Machine learning is a form of AI (artificial intelligence) in which a system can execute certain tasks without receiving specific instructions to do so. While many larger corporations have beeninvesting heavily into AI, machine learning is becoming much more common to the point where even smaller businesses can use machine learning to more effectively market to their target audience. As machine learning is becoming better understood, the uses for it are increasing throughout the marketing industry.
Machine learning is possible through the use of algorithms that can be leveraged to analyze available data and then make decisions based on that data. In simpler terms, the system makes predictions or identifications based on the data it has access to. It uses specific signals or factors to form its decisions. The system then adjusts its parameters by comparing its predictions to the actual outcome– in essence, the system is “learning.”
Machine learning not only allows marketers to make decisions based on available data, but those decisions can be made automatically via machine learning, which means marketers can let the system analyze the data and execute actions based on that data. As you can imagine, this can be incredibly helpful in many different ways. The following are ten particularly helpful ways in which machine learning can make marketing easier and more effective:
Most marketers realize that targeting more specific audiences versus a broad audience using more personalized content tends to be much more effective. The challenge is knowing what kind of content to create and for who. Machine learning can help by collecting data on previous content engagement and then segmenting your content and customers to determine not only what kind of content garners the best results, but who the content should target.
Let’s say your company sells pet food. The use of machine learning could, for example, help you determine that based on your customer information and past engagement, your most popular type of content focuses on the subject of puppies. Machine learning will also be able to determine what segment of your audience you should target with this content based on the information it has on your customers and leads.
For example, a customer who has bought puppy-related products from your e-commerce page or has read your puppy-related blog posts will likely be interested if you target them with an ad for puppy food. Everything except for the actual creation of the content can be automated, making the entire process much less time-consuming and much more efficient.
Machine learning software can track what visitors do on your website in real time and predict what actions you should take based on their activity. This can help improve your customer service in the following ways:
Properly pricing products and services can be tricky business. Price your products or services too low and you can lose profit, while pricing them too high can cost you customers. Machine learning can be used for price optimization by employing data analysis techniques to understand how customers will react to different pricing strategies. Price optimization tools can even predict the impact of sales promotions or estimate what price will help move a certain number of products within a certain amount of time.
Machine learning can factor in data that includes competitor price points, weather, season, company objectives, local demand, and operating costs to determine the initial price, the best price, the best promotional prices, and the best discount prices.
Many businesses have begun implementing recommender systems, which use machine learning to analyze the past preferences of a customer to predict future preferences. Netflix is probably the most high profile business to make use of a recommender system, using it to recommend movies and TV shows based on their subscribers past viewing habits. Implementing a recommender system will allow you to more effectively recommend products and services that a customer is more likely to purchase. Not only can this help increase sales, but it will also increase the trust that your customers have in your brand.
Churn occurs when a customer stops doing business with your company. Preventing churn can help you save a significant amount of money because the cost of retaining an old customer is much less than the cost of acquiring a new customer. Machine learning can help predict churn by taking into account previous customer churn along with a variety of customer data, including renewal status, subscriptions, lifetime account durations, customer support data (such as the number of issues reported by a customer and whether they were resolved), account activity, and whether their onboarding was successful.
By predicting customer churn, you can take steps to prevent it from happening. For example, if you notice that a customer hasn’t been active on your website, you can target them with personalized offers. If they subscribed to a service and the subscription is about to lapse, you can send them some kind of promotional offer to encourage them to resubscribe.
Machine learning can help you to more effectively manage your leads. One of the challenges that marketers and sales personnel deal with is figuring out how to engage with a lead and when to do so. Many CRM solutions allow you to score your leads based on their behaviors and actions. The higher the score, the more qualified the lead is as a prospect. This prevents you from wasting time engaging a lead that may only have a passing interest in your brand and will help prevent highly qualified leads from falling through the cracks.
Besides the ability to score leads, machine learning also allows you to segment your leads and customers based on the data you have on them. Segmentation gives you a better idea of who your audience is and allows you to target leads and customers with more personalized ads and content. The ability to score your leads and segment them will help to improve your MQL (marketing qualified leads) to SQL (sales qualified leads) conversion rate.
Content marketing is one of the most important elements of any inbound marketing strategy. Machine learning can help to optimize your content in a variety of ways. Besides identifying subject matter relevant to your audience based on how your content has performed in the past, machine learning can also help in the creation of your content.
Although AI isn’t quite where it needs to be to be able to generate content that reads like it was written by an actual human, it can be used to generate updates or reports. It can also be used for simpler tasks, such as generating titles for your content based on your keywords and the titles you used in the past and how effective they were.
There are machine learning tools available that can analyze the content that you write and provide recommendations based on your focus topic. Then they can run predictive analysis to see how your content would fare on search engines and how tweaking SEO could affect your search engine rankings. On top of that, there are now tools available that can analyze the emotional tone of your content to ensure that it’s consistent.
Media monitoring is essential to tracking the reputation of your brand, locating where your audience is, and handling negative attention whenever it comes up. Machine learning algorithms can not only identify mentions of your company name throughout social media as well as the web in general, they can also be trained to identify patterns and logos in images or videos. Such algorithms can even filter out mentions that may have similar words as your company name but have nothing to do with your company.
Sentiment analysis is a method of machine learning that allows bots to go through customer reviews and feedback to determine whether the comments are positive, negative, or neutral. This can be done on social media as well, allowing you to sift through thousands and thousands of reviews and comments to get an idea of what the perception of your company is. Sentiment analysis can be helpful in identifying negative comments that you can then have someone personally address (and improve your customer service) as well as identifying customers who have provided high praise. Such customers can then be turned into brand ambassadors.
Installing chatbots or virtual assistants on your social media pages or on your website can help to greatly improve engagement with your leads and customers as well as improve your customer service. It’s impossible (unless you’re a large corporation with unlimited resources) to have humans available to chat with visitors at all hours of the day and night. Chatbots have the ability to target visitors, make recommendations based on visitor behavior and responses, and analyze sentiment based on visitor responses. This allows them to engage with visitors in a personalized way that can help keep visitors on your site longer. It’s also a way to reduce the amount of time it takes for visitors to get a response for common questions and concerns.
Machine learning is already being leveraged by big and small companies alike, and there’s no doubt that it will become even more important to marketers in the near future as new machine learning algorithms are developed. Not only can machine learning help reduce the amount of time spent on tedious tasks, but it can help you to better understand your customers as well as help you to engage with them more successfully.