and watch it make predictions!
Akkio is a no-code AI tool that supports marketers with personalization, automation, increased ROI, faster decision making, and reduced errors.
Akkio is used by some of the most innovative companies to create predictive models that solve real problems faster than ever before. Try Akkio today and see how AI can turbocharge your marketing efforts.