Introducing artifical intelligence (AI) to your marketing operations can launch the department to new levels of customer engagement and data-driven strategy. Not only can this technology allow marketing teams to scale their output, but it can also provide game-changing insights based on campaign data.
According to Hubspot, a staggering 80% of industry experts integrate some form of AI technology into their marketing operations. This rapid market adoption of AI has led some companies to claim they can use the technology without doing the actual work required to build a custom AI model. This alarming trend means some serious homework is needed before selecting an AI tool for your marketing department.
This article will give you an overview of the most common use cases of AI in marketing, as well as useful tips and questions to ask when assessing AI vendors.
What is Artificial Intelligence?
Artificial intelligence is a field of computer science aiming to replicate human intelligence in machines and software. AI is achieved by developing algorithms, models, and systems that enable computers to perform tasks traditionally associated with human intelligence.
AI technology can analyze vast volumes of data, identify patterns, and make predictions or decisions based on that data. This capability sets AI apart and offers a wealth of opportunities across various industries, including marketing.
The impact of AI on marketing tasks cannot be overstated. This technology not only allows marketing departments to scale their activities but also allows them to accomplish previously impossible tasks.
A (short) history of AI
The field of Artificial Intelligence was first introduced by John McCarthy in 1956 and was shortly followed by foundational theories such as the Turing test to measure a machine’s intelligence. Artificial intelligence developments were halted for a few decades because of the limitations of computers at the time.
The early 2000s saw a resurgence of AI due to the Internet and massive improvements in computational power. 2010 and on saw further improvements in deep learning and neural networks, allowing for the AI capabilities we know today.
The very first step in selecting an AI provider is to identify which type of model your organization requires. There are three main types of AI tools being used by marketing professionals:
These models are built on a neural network architecture and are trained by analyzing large amounts of text from books, websites, articles and other sources. This training allows the model to understand the analyzed data but also to make links between concepts. LLMs can then execute tasks like creating new text, answering questions, translating languages, summarizing content, creating custom images, altering existing images and more. These models can also be trained with specific content to be able to handle specialized tasks like annotating legal documents or writing computer code.
Common examples of generative AI would be ChatGPT, Bard and DALL-E.
Predictive models are trained using historical data from multiple sources to predict the success or outcome of a certain business task or decision. Common uses could be stock market returns to guess stock picks, healthcare data to determine statistics like the average time before a patient sees a doctor or advertising data to determine the ROI of a specific ad creative.
Common examples of predictive AI are IBM Watson, Salesforce Einstein and Hippoc.
Common Marketing AI Use Cases
The main reason why AI tools are finding their way into most marketing departments is that they allow them to use the wealth of data they produce but are understaffed to use.
Here are the marketing tasks where AI has the most impact:
Determining which audience you should be broadcasting your content to is largely a subjective analysis when done by humans. AI can weed through the noise and make data-backed decisions to find the right audience for your business.
As consumers constantly see ads and emails from companies, all these marketing efforts become a blur. Successful campaigns hinge on the ability to personalize the content to the behavior and needs of the targeted audience.
AI can achieve personalization in several different ways. It can sift through consumer data to identify patterns, dynamically customize content on a website based on past interactions, provide tailored customer service via chatbots and more.
Marketing departments have to tackle several repetitive tasks like A/B testing and campaign optimization. Not only are these tasks time-consuming, but they are often difficult for humans to execute in a timely manner. AI can speed up these processes and provide more efficient results.
Ad and email campaigns produce tremendous amounts of data that are often underutilized or unused because of a lack of appropriately trained staff. The type of data being analyzed will change the output, but it’s safe to say that all marketing departments could use more data analysis.
A common instance of data-driven insights is sentiment analysis through Natural Language Processing (NLP) to determine whether social media comments are positive or negative. Another example of AI data-driven insights is the recommendations provided by Hippoc since the predictions are based on advertising historical data.
Recognizing Real AI
AI has become a buzzword used by a slew of different products. While it might be easy to point to a product like ChatGPT and say, “That’s AI”, it can be a bit harder to recognize in a more complicated product offering.
Real AI vendors will be able to give you advice on the type of AI needed to solve your issue without overdoing it. For certain problems, a deep learning algorithm would be overkill, and a simple linear regression would serve you better.
Here are the 4 key pillars that must be present in a true AI product:
- Data-driven operation: AI models are made to analyze data, and they must have the ability to make decisions based on that data. There can be varying levels of human guidance in the process, but ultimately AI models are made to analyze data and provide answers based on it.
- Learning capability: AI models can evolve and refine their algorithms as they encounter new data. The more data gets fed into an AI model, the better the results will be over time.
- Automation: Automating tasks is a core feature of AI models and, ideally, should be achievable without human input. These tasks can range from simple data analysis to pattern recognition and even decision-making.
- Complex problem solving: One of the main strengths of AI models are extremely large and rapidly changing data sets that would be virtually impossible to analyze by humans.
Fake AI red flags
As AI inundates the business world with great success, several companies have cashed in on the trend by cutting corners. Boasting AI capabilities is considered a surefire way to success these days, and if consumers aren’t properly informed, it’s surprisingly easy to sell fake or faulty AI software.
Using a fake AI product can have far-reaching implications. Not only will fake AI provide you with likely erroneous information, but it can also cause serious legal and cybersecurity issues. A failed project due to a bad AI tool will also gravely undermine the technology to your employees and potentially make them reticent to use these types of tools at all in the future.
Here are a few giveaways that a product might not be backed by an actual AI model.
- Lack of transparency: AI is a labor of love. Companies boasting this technology should be excited and enthusiastic to explain how their model works. If an AI provider is being evasive about its models, exercise caution.
- Overpromising: If it sounds too good to be true, it probably is. AI is a revolutionary technology, but if someone is proposing a solution you’ve never heard of before, remain wary until proof has been provided.
- Data security concerns: A shocking amount of AI products are simple “wrapper” software around an open-source AI model like ChatGPT. While there isn’t technically anything wrong with this practice, it can quickly lead to cybersecurity issues for larger companies if the software isn’t carefully built. Always ask for an AI company’s security measures before feeding data into their product.
Selecting the Right AI Tool
Before you start the search for an AI tool, it’s important to determine the tasks you want to delegate. Many AI providers claim to be able to accomplish multiple different assignments but like any software, it’s often best to select a specialist to get the best results.
Here is a helpful checklist to evaluate vendors and how involved they are with their AI model:
- Technical expertise: Ask for the credentials and qualifications of the people actually working on the AI model.
- References and case studies: Request references from existing clients and case studies demonstrating the capabilities and successes of their AI model. You should also ask if the AI model is being trained on your data and if they can provide you with accuracy.
- Data handling and security: Verify the security measures put in place by the vendor, particularly if you plan to feed customer data into the AI model.
- Scalability and integration: Discuss how you plan to integrate the tool into your workflows and ask them for their advice in doing so.
- Pricing and ROI: Study their pricing model and ensure it remains realistic within your projected ROI calculation. Some AI models can get quite expensive quickly, depending on the amount of processed data.
Integrating AI within Workflows
A common mistake when integrating AI into your business is to assume it will perform better than your previous workflow without measuring the results. While AI often provides a big efficiency boost, it’s not a silver bullet that can be applied to any situation.
Here are some useful best practices to assess the work executed by AI:
- Validation of results: Split your data into multiple subsets to train and test the AI model accordingly. This process will ensure the AI model is performing correctly and consistently across tasks.
- Determining the right metrics: Accuracy is an important metric for AI, but it shouldn’t be the only one you are monitoring. Make sure to also take into account the proportion of true positive predictions, the model’s ability to discriminate between classes and traditional marketing metrics like conversion rate.
- Benchmarking AI vs your other options: If your budget allows it, it’s always best to run two AI tools side by side for a month or two to determine the one best suited to your needs. This step will also give you a better assessment of the data quality provided by the AI models.
AI Educational Resources
At the end of the day, AI is a complex technical field, but there are several reliable sources that can be studied to gain the knowledge required to make informed decisions.
Here are the ones we recommend:
- “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” by Eric Siegel: This book delves into the predictive power of AI in marketing, offering practical insights on how to harness it.
- “Artificial Intelligence for Marketing: Practical Applications” by Jim Sterne: Explore practical AI applications in marketing, from segmentation to content optimization.
Articles and Blogs
- Hippoc blog
- HubSpot Blog: HubSpot regularly publishes articles on AI in marketing, covering topics like chatbots, predictive analytics, and personalization.
- Marketing AI Institute Blog: This blog is dedicated to AI in marketing and offers in-depth insights into various AI technologies and strategies.
Webinars and Online Courses
- Coursera’s “AI For Everyone” by Andrew Ng: A beginner-friendly course by one of the leading experts in AI, providing a broad understanding of AI’s impact on various industries, including marketing.
- edX’s “Artificial Intelligence (AI) for Business”: This course focuses on AI applications in business, including marketing and customer experience.
Selecting an AI tool for marketing can drastically improve the results you typically get from that department. AI can execute essential marketing strategy tasks like data cleaning and analysis that often require highly skilled individuals who can be difficult to hire. However, this technology can rapidly be a costly mistake if the sourcing process isn’t done correctly.
Your first AI tool will influence the way your company perceives the entire field. For that reason, an informed choice must be made from the get-go. Make an impact by introducing a first AI tool that accomplishes tasks that were previously impossible, such as Hippoc’s ad ROI predictions.
If you’ve read this whole guide, we’d love to give you a quick demo of our product’s capabilities. Contact us here.