AI for Predictive Analytics: Everything You Need to Know
The story I'm about to tell you is trash. Literally.
Budget Dumpster is a company in a simple line of business: It rents dumpsters to homeowners and contractors. The company has a ton of competitors. Sometimes, the competitors are small local companies in one of Budget Dumpster's markets. Other times, the competitors are huge, national firms with serious clout.
In both cases, Budget Dumpster has a talented, but lean, marketing team whose job it is to win business, no matter who they're up against.
That meant the company had a problem.
"The tools we were using didn't have the breadth of intel we needed to understand what our competitors were doing, and the intel wasn't getting surfaced fast enough," Budget Dumpster's CMO told Crayon, an AI-powered competitive intelligence tool.
Budget Dumpster used Crayon's AI-powered competitive intelligence to learn exactly what its competitors were doing online. The company used the AI tool to inform decisions about marketing campaigns, advertising, and business strategy—all with limited time and budget.
That's because AI is able to analyze large sets of data, including competitor data, at scale, providing predictive analytics that tell you not only what's happening, but what you should do about it.
And, while you might not work in waste management, you definitely deal with a lot of garbage when it comes to marketing analytics:
- Incomplete data to analyze
- Lack of time to properly analyze data
- Lack of resources to properly analyze data
- Insights from data aren't useful
- Insights from data are too slow
The simple fact is:
As marketers, we have too much data, not too little, from lots of different sources. The insights buried in that data might be pure gold. But they require serious time and money to extract - if it's even possible to get at those insights in the first place.
That's where artificial intelligence and machine learning come in. AI is an umbrella term for a range of technologies that allow machines to perform cognitive tasks - like seeing, writing, moving, reading, or analyzing data - as well or better than humans.
AI is used all around you, even if you don't realize it. It powers every voice assistant, like Alexa. It's behind every email client that reads emails and filters out spam (all of them). And it's what makes Amazon recommendations, Netflix recommendations, self-driving cars, and hundreds of other technologies possible.
AI isn't just for big tech companies. It's disrupting finance, healthcare, manufacturing, and dozens of other industries. And the technology is making major waves in marketing and sales. In fact, McKinsey predicts AI could unlock up to $2.6 trillion in business value in marketing and sales.
Nowhere do we see this impact more than in predictive marketing analytics, a discipline that's been around for awhile, but is only recently unlocking its true potential thanks to AI.
What Is Predictive Analytics?
Predictive analytics uses machine learning to predict outcomes using historical data. Predictive analytics platforms and tools do this using machine learning that informs a predictive model. Machine learning is an AI technology that finds patterns at scale within datasets. Machine learning can then use what it learns to predict future patterns from your analytics, often using regression analysis techniques in the predictive model.
A predictive analytics tool has many use cases across business and marketing, all related to predicting future outcomes and/or future behavior. It can do everything from predict customer churn to forecast equipment maintenance to detect potential fraud. These predictive capabilities can prevent serious loss or create significant business value.
Often, predictive analytics software relies on big data, or extremely large datasets, to do predictive modeling. Now that companies have access to more big data than ever, they increasingly rely on predictive modeling to figure out what will happen next-especially in marketing.
AI and Predictive Analytics
The smartest AI technologies are, quite literally, prediction machines. They use algorithms to analyze large sets of data, in order to optimize towards a goal. As they optimize, they learn over time to improve their results.
Thanks to the transition from traditional to digital marketing, we marketers now have a lot of data at our fingertips. We have access to web analytics through Google Analytics, HubSpot, or other analytics and CMS platforms. We have robust CRM systems and customer data platforms. And, we have tons of data from promotional channels like search engines, advertising, social media, and more.
Unfortunately, we're only human.
While some of us are great at extracting insights from this mess of data, most of us aren't really that talented at it. And even the analytics rockstars among us have limited time and cognitive capacity to develop insights for marketing teams in real-time.
But, with AI, we can start to unleash true predictive analytics across our marketing organizations. SAS has this to say about the benefits of predictive analytics in marketing:
Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.
This is just the beginning.
A predictive model powered by AI can take the data you already have, and unlock immense value from it. AI-powered data analytics can tell you what's going right and wrong with your website, predict which leads to score as potential customers, surface insights on your competitors, and predict what your target audiences want to buy and consume.
That power has marketers adopting AI-powered analytics tools.
Using our AI Score for Marketers assessment tool, we asked hundreds of professionals to rate the value of intelligently automating more than 60 common AI use cases. We then cataloged the top 25 use cases for AI in marketing . Many of these top use cases were related to analytics data or insights gained from analytics:
- Create data-driven content.
- Discover insights into top-performing content and campaigns.
- Adapt audience targeting based on behavior and lookalike analysis.
- Predict content performance before deployment.
- Forecast campaign results based on predictive analysis.
- Determine goals based on historical data and forecasted performance.
- Score leads based on conversion probabilities.
- Build dynamic charts and graphs to visualize performance data.
The value is clear: AI helps you get more out of your analytics. In fact, AI-powered analytics are helping marketers and brand win in three big ways:
- Increase revenue by analyzing and acting on data at scale.
- Reduce costs by acting on that data faster and automatically.
- Build a massive competitive advantage with both superior insights and superior speed.
Are you beginning to see why AI-powered analytics is such a big deal?
Descriptive Analytics vs. Predictive Analytics vs. Prescriptive Analytics
When you research predictive analytics, you'll often hear two other related terms: descriptive analytics and prescriptive analytics.
Descriptive analytics is what we call looking at historical data to learn something. Your standard reporting and analytics activities would be categorized as descriptive. Descriptive analytics activities often occur manually, with human analysts interpreting results from data platforms.
Predictive analytics takes this one step further. It applies machine learning to the descriptive analytics data to predict what outcomes might be likely to occur in the future. The human then makes decisions to make based on these predictions.
Prescriptive analytics also uses machine learning, but takes analytics activities to their logical conclusion. Instead of the human making decisions based on the machine's predictions, the machine uses its own predictions to make recommendations about what actions to take. In this way, prescriptive analytics is the natural upgrade from predictive analytics.
However, the efficacy of any analytics activity-descriptive, predictive, or prescriptive-is highly dependent on the data at your disposal and the ability of the human or machine to make effective predictions and recommendations.
Predictive Analytics Use Cases
Today, we're seeing marketers use AI-powered predictive analytics in a few key ways to increase revenue, reduces costs, and build competitive advantage.
Human analysts can do a good job of surfacing insights from analytics platforms. But they can't do so consistently and at scale. AI, however, excels at detecting patterns from large datasets. It also often detects patterns humans miss. These insights can give marketers a competitive edge.
For instance, Google Analytics now uses machine learning to answer common questions you have about your data, including "Why did my users change last week?" and "Any anomalies in number of users last week?" and dozens of others. Google uses AI to analyze your data nearly in real-time, then deliver the appropriate response.
Other AI-powered platforms do the same for your proprietary business data . Some solutions, given the right data , can answer questions about business problems you'd like to solve and do predictive modeling by analyzing your data and making predictions about how to solve your challenge.
It stands to reason that AI systems - prediction machines - do a pretty good job of predicting. And indeed they do.
Today, there are AI-powered analytics systems that can analyze what your competitors are doing online. The data includes everything from product and pricing changes to personnel announcements to content strategy. These systems then predict which competitor moves matter most to you and your business. This type of competitive intelligence becomes critical to marketing organizations trying to help their brands win market share.
There are also AI-powered tools that give you these types of deep insights about your target audiences. It's possible because companies are applying sophisticated AI to data on online audience interests, demographics, and psychographics. They're also analyzing behavior online and across social media. The result? Predictions about exactly what your audience wants to buy, see, and consume.
Unify Your Data
AI-powered analytics platforms can help you close the loop on all your reporting across first- and third-party sources.
For instance, some AI tools can unite data from different first-party sources into a single unified customer view across channels, so you have everything in one place. From there, these tools then apply machine learning to this unified data to determine someone's likelihood to become a customer and build more sophisticated lead segments.
AI is also being used in call tracking and analytics to connect call center sales to marketing activities. This includes using AI for everything from closing the loop on attribution across channels to dynamically routing calls between reps and teams.
Predictive Analytics Tools
There are a number of vendors to investigate who offer the capabilities described in this article.
Read below the description of each one, and click the orange buttons to learn more about each predictive analytics solution.
Note: Where possible, company descriptions come from Crunchbase or company websites.
Adobe Analytics uses AI to analyze data from different online and offline sources, then visualize insights from your data.
Crayon is a market intelligence company that uses AI to help businesses track, analyze and act on everything happening outside their four walls.
Google Analytics is a commonly used data analytics platform that incorporates Google's machine learning to deliver insights about your data. The AI-powered advanced analytics provided by the tool are the backbone of many organizations' reporting.
Helixa develops technology and SaaS products that use artificial intelligence, machine learning, and other emerging technologies to combine disparate datasets and efficiently extract advanced research insights from big data.
About Mike Kaput
Mike Kaput is Chief Content Officer at Marketing AI Institute and a senior consultant at PR 20/20. He writes and speaks about how marketers can understand, adopt, and pilot artificial intelligence to increase revenue and reduce costs. Full bio.