What if you could automate the repetitive and often tedious aspects of your marketing process? With AI, that's now possible. While machine learning has been applied to a variety of tasks, from banking to healthcare, it is now being used by companies of all sizes to improve their marketing efforts.
The digital world is becoming hyper-saturated with data. By using machine learning, we can make better predictions and decisions in our marketing campaigns, from lead scoring to churn minimization and beyond.
At the same time, many marketers still struggle with understanding how AI works and what they can do with it. To help you better understand how ML can benefit your marketing strategy, we're going to explain what AI marketing is, and highlight some of the most common uses for machine learning for marketing.
Machine learning is a type of artificial intelligence that uses algorithms to make predictions and decisions based on data. It's used in many different areas of modern life, from healthcare to finance to advertising, and it can be applied directly to marketing activities like lead scoring and email marketing.
In the context of marketing, it's an effective tool because it can provide insights into customer behavior that would normally be overlooked. For example, a company may have a lot of data about people who filled out a contact form on their website, but they don't know if those people will convert, or how to optimize their website for better lead flow. With machine learning, they can train models to make predictions about who is more likely to buy.
At its core, machine learning refers to the ability of computers to learn without being explicitly programmed. In marketing, this means that a computer can identify patterns in data and apply those patterns to predict future outcomes with a high degree of accuracy. For example, a machine learning model may be able to predict which leads will convert based on their past behavior—and then take specific action to optimize their experience for greater success.
Machine learning algorithms are becoming increasingly sophisticated, enabling computers to automatically improve their performance over time by continuously collecting new data and then using that data to improve their decisions going forward—all without human intervention required.
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.
On a technical level, Akkio’s AutoML will group, sort, and sift through the large quantities of information to look for patterns in the customer journey data and make predictions. For example, perhaps high-conversion customer profiles are those that have watched over a certain number of videos on a company's website. Based on this data, a company might work to expand and improve its video content library.
Basic business strategy tells us that customer acquisition is key to long-term growth, but it can be incredibly difficult to measure the impact of marketing efforts.
Marketing teams are often responsible for multiple aspects of their company's strategy, from product development to pricing decisions. This makes it very difficult to know which actions will have the most impact on revenue generation over the long term—and machine learning can help with this by helping companies better understand their customers and take action on that insight.
For example, let's say a company sells products on an eCommerce platform like Shopify or BigCommerce. Using Akkio, they might train a model that predicts which customer segments are most likely to convert (based on previous conversion rates), then send targeted emails or place ads in specific channels to those customers. This will help them create a higher average order value, which is an important metric for growing revenue over time.
Marketing teams are also responsible for creating customer personas, defining their target market, and crafting messaging based on that data—but it's often difficult to know if they're doing it correctly or have any insight into how different groups of people respond differently to the same marketing message.
Data science can provide a number of insights into customer characteristics that can be used to improve targeting and messaging. For example, you might use machine learning to determine what language a user prefers in emails based on their geographic location, or whether users are more responsive to pop-ups or banners depending on the device they're using.
The result of all this is more effective marketing that can improve company growth and profitability.
Let's take a look at 13 examples of machine learning for marketing. This list isn't exhaustive; there are countless applications for AI in marketing, including things like natural language chatbots and intelligent landing pages. We'll focus on what we consider to be the most practical examples of how machine learning can be used to improve business outcomes today.
Predictive modeling, in the context of marketing, helps companies predict future customer behavior, sales figures, lead conversion, or any other metric in historical data. Let’s see how you can use machine learning for marketing through seven examples.
Recommender systems can be used to surface information that a user may enjoy. For example, if you wanted to find out what movies to watch based on your preferences, or what music to listen to next based on the type of mood you’re in, recommender systems can be very useful.
Netflix uses machine learning to predict what you'll want to watch next. If you start watching a movie and get bored, Netflix’s AI system will recommend another movie for you. It uses historical data on users' viewing behaviors to make these recommendations. For example, if you watched The Hunger Games, it might recommend Squid Game next.
As The Motley Fool reports, Netflix’s recommender engine is said to save the firm $1 billion a year through decreased churn and higher retention. In other words, AI is a game-changer for Netflix, and a big part of why they're in the coveted FAANG (or now MANGA) group. Amazon is another famous example of recommendation engines in the real world.
Airbnb uses predictive modeling to recommend places that visitors might want to stay when they visit a new city. This recommendation service helps travelers find the perfect space for their needs, based on factors like their previous stays, as well as amenities and location.
Additionally, they use AI for smart pricing, since hosts often don't know how to set the perfect pricing according to current supply and demand, as well as the listing’s specific details. Finally, Airbnb even uses AI to vet guests based on third-party data, to avoid bad actors.
Airbnb’s tremendously successful IPO, which resulted in a valuation higher than that of Marriot and Hilton combined, is largely thanks to innovations like these. Using AI, Airbnb has shown that they have what it takes to get ahead of the competition.
Twitter is more than just another social media platform. It has been described as a consumer insights engine, and that couldn’t be more true. Through machine learning, they’re able to intelligently crop images, recommend relevant timelines and content to keep users scrolling, and even filter out hate speech.
These are just a few of the ways AI helps Twitter make their service better for users, which plays an important role in their battle for social relevance and staying power. With marketing automation tools like Akkio, you can automatically measure the sentiment of tweets that reference your offering, which is valuable for market research, marketing initiatives, or even to innovate new products by figuring out what customers want.
Machine learning lets you comb through historical customer data to find patterns in churn, and predict which customers will churn next.
Companies like Spotify use machine learning to predict when a customer will churn so they can take action before the customer leaves. They do this by looking at demographics, past user behavior, and other data formats to predict future actions.
With this technology, these companies can maintain high retention rates, which in turn increases revenue and boosts the bottom line. For instance, if they predict that a customer is about to churn, they can offer incentives for them to stay, like a discounted subscription rate for 3 months. As a result, they can use machine learning for marketing to effectively boost customer lifetime value.
Lead scoring is essentially the science of predicting which leads are likely to convert, while sales funnel optimization is optimizing the sales funnel based on historical sales data, to better focus sales efforts. This can help optimize your marketing and sales spend, and improve conversions.
Without lead scoring, sales teams would have to manually sort and review thousands of leads every month. With machine learning, those same teams can use a lead scoring model to automatically identify the most promising leads and prioritize their time and attention—allowing them to increase the productivity of their team while also reducing costs.
Even in a B2C context, lead scoring can be an incredibly powerful tool, such as by helping online retailers understand which products a user is likely to buy based on their past behavior, and showing the right ad to the right person at the right time.
Companies in all industries are using machine learning to optimize marketing spending. DoorDash is one example of a multi-billion-dollar firm using the technology to lower costs by 10 to 30 percent, while still reaching the same number of customers.
Optimizing marketing spending is traditionally an intractable problem that has stumped many companies. By using AI, they can work to increase their revenue per customer, while reducing marketing costs.
Customers interact with your business through textual feedback, including product reviews, tweets, form submissions, emails, and more. It is essential that you are able to understand their sentiments. With machine learning based natural language processing, it’s easy to build and deploy everything from negative tweet flagging to targeted nurture campaigns.
This enables unprecedented insight into your customers, their needs, and what they are saying about your brand. It also gives you the ability to react quickly to negative sentiment—and use that insight to improve your product or service.
For example, if a customer posts a negative tweet about your offering, this information will be invaluable for improving your product or service in the future, while providing an opportunity to win back the customer.
A large consumer electronics company used Akkio to build machine learning models that categorize and prioritize product feedback, increasing efficiency and allowing engineering teams to focus on what matters: Improving products and fixing issues.
Digging through all the feedback to find the most valuable insights is time-consuming work for teams of analysts. Using Akkio’s no-code NLP, they were able to build models that could identify sentiment on a scale previously impossible, enabling more actionable product decisions for their engineering teams.
Forecasting is one of the most common use cases for machine learning. It allows you to predict your future revenue, what your costs will be, or even commodity prices. This helps you make better decisions about inventory, predict campaign responses, and more.
When running advertising campaigns on Facebook, you can choose to target people who have previously interacted with your brand or expressed interest in your product field - a form of forecast targeting and campaign response prediction so you focus your ads on people who are most likely to convert.
Forecasting also helps you make better product decisions—such as whether or not to release a new feature or product. If you know that your company relies heavily on email signups for revenue, for example, then it would make sense to prioritize development efforts towards improving your email signup flow. This will help boost signups while decreasing churn.
In the retail industry, machine learning is being used for pricing. By understanding how demand fluctuates and which products are selling at what price, retailers can make better decisions about pricing their inventory. This helps them stay competitive, while also maximizing profits.
With machine learning, you can target promotions and discounts to specific customers who are most likely to convert. This helps you increase revenue and decrease customer acquisition costs.
E-commerce companies use machine learning to target promotions and discounts to specific customers who are most likely to convert. For example, if you have a customer who has shown interest in your products but has never made a purchase, you could send them a discount code to encourage them to buy something from your store.
Additionally, you can use machine learning to segment your customers so you can offer them the most relevant deals. For example, if you have a customer who always buys high-end products, you could send them coupons for other luxury items they might be interested in. On the other hand, if you have a customer who only ever buys on sale, you might want to send them notifications when items they’ve shown interest in go on sale.
With machine learning, you can optimize your marketing campaigns in real-time so they're always performing at their best. This includes things like A/B testing different ad copy or campaign strategies, as well as optimizing landing pages for conversion rate optimization (CRO).
You can use machine learning to A/B test different ad copy so you can find the best performing headlines, descriptions, call-to-actions (CTAs), and images for your ads. This ensures that your campaigns are always optimized for performance, helping you get more bang for your buck.
You can also use machine learning to optimize your landing pages for conversion rate optimization (CRO). This includes things like testing different headlines, copy, images, and CTA buttons to see what works best. With machine learning, you can automatically test and deploy the best performing variants so you're always getting the most out of your campaigns.
Personalization is key to providing a great customer experience. With machine learning, you can personalize your website, emails, ads, and more to each individual customer. This helps improve conversions, engagement, and retention.
You can use machine learning to personalize your email marketing campaigns so they're more relevant to each recipient. For example, you can segment your list by interests and send different emails to different groups. Or, you can use data from past interactions to send more targeted emails. For example, if a customer abandons their shopping cart, you could send them a discount code to encourage them to complete their purchase.
You can also use machine learning to personalize your website so it's more relevant to each individual visitor. This includes things like showing different homepages or offers based on user location or past behavior. It also includes things like product recommendations and personalized search results. By personalizing your website, you're providing a better experience for your visitors, which helps improve conversions and retention rates.
Customer segmentation is the process of dividing customers into groups based on shared characteristics so you can market to them more effectively. With machine learning, you can automate this process so it's more accurate and efficient.
With Akkio, you can easily build and deploy models that use the latest machine learning techniques to provide actionable insights into your customers.
Akkio is a platform that helps you go from data to deployed prediction models in a matter of minutes, without needing any technical expertise. Since it’s fully no-code, it can be used by anyone - marketers, small business owners, affiliate marketers, as well as data scientists.
You'll be able to effortlessly scale your machine learning efforts with on-demand compute. In addition, you can easily integrate machine learning into your existing applications and services.
Gartner famously reported that 85% of AI projects fall short, largely due to their complexity. Akkio can help you implement the AI marketing use-cases above without needing to build your own models from scratch. Our engineers have built out our platform to make it easy for you to get started.
For example, we use Neural Architecture Search in the background, after automated data processing, to use the best available models for your data. After that, our robust deployment pipelines will automatically scale your trained models to any level of usage.
Suppose we wanted to predict whether any given user will churn. We can connect a historical customer dataset, with a column on churn. We simply upload it to Akkio, in the simple visual interface, as seen below.
After our dataset is connected, we select the column we’d like to predict. Again, it’s simply “churn” here, so we select that and hit “Create Predictive Model.”
After a few brief moments, we’ll have an accurate churn prediction model that we can deploy in any setting. Whether you’d like to make real-time predictions in CRMs like Hubspot, predict on big data in Snowflake, or connect to thousands of other applications with Zapier, it can all be done effortlessly with Akkio.
For a visual explanation, see this simple video demonstration. Ultimately, AI won’t replace your unique human intelligence, but it can augment it to great effect.
To recap, machine learning in digital marketing can help companies make better decisions along the entire customer lifecycle, from lead scoring and sales funnel optimization to churn reduction. By gaining insights into customer sticking points, you can improve the customer experience, increase customer engagement, and reduce customer churn in the process.
By using Akkio’s platform, you can easily build and deploy models that use the latest machine learning techniques to provide actionable insights. You'll be able to effortlessly scale your machine learning efforts with on-demand compute. Further, you can easily integrate machine learning into your existing applications and services.
To get started, sign up for a no-risk, free trial and build your first models in minutes.