The Best Free and Paid Facebook Analytics Tools
By Kit SmithSep 30
Right now, a certain buzzword compliance cloud surrounds discussions of artificial intelligence (AI). Many technology products and services would like to bask in its reflected glow. Plenty of terms have enjoyed or endured similar moments — big data, IoT, even gluten-free.
But that doesn’t mean AI isn’t real. Or powerful. Or useful. In fact, its commercial applications are really just beginning to show their worth.
In this post, we’ll look at the ways in which AI can be used to help companies extract consumer insights from the enormous volume of structured and unstructured data to which they have access. But first a definition:
Artificial intelligence is intelligence displayed by machines, in contrast with the natural intelligence displayed by humans and other animals.
Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving.”
Within the discipline of AI are a set of technology building blocks that include:
Computing power has increased dramatically over the last couple decades, while its relative cost has dropped. Likewise, data storage. This puts the compute and storage resources necessary for AI within the grasp of even small businesses. AI technologies make it possible to analyze enormous data sets much faster and more accurately than humans.
When applied to the fire hose of social media data, AI-powered analysis can reveal consumer insights that might otherwise remain hidden. It can find patterns in the marketplace and consumer behavior that lead to product refinements and new products, more effective marketing campaigns, and innovative ways to engage with communities of customers and influencers.
The table below shows types of social media data and tasks to which AI can be applied.
So, with a conceptual framework in place, let’s look at a tangible example showing how AI can analyze social media data in ways that are either much faster than manual analysis or were simply not possible with traditional research and data gathering methods.
Based in Chicago, Levy Restaurants offers premium vending and food services to major entertainment and sports venues, such as the Scottrade Center in downtown St. Louis — home to the NHL’s St. Louis Blues. The company wanted to refine this venue’s restaurant concepts to appeal to the changing preferences of the city’s sports fans, especially in the coveted 20 to 30 age brackets.
AI-powered analysis of social conversations revealed a strong interest in fusion cuisine, which combines elements of different culinary traditions. Levy’s modified its restaurants to meet this demand, and within half a season, the same locations generated more revenue than in the entire previous year.
Only the convergence of AI, analytics, and social media made this possible for several reasons. Focus groups and surveys could never provide the breadth and volume of consumer opinion and preference that social media can. In-venue interviews might seem promising until you consider the logistics and the fact that you’d be interrupting the game experience, which could easily provoke some very negative social conversations.
Finally, these traditional research methods are time-consuming and resource intensive. In the time it takes to set up a focus group — let alone conduct it and analyze the results — AI technologies could analyze and classify thousands of conversations about food preferences at sporting events. It’s just no contest.
It’s easy to understand how AI can deliver speed, accuracy, and scalability. After all, artificial intelligence is machine intelligence. But what’s often lost in the discussion about AI’s power is its ability to help us understand nuance and context. Very important characteristics of human communication.
Understanding those characteristics means you can answer more complex questions. You can dig into why people behave the way they do and what subtle drivers shape their preferences.
AI can analyze qualitative data, usually in the form of text, by training algorithms to analyze sentiment at a very granular level. And the more data you have, the more sensitive the analysis becomes to the nuances you wish to surface. Three examples illustrate this capability.
Images also affect how we process information. In 2003, a Harvard student worked with a South African bank, sending 50,000 letters with offers for short-term loans. The letters varied the interest rate and included other psychologically-influential cues. It turned out that adding a picture of a happy female to the letter had as much positive impact on the response rate as dropping the interest rate by four percentage points!
As AI advances, it’s fair to ask whether it will eventually replace human analysts. Not anytime soon because algorithms still need the “human touch.” Algorithms work best on closed systems — a Go board, for example. The algorithm needs a human analyst to describe the environment in which it will run.
And, the consumer insights generated via AI-enabled tools will still be applied by humans. These tools will become smart assistants to analysts as their roles evolve. The speed, power, and pattern recognition capabilities of AI coupled with people pointing them to the right questions will create the best of both worlds.