CEO/Founder at Junction AI – We Take the Guesswork Out of Successful Digital Ads on Google and Facebook with AI.
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Being a digital marketer today demands a commitment to continuous learning. Achieving success means constantly adapting and engaging with new platforms, channels and technology.
As AI machine learning takes root, it comes with its own set of demands including terminology. Understanding the lingo is key to confidently taking advantage of AI-enhanced marketing.
The good news is this is not about the technology — it’s about understanding what AI can do and how to apply it.
Here are five practical terms that I believe need to be standard knowledge for today’s AI-enhanced digital marketer:
1. AI Analytics
Many marketers are familiar with data analytics, but how many understand the difference between data and AI analytics? This causes confusion and sometimes rejection of AI analytics because it does not conform to the normal data analytics approach used in A/B testing and live experimentation.
Data analytics is based on a human assumption, a human querying data to probe for a relationship. Analytical benefits can be limited by our assumptions, bias and lack of time to analyze all potential relationships.
AI machine learning analyzes all possible relationships between all data. A human does not need to hard-code a data query to generate a prediction for each possible data combination. This scalability alone provides marketers with a massive opportunity — analytics that deliver new and unknown insights to engage audiences.
For example, we’ve seen AI analytics predict that using a musical instrument in an image will deliver more clicks, and with specific insights to include a ukulele. A marketer did not have to A/B test every musical instrument to determine which had the biggest impact. Imagine the time and cost to discover this opportunity without AI.
2. Predictions
AI machine learning delivers predictions. Most of us already use AI predictions daily, like route options in Google Maps or when Amazon suggests other products. These are all based on using the underlying data and an input — your destination or last purchase — to generate a prediction.
But predictions are just that — predictions. You may choose to take an entirely different route home because you prefer the scenery or need to stop at a store.
Marketers can use predictions to improve results by targeting relationships they did not know about before to generate more targeted creative, avoid risks, and more effectively use their time and funds.
3. Concepts And Insights
AI is a machine. AI analytics deliver feedback that we often call “concepts” and “insights.” To get value from analytics, marketers have to change how they interpret the results; they need to unpack the concepts and analyze these insights to understand how and what to apply and what is relevant to their brand, strategy and audience.
Concepts and insights reflect the analysis of data and the relationships in the data. A concept might be “garden.” Unpacking this concept is critical because the insights may include elements such as plants, nursery, flowers and trees.
But this doesn’t mean every ad must include copy and images of only a garden. If the marketing message is about selling garden plants, enhancing the creative with flowers and plants may have a positive impact. But if we are selling indoor pots, we are sending the wrong message, and are unlikely to engage the audience to buy indoor pots.
4. Confidence And Probability
AI machine learning delivers predictions. Using a prediction to make better decisions relies on using judgment. Probability and confidence help inform our judgment on how reliable or risky it may be to use the prediction.
Probability and confidence reflect the likelihood or certainty of the prediction occurring. An AI image analysis may indicate that there is a 92% probability that the concept it sees is a table. Before deciding that it is, in fact, an accurate prediction, we need to understand the confidence in the probability that it is a table.
Surveys and polls are a great common example. A poll might indicate that 67% of consumers will reduce spending if prices rise by 10%. We know we can more than likely trust this prediction when the confidence interval is 95% +/- 5%, but if the confidence is only 40%, +/- 10%, we should be warier of adopting this prediction.
5. AI Models
An AI model is the practical application of what we want an AI to do — its function. There are many types of models for all types of different situations, specifically targeted or more general. The model incorporates the data, settings, formulas and algorithms needed for training the AI.
Models are what we use to test or challenge with an input in order to generate an output. For example, AI image analysis will attempt to recognize objects in an image we supply. It will have undergone training on a model to learn to identify the various objects (i.e., cats, trees, balloons, etc.). The object identified will include a probability that it is that object and a confidence interval. The better the model, the better the AI will be at this function.
As marketers, we are directly responsible for the data used in the model. We generate vast quantities of data. This data is the building block to the whole AI capability — without it, there is no model and no AI machine learning.
AI-enhanced marketing will increasingly change the role a marketer plays and where we add value. With machines crunching our data and rapidly delivering predictions, how we analyze and use predictions to make better decisions relies on judgment. This is a uniquely human skill and the highest value-add activity. The first step is understanding this new language.
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Author: Vance Reavie, Forbes Councils Member
