A recent study by Price Waterhouse Cooper (PwC) estimates that by 2030, artificial intelligence (AI) will generate more than USD 15 trillion for the global economy and boost local economies by as much as 26%. (1) But what about AI’s potential specifically in the field of marketing?
From customized content creation to task automation and data analysis, AI has seemingly endless applications when it comes to marketing, but also some potential risks. Here are some key definitions, benefits, use cases and finally a step-by-step guide for integrating AI into your next marketing campaign.
What is AI marketing?
AI marketing is the process of using AI capabilities like data collection, data-driven analysis, natural language processing (NLP) and machine learning (ML) to deliver customer insights and automate critical marketing decisions. Today, AI technologies are being used more widely than ever to generate content, improve customer experiences and deliver more accurate results. Before choosing an AI tool, organizations should fully explore the different types of AI marketing applications available and look at how they’re being used by other businesses.
AI in marketing use cases
Examples of AI in marketing abound as more and more organizations look to it to help them improve everything from their social media posts to their email marketing and content marketing efforts. Here are some examples of ways enterprises are increasing their use of AI to help them achieve marketing goals.
- Content generation: The recent launch of OpenAI’s generative AI platform ChatGPT in November of 2022 has prompted a flood of new use cases for AI. AI used for content generation can save marketing teams time and money by creating blogs, marketing messages, copywriting materials, emails, subject lines, subtitles for videos, website copy and many other kinds of content aimed at a target audience.
- Audience segmentation: AI helps businesses intelligently and efficiently divide up their customers by various traits, interests and behaviors, leading to enhanced targeting and more effective marketing campaigns that result in stronger customer engagement and improved ROI.
- Customer service chatbots: Increasingly, marketers are exploring the possibilities of enabling AI chatbots to enhance certain aspects of customer service. Once trained, these bots can interact with customers no matter where they are on their customer journey, help resolve tickets quickly and effectively and increase customer satisfaction.
- Programmatic advertising: Programmatic advertising is the automation of the purchasing and placement of ads on websites and applications. AI has significantly enhanced organizations’ abilities to conduct programmatic advertising using customer history, preferences and context to deliver more relevant ads with higher conversion rates.
- Search engine optimization (SEO): Deploying an AI solution to enhance search engine optimization (SEO) helps marketers increase page rankings and develop more sound strategies. AI can help marketers create and optimize content to meet the new standards.
- E-commerce: AI is helping businesses improve their e-commerce programs and digital marketing capabilities by giving them a more nuanced understanding of their customer’s needs and buying habits, automating tasks and simplifying workflows.
The benefits and challenges of using AI in marketing
As with other new technologies, there are both benefits and challenges to using AI for marketing purposes. From maintaining the quality of the large data sets needed to train AI to complying with the field’s ever-expanding privacy laws, organizations that haven’t used AI before are understandably cautious. But enterprises who have made the investment and identified an AI marketing solution tailored to their needs are enjoying many advantages.
- Faster, smarter decision-making: Marketing teams equipped with cutting-edge AI tools can see the impact of their marketing efforts in near real-time and adjust their tactics accordingly. AI marketing platforms can create AI marketing strategies and analyze data faster than humans using ML algorithms and recommend actions informed by sentiment analysis from historical customer data.
- Improved return on investment (ROI) on marketing initiatives: AI marketing tools can help marketers identify actionable insights from data generated by a campaign in near real-time. Additionally, the same tools can help identify the right channels for a media buy and even the optimal placement of an ad based on customer behavior. Modern AI marketing solutions help stakeholders ensure that they are getting the most out of their investment in a campaign.
- More accurate measurement of KPIs: Digital campaigns generate more data than humans can keep up with, which can make measuring the success of marketing initiatives difficult. AI-enhanced dashboards help marketers link the success of their efforts to specific tactics they’ve deployed, helping them better understand what’s working and what could be improved.
- Enhanced customer relationship management (CRM) capabilities: AI technologies help marketing teams improve their customer relationship management (CRM) programs by automating routine tasks like the preparation of customer data. They can also reduce the likelihood of human error, deliver more personalized customer messages and identify at-risk customers.
- More meaningful insights from customer data: Today, many marketers struggle with the sheer amount of data available to them when they’re planning a campaign. AI can help by performing predictive analytics on customer data, analyzing huge amounts in seconds using fast, efficient machine learning (ML) algorithms. It uses the data to generate insights about future customer behavior, suggest more personalized content and spot patterns in large data sets for marketers to act on.
- Training AI solutions: Just like humans, AI requires significant training to learn a new task. For example, if you want an AI solution that will talk to your customers in an engaging way, you’ll need to invest the time and resources necessary to teach it. To build an application like this, you will need a large amount of data about customers’ preferences and, potentially, data scientists who specialize in this kind of training.
- Ensuring the quality and accuracy of data: AI solutions are only as strong as the quality of the data they are trained on. This is true no matter how technically advanced a tool is—if the data it’s been trained on isn’t accurate and representative, the answers and decisions it generates will be low quality and ineffective.
- Complying with privacy laws: Because AI is trained on personal customer information, the laws surrounding what can be used must be strictly followed. Companies who deploy AI for marketing purposes need to be able to adhere to consumer data regulations or they risk incurring heavy fines and reputational damage. According to HFS Research, reputational damage from errant AI deployments appearing in the media has increased the pressure in both Europe and North America for greater regulation.
A step-by-step guide to incorporating AI into your marketing strategy
Follow these five steps to effectively incorporate AI into your next marketing campaign.
Step 1: Establish goals
The first step to integrating AI into a marketing campaign is to set out goals and expectations. Assess what worked and didn’t about past campaigns and outline the ways in which you hope AI can help improve your results in the future. Once stakeholders have aligned on expectations, it will be easier to choose an AI solution and set meaningful key performance metrics (KPIs) to evaluate its success.
Step 2: Acquire the right talent
Data scientists or engineers with a background in AI, machine learning and deep learning don’t typically sit on marketing teams, but their expertise is necessary for successful AI marketing initiatives. To address this issue, organizations have a choice—they can either make the investment to hire the data scientists and engineers they need, or they can go to a third-party vendor for help training and maintaining their AI marketing tool. Both approaches have their advantages and disadvantages, primarily around the level of investment an organization is willing to make.
Step 3: Adhere to data privacy laws
One of the biggest challenges facing AI marketing solutions is the use of customer data for training and implementation purposes without violating privacy laws. Throughout the training process, organizations must find ways to maintain their customers’ security and privacy or face heavy fines.
Step 4: Test the quality of data
The success of an AI marketing tool depends on the accuracy and relevancy of the data it’s trained on. AI tools that are trained on data that doesn’t accurately reflect customer intentions will fail to provide useful insights into customer behavior or make useful strategic recommendations. By prioritizing the quality of their data, enterprises can ensure that their AI solutions will help them better achieve the outcomes they seek for their marketing programs.
Step 5: Choose the solution that’s right for you
Organizations selecting an AI solution have many different platforms and capabilities to choose from. If they’ve followed the first four steps carefully—laying out their goals, hiring the right talent and ensuring the quality and accuracy of their data—the last step should be the most straightforward: Choosing the tool that’s right for them.
AI marketing solutions
Today’s most effective AI marketing solutions utilize AI and ML technologies to enhance customer experiences and deliver meaningful insights to marketers swiftly and accurately.
Discover 6 AI marketing use cases
There’s a good chance that AI can help you automate and improve the online marketing activities you or your teams undertake. Here are some use cases of AI in marketing.
1. Content moderation and generation
Moderating content across all social media platforms is a popular example of a time-consuming and error-prone activity. Automation is the best to get this right, and you needn’t look further than AI-powered platforms to make moderation easy.
2. Ad targeting and analysis
Who do you want your ads to target? What are your customer personas? Where can they see your ads, when, and how?
AI platforms help you create targeted ads that get the right people to engage at the right time. They also allow you to analyze your ads before they even go live.
For instance, predicts ad performance before you test it. Ads are expensive—and may burn holes in your wallet if you keep testing without evaluating their performance. Irrelevant ads can turn customers off and make them lose trust in your business.
How can AI help?
- Test your ad content before publishing.
- Train your AI model with historical ad data to get better results.
- Get data-driven performance metrics and improve conversion rates.
- Lower costs by choosing relevant ads that work and convert.
An example of this in action is Facebook. Facebook focuses heavily on targeted advertising to achieve the highest market cap and edge out the competition. Deep Learning also plays a big part here, as Facebook uses Deep Neural Networks to determine which ads to show to whom intelligently.
3. Social media listening and brand awareness
Social listening involves tracking online mentions of your brand and repurposing them to increase brand awareness. Doing this manually is inconvenient, time-consuming, and it is impractical to scan across all platforms and mentions.
You can then cut through the noise and choose which social engagements to prioritze.
Why use an AI solution for social listening?
- Understand the tone and intent.
- Discover social media trends and optimize your messaging.
- Discover potential customers to start conversations with.
- Find influencers to collaborate with.
- Actively engage with your audience.
- Address complaints or other issues mentioned on social channels.
- Monitor your social performance and adjust as needed.
4. Churn predictive analytics
AI can also be used to help predict customer churn. How? By analyzing the responses to your Net Promotor Score survey, general customer sentiment, and previous communications with the client.
You likely have tons of data from previous interactions with clients. Some of these clients are still with you; others aren’t. By analyzing past conversations and communications with clients you’re able to identify common characteristics amongst customers who’ve stopped using your services.
This can help you step in and nurture user relationships before it’s too late.
5. Data analysis
AI analytics use Machine Learning techniques to analyze data on the go, provide key customer insights, uncover patterns, and establish data relationships. With AI tools, you can determine what works and doesn’t for your business and how your customers interact with you.
Data-based insights facilitate decision-making. You can easily decide how to adjust your marketing campaigns based on the information at hand.
6. Image recognition
Image recognition is a staple in Artificial Intelligence. It uses Deep Learning to detect patterns in images and videos. You can identify places, objects, and actions to learn more about your customers and auto-tag and label items when needed.
This helps marketers segment customers and create more targeted campaigns. You can upload an image data-set, give it labels of your choice and connect to your workflows. Any new images that flow in will be analyzed and automatically given the correct labels and sent in the right direction.
Check out this quote from Veneka, one of our image recognition users. Veneka use Airtable to keep track of inventory but thanks to Levity, their Airtable dashboard is now automatically updated, no need to manually classify and tag images as they come in. This also means their online stores can be instantly updated.
“Manual tagging slows down our onboarding efforts. With Levity, we identify and tag 60+ product attributes accurately, significantly speeding up how quickly our stylists use new products in their capsules.” — Shivika Sinha, CEO Veneka
An example of image recognition is Facebook’s DeepFace. Not only does it analyze images for tags and descriptions but also trains its AI models enabling precise recognition with minimal errors. 7
AI is a complex technology, but that does not mean it has to be complex to implement.
There’s a good reason why some companies are reluctant to adopt it—they lack adequate IT infrastructure and talented developers. Many small businesses cannot afford to set up a sophisticated AI framework. They just don’t have the budget and choose to look the other way. But there is a way to overcome this. No-code AI tools like Levity are a great solution for businesses. You can create and train your AI model with your own data without any coding skills. You can also use a pre-made template to give you a head start. No need to set up any elaborate infrastructure or have a engineering team to automate your workflows