Predictive Analytics. AI-powered analytics. Machine Learning. As artificial intelligence has grown more prevalent, these terms are often used interchangeably. So, what is the difference between them and why should marketers care?
So, you’ve integrated all your data, determined your KPIs, and built various reports to track your success. For many organizations, this might be the final goal of their data operations.
However, this is only halfway on your journey to fully utilizing your data. To extract the most value, data needs to be cross-referenced and analyzed in-depth, to discover connections, correlations, and dependencies.
Done correctly and you can begin making crucial predictions about the future instead of simply looking retrospectively at events that have already happened. And for this, you need to move one step further into data analytics.
What is Data Analytics?
This is where data gets interesting. Once you have all your data in one place, feeding metrics and KPIs, and all of those KPIs formed in visual reports - you can start analyzing and creating actionable insights.
What do we mean by ‘actionable insights’? Well, a bunch of random numbers and graphs on their own is only going to tell you so much - no matter how beautifully designed they are. However, by understanding what your reports are telling you and how to act on this information, you can make effective and timely decisions to support your business.
For example, you can:
- Understand which channels are bringing the most customers - and optimize your marketing budget accordingly
- Learn which groups of customers spend the most and what they are spending their money on - and target them with specific offers and sales
- Reveal which customers are least likely to make a second purchase and why - and develop an appropriate customer retention strategy
Now, you can choose to do this by yourself - many companies have teams of data analysts whose job is to support decision-making based on data. Or you can let a computer do it for you, which is the next step on the road to advanced data analytics.
Visualizing your reports is key when you are trying to understand the power of information at hand. Here are 20 chart types you can use to visualize your data
What is Augmented or AI-Powered Analytics?
Augmented Analytics is where you enhance your data analysis process by utilizing artificial intelligence to sift through it and draw out insights. While AI can sound a little too futuristic to be true, the reality is that technology has advanced so rapidly that actually that AI is now widely available and already being used by various industries across multiple scenarios.
One of the key benefits of AI-powered analytics is being able to uncover key anomalies which would be impossible to spot by conventional analysis. For example, it might be able to spot a drop in conversion rates among a specific customer segment that would be missed if you are only looking at aggregate data, or it can identify a dip in ROAS on a specific campaign that again might be missed by humans monitoring campaign performance.
Put simply, AI can analyze data, find patterns, reveal anomalies, and generate insights at a scale, speed, and level of detail impossible for individual human analysts. This alone is enough to fundamentally change how most businesses approach their data. However, if that wasn’t enough, this doesn’t cover one of the most important applications of AI-powered analytics - making predictions about the future.
Want to learn more about Augmented Analytics? We have all the details in a whitepaper named “What Is Augmented Analytics and How Can It Transform Your Marketing”
What is Predictive Analytics?
Predictive analytics is the process of using historical data to find patterns and trends and make forecasts that can predict future developments. And, when it comes to analytics, this is a game-changer, making the difference between only looking backwards at what has already happened, and looking forwards and being proactive about what is (most probably) going to happen.
While strictly speaking this can be done by human analysts, the excruciating complexity of predictive analytics is AI’s playground. By using the power of machines, marketers can draw out incredibly sophisticated predictive insights that would be extremely costly and time-consuming using human analysts, if not completely impossible.
Either way, it’s an extremely useful technique for marketers, as it enables them to predict campaign effectiveness, inform on high-potential markets and demographics they should target, or forecast and reduce churn rates. Or, it can also be used to forecast media budget allocation at a total brand/campaign or channel level based on what is the most effective at achieving your KPIs.
Likewise, predictive analytics can identify potentially costly problems as early as possible before they become a major issue. For example, CPC on a current campaign might look OK today, but AI-powered predictive analytics might reveal a hidden trend that shows it actually tanking by the end of the quarter. Armed with that knowledge, marketers can adjust their strategy proactively rather than waiting for a campaign to finish before analyzing the results and seeing where they went wrong.
To get the most out of your data, analytics is a crucial step. But how you approach that depends on your business. Whether you choose to employ a team of analysts to pull out the valuable insights you need or utilize AI technology to accelerate that process. However, analyzing the data as much as possible should always be the goal - in particular analyzing the data to be able to make predictions about the future so that you aren’t only ever looking back at what has already happened, but are prepared for what is going to happen.
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