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AI in Analytics: Top Use Cases and Tools

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Artificial intelligence can give you analytics superpowers.

AI is a collection of technologies that excel at extracting insights and patterns from large sets of data. AI can use those insights and patterns to make predictions about what drives outcomes. It can even learn to improve its predictions over time.

This makes AI perfect for anyone who uses analytics data to make decisions. We're talking data analysis using systems like Google Analytics, automation platforms, business intelligence systems, content management systems, and CRMs.

With AI in analytics, you can get more value out of the data you already have, unify that data, and make increasingly valuable predictions based on your data.

This all sounds great...

But how do you actually get started with AI analytics?

This article is here to help you take your first step.

At Marketing AI Institute, we've spent years researching and applying AI in marketing and business. Since 2016, we've published more than 700 articles on the subject. And we track 1,500+ sales and marketing AI companies with funding north of $6 billion.

That means we can help you demystify AI analytics. (And, don't worry, you don't have to be a data scientist to do it.)

Let's dive in.

What Is Artificial Intelligence?

It helps to know what AI is before you use it for analytics.

What Is Artificial Intelligence?

If you ask 10 different experts for a definition of AI, you'll get 10 different answers. We like a simple, effective definition from Demis Hassabis, CEO of DeepMind:

AI is the "science of making machines smart." That means we can teach machines to mimic human intelligence. We can give them the ability to see, hear, speak, write, and move.

So, AI is really a group of technologies. You may have heard of some of them like: machine learning, deep learning, neural networks, natural language generation (NLG), and natural language processing (NLP).

At this stage, it's not important to know what every piece of AI terminology means.

You just need to understand how AI is very different from traditional software.

As an example, take a traditional non-AI tool like your standard accounting software. No doubt this software is useful, quickly and automatically performing calculations for you.

But it relies entirely on humans to work. Its rules are written and updated by human programmers. And it requires human professionals to provide it with data, information, and guidance to do its job.

Most importantly, the tool only improves its performance if human programmers go in and update it. (Anyone who has dealt with outdated or ineffective software knows this can sometimes take a long time.)

AI tools are very different.

AI tools have the ability to learn and improve on their own. They still need to be built and managed by humans. But they can adapt to past outcomes and future data to increase their performance on their own.

You may not realize it, but AI is everywhere in daily life.

Your smartphone relies heavily on AI.

AI-powered facial recognition allows you to unlock your phone with your face. This AI has learned to accurately and consistently identify faces. It's able to do this because it has learned from training on millions of other faces.

AI-powered voice assistants help you search and shop. They're able to deliver the right results because they learn from you and other consumers which results are correct, and improve their output accordingly.

And AI powers predictive text, learning to predict what you'll type next with a high degree of accuracy because it has learned to improve from billions of other users.

AI is also at the heart of many leading companies.

Tesla's self-driving cars rely on AI that sees and moves to navigate the road.

Amazon and Netflix rely on AI to serve up product and content recommendations.

Google relies on AI to complete your emails and power intelligent search.

There's a reason AI is so popular...

Big data. Companies now have enormous amounts of data generated by digital activities. This big data acts as fuel for AI systems, which require tons of data to make predictions. Now that all this data exists, companies have rushed to develop more (and more advanced) AI solutions to extract insights from it.

In the process, AI systems are getting smarter, faster, thanks to all the data they have to work with. This creates a virtuous cycle of improvement that is fueling huge growth in the market for AI-powered solutions.

(Turns out big data is a big advantage.)

Here's a real-world example in business and marketing:

An AI tool exists that writes marketing email subject lines for you.

It uses training from humans (sample of the company's past marketing emails) to learn. But then the tool drafts its own email subject lines and improves them over time.

As time goes on, this AI tool gets better and better at writing subject lines, until it is better at writing subject lines than human copywriters.

Now, imagine the power of AI applied to any piece of data analytics technology...

It's already happening, and it can transform how you do business.

Demystifying AI in Analytics Jargon

Let's quickly clear up confusion around different advanced analytics terms and how they relate to AI...

Demystifying AI in Analytics Jargon

(If you're already an analytics pro, you may want to skip to the next section on use cases.)

One is predictive analytics. This is when a machine uses historical data to make predictions about the future. Whether you know it or not, sophisticated predictive analytics solutions use AI to make these predictions. So, when you hear this term mentioned, it's likely AI is involved.

You may also hear the term prescriptive analytics. Prescriptive analytics means a machine not only makes predictions, but also prescribes what to do next. Prescriptive analytics tools rely on AI to do this.

Another popular term that often comes up is augmented analytics. Augmented analytics is when AI is used to automate parts of the analytics process that would be performed by a data scientist or a data science team. These include tasks around data preparation and getting insights out of datasets. Basically, this type of analytics uses AI to make the human side of data analysis easier.

Finally, you may hear the term descriptive analytics. This means a platform provides you with historical data. It describes what has happened. All AI and non-AI tools have a descriptive analytics component. But not all descriptive analytics tools use AI.

These distinctions are important to understand as you dive into AI analytics technology. Too often, vendors will say a tool is predictive or prescriptive, when it's actually just descriptive.

Alright, now that this out of the way, let's get to the fun stuff:

How do you use AI in analytics?

AI in Analytics Use Cases

Here are just a handful of the most powerful use cases we've found for AI in analytics today.

AI in Analytics Use Cases

Discover New Insights

AI excels at finding insights and patterns in large datasets that humans just can't see. It also does this at scale and at speed.

Today, AI exists that will answer questions you ask about your website data analytics. (Think "Which channel had the highest conversion rate?")

An AI analytics tool can also recommend actions based on opportunities its seeing in your analytics.

Predict Business and Marketing Outcomes

AI can help you predict outcomes and successful courses of action.

AI-powered systems can analyze data from hundreds of sources and offer predictions about what works and what doesn't.

AI can also can deep dive into data analytics about your customers and offer predictions about consumer preferences, product development, and marketing channels.

Unify Analytics and Customer Data

AI can be used to unify data across platforms. That includes using the speed and scale of AI to pull together all your customer data into a single, unified view. Artificial intelligence is also capable of unifying data across different sources, even hard-to-track ones like call data.

Forecast Demand

Thanks to its predictive abilities, AI can use your analytics data to forecast product demand based on available stock, seasonal trends, past purchase behavior, and more.

Based on this data analysis, businesses can improve how they stock products, purchase inventory, or purchase materials. They can also use AI demand forecasting to plan other business or marketing investments.

Top AI Analytics Tools

There are hundreds of AI analytics tools out there—here are some of the best ones worth a look.

Top AI Analytics Tools

  • Adobe Analytics — Adobe Analytics uses AI to analyze data from different online and offline sources, then visualize insights from your data.
  • BlueConic — BlueConic is a customer data platform that turns customer data into person-level profiles for marketing purposes.
  • Crayon — Crayon is a market and competitive intelligence tool that enables businesses to track, analyze, and act on everything happening in their market.
  • Google Analytics — Google's popular analytics tool has powerful AI baked right into it. Its AI-powered Analytics Intelligence feature uses machine learning to surface insights, answer your analytics questions, and model conversions.
  • Google Cloud — Google Cloud's smart analytics solutions use machine learning to get insights into and make predictions about business outcomes.
  • Helixa — Using AI, Helixa helps you produce detailed personas based on audience interests, demographics, and psychographics.
  • Invoca — Invoca is an AI-powered call tracking and conversational analytics tool that brings the depth of marketing analytics traditionally limited to digital consumer interactions to the world of human-to-human selling.
  • IBM Watson + IBM Planning Analytics — IBM Watson powers IBM's prescriptive analytics tool. Using AI, the tool can make predictions across finance, operations, and sales.

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