When advancing technology – like artificial intelligence (AI) – becomes part of the everyday, everyone rushes to prove their expertise. But like anything else, you’ve got to do your due diligence – because some information floating about isn’t quite accurate.
Here are some myths about AI analytics competitors want you to believe – and the truth they aren’t equipped to tell.
AI Is a Replacement for Human Involvement
This couldn’t be further from the truth. AI’s job is to enhance human capabilities – speeding up tasks that would take weeks or months to do by hand.
In addition to human programming of the algorithms involved, AI analytics require human interpretation and refinement to continually teach AI systems what is needed. This information is brand and use-case specific. No system can surface brand-tailored social insights without human guidance.
This is to say nothing of the fact that only humans can interpret and apply the insights discovered by AI-boosted social analytics.
If anyone says you can “set it and forget it,” walk away. Think of AI as a member of your team rather than a replacement for them.
You Only Need One AI Technology to Succeed
Social analytics tools providers without the means to offer more will tell you a multi-faceted approach to AI is “fragmented.” It’s easy to fall for that when you’re playing catch-up, trying to integrate unfamiliar tech to avoid being behind the curve.
But actually, you can’t get the comprehensive view of your audience needed without bringing a few modalities together. It’s similar to using print, television, radio, and social media in your marketing strategy. This isn’t fragmented – it’s covering the spread. And entirely necessary.
Thus, our Next Generation AI technology is comprised of a cohesive unit of analytics derived from expert systems, machine learning, and deep learning. Each of these approaches offers something different, so it makes sense that it takes all three working together to deliver the high accuracy and nuance needed by brands today.
Market research company 113 Industries embodies this idea. They use AI machine learning and NLP technologies to help their clients understand their customers better. They cite NetBase NLP technology as standing out for our ability to capture information, but also to reduce the noise that so often accompanies less analytics results.
This speaks exactly to our point, as our Natural Language Processing technology is powered by all three of the AI technologies referenced earlier – expert systems, machine learning, and deep learning.
This type of nuanced NLP reveals consumer conversations related to buying, using and rejecting products, as well as repeat buying and behavior conversations.
This helps 113 Industries and other brands understand what causes consumers to use a product in a certain way, leading to the discovery of compensating behaviors – i.e., hacks consumers use when a product doesn’t do what it’s supposed to do.
With this information they help brands like Ocean Spray, Hershey, Coca-Cola, and more develop products that solve these hacks. Says Razi Imam, Co-Founder and CEO of 113 Industries, “When they see a product that satisfies an unarticulated need, that product flies off the shelf. That is what we have specialized in using NetBase.”
You Don’t Need AI in Your Social Analytics
This lie is easy to believe if you’re a smaller brand with a smaller budget, thinking you have to settle for less comprehensive tools.
The reality is, like UPC barcode scanners, digital cash registers, websites, and many other technologies brands have had to adopt over time, AI analytics are an investment worth making now, but you have to choose the right tools. Here’s why:
- Accuracy: Data that isn’t accurate is worthless – and you can’t achieve accuracy without state-of-the-art tools
- Speed: Data that comes in less than real-time puts you behind competitors who are reacting to data in the moment – you’ll never get ahead
- Transparency: Any AI system can summarize data at the macro level – but it’s hard to trust “black box” data when you can’t understand how it was analyzed, or see the details at the micro level when needed.
- Control: Overarching analytics are great – to a point. When you have a specific use case or are working in a niche industry, you need to be able to optimize to find the data that matters most to you.
(Our AI analytics do all of the above, FYI.)
And here’s one last thing AI isn’t: going away.
It’s taken decades to get to this point, and the technology is only going to get better. Starting now means your brand gets to evolve with AI, instead of struggling to catch up later. Especially if you choose a provider focused on innovation as a guiding principle.
At the same time, you’ll have access to the best analytics available – the kind that allow you to connect to your target audience in ways that were unthinkable just a few years ago. That’s worth the investment, right?
Keep this post in mind as you make your choice, and you won’t have to change horses mid-race.
For more on Artificial Intelligence Analytics, check out the rest of our AI Series:
- Why Is Next Generation AI Best in Class?
- Artificial Intelligence Analytics 101
- What AI Isn’t, But Competitors Will Have You Believe (Currently viewing)
- Early Adopter’s Guide to AI (Coming soon!)
- Advanced AI Sentiment Analysis Considerations to Plan Ahead For (Coming soon!)
- What is Predictive Marketing & How Sentiment Unlocks It for You (Coming soon!)
- Demystifying AI in Sentiment Analysis (Coming soon!)
Ready to invest in our cohesive AI analytics tools? Reach out for a customized demo to learn more.
