AI for Predictive Analytics Marketing: What 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?
Analytics refers to any effort to analyze data, so you can find patterns in it.
There are a few types of data analytics, and understanding predictive analytics for marketing requires knowing the difference between them.
Descriptive analytics is the use of data to describe the past and current state of your business. For example, you can use descriptive analytics to answer questions like, “What is the average age of customers who bought my product in the last week?” or “What are the most common reasons customers buy my product?”
Predictive analytics is the use of data to predict the future of your business. For example, you can use predictive analytics to answer questions like, “What is the probability that my customers will buy my product in the next month?” or “What is the total value of customers who will buy my product in the next month?”
Prescriptive analytics take descriptive analytics and predictive analytics, then actually suggest a course of action based on the insights these analytics techniques provide.
In many cases, prescriptive analytics is still in its infancy, with tools and systems just coming online that can actually recommend courses of action.
Predictive analytics, as well as prescriptive analytics, are powered by artificial intelligence and machine learning. AI and machine learning hold the promise of providing unparalleled levels of business intelligence and actionable insights to brands.
As more intelligence and insights are discovered thanks to predictive and prescriptive analytics, more business value is unlocked.
That’s why the global predictive analytics market is projected to grow to $21.5 billion by 2025, from $7.2 billion in 2020-an almost 200% increase.
Predictive analytics can be used across any industry or business function that has adequate amounts of data in the right format for use by advanced analytics tools and machine learning models.
Some examples of where predictive analytics can be used include:
- Forecasting inventory and sales.
- Predicting which consumers may be credit or insurance risks.
- Enhancing manufacturing production and limiting machine downtime.
- Predicting equipment failures in plants and utilities.
One major use case for predictive analytics powered by AI is marketing.
In fact, by using predictive analytics marketing, you can run campaigns of unprecedented complexity that produce significant ROI in terms of traffic, leads, and sales.
There are a number of use cases and tools you can use to get started implementing predictive analytics in your business. In this article, we’re going to review the major ones across marketing functions specifically.
What Is Predictive Analytics for Marketing?
Predictive analytics for marketing works just the same way as predictive analytics across other business functions: You use descriptive marketing analytics about what is currently happening and what happened in the past related to your marketing efforts, then predict what is going to happen in the future.
In short, you use it to do predictive marketing. You use predictive analytics to forecast which marketing decisions will produce the best results. That could include many different outcomes, including:
- Which blog topics or social media content will cause prospects to engage with your brand?
- Which offers will work best with which audience segments?
- Which content or offers will work best at different stages of the customer journey?
- Which audience segments are most likely to convert into warm leads or paying customers?
- Which digital marketing campaign or channel will be most effective at improving specific metrics?
- Which consumer segments are most likely to become customers?
- Which consumer behavior is most likely to result in a purchase?
- Which consumer segments or channels will result in the highest customer lifetime value?
So, why should you use predictive analytics? Because it produces marketing ROI.
Traditional marketing uses only descriptive analytics, or no analytics at all. This is a mistake. While you may still see success, you are only learning from a marketing effort that has already happened-a marketing effort that costs time and money to execute.
It can get extremely expensive, not to mention dangerous for your career and business, to learn what works and what doesn’t. Unless you find a winning formula fast, you risk running out of budget, missing marketing goals, and racking up more failures than your bosses will tolerate.
Predictive analytics for marketing solves this problem.
With the right approach, you can determine what actions, investments, and channels are most likely to drive marketing success, improving your win rate and helping you hit key marketing goals.
That’s why predictive analytics is so important for every marketing team to embrace and every marketing strategy to include.
There’s just one problem:
A human digital marketer isn’t that good at making these types of predictions. Even the best human analysts can’t keep up with available data in real-time and at scale. We also can’t process the volumes of data required to make accurate, statistically significant predictions.
This is where artificial intelligence comes in.
To get started with predictive analytics in marketing, you need artificial intelligence. Specifically, you need an AI-powered tool or platform that uses machine learning to make predictions. To do that, you need to determine what predictive analytics use case you’re trying to solve with AI, then find appropriate tools or platforms to pilot.
The rest of this post shows you how.
Use Cases for AI in Predictive Analytics Marketing
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.
AI Tools for Predictive Analytics Marketing
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.