AI in marketing: explanation, opportunities, examples
- Günther Klee
- 15.06.2023
- Digitalisierung
Table of contents
What is artificial intelligence (AI) in marketing?
In the context of marketing, artificial intelligence refers to the use of algorithms and statistical modelsto support marketing employees or even to automate tasks completely. Which method(s) from the broad field of AI are actually used depends on the task. Algorithms from the field of machine learning (including deep learning) are very often used to cover a wide range of tasks, from analysis, prediction, and optimization to, increasingly, content creation. Machine learning has a wide range of applications, but the same applies here: “If you have a hammer, every problem looks like a nail.” Often, very good results can be achieved with ‘traditional’ statistical approaches (e.g., (z.B.: forecasting models) ) with less effort. Such models are much easier for ‘real’ employees to understand and interpret than an automatically generated machine learning model or even a deep neural network.
What opportunities does AI offer in content marketing?
AI can take over the creation of content such as articles, product descriptions, and social media posts to a certain extent without human support. In addition, generated content can be tailored to individual persons in a granular way. Based on collected data such as preferences, behavior or demographic information, AI can create personalized product recommendations and content. The distribution of content can also be optimized with the help of AI: by analyzing user behavior, it is determined how and when content should be published. An AI can assist a human employee in content creation. A common use case is content optimization - for example, AI analyzes which types of content work best for which target groups. Using A/B tests, AI can gain insights into the performance of different variants and make recommendations. Content can also be optimized with regard to search engines by using AI to perform keyword analysis and create meta tags and meta descriptions.
AI tools in content marketing: creating texts and articles
- ChatGPT… probably needs no introduction - it can generate texts and answer questions, with the results being output in natural languages. In addition to writing articles, posts, letters, etc., the chatbot can also be used to output code sections or optimize texts according to quality criteria entered.
- Grammarly is a grammar and spelling checker that uses AI to detect errors in texts and then provides suggestions for improving grammar, spelling, and writing style.
AI tools in content marketing: content personalization and product recommendations
- Sitecore Personalize creates personalized content and evaluates its performance (A/B tests) to analyze the individual preferences and behavior patterns of users on different platforms and to play out customized content accordingly.
- Clerk.io is an AI plugin for creating personalized product recommendations and for navigating a web shop. Machine learning algorithms are used to generate personalized recommendations based on user behavior.
What opportunities does AI offer in online marketing?
The great strength of AI tools lies in their ability to analyze huge amounts of data – enabling them to identify trends, recognize patterns and make predictions. AI in its various forms is an important tool for making a data-driven marketing strategy feasible in practice. AI tools find and analyze relevant factors to create and optimize target group profiles . This enables ads to be targeted more precisely to those users who are most likely to respond to them (and subsequently convert).
By creating and delivering personalized ads based on users' individual behavior and interests, ads that are more relevant and appealing to the user are created, thus increasing conversion rates. Large amounts of data are analyzed in near real time, providing instant insights into the performance of ads and campaigns. AI tools can use machine learning to anticipate the likelihood of clicks/conversions for specific ads and target groups. This helps marketers a great deal with budget planning and prioritizing promising campaigns.
AI tools in online marketing: target group analysis
- Google Analytics is well known and uses AI to segment target groups based on various criteria (e.g., demographic characteristics, behavior, interests). It focuses mainly on analyzing website and app data to gain insights into user behavior and to measure the effectiveness of marketing campaigns.
- Sitecore CDP allows the consolidation of customer data from internal (apps, shops, websites, etc.) and external (partner companies, Google, etc.) sources. Its focus is on centralized management, with the aim of a comprehensive analysis of target groups and their customer journeys. Machine learning is used to detect patterns and similarities in large amounts of data, but also to enable the consolidation of structurally different data sets.
AI tools in online marketing: Performance
- AdRoll is used for display advertising and retargeting – with the help of AI algorithms, ads are optimized based on user behavior. By evaluating data such as click behavior, interests, or location of users, it is able to play out highly relevant ads.
- Google Ads uses AI in a variety of ways: for example, in smart bidding, bids are automatically adjusted to improve performance. Responsive search ads, on the other hand, use the technology to evaluate different ad combinations and achieve the best results.
Marketing examples of AI use cases
Example: Predicting customer lifetime value
An e-commerce company wants to predict the CLV for its customers in order to develop personalized marketing strategies and thus improve customer loyalty. The company collects historical data about its customers, including information such as buying behavior, order history, purchase frequency, average order value, interactions on the website, etc. Various algorithms can be used to predict the CLV. One possible approach would be a neural network that is trained on the historical customer data. The neural network analyzes the characteristics of the customers and learns complex relationships between these characteristics and the future CLV. After training, the trained model can be used to predict the CLV for new customers. Based on the customer data that the company has about a new customer (e.g. first order, product category, geographic information, etc.), the model can estimate the future CLV for this customer. Subsequently, companies will develop personalized marketing measures to promote customer loyalty, implement cross-selling strategies or make discount offers that increase or maximize CLV.
Example: Cancellation prevention through AI
A streaming service provider wants to use artificial intelligence to develop customer identification and prevention strategies to retain customers who are considering canceling. The company collects extensive data on customer behavior: usage behavior, interactions with the platform, content preferences, and much more. A machine learning algorithm is developed, or more precisely, trained on the collected data. The algorithm learns to recognize patterns and correlations between various customer characteristics and the likelihood of cancellation. For example, the model could determine that customers who prefer certain genres have a higher level of loyalty to the streaming service and are less likely to cancel. The company's goal could be to use the customer data of a particular customer (e.g., inactivity, decrease in usage frequency, change in interaction patterns) to calculate the likelihood of cancellation. Based on these predictions, targeted measures are taken to reduce the likelihood of cancellation: for example, an email campaign with individually tailored special offers.
Example: Price optimization through AI
An online travel agency wants to optimize its pricing strategy for airline tickets to maximize revenue. The company has access to extensive data on past bookings, prices, demand, seasonality, routes, and other relevant factors. One possible approach is to train a machine learning model that analyzes the price elasticity of demand. The model can determine how booking rates behave at different prices and which factors influence the price sensitivity of customers, e.g. travel trends, holiday periods or the popularity of certain destinations. Based on these insights, the model can develop dynamic pricing strategies. It can make price adjustments in real time to maximize demand and optimize the utilization of flights. Factors such as aircraft utilization, the competitive situation, seat availability and individual customer preferences are taken into account. The company can also apply the principle of dynamic pricing, in which prices are automatically adjusted based on the principle of supply and demand. Competitor prices are monitored and incorporated into the optimization process, resulting in competitive prices with the highest possible profit margin.
Conclusion: AI-based marketing is becoming more and more prevalent
Artificial intelligence is making inroads into all companies. The use of AI in marketing is particularly advantageous for companies that generate and process large amounts of customer data. This is typically the case in the following industries:
- E-commerce companies: e.g., for product recommendations, price optimization, inventory management, fraud detection
- Financial service providers: e.g., for analyzing customer behavior, fraud detection, risk assessment, personalized financial recommendations
- Telecommunications companies: e.g., for segmenting customer groups, personalized offers, churn prevention, optimizing customer experiences, chatbots in customer service
- Media companies: e.g., for personalized content, recommendation algorithms, target group analysis, content creation.
- Travel and hospitality: e.g., for personalized offers, price optimization, customer segmentation, booking via chatbot interaction, speech recognition technologies
- Healthcare: e.g., for personalized health recommendations, patient segmentation, early detection of diseases
These prime examples are pioneers in AI-supported marketing.
However, the technology is by no means limited to these industries, and companies outside these sectors can also benefit significantly - provided they have sufficient amount of data of sufficient quality on customers and prospects.