All about AI Marketing: Definition, Use Cases, Tools
In recent years, a growing number of marketers have turned to technology to streamline their operations and enhance user experience and satisfaction. Artificial intelligence-powered marketing platforms are an example of such innovations helping businesses worldwide to optimize planning processes and a lot more. These platforms allow marketers to gather more detailed information about potential customers, thus allowing them to generate leads more effectively and lessen their workload.
What is Artificial Intelligence Marketing?
AI Marketing means implementation of AI-based tools that assist marketers in their operations or even replace them. Artificial intelligence in marketing can be used to automate decision-making, data collection, processing, and analysis, which saves time for more complex tasks that cannot be performed by a machine.
Examples of AI Application in Marketing:
- Data Analysis: Marketing teams now can rely on AI-powered tools to automatically sort through enormous amounts of data from different systems and media campaigns. Not only are algorithms tireless, but they also are able to take into account numerous factors at once and identify a pattern that a human might miss. And that is exactly what you want when you have to understand how advertising, audience demographics, macroeconomic situation, seasonality and other factors affect the business KPIs. AI-based data analysis helps to identify the contribution of marketing channels to sales or the number of new customers faster and more accurately. AI is also implemented in ETL systems to process and categorize data according to set taxonomies. Their main task is to identify what ad campaign or format specific information is related to.
- Natural Language Processing (NLP): It is used by customer support chatbots and personalization tools to create a human-like language that enables instant communication with clients. Chatbots proved helpful to marketers at the customer acquisition stage and are used in cross sales and upsales strategies. They can answer most questions they receive and reduce customer acquisition cost by getting more people into your sales funnel than an employee would.
- Media Buying: The decision-making process for selecting which media channels and formats to use in order to reach a business’s intended audience and achieve a maximum return on marketing investments. To make sure such a system can function in the cookieless world, you need a DMP system that would collect user profiles for retargeting campaigns and send this data to media buying systems.
- Automated Decision-Making: AI assists businesses in making informed decisions regarding company expansion or marketing strategies. AI-based solutions for media mix management can perform budget optimization and generate forecasts after analyzing historical data.
- Content Generation: Utilized by marketing teams to produce concise yet complete material for campaigns, such as video captions, newsletter message lines, internet text, articles, and more.
- Real-time Personalization: Real-time personalization means altering a source of marketing information to encourage a certain activity, like visiting a webpage and making a purchase, by using data from prior customer interactions with the resource to determine current preferences.
Types of AI Marketing Solutions
Marketers who use artificial intelligence to forge relationships with consumers or optimize spends have become increasingly common. The following elements and methods of AI-powered tools help digital marketers to bridge the gap between vast amounts of consumer or sales data and practical next steps in their upcoming campaigns.
It’s the process of teaching AI algorithms by feeding them information. By analyzing the data, algorithms gain experience and knowledge about how to act in various situations. Such trained programs enhance digital marketing campaigns based on prior results analysis and save time and labor resources.
Big Data and Analytics
The wide usage of digital media generates big data, which enables marketers to precisely distribute value across channels and evaluate the effectiveness of their efforts. The current data excess makes it challenging for many marketers to decide which data sets are worth taking into account; however, artificial intelligence can help in evaluating the data, choosing the most significant components for upcoming marketing activities, and quickly sorting through all of that data by reducing it to the most important information. Digital marketers can use tools powered by artificial intelligence to automate the process of analyzing large amounts of data and obtain insights about their target market and marketing spend.
AI Marketing Platforms & Tools
Marketers can use Bayesian models to assess each customer’s level of openness to various digital marketing initiatives. Marketing Mix Modeling is another reliable tool for evaluating the effectiveness of advertising. AI takes this tool to a completely new level: now you can see the contribution of each media channel to sales or other business KPIs, choose an optimal budget allocation for future periods and create a data-driven media plan. This detailed comparison of top AI-based marketing mix modeling solutions will help you select the one that suits your requirements better.
Challenges for AI Marketing
Artificial intelligence has proven to be useful for many marketing teams that enables them to make timely and data-driven decisions. But since this technology is still very young and continues to develop, there are some challenges associated with integrating AI-based tools into your marketing management process.
To become a valuable asset for a marketing team an artificial intelligence tool needs to be trained and taught just like humans need to study a subject to become experts in it. Simply put, AI the training process consists of data consumption and analysis, that is why the outcome completely depends on the data quality. If an AI-solution is trained with data that is not high-quality, complete and accurate, the results it will generate are going to have zero value. To make useful AI models, one needs to collect a statistically significant amount of data for various market conditions and marketing activities. In underrepresented combinations, AI models can make unreliable predictions. However, in practice, there is usually a lack of diversity in combinations, so marketers should take into account uncertainty of predictions when relying on AI-driven marketing planning.
Marketing teams must make sure that they are using customer data in compliance with laws like the GDPR in order to avoid significant fines and harm to the brand reputation. This is because consumers are becoming more aware of how their personal data is used and governments are enforcing stricter regulations on how data is stored and shared. Make sure that the artificial intelligence marketing tools you use adhere to active regulations when it comes to client data management. It’s also important to keep up with changes in legislation, because there have been updates recently. For example, Google Analytics, which includes some AI features, was even banned in some European countries in summer 2022 and in the rest of the world its use is becoming limited because of the phase-out of third-party cookies.
Stakeholders and CFOs usually want to see numbers that objectively demonstrate how useful an initiative is. While you can easily calculate ROI or the number of new customers, it is quite difficult to estimate the impact of AI on customer experience or brand reputation. That is why marketers should make sure they have the necessary evaluation tools to attribute such results to their AI investments. Usually most people want quick returns, but some marketing activities work in the long run. Marketers should be ready to defend the importance of the long-term benefits, define timeframes and some quantitative results.
Deployment Best Practices
There aren’t yet any best practices of AI marketing tools deployment because AI is still a relatively new technology.
Keeping up with Changing Marketing Landscape
A new age in marketing has arrived because of artificial intelligence. Marketers need to stay on top of both the roles that will disappear and the new ones that will emerge. According to one research, roughly half of occupations currently held by marketing analysts and professionals will eventually be replaced by marketing technology.
Implementing an AI solution
Marketers should first create a thorough plan for incorporating AI into their marketing strategies and operational procedures in order to guarantee a return on their investment. As a result, they will be able to avoid costly mistakes and start using their new technology right away. Here are some practical tips that will help you get the most of AI marketing.
Decide on Goals
Setting precise objectives that will help you measure success further on the way is crucial when implementing any initiative and AI marketing tools are no exception. For example, if your goal is to enhance customer experience, you might want to identify clear KPIs that will show where you stand in terms of reaching your goal. Later these KPIs will be used to assess the effectiveness of AI-enhanced marketing strategy. Business KPI examples are user acquisition or retention as well as results that are not so easily quantified, such as brand awareness, intention to buy or an NSP score. So when deciding on the goals you should also understand how you are going to measure the results.
Data Privacy Standards
Any AI marketing platform you use must strictly adhere to privacy laws while using customer data and must only gather the information required for marketing activities. At the same time make sure the tool guarantees the best result under the existing circumstances and its performance is not deteriorated by privacy restrictions.
Data Quantity and Sources
In order to start doing AI marketing, digital marketers require extensive access to data. By consuming it an AI tool learns everything about customers, media placement history, sales and other important information. After that it can assist marketers in content generation or budget optimization, whatever task it was intended for. Train data sources include the company’s CRM, website, application, advertising agency, etc. Incorporating second and third-party data covering geography, seasonality, as well as other factors that might affect consumer actions will guarantee a more comprehensive training dataset.
Hire Data Science Experts
For marketing teams that don’t have employees with experience in data science and AI, handling vast quantities of data presents a variety of issues. Businesses need expertise from outside sources to manage successful AI-focused marketing initiatives, including data gathering, analysis, and tool setup. Usually AI-based solutions offer consultation services or provide experts to give their assistance and guidance about the tool. There are also startups where you can hire a part-time expert in such spheres as marketing, AI, data science, etc.
Maintain Data Quality
As an AI-enabled marketing system collects and goes through greater volumes of data, its ability to make decisions will increase. For an AI marketing program to be successful, the data must be accurate and standardized. Before using any AI initiatives, properly set up processes for data collection and categorizing. Data management teams can create methods for regular maintenance and cleaning. Below you can see the seven essential components of high-quality data.
How to Choose an AI Marketing Platform
The widespread consensus in the digital sector is that including AI in current initiatives will improve the success of campaigns. Identify the areas in your work process that can be improved and see if it can be done with an AI-powered solution. If some operations are too labor-intensive or not effective enough, there is a potential place for AI to step in.
It’s also a huge advantage if you understand how the platform’s algorithms function. Knowing everything about the test sample and the limitations will help you to interpret the obtained results. If you know how a system works, you will see the reasoning behind its decisions and forecasts. Be careful about using solutions that lack visibility into their functioning.
Benefits of Using AI in Marketing
Companies looking to integrate artificial intelligence marketing into their digital initiatives might benefit from a variety of scenarios. For instance, AI marketing may reduce risk, speed up processes, increase customer satisfaction, and raise sales. Some improvements, like sales volume, can be measured, while others are not so easily evaluated (for example, customer satisfaction).
Increased Campaign ROI
Marketers can utilize AI technologies to examine historical campaign data to increase advertising effectiveness and engage more customers. By using data to gather insights and acting on them in real-time, marketers will be able to optimally allocate budgets across media channels and increase return on investments. Here it is also crucial to understand on what methodology an AI marketing platform is based.
Improved Customer Experiences with Real-time Personalization
Marketers can use artificial intelligence to serve a user a customized offer at the right point based on their profile or purchase history. For example, you can send a message with a discount coupon to a client that added items to their cart but didn’t buy them. This might stimulate them to go through with the purchase. Such personalized offers can also stimulate brand loyalty and customer satisfaction.
Better Marketing Measurement
Digital marketing activities generate significant amounts of data, which firms can find overwhelming to keep on top of, and sometimes, find it hard to attribute the results to specific activities. Relying on AI-generated dashboards helps faster analyze campaign performance. It is even better if your platform can take into account offline channels too and identify the mutual effect digital and traditional media have on each other.
Make Decisions Faster
With machine learning and AI, marketing departments may now use tactical data analysis for quicker decision-making. Team members have time to concentrate on projects which will eventually guide marketing efforts assisted by machine learning. Marketers can use AI-generated forecasts to select the most appropriate form of media instead of delaying their thinking up until the campaign end. AI-powered tools may especially come in handy at times of crises, when the situation changes dramatically and established procedures won’t do anymore. With the right tool you can take into account such factors as global inflation or the COVID-19 pandemic.
10 Applications of Artificial Intelligence in Marketing
Marketers leverage AI marketing in a range of industries, including finance, politics, leisure, medical, and commerce. Below you can see ten AI marketing use cases that enhance marketing performance.
1. Bidding for Programmatic Media Buying
Two reasons for which you should use AI-powered Programmatic Media Buying are it’s fast and flexible. Discussions about media plans or optimal placement spots can take hours, and AI on the contrary makes that decision in less than a second. Artificial intelligence analyzes a user’s search history, location and other factors to bid for ad space relevant to target audiences in real-time. Buying advertising in real-time based on data gathered on a buyer’s preferences is one example of how machine learning can increase adaptability. The same technology allows marketers to synchronize TV commercials and digital advertising. It helps to collect search data and direct users to lead generation forms or call centers.
2. The Right Approach
When companies generate marketing messaging, in an effort to persuade customers to purchase the good or service, marketers may appeal to their impulses, sense of humor, as well as motivation. Machine learning and AI marketing can identify which communications clients have responded to and build a more thorough personal profile. As a result, marketers may tailor communications to their consumers’ needs and preferences.
3. Granular Personalization
Consumers today expect highly customized experiences. Marketers should take a customer’s preferences, past purchases, location, and additional data factors into consideration when creating messages. Thanks to AI that goes beyond conventional demographic data, marketers may now acquire a more detailed, customized insight into consumer requirements and wants, helping businesses in developing experiences based on the distinctive preferences of their clients. Another example of AI-enabled personalization is atomic content, or tiny morsels of content. This customized content includes relevant images, videos, and posts. You could have come across personalized playlists on music streaming services, well, if you liked it, you should thank AI for that.
4. Using Chatbots
Chatbots may be programmed to respond to everyday interactions, relieving precious customer service staff. They can thus dedicate greater time and care to urgent and more complex situations.
5. Predictive Marketing Analytics
Marketing teams may use customer data they get every day more effectively with predictive analytics, as this combines machine learning, algorithms, and models, as well as forecasting datasets. Digital marketing professionals may better place ads when they’re more aware of when or where consumers will look for them. For example, a marketplace can show users ads for things they are likely to need based on their purchase history. A bank may offer a renovation loan to clients that have taken out a mortgage loan.
6. Marketing Operations
The use of artificial intelligence by marketing teams can boost productivity and simplify processes. AI enables marketers to automate tasks like categorizing marketing data, responding to frequently requested questions, and carrying out security authorizations. This makes time available for more strategic and analytical tasks. AI tools can analyze workload that marketing teams currently have and distribute tasks more evenly.
7. Dynamic Pricing
A brand’s competitiveness can be increased by AI by utilizing demand pricing. Huge volumes of competitive and historical data can be analyzed by AIs to recommend current prices for products. Retail businesses have found this method to be very beneficial for driving up sales and differentiating themselves from rivals. To accommodate consumer demand for particular products, they can even change prices. AI can also analyze brand’s competitors and offer the best price that will result in conversion.
8. Next Best Offer
Next best offer or NBT is a highly personalized offer that is delivered to a user at the most suitable time, via the most convenient channel. This approach is based on predictive analytics where AI algorithms process past consumer data and try to guess what they will want even before the idea comes to their mind.
9. AI generated images and content
An AI text generator introduced by Alibaba can write hundreds of pages in a second, and numerous brands are already using it to write product listings on the marketplace. AI tools can also be used to write SEO texts. And to get an image for an ad you just need to type what you want to see, and an AI algorithm will turn your words into a picture.
10. Native advertising platforms
AI analyzes content on native ads platforms and finds the best spots to insert a link to a product or service.
The future of AI
As you can see AI-powered tools are widely used to perform different marketing tasks even though it’s a new technology. It seems that we will rely on artificial intelligence even more in the future. According to Gartner by 2022, AI will replace about 33% of data analysts in marketing. If you are still not using AI marketing platforms to analyze customer data, write emails, optimize budget allocation and increase ROI, it’s probably the right time to start.