Capgemini research shows that 75% of organizations using machine learning in their marketing efforts experience a sales increase of over 33% for new products and services.
Today, marketing and machine learning are becoming increasingly intertwined, enhancing the other. Unsurprisingly, machine learning has found numerous applications in the marketing sector. In this article, we’ll explore how machine learning algorithms are being used in marketing.
According to Forrester’s Global State of AI Online Survey, AI and machine learning drive growth in several critical business areas. Organizations reported that machine learning helps improve:
- Customer experience (57%)
- Product and service enhancement (44%)
- New business models (43%)
- Development of new products and services (41%)
- Customer satisfaction (33%)
You don’t need to be a data scientist to recognize the value of these improvements. Machine learning is genuinely transforming marketing by optimizing analysis, enhancing customer experiences, and aiding in the development of new products and services.
In the coming years, machine learning is expected to become a standard tool in the marketing industry. Every leading marketing company will likely have machine learning solutions at their disposal.
Before that time arrives, let’s explore some of the current applications of machine learning in marketing and examine its potential impact on the field.
For good reason, machine learning is becoming a hot topic in marketing analytics. It enables you to analyze marketing campaigns, assess their effectiveness, and identify areas for improvement. The same goes for website performance analysis. One of the most advanced tools in this field is Google Analytics (GA), which incorporates a machine learning algorithm called Analytics Intelligence.
According to Google, Analytics Intelligence is a set of machine learning features that help you understand and act on your data more effectively. It offers three essential functions:
The conversion probability feature is precious. It calculates the odds of a specific customer making a purchase, providing a personalized score for each user. Google's machine learning algorithm evaluates transactions and assigns a conversion probability score between 1 and 100, with one being the least likely and 100 the most likely to convert, typically over one month.
This allows marketers to estimate the likelihood of increasing or maintaining sales shortly.
As a machine learning tool, Analytics Intelligence becomes more accurate with increased data. But this isn't limited to your account alone - Google has one of the largest datasets of customer web activity. The more marketers integrate GA Analytics Intelligence into their campaigns, the more effective and precise the tool becomes.
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There are at least two essential ways to enhance customer experience with the aid of machine learning. First, machine learning applications can offer personalized product recommendations to help the customer find what they want. Imagine an eCommerce website with thousands of products.
Based on customers’ behavior and activity on your website, a machine learning algorithm can establish a given customer’s interests and preferences. Thanks to that, the same algorithm can recommend a product that matches their requirements.
For instance, it can be done with the usage of the TensorFlow and Google Cloud Platform[5]. TensorFlow is a machine learning algorithm with an API for training and predictions that resembles common machine learning tools. You can use it (along with GCP) to build a real-time recommendation system for your eCommerce website.
Another critical application is related with direct communication. Chatbots are the next milestone in customer service, and machine learning can improve them outstandingly! According to a recent study, conducted in Poland by the consulting company PwC[6], most customers perceive high-quality service as fast, problem-solving, and convenient. Customers estimate that these needs are best met by the channels in which the client has direct contact with the customer service. Internet chats were indicated by 77% of surveyed customers. And chatbots are a perfect way to make this channel available 24-7.
How can machine learning improve chatbot? With the database of previous interactions, it is possible to develop chatbot’s own consciousness of the text. This, in turn, can be used to “teach” ML algorithm how to “talk” with human customers and respond to various queries and problems.
There is still a lot to accomplish, but chatbots get better and better every year, so we can expect that, within the coming years, they will become self-sufficient.
Let's explore how machine learning algorithms can transform marketing. Here are four key ways machine learning is driving significant change in the industry, focusing on increasing sales and improving customer retention:
Personalized Recommendations: Machine learning can provide personalized product recommendations based on user behavior. For example, an eCommerce website with thousands of products can use algorithms like TensorFlow and Google Cloud Platform to recommend items tailored to individual preferences.
Improving Chatbots: Chatbots are becoming a crucial part of customer service. Machine learning enhances their ability to interact with users by analyzing previous interactions to improve their responses. According to a PwC study in Poland, customers value fast and effective service, which chatbots can provide 24/7. Machine learning enables chatbots to understand better and respond to customer queries, making them more efficient.
Survey Automation: Machine learning can expedite the development of new products and services by conducting and analyzing surveys more rapidly than human surveyors. This helps companies gather extensive market data quickly and create products that meet current demands. For example, a car manufacturer can use survey data to design vehicles tailored to regional preferences, such as four-wheel drives in the U.S. and hybrids in Europe.
SEO and Content Strategy: Machine learning can significantly impact SEO by identifying the types of content, questions, and headlines likely to attract clicks. Algorithms help estimate which content will perform best, thus improving search engine rankings and click-through rates. Additionally, A/B testing can be enhanced with machine learning to determine the most effective content variations. Machine learning can also analyze competitors' websites to refine your marketing strategies.
Marketing Automation Tools: Machine learning can now automate many marketing tasks. Tools like SALESmanago Copernicus use AI to automate activities such as Google Ads, Facebook Ads, email marketing, and discount systems. Copernicus analyzes transaction and behavioral data to provide personalized product recommendations and automate content delivery based on customer behavior.
Machine learning is a game-changer in marketing, offering enhanced customer service and more effective promotional strategies. You don't have to build everything from scratch if you're considering integrating machine learning into your marketing efforts. AIdentico can assist with tools, solutions, and expert guidance to help you achieve the best results with machine learning and AI.
We're here to discuss your challenges and ideas. Contact us today to explore how we can support your business with cutting-edge machine-learning solutions.
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Machine learning greatly enhances marketing by improving customer experiences, aiding product and service development, and optimizing content and automation. Research indicates that organizations using machine learning in marketing see over a 10% increase in sales of new products and services.
Machine learning enhances customer experience through personalized product recommendations and advanced chatbots. Algorithms analyze customer behavior to suggest relevant products and improve chatbots, enabling them to efficiently respond to customer queries and provide a more engaging experience.
In marketing analytics, machine learning helps analyze campaigns, evaluate their effectiveness, and identify areas for improvement. Tools like Google Analytics Intelligence leverage machine learning to answer questions about campaigns, analyze data for changes or opportunities, and model conversion probabilities to forecast future sales.
Machine learning algorithms can conduct extensive surveys and analyze the resulting data to understand customer preferences and market demands. This enables companies to create products and services that align with market needs, leading to more successful product development.
Machine learning algorithms estimate which types of content are likely to resonate with target audiences, enhancing SEO efforts. They also facilitate A/B testing to determine the most effective content variations, helping companies create more targeted and engaging content.
Machine learning automates marketing activities such as Google Ads, Facebook Ads, email marketing, and newsletters. Tools like SALESmanago Copernicus utilize AI to analyze transactional and behavioral data, offer personalized product recommendations, and streamline marketing processes for greater efficiency.
Yes, machine learning algorithms can analyze competitors' websites and marketing strategies. This allows companies to gain insights into competitors' tactics and adjust their marketing campaigns to improve effectiveness.
No, companies do not need to build machine learning infrastructure from scratch. Providers like Addepto offer tools, solutions, and consultancy services to help businesses implement and benefit from machine learning and AI in their marketing strategies.
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