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There’s a lot happening in the world of AI right now. As AI’s capabilities evolve, you’re going to see more and more headlines referencing it. Comparisons have been made to the 1850s Gold Rush as tech companies are clamoring to announce the next big breakthrough. The heavy-making news right now is OpenAI’s ChatGPT. There are some ethical issues with ChatGPT’s capabilities, like being able to write entire essays for students, that are popping up and gaining attention, while other industries are partnering with them with open arms. Whatever side you’re on, it’s important to educate yourself on this up-and-coming technology.
Maybe you’ve thought to yourself: “I hear all these AI buzzwords, but what do they really mean?” In the case of data science and data analytics, buzzwords have become a way of talking about a business area that is often poorly understood and fraught with myths and misconceptions. However, there are several AI and data science buzzwords that are important for marketers to understand.
By using the real function of these buzzwords, marketers can automate sophisticated aspects of their responsibilities that previously had to be completed manually. By mastering not only these terms, but also their uses, marketers can significantly increase their value to the business by bringing invaluable data, predictions, and insights that would otherwise go unnoticed.
In this post, we’ll cover some basic definitions as well as the top eight AI and data science buzzwords. We’ll also provide examples that can make you a more sophisticated marketer. Without further ado, let’s dive in!
AI is when computers or systems mimic human processes or decision-making abilities. It takes in information and context regarding a problem and provides advice or solutions.
Example: Customer calls can be streamed live through an AI system that finds relevant internal documents to assist the sales or support agent in providing the information a customer needs. Based on the suggested results the agent selects, the AI algorithms improve over time at surfacing the right materials. Think about how this could improve your customer experience and how much time it could save your marketing response or customer service team.
The combination of computer science, statistics, modeling, and artificial intelligence. A cross-discipline focused on getting the most from modern data-rich technical environments.
Example: Data scientists can work with your B2B e-commerce site data to combine sales data from an operational database with customer data from your site’s CRM. They can evaluate trends and correlations between purchase types to suggest certain patterns of customers fit certain ad campaigns. Those patterns are tested using A/B testing of ad serving, and validated models are made available via an API.
With the help of data science, you can build smarter ad campaigns by discovering and combining the most successful marketing channels (e.g. voice, text, and/or email) and different targeting strategies that less savvy marketers are likely unaware of.
A combination of factors (mobile, IoT, cloud) have driven increases in the amount and rate of data ingested by many systems. There is no set threshold for the size of big data, it is merely a term to refer to the modern challenges and opportunities of processing and gaining insight into an environment that creates a lot of data. Commonly, such data is ingested into a scalable clustered environment.
Example: With the help of big data, a national brand can track sales made via phone calls from all retail locations and aggregate this data into models that reflect each regional advertising campaign. These new insights can be made available in real time so that marketers can make rapid adjustments to their decisions and budgets.
ML uses historical data to teach a computer to detect patterns in order to describe, classify, or predict new examples. Machine-learning algorithms are often used as tools to create AI applications.
Example: A data scientist could help you use machine learning to cluster calls based on keyword classification, campaign, and time of day to find trends that can drive new advertising strategies. Using a machine-learning solution for phone calls, marketers can analyze thousands of phone calls to look for patterns, topics, and other actions that are happening on the phone that you could never notice by listening to a sample of calls.
Refers to using an algorithm to create a model, which is a way to evaluate new items of data to classify or predict something of interest to the user.
Example: If you have specified an ad campaign in Google Ads to target a certain market or demographic, you have actually already created a model before. You can use more complex modeling to detect calls over a certain duration that do not end in a sales result status. You can then flag these calls for follow up. Using modeling in this manner can help you understand how to improve your customer experience and increase sales.
Similar to modeling, predictive analytics is a term for traditional business intelligence and trend analysis that is focused on forecasting.
Example: Using predictive analytics, marketers can take large amounts of unstructured data and be able to predict future trends. For example, using past seasonal fluctuations in sales-based phone calls, a large company could appropriately staff their call center and ramp up their marketing and advertising budget for the upcoming holiday season.
Processing text in order to classify, cluster, search, or extract information. This processing enables other data science tasks, such as machine learning.
Example: Content marketers can use NLP to run marketing assets through a process called topic modeling. This process determines a score for each topic that is surfaced, which is made up of words that often go together. These topic scores are used to provide relevant articles for a site user to read next.
Using NLP in this example allows content marketers to automatically provide users with appropriate and relevant content for the next step in the customer journey. Creating this type of user experience manually could take hours of reading through existing content and manual scoring.
In order to understand unstructured data, it is useful to start with contrasting it with structured data. What is structured data? Structured data is a simple form that is able to be operated on mathematically or logically in a direct manner.
If you have a list of customer names, age range, and location, that is all structured data. The names serve as a label and you could count the occurrences of common names. The ages and location may be numeric, and you could rank or summarize the numbers. In contrast, pictures of the customer or descriptions of them in long-form text would be unstructured data. In order to do any kind of summarization or classification based on those fields, you would need to process them first.
Example: In the case of the conversations, a phone call transcription is unstructured data. When modeling or machine learning is placed on top of a conversation, you need to decide how to work with the transcript. Sometimes keywords or word counting can be appropriate. Other times, more sophisticated techniques like topic modeling make sense.
At Invoca, our data scientists make these decisions frequently, as we specialize in turning phone call insights into conversations. We can help marketers take unstructured data from phone calls and turn it into actionable information that helps improve caller experience.
The area of data science is ever-changing, and these terms continue to evolve over time. This is a snapshot of where things stand today. Rather than be intimidated by the pace of change, we choose to embrace the new techniques and tools that can help drive our customers to improve efficiency and foster innovation and success.
Artificial Intelligence includes multiple types of technologies and techniques, and machine learning is included under its umbrella as a subcategory. Machine Learning is centered more around teaching algorithms to make choices or predictions based on the data it is fed.
Artificial intelligence recognizes patterns in data, and uses those patterns to make decisions or predictions based on algorithms and statistical models it is taught to analyze. Systems are given a large amount of data to learn from, the data is analyzed, and then predictions are formed. With time, the more AI is interacted with and fed, the closer its predictions will be to becoming true.
AI is divided between being “Based on Capabilities” and “Based Functionalities,” and they each have several subcategories included with them. We’ve listed and provided examples below to give you a general idea:
The benefits would be almost endless but the main takeaway is that AI would simultaneously increase efficiency, increase savings, and improve decision-making as the data it relies on increases and becomes more advanced.
We’re all hoping for a more positive outcome for this world than the one that’s created in the Matrix because of technology. Hopefully, the advancements in robot technology continue and they’ll be able to take on the dangerous jobs that we just don’t need to be doing anymore. Increased automation and improved decision-making will allow for more efficiency and forecasts will improve as more and more data is collected. With great power comes great responsibility, so the legal and ethical ramifications will need to be addressed, and the consequences are bound to be a subject of great debate.
Want to learn more about the latest AI trends for marketers? Check out these resources:
