The AI (artificial intelligence) industry is growing rapidly and advancing very fast. The current pace of advancement in computer vision, natural language processing, deep learning neural networks, machine learning algorithms are growing at an exponential rate. There is a great deal of opportunity and real value in leveraging these technologies and improving the efficiency and effectiveness of today’s online marketing efforts.
Read in this article:
- Define AI in marketing
- Common AI applications
- Advantages and Disadvantages
- Determine the ROI of AI marketing
- Get started with AI in marketing
AI marketing refers to the use of advanced machine learning (ML) algorithms to automate marketing functions, maximize return on ad spend, and achieve better targeting while personalizing messaging based on user preferences. It is also known as ML marketing.
Companies are realizing that they need another layer of sophistication in their digital marketing efforts to maintain competitive advantage for what will be an increasingly crowded marketplace.
Insightful marketers are beginning to understand the mantra “Technology drives decisions.” In a world where there’s been a 250% increase in targeted advertising from 2015-2021, marketers must adopt new innovative technologies and approaches in order become more efficient and stay one step ahead — often by automating or utilizing AI powered solutions.
What is Artificial Intelligence Marketing?
In its simplest form, AI for marketing means the use of intelligent computer systems to make and implement marketing decisions without human intervention.
Artificial intelligence, especially in the digital marketing world, has been defined in a number of different ways, but generally speaking AI is sometimes considered to mean any relatively long-term goal that is achieved through systematically and intelligently applying expert systems (programs that write and improve themselves).
From this broader definition we can recognize that AI in marketing and advertising can encompass a number of specific applications with varying degrees of sophistication.
Here is how we define AI in marketing:
Artificial Intelligence Marketing (AIM) is the use of ML and AI enabled algorithms to automate, enhance and scale the digital marketing efforts of a company. The goal of AIM is to elevate marketing activities by predicting consumer behavior and delivering increasingly relevant messaging, products and offers to the consumer.
The main types of AI used in marketing are:
- Natural language processing (NLP)
- Predictive analytics
- Customer sentiment analysis
- Deep learning systems
- Recommendation algorithms
- Computer vision
Common Applications of AI in Digital Marketing
AI has the potential to completely change how we approach digital marketing and advertising. In fact, if you look at how important data is in business today, it’s clear that artificial intelligence will be the backbone of making sense of all this data.
AI can be used to augment the online marketing and advertising capabilities of a company as well as assist traditional marketing activities.
For example, AI lets personalize campaigns based on user preferences, determine marketing campaigns that are most likely to meet business objectives, develop predictive models for future outcomes, and help determine the best time to market a given piece of content.
More specifically, AI can be used in marketing automation software to automate tasks that were previously performed manually by marketing professionals. This ranges from customer segmentation, lead management, campaign creation and optimization, social media marketing, lead nurturing, real-time bidding management.
— AI for customer segmentation
From a marketing perspective, it is important to determine what segmentation of customer populations will generate the most profitable results. It’s no longer a question of “how do we market to everybody?” The answer used to be “broadcast a message” and hope that it appeals to your target market. This can come in the form of paid advertising, search engine optimization, or building out a comprehensive social media presence.
Now, it’s about creating highly targeted advertising that allows you to pinpoint your ideal customer. With AI, you can create highly targeted and hyper personalized online ads that speak to specific groups of people or even special individuals, based on their interests and buyer intents. These potential customers will see your ads when they are more likely to make a purchase, which increases the likelihood that they will convert into actual customers.
— AI for campaign automation and optimization
Artificial intelligence and marketing automation go hand in hand. According to a 2021 report from Adobe, “Automated campaign management is evolving from a fixed set of rules to the deep learning based intelligent systems that can adapt and learn from changes in audience”.
In the future, this AI will give marketers the ability to track, segment and personalize customer journeys based on their emotional traits. This means that campaign automation will move from being a hands-off process involving automated tasks to being an interactive one involving human participation.
For example, IBM Watson uses deep learning networks to recommend the best type of content to publish on a given day, based on historical performance and up-to-date statistics. If an email brings in more new subscribers than you were expecting, you can use AI to automatically create a new email campaign that better targets that audience. You can also use it to provide more relevant information about your listeners without having to send out numerous messages with the same reworded information.
.— AI for social media marketing
AI is an excellent way to augment the marketing mix for social media on LinkedIn, Twitter, YouTube, TikTok and Facebook.
Social media is still primarily considered a place to build brand awareness, invite potential customers to discover your product, and gain feedback. But AI is starting to change the way social media is used.
Using social media insights, marketers can learn how to craft content that optimizes their marketing campaigns. Right now, this is primarily being done using AI to identify key influencers who are relevant to your target audience. You can use this information to optimize your social media marketing campaigns by identifying the best places to post content and utilize influencers’ personal networks.
Deep learning, which allows machines to learn without being explicitly told what to do, generates insights about potential customers’ emotional states.
— AI for customer experience
AI can be used to improve customer experience (CX) in a number of ways.
Customer experience optimization is the term used to describe the work that marketers do to optimize the experience that customers have with their brand. The goal is to make sure that your customer’s interactions with your company are seamless; they should know what to expect at every stage of the CX process and how your brand will interact with them throughout.
For example, an AI system could automatically generate an email template for each new customer, based on their history and preference data.
— AI for lead scoring
AI can be used to improve the accuracy of lead scoring in a number of ways.
For example, AI could create bespoke lead scoring models that work much better at distinguishing between potential customers and non-customers, based on the amount of information they have provided about their intents and preferences.
You could also use AI to automate decisions around the quality and price level of leads. In this way you do not have to put time and effort into manually choosing the best leads for each customer opportunity.
— AI for SEO
Artificial intelligence marketing technologies can be used to improve an SEO strategy. AI can be used to analyze the search queries of your audience and the content they are sharing, so you know how to target your keyword research and content production efforts.
AI analyzes consumer interest, dominate search terms, and identify topics that are trending on Google so you know what subjects are relevant to your industry and which keywords you should focus on in order to boost awareness about your brand.
— AI for lead nurturing
Lead nurturing is a digital marketing activity that takes place after a potential customer has expressed interest in your product and performed an initial action such as filling out a form or downloading an e-book. It typically involves sending out a series of emails that extend the sales automation AI cycle and encourage further action.
AI can be used to create new lead nurturing campaigns with variable content. For example, you could do something like auto-generate a sales video based on the buyer’s interests and intended product use which you might not otherwise have known. You could also create automated content such as email courseware or increase your online availability without requiring an email address from a specific user.
— AI for marketing predictive analytics
Predictive analytics is a marketing modelling activity that uses historical data to make predictions about future outcomes. AI can be used to create predictive models to estimate the likelihood that a customer will respond to a specific piece of content or that a given product/service will succeed in a given market.
There are many different AI applications that can be used to augment or optimize marketing and advertising initiatives. AI can provide a competitive advantage, automate the mundane tasks that take up a lot of time and effort, and streamline a number of end-to-end customer journeys.
— AI for account-based marketing
Using AI for account-based marketing requires the ability to create an accurate customer profile. Using machine learning, you could create a product/service that identifies the needs of your ideal customer based on historic data. This could be done in combination with information about their personal preferences and company size, to give you a better idea of who your best customers are.
For instance, IBM iX works with clients on their account-based marketing efforts. By analyzing data from multiple sources, they are able to help companies generate insights about their existing customers and identify potential customer accounts. This helps you prioritize your target accounts and use your resources more efficiently—as well as increase the effectiveness of your account-based marketing program overall.
The Advantages and Disadvantages of AI Marketing
As artificial intelligence grows in popularity, so does the interest in the potential of the technology. But while many see AI as a solution to a number of problems, there are some who believe it could be a threat to economic stability and moral values. While some have found the potential for AI to be promising, others have been more reserved.
In 2015, MIT researchers proposed that if machines were given free reign over areas such as healthcare and education, they could better serve society than humans. In 2016, UNICEF launched an awareness campaign designed to educate children on how AI can make them better leaders in their future lives using video games and robot assistants.
Back to marketing, AI has the potential to truly transform the way that marketers operate, however, it’s important to understand both the advantages and disadvantages of AI so you can reap the benefits without succumbing to the potential pitfalls.
The main three advantages to AI as a marketing tool include:
- Improving marketing analytics by identifying weak points in a campaign.
- Sifting through vast amounts of data to find customer insights that might otherwise get overlooked.
- Eliminating repetitive and monotonous tasks.
The main three disadvantages to AI as a marketing tool include:
- AI cannot truly tell an authentic story. The decision making process needs to be left to humans so they can guide the AI to get the most out of it
- Some brands, especially smaller ones, aren’t able to afford solutions that are cost-prohibitive.
- Some consumers are more comfortable interacting with humans rather than machines.
As with any tool, there are both advantages and disadvantages. For many marketers there is still a lot of uncertainty surrounding AI—both in terms of its potential capabilities and its real world application.
How to Determine the ROI of AI-powered Marketing?
Businesses are beginning to invest in AI solutions but there is some concern over how the ROI on these investments can be properly measured. A new study by Oxford Economics aims to provide some clarity around the question of how AI can provide value for not just marketing but the enterprise as a whole. The report outlines 6 recommendations on how top marketing executives can evaluate, implement, and measure AI successfully using real-world examples.
The 6 recommendations include:
- Define clear goals. Start with clearly defined goals that focus on business outcomes rather than technology capabilities. This will help set expectations for what their ROI might be and which solutions are worth investing in.
- Establish a data-driven approach. The more data you have, the better. The best way to achieve this goal is to establish a governance model that includes all teams involved in the AI project. Make sure everyone is aligned with the same purpose.
- Take advantage of every available resource. Don’t be afraid to take advantage of existing data sources to gain an initial foothold in your business knowledge base. Start with simple techniques, such as using text analytics tools, and use them iteratively throughout the project. Make sure that these tools are relevant and useful for your business outcomes instead of just another tool on the marketing tool belt.
- Look for the right partners. Successfully implementing AI requires support from all stakeholders in the business. You need to find the right partners that can offer additional expertise in areas where your team lacks expertise, prove their value quickly, and provide a realistic ROI estimate for your investment.
- Measure early and often. Don’t wait until you have completely defined your data governance model or until you have implemented all of your AI tools to begin measuring the impact of your investment. Start by deploying a QA process before developing an AI solution to make sure you understand what a robust system looks like.
- Always be open to change. AI will change over time just as any other technology will. Be flexible enough to adjust your approach and improve your systems as needed.
The report also offers a deeper dive into the various ways marketers can combine their existing information with new insights from AI technologies to do things like:
- Improve customer targeting and segmentation
- Improve campaign performance across the board
- Use big data tools to improve AI training and data quality. They also outline the pros and cons of implementing each of these approaches, including their potential for ROI, time investment, and risk factors.
How can Companies Get Started with AI Marketing Software?
There are many different ways to start incorporating marketing AI into your marketing strategy, but it’s important to remember that this is a tool that should be used in conjunction with other tools so it doesn’t get in the way of your success.
The kind of AI marketing software you want to use will depend on the size and scope of your company. Smaller or more straightforward AI solutions can be implemented quickly and with limited resources. However, large companies with significant resources may need to take multiple approaches in order to reach their goals.
If you’re looking for simpler solutions, you might consider evaluating individual features like text analytics or sentiment analysis instead of deploying an entire system like IBM’s Watson for Marketing.
Here 9 examples of AI marketing software:
- Looker for big data marketing analytics.
- Salesforce Einstein for sales, marketing and CRM.
- SEO toolkits from Moz, Ahrefs, SemRush and Alexa that help marketers analyze audiences and increase website traffic.
- Drift.com, Intercom.io, Conversica and Livechat.com to implement virtual chatbots and conversational customer engagement solutions.
- Bing Ads, Google Ads, LinkedIn Ads, Facebook Ads, and other pay-per-click and social advertising platforms for direct response marketing.
- Google Analytics, Adobe Analytics, and other real-time analytics platforms to help you measure the success of your marketing by tracking data on your website traffic and conversions.
- ZoomInfo and their Clickagy DSP bring together information about people, products, and businesses to create a digital footprint for your marketing efforts.
- HubSpot, Zoho CRM, EngageBay, Pipedrive, ActiveCampaign to help automate CRM and marketing processes.
- Grammarly Business, Zoho Writer, QuillBot, ProWritingAid and ShortlyAI that use AI to improve your writing skills and create unique, high-quality content.
The value of AI for digital marketing is still being challenged. The expected outcomes are easy but the ROI is hard to determine. The question, “How will AI affect my business in the next year?” needs to be answered quickly. Otherwise you risk being left behind by your competitors who have embraced the new technology.
AI has the power to disrupt online marketing and help marketers deliver results in a more effective way. Improved personalization, better targeting and ROI is not a question of “if” but “when?” There are still many questions that need to be answered before AI is widely used in digital marketing, sales and CRM. However, the potential ROI can’t be ignored for any marketer in today’s fast-paced world.