How Machine Learning Can Be Used in Marketing
Did you know that an average marketer spends about 16 hours a week on repetitive tasks, according to a HubSpot survey? The most time-consuming activities are content creation and collection, organizing, and analyzing marketing data from different sources. In this article, we’ll talk about how to delegate some marketing tasks to machines that can learn.
Data is the Key
The data used by marketing-focused machine learning algorithms can be used for many kinds of beneficial solutions for businesses looking to secure or obtain a foothold in the market. However, while ML algorithms can operate autonomously, the quality of their results entirely depends on the machine learning engineers providing the training data. Without high-quality data, the algorithm will not provide useful results.
When data scientists prepare the data, they often require a domain expert assisting in data categorization and labeling. Otherwise, the correctness of data may fail due to the wrong understanding of the final output expected from the machine learning model, incorrect categorization of data, and human error.
Read also:Data Quality in Machine Learning
Let’s look at machine learning use cases in marketing from the point of view of marketing professionals and AI engineers.
ML in Analytics and Data mining
Dealing with gigabytes of information is an arduous task for the human brain but it can easily be handled by machines. For example, ML algorithms of the SocialMiningAI tool search in real-time through millions of public social posts to identify prospective customers with high intent to make a purchase. With the help of natural language processing (NLP) and image recognition algorithms it’s easy to identify a potential lead who is interested in a specific service or a product.
Also, using machine learning algorithms for social listening gives a powerful tool for reputation management and brand awareness campaigns, saving hours of work for brand managers. Modern algorithms are able not only to quickly process huge amounts of data, monitoring mentions of your organization or keywords that are relevant to your business, but also to understand patterns, providing valuable insights for marketers.
For example, ML-based social listening can help marketing managers find brand ambassadors, study customer feedback and their experience in using the product to improve it or create effective marketing campaigns.
ML for Enhanced Customer Segmentation
Every marketer knows that customer segmentation is the key to the successful functioning of marketing in a company. Dividing your audience into groups with common needs and interests allows you to target more effective messages for each group of customers. Machine learning algorithms for marketing not only allow businesses to automate this process, but also to find hidden patterns in the data that elude the human eye. Thanks to this, you can create more specific small segments and target them with the most personalized marketing campaigns.
Having an ML algorithm find the best segmentation strategy automatically is not only more efficient, but it can lead to better conversions. As the business reaches their target audience with greater focus, they can be much more persuasive and effective at converting a potential customer into a regular buyer. Most of us have seen this “magic” on Facebook or Instagram, being offered exactly the same product or a service you were searching or talking about.
Clustering machine learning algorithms are unsupervised machine learning algorithms which means they use untagged data and can be used to discover natural patterns in the dataset. For example, K-clustering works on the principle of finding similarity or closeness between the data points and clustering data based on that. K here means the number of possible clusters.
An example of a Clustering algorithm in action
To describe the essence of clustering algorithms let’s consider the following example. Let’s assume you have three friends: Alice, Bob, and Emma. All of you have different preferences in movies and snacks. For example, you and Emma both like westerns and popcorn, but Alice and Bob both like chips and musical movies. So, you and Emma tend to spend time together and you are more close to each other than to Alice and Bob. The same for Alice and Bob: they tend to be closer to each other then to you and Emma. So, you and your friends form two clusters based on your preferences.
In clustering analysis we can accurately calculate the distance between people in groups, we can estimate the number of groups and with the help of domain experts, describe those groups.
ML for Optimizing & Automating Marketing Campaigns
Machine learning can be used to optimize and automate marketing campaigns. This opens up opportunities not only to improve the efficiency of launching and operating these campaigns, but also for saving time and money that could be spent on other projects. Usually, marketing specialists spend hours to find ways to increase conversions or optimize campaign budget. Machines can do this much faster and more efficiently.
For example, advanced machine learning is what makes Google Ads a leader among advertising services. Features such as smart bidding, data-driven attribution or responsive display ads allow marketers to rely on the system to select the most optimal ad campaign settings based on campaign objectives. These self-learning capabilities of the algorithms apply to other marketing tools as well, allowing you to test different headlines, email subject lines, images, CTAs, and other variables.
Optimizing Marketing Campaigns
Marketing campaigns can be optimized without machine learning using A/B testing. However, this is usually only feasible when using only one or two variables. Machine learning on the other hand, can use many more variables at once when testing the effectiveness of different types of campaigns. After performing this testing, it can report the results back to the marketer. The most effective variation of the campaign can then be used.
Some ML-based marketing tools can automatically select the marketing channel based on your marketing strategy. For example, Mercato uses smart algorithms to attract customers to their website using geo-optimized promotion across several digital channels. Cost-effective cross-channel execution is undoubtedly something that can optimize the entire marketing process of a company, and ML is the main tool for achieving this.
Marketers need to look at their analytics daily. It can often be difficult to see complex patterns in the data that can have profound impacts on a business. It also can be challenging to find potential opportunities within this data. Spending the time necessary to recognize these potential opportunities could be costly. However, a machine learning algorithm can easily do it for you.
As an example, an ML algorithm can identify unusual spikes in data and alert a marketing manager of interesting findings such as the time of day at which the most conversions are carried out, and spend the budget exactly when it will bring the maximum result. If an ML algorithm can process and analyze campaign data automatically, this leaves time for marketing managers to spend more time on other aspects of their work.
A Step Further: Automatically Generated Content
Beyond automating and optimizing content, machine learning can go so far as to enable a computer to create content automatically that looks as if it was created by a human. This can be done through generative adversarial networks, or GANs, for images. A generative network generates content, while an adversarial network detects and eliminates unwanted results. After many iterations, the result of the project will include new image content that can be used to enhance marketing campaigns.
For automatic text generation, BERT or GPT approach is usually used. For example, BERT can generate the whole article on any topic with just a few sentence inputs. The framework has large informational datasets trained on Wikipedia and Google’s BooksCorpus. Even if marketers don’t use the generated text in its purest form, they can take new ideas from it or edit it, which still speeds up the content creation process compared to if you had to do it from scratch.
One of the recent approaches that been increasingly popular in marketing automation came with the release of ChatGPT model, which is the latest conversational neural network by OpenAI. ChatGPT is the most advanced GPT model out there with the capabilities to support users on a huge database collected before 2021. However, with its API available, the model is also customizable to solve mundane marketing tasks like data collection, copywriting, or lead generation.
By using such ML-based marketing tools, businesses can automate more of their content creation processes and divert their focus toward strategy and data analysis.
Read also:6 use cases for ChatGPT
Machine learning algorithms are great at recognizing patterns, especially in ecommerce contexts. When users visit ecommerce storefronts and purchase items, all the information about their activity and purchases can be recorded and anonymized to be used for marketing campaigns. One of the most useful applications of this example is to increase customer loyalty with personalized suggestions also known as recommendation systems.
Creating a machine learning recommendation engine is usually based on one of two methods: collaborative filtering or content-based filtering. The first model uses data about the preferences of users with certain similarities and, based on them, issues recommendations to the user. The content-based approach uses product features in which the user was interested to recommend other products similar to what the user likes. For some systems, a combination of the two approaches can also be used, as Netflix did for example.
If we know what a consumer bought in the past, we can reasonably predict what they may want in the future with machine learning. In one of our retail projects, we used Mlxtend, Pandas, associative rules, and Apriori algorithm to empower the system to offer relevant accompanying goods that customers tend to buy together with any selected product and drive additional revenue. This is an example of an ML-based recommendation system.
Smart recommendations increase user engagement and drive brand loyalty. This is something Spotify has learned. Spotify’s ‘Discover Weekly’ playlist utilizes machine learning to tailor music suggestions to users. The intent behind this is to keep users listening on their platform and to keep them subscribed. This works by collecting listening data about users. When a user listens to a particular song, the algorithm looks at what other people who listen to that same song listen to. The ML algorithm can then make a suggestion that is added to the Discover Weekly playlist for the user to listen to and enjoy. As a result, Spotify can better maintain its customer loyalty.
Business to Business
This technology isn’t just useful for B2C sales, but also for B2B. Zylotech and Bombora are unifying their machine learning and data focuses to help businesses identify which potential business customers are ready to make a purchase. This can enable a business to know exactly when they need to initiate a personal conversation with the customer to make the sale. The information that they gather can also be used to better understand the customer and make the sale more personal.
Since machine learning models for marketing greatly simplify the recognition of patterns in data, they are also very useful for forecasting consumer purchases, lifetime value of customers, and other data in the future.
When it comes to managing product supply as well as dynamic pricing, demand forecasting powered by machine learning can help immensely. By using past data to predict when consumers will be more likely to buy certain products, businesses can stock up on products early to meet the demand. They can also prepare marketing campaigns to inform customers of sales that best compete with other businesses during those times.
When using a machine learning algorithm and suitable data, businesses can predict what consumers will purchase in the future and when. This can be helpful for financial planning, but it can also be useful for planning when to run marketing campaigns for certain products. If you know that consumers will be more likely to purchase items at a certain time of the year, you can increase conversions during those times to net even more gains.
By using past data, machine learning can better understand how much value each customer contributes to the business. This can be used to predict the lifetime value of customers to a business. This information is useful not only for investors, but also for the company’s long-term decision making.
Conversely, when a consumer leaves the system through unsubscribing from a newsletter or another program, they contribute to a business’s churn rate and can negatively impact their metrics. Machine learning can better analyze trends in a business’s churn rate and help marketers understand what went wrong and when. By understanding the demographics and behaviors of users who leave the system, marketers can come up with strategies to reduce their losses.
Natural Language Processing & Chatbots
Considering the use cases of machine learning in digital marketing, it’s impossible to skip AI assistants. Today, it’s one of the most powerful modern tools to improve customer service and optimize marketing efforts.
AI assistants can play the role of online consultants increasing the level of customer engagement with the brand. Offering product recommendations or discount coupons, providing order information or the nearest store address are just a few tasks that AI assistants can do. Modern AI assistants are getting smarter and can perform a wide range of tasks, giving the impression that the buyer is communicating with a real person.
Read also:AI Virtual Assistant Technology Guide 2022
For example, Samsung introduced an AI-powered virtual avatar called Neon a few years ago. This is an AI assistant that looks like a human hologram, can communicate with you and act like a real companion. Neon avatars are customized as per the client’s requirements and can be used in a variety of applications.
Since not every business can master the creation of its own AI avatars, their simpler counterpart can be ML-based chatbots. Due to advances in natural language processing, chatbots can make certain aspects of a business’s customer service much more efficient. Chatbots can’t handle every job, but they can help customers with tasks like troubleshooting and even purchases. In situations that a chatbot can’t handle, the program can automatically escalate it to a human customer service representative. This process can help save money on customer service operations while improving quality of service.
The Future of Machine Learning in Marketing
Machine learning is improving by the day, but the amount of data that we collect is also ever growing. The more high-quality data that we have, the better our predictions and pattern analyses will be. However, circumstances are always changing, so businesses need to stay on top of the curve in order to maintain their relevance in the market. Machine learning will also affect businesses in many ways other than just marketing technology that you’ll need to prepare for in order to keep up to date.
Keeping your business on top or solidifying its place in the market if you don’t already have a foothold are objectives that may only be accomplished with innovation and creativity. Machine learning in marketing and sales is a great way to start adopting new technology strategies. Consulting data science and machine learning experts will help you to begin the process of bringing your business up to date.
If you’re ready to take your marketing strategies into the future and supplement machine learning marketing use cases with your project, reach out to us today to start the conversation. Our machine learning development team at MobiDev is here to help you succeed.
The article is reviewed by Yevhen Krasnokutsky, AI/ML Solution Architect at MobiDev