Amanda, director of marketing, is watching two important campaigns unfold. Both are vital to her success— and to her bid for the VP slot. If she can keep revenue growing and inventory where it needs to be, all while launching a new line of running shoes, she’ll be in a great position. It’s an important launch that will confirm the company’s dominance in the space and introduce new and evolving segments that will replace currently fading segments. With everything that’s on the line, she knows she’ll need the best technology she has at her disposal.
Today, artificial intelligence (AI) and analytics make a good team. But in just a few years, AI-powered analytics will help you create and deliver on all your most important strategies. Like Amanda, your success depends on your ability to focus energy where it counts. And with AI-powered analytics, you and your team can concentrate on the initiatives that really matter instead of burying yourself in reports.
Basic analytics packages report what has happened and help you investigate why. But to stay relevant today, you have to do more. You have to get on top of the mountains of data your customers generate as they engage through an ever-growing array of channels and devices. And then you have to turn around and use that data to create compelling experiences on every channel that delight your customers and exceed even their highest expectations.
Analytical tools that simply summarize and report what has happened are valuable. But these reports only answer the questions you know enough to ask. With so much data, there are plenty of insights that you don’t even know exist. And to get at them, you need AI and machine learning.
Action is the goal. Analysis and insights are means to an end, not an end in themselves. AI and machine learning can identify the best action, and you can decide on situations where you want to automate business processes. They assist, augment, and amplify your work, lifting stress and tedious activity from your shoulders. And when done right, these insights can empower everyone in your company, not just a few specialists. All of this is why almost 85 percent of executives believe AI will enable their companies to obtain or sustain a competitive advantage, according to a MIT Sloan study.
What AI does for analytics.
Without AI, analytics is a tool to understand what has happened based on data you have selected and questions for which you have prepared answers. There is significant effort to create the reports and dashboards, but far more effort is involved in using them. You study the data to find problems, solutions, opportunities, risks; to verify all is well; and to understand what has changed, at what rate, and to what effect. You won’t find the things you don’t know that you don’t know, because your dashboards can only report what they are designed to report.
You can look at reports for months before you see an event that is truly significant. Or you can see a significant event and spend hours, days, or weeks trying to determine what really happened or how to respond. Understanding why something is significant is just as critical, if not more so, than simply recognizing that it happened.
For example, a basic analytics tool can send you alerts for events, such as when the number of online banking visits per hour drops below a threshold you set. As a result, you’re bombarded with alerts on Sundays, holidays, Super Bowl Sundays— any time people are not interested in banking. This trains you to ignore the alerts, and when the day comes that there’s actually something you should have responded to, you’re probably in trouble. With machine learning , however, your analytics tool would recognize patterns of activity and alert you only when something was truly unusual.
Here’s another example. As a marketer, you’re making educated guesses about how to respond to what little you know about events. You notice that Californians coming to you from Facebook view your top running shoe. You could reasonably assume that any Californian directed from Facebook should be shown that running shoe. But there are certainly dozens of other contributing elements to that action, and in reality, it may be that the Facebook element is actually irrelevant. Machine learning identifies the complex patterns of behaviors among all visitors and predicts what content will be most effective—whether that’s a running shoe, a video, or a review of running gear.
As these examples show, machine learning and AI paired with analytics have the power to truly help marketers achieve their most ambitious goals. Research by consulting firm Capgemini bears this out as well. According to their research, three out of four organizations implementing AI and machine learning have increased their sales of new products and services by more than 10 percent.
How AI and machine learning make analytics easier.
Gartner predicts that by 2022, one in five workers engaged in mostly non-routine tasks will rely on AI to do a job. Analytics is no exception. Beyond opening new opportunities outside of diagnostic analytics, AI and machine learning bring other significant benefits to an analytics practice. For example, they can take over tedious tasks that deflect your attention from strategy. Many of these tasks involve building and maintaining rules that would guide the analysis of data. These tasks are critical, but by automating them you can focus on the message, the creative, and the content, as well as responding to what is happening.
Using AI and machine learning to move from rules-based to AI-powered analytics brings significant benefits.
Warn you whenever activity is greater or lesser than average.
- Rules-based analytics: You set a threshold for activity (e.g., “200–275 orders per hour”) and then manually investigate whether each alert is important.
- AI-powered analytics: Your analytics tool automatically recognizes that activity is unusual for this moment in time and determines that the event is worthy of an alert.
Conduct a root cause analysis and recommend action.
- Rules-based analytics: You manually investigate why an event may have happened and consider possible actions.
- AI-powered analytics: Your tool automatically evaluates what factors contributed to the event and suggests a cause and action.
Evaluate campaign effectiveness.
- Rules-based analytics: You manually set rules and weights to attribute the value of each touch that led to a conversion.
- AI-powered analytics: Your tool automatically weights and reports the factors that led to each successful outcome and attributes credit to each campaign element or step accordingly.
Identify customers who are at risk of defecting.
- Rules-based analytics: You manually study reports on groups of customers that have defected and try to see patterns.
- AI-powered analytics: Your tool automatically Identifies which segments are at greatest risk of defection.
Select segments that will be the most responsive to an upcoming campaign.
- Rules-based analytics: You manually consider and hypothesize about the attributes of customers that might prove to be predictive of their response.
- AI-powered analytics: Your tool automatically creates segments based on attributes that currently drive the desired response.
Find your best customers.
- Rules-based analytics: You manually analyze segments in order to understand what makes high-quality customers different.
- AI-powered analytics: Your tool automatically identifies statistically significant attributes that high-performing customers have in common and creates segments with these customers for you to take action on.
Steps to success with analytics and AI.
Amanda’s cross-functional team is managing the most strategically significant campaign of the year. They share a goal and their AI-powered analytics platform gives them a set of connected tools that enable them to be more effective as a team.
To be successful, your marketing organization doesn’t need to learn how artificial intelligence works or how to create an effective algorithm. When getting started with AI-powered analytics, here are a few things to consider:
1. Make sure your analytics tool includes AI capabilities. Otherwise you are at a competitive disadvantage, delivering substandard campaigns. “Good enough” analytics is not good enough anymore. Without artificial intelligence, you are spending your time trying to understand what has happened while your competitors are taking action.
2. AI-powered capabilities should be usable across your organization, not just by specialists. Novices or occasional users should be able to get useful, actionable insights quickly and easily on their own. If you have a data science team, don’t spend their scarce time and resources creating reports. Everyone should be empowered to find the insights they need, when they need them, doing their job in the moment that matters.
3. Establish the practice of cross-functional teams. This includes analysts, marketers, business, and IT to guide how analytics are best applied and to leverage the results.
4. Make sure early projects are important and impactful. Evaluate the success of projects-based business results.
5. Be confident. AI is there to assist you and to make your work—maybe even your life—better.
It’s time for AI-powered analytics.
Gartner states that in 2021, AI augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity. The Consumer Technology Association reports that firms adopting AI at scale or in a core part of their business report current profit margins that are 3 to 15 percentage points higher than the industry average in most sectors. In the next three years, these AI leaders expect their margins to increase by up to five percentage points more than the industry average.
This a train you want to be on, and it’s already leaving the station. Analytics is an area of increasing AI maturity, and now is the time to invest. With AI-powered analytics on your side, you’ll pull ahead of your competitors and win the hearts and minds of every customer.