Many companies - and the marketing teams that support them - are rapidly adopting intelligent technology solutions to encourage operational efficiency while improving the customer experience. Through these platforms, marketers are able to gain a more nuanced, comprehensive understanding of their target audiences. The insights gathered through this process can then be used to drive conversions while simultaneously easing the workload for marketing teams.
What is Artificial Intelligence (AI) Marketing?
AI marketing uses artificial intelligence technologies to make automated decisions based on data collection, data analysis, and additional observations of audience or economic trends that may impact marketing efforts. AI is often used in marketing efforts where speed is essential. AI tools use data and customer profiles to learn how to best communicate with customers, then serve them tailored messages at the right time without intervention from marketing team members, ensuring maximum efficiency. For many of today’s marketers, AI is used to augment marketing teams or to perform more tactical tasks that require less human nuance.
AI marketing use cases include:
- data analysis
- natural language processing
- media buying
- automated decision making
- content generation
- real-time personalization
It’s clear that artificial intelligence holds a vital role in helping marketers connect with consumers. The following components of AI marketing make up today’s leading solutions that are helping to bridge the gap between the expansive amounts of customer data being collected and the actionable next steps that can be applied to future campaigns:
Machine learning is driven by artificial intelligence, and it involves computer algorithms that can analyze information and improve automatically through experience. Devices that leverage machine learning analyze new information in the context of relevant historical data that can inform decisions based on what has or hasn’t worked in the past.
Big Data and Analytics
The emergence of digital media has brought on an influx of big data, which has provided opportunities for marketers to understand their efforts and accurately attribute value across channels. This has also led to an over saturation of data, as many marketers struggle to determine which data sets are worth collecting.
AI Platform Solutions
Effective AI-powered solutions provide marketers with a central platform for managing the expansive amounts of data being collected. These platforms have the ability to derive insightful marketing intelligence into your target audience so you can make data-driven decisions about how to best reach them. For example, frameworks such as Bayesian Learning and Forgetting can help marketers gain a clearer understanding of how receptive a customer is to a specific marketing effort.
Challenges for AI Marketing
Modern marketing relies on an in-depth understanding of customer needs and preferences, and then the ability to act on that knowledge quickly and effectively. The ability to make real-time, data-driven decisions has brought AI to the forefront for marketing stakeholders. However, marketing teams must be discerning when deciding how to best integrate AI into their campaigns and operations. The development and use of AI tools are still in early stages. Therefore, there are a few challenges to be aware of when implementing AI in marketing.
Training Time and Data Quality
AI tools do not automatically know which actions to take to achieve marketing goals. They require time and training to learn organizational goals, customer preferences, historical trends, understand overall context, and establish expertise. Not only does this require time, it also requires data quality assurances. If the AI tools are not trained with high quality data that is accurate, timely, and representative, the tool will make less than optimal decisions that do not reflect consumer desires, thereby reducing the value of the tool.
Consumers and regulating bodies alike are cracking down on how organizations use their data. Marketing teams need to ensure they are using consumer data ethically and in compliance with standards such as GDPR, or risk heavy penalties and reputation damage. This is a challenge where AI is concerned. Unless the tools are specifically programmed to observe specific legal guidelines, they may overstep in what is considered acceptable in terms of using consumer data for personalization.
It can be difficult for marketing teams to demonstrate the value of AI investments to business stakeholders. While KPIs such as ROI and efficiency are easily quantifiable, showing how AI has improved customer experience or brand reputation is less obvious. With this in mind, marketing teams need to ensure they have the measurement abilities to attribute these qualitative gains to AI investments.
Deployment Best Practices
Because AI is a newer tool in marketing, definitive best practices have not been established to guide marketing teams’ initial deployments.
Adapting to a Changing Marketing Landscape
With the emergence of AI comes a disruption in the day-to-day marketing operations. Marketers must evaluate which jobs will be replaced and which jobs will be created. One study suggested that nearly 6 out of every 10 current marketing specialist and analyst jobs will be replaced with marketing technology.
How to Use AI in Marketing
It’s important to begin with a thorough plan when leveraging AI in marketing campaigns and operations. This will ensure marketing teams minimize costly challenges and achieve the most value from their AI investment in the least amount of time.
Before implementing an AI tool for marketing campaigns, there are a few key factors to consider:
As with any marketing program, it is important that clear goals and marketing analytics are established from the outset. Start by identifying areas within campaigns or operations that AI could stand to improve, such as segmentation. Then establish clear KPIs that will help illuminate how successful the AI augmented campaign has been – this is especially important for qualitative goals such as “improve customer experience.”
Data Privacy Standards
At the outset of your AI program, be sure that your AI platform will not cross the line of acceptable data use in the name of personalization. Be sure privacy standards are established and programmed into platforms as needed to maintain compliance and consumer trust.
Data Quantity and Sources
In order to get started with AI marketing, marketers need to have a vast amount of data at their disposal. This is what will train the AI tool in customer preferences, external trends, and other factors that will impact the success of AI-enabled campaigns. This data can be taken from the organization’s own CRM, marketing campaigns, and website data. Additionally, marketers may supplement this with second and third-party data. This can include location data, weather data, and other external factors that may contribute to a purchasing decision.
Acquire Data Science Talent
Many marketing teams lack employees with the necessary data science and AI expertise, making it difficult to work with vast amounts of data and deliver insights. To get programs off the ground, organizations should work with third party organizations that can assist in the collection and analysis of data to train AI programs and facilitate ongoing maintenance.
Maintain Data Quality
As machine learning programs consume more data, the program will learn how to make accurate, effective decisions. However, if the data is not standardized and free of errors, the insights will not be useful and can actually cause AI programs to make decisions that hinder marketing programs. Prior to implementing AI marketing, marketing teams must coordinate with data management teams and other lines of business to establish processes for data cleansing and data maintenance. When doing so, consider the seven essential data dimensions:
Selecting an AI Platform
Selecting the right platform or platforms is a crucial step in getting an AI marketing program off the ground. Marketers should be discerning in identifying the gaps that the platform is trying to fill and select solutions based on capabilities. This will revolve around the goal marketers are trying to achieve – for example, speed and productivity goals will require different functionality than tools used to improve overall customer satisfaction with AI. One thing to keep in mind when selecting a tool is the level of visibility you will need regarding why an AI platform made a certain decision. Depending on the algorithm in use, marketing teams may get a clear report on why a certain decision was made and which data influenced the decision, while algorithms working on a more advanced level with deep learning may not be able to give as definitive reasoning.
Benefits of Leveraging Artificial Intelligence in Marketing
There is a myriad of use cases for AI in marketing efforts, and each of these use cases yields different benefits such as risk reduction, increased speed, greater customer satisfaction, increased revenue, and more. Benefits may be quantifiable (number of sales) or not quantifiable (user satisfaction). There are a few overarching benefits that can be applied across AI use cases:
Increased Campaign ROI
If leveraged correctly, marketers can use AI to transform their entire marketing program by extracting the most valuable insights from their datasets and acting on them in real time. AI platforms can make fast decisions on how to best allocate funds across media channels or analyze the most effective ad placements to more consistently engage customers, getting the most value out of campaigns.
Better Customer Relationships & Real-Time Personalization
AI can help you deliver personalized messages to customers at appropriate points in the consumer lifecycle. AI can also help marketers identify at risk customers and target them with information that will get them to re-engage with the brand.
Enhanced Marketing Measurement
Many organizations have trouble keeping pace with all of the data digital campaigns produce, making it difficult to tie success back to specific campaigns. Dashboards that leverage AI allow for a more comprehensive view into what is working so that it can be replicated across channels and budgets allocated accordingly.
Make Decisions Faster
AI is able to conduct tactical data analysis faster than its human counterparts and use machine learning to come to fast conclusions based on campaign and customer context. This gives team members time to focus on strategic initiatives that can then inform AI-enabled campaigns. With AI, marketers no longer have to wait until the end of a campaign to make decisions, but can use real-time analytics to make better media choices.
7 Examples of Artificial Intelligence in Marketing
AI is being used in marketing initiatives in a multitude of use cases, across a broad array of industries including financial services, government, entertainment, healthcare, retail, and more. Each use case offers different results, from improvements to campaign performance, to enhanced customer experience, or greater efficiency in marketing operations.
There are numerous ways businesses can take advantage of machine learning to create a more comprehensive marketing plan. Consider the following:
1. Bidding on Programmatic Media Buys
A problem that marketing teams often encounter is deciding where to place advertisements and messaging. Marketing teams can create informed plans based on user preferences, but these teams are not flexible or agile enough to alter the plan in real time based on the latest consumer information. AI is being used by marketers to mitigate this challenge through programmatic advertising. Programmatic platforms leverage machine learning to bid on ad space relevant to target audiences in real time. The bid is informed by data such as interests, location, purchase history, buyer intent, and more. This enables marketing teams to target the right channels at the correct time, for a competitive price. Programmatic buying exemplifies how machine learning can increase marketing flexibility to meet customers as their needs and interests evolve.
2. Select the Right Message
Across channels, different consumers respond to different messages – some may resonate with an emotional appeal, some humor, others logic. Machine learning and AI can track which messaging consumers have responded to and create a more complete user profile. From there, marketing teams can serve more customized messages to users based on their preferences. For example, Netflix uses machine learning to understand the genres a certain user is interested in. It then customizes the artwork that user sees to match up with these interests. On the Netflix Tech Blog, they explain how they use algorithms to determine which artwork will most entice a viewer to watch a certain title, saying:
“Let us consider trying to personalize the image we use to depict the movie Good Will Hunting. Here we might personalize this decision based on how much a member prefers different genres and themes. Someone who has watched many romantic movies may be interested in Good Will Hunting if we show the artwork containing Matt Damon and Minnie Driver, whereas, a member who has watched many comedies might be drawn to the movie if we use the artwork containing Robin Williams, a well-known comedian.”
Credit: Netflix Tech BlogWhen AI and machine learning are used, these platforms can gather valuable data on customers that allow marketing teams to increase conversion rates and improve the customer’s experience. Marketing teams can then analyze all of this data to create a more nuanced view of the customer, even considering additional factors such as if a user would have watched a title regardless of the image, and how that plays into future messaging.
3. Granular Personalization
A highly granular level of personalization is expected by today’s consumers. Marketing messages should be informed by a user’s interests, purchase history, location, past brand interactions, and a host of other data points. AI helps marketing teams go beyond standard demographic data to learn about consumer preferences on a granular, individual level. This helps brands create curated experiences based on a customer’s unique tastes. For example, Spotify uses AI to create customized playlists based on what a customer has listened to in the past, current hits across genres, and which music is being talked about. It uses these datasets to create customized playlists for users and to create genre playlists based on artists that appear in conversation, in articles, etc. This has helped Spotify to become a top streaming service and emphasize customer experience through personalization.
Another trend based on AI-enabled personalization is atomic content. Here, AI learns customer preferences and pulls pieces from a library of content to create a customized email or offer for a client featuring relevant images, videos, or articles.
4. Chatbots and Conversational Experiences
With the development of natural language processing through AI, chatbots are now being used to augment customer service agents. Customers with more basic queries can refer to chatbots which will give immediate, accurate answers. They will be able to leverage past questions and historical data to deliver personalized results. This gives time back to customer service agents to work on complicated requests that need more human nuance.
5. Predictive Marketing Analytics
With so much data coming, marketing teams are having a hard time actually deriving insights from it. AI allows marketing teams to make the most of this data using predictive analytics, which leverages an assortment of machine learning, algorithms, models, and datasets to predict future behavior. This can help marketing teams understand the types of products a consumer will be looking for and when – allowing them to position campaigns more accurately.
For example. Amazon uses predictive analytics to suggest products to consumers based on past purchases and behaviors, increasing conversions and customer satisfaction. AI can also be used to help marketing teams more accurately track attribution, allowing teams to see which campaigns contributed most to ROI.
Credit: Woo Commerce
6. Marketing Operations
Another key use case for AI in marketing is to increase efficiency across various processes. AI can help to automate tactical processes such as the sorting of marketing data, answering common customer questions, and conducting security authorizations. This allows marketing teams more time to work on strategic and analytical work.
7. Dynamic Pricing
AI can help make brands more competitive by enabling dynamic pricing. AI platforms can suggest optimal prices for products in real time by evaluating huge quantities of historical and competitive data. This strategy has been especially effective in retail. It allows brands to adjust prices to reflect demand for certain products, boost sales, and edge out the competition.
Predictions and Trends for AI Marketing
While AI is still largely new to the marketing space, it promises to only grow in popularity. There are a few AI trends marketers will see over the next few years and should begin to adapt to:
AI is Growing:
- Gartner has predicted that by 2022, AI will replace about 33% of data analysts in marketing.
Teams Will Scale Through AI
Marketing teams will be put under increased pressure to demonstrate marketing value and ROI to executive stakeholders. Teams will leverage AI solutions to drive these targets and better allocate funds towards successful campaigns and provide the marketing metrics that demonstrate the value of campaigns.
Marketing Leaders Who Don’t Leverage AI Will Be Replaced By Those Who Do
According to Gartner, those responsible for marketing insights will no longer be as competitive in this changing marketing landscape. The majority of those surveyed by Gartner employ AI solutions in their marketing strategy or are planning to. Only 13 percent do not see a use for it in the next three years.