From Data to Money – 3 Key Steps to Data Monetization
Data isn’t worth much by itself.
To make money from data, you need to put it into context, understand it, and then turn it into information.
This article explains how.
To Turn a Profit, First Turn Data into Information
Below, we’ll go over 3 key steps that will help you turn data into profit. But to do that, data must first be transformed into information.
So what’s the difference between data and information?
Data is simply numbers. Disconnected facts don’t offer actionable advice. An encyclopedia entry on monetization may tell you what monetization is, but that won’t give you much actionable information. A how-to blog article on monetization, on the other hand, will do just that.
The key difference is that encyclopedia articles present data. This data is often organized chronologically and topically. But to go deeper than surface facts, you need to turn that data into information.
Here’s how:
- Discern patterns. When we analyze data, we look for patterns and trends that present themselves.
- Look for stories. Patterns tell stories, and these form the essence of information – which is actionable.
- Focus on the big picture. Data is discrete, small-picture chunks. When we use visualization tools and analytics tools, we’re trying to extract information from that data in order to see the big picture.
The complex world of data analytics has already hit the marketing world in a big way. And to stay competitive in this world, we all need to stay current on analytics trends and best practices. Dimensions in data, for instance, will become a new standard in marketing analytics.
But whether you use next-level analytics tools or reporting tools from five years ago, you’ll follow the same essential analytics process…
Understand, Activate, Predict
To make the most of data, we need to first collect it. This isn’t a problem for most companies, who are on board at least one major analytics platform.
But understanding, activating, and using that data are different stories. As an industry and a science, big data and data science are still in the early stages.
Turning data into profit isn’t difficult.
These three stages form the essence of any good analysis:
1. Understanding the Past
First and foremost, data gives us insight into what worked and what didn’t. Reports are valuable tools that provide great details and insights into marketing efforts.
But immediately we run into a problem: information overload. Too much data – without the right tools to make sense of that data – will just overwhelm.
The data-savvy marketer will make use of several tools that actually make this data actionable:
- Visualizations are one of the best ways to make sense of data. The more “infographic” a visualization tool, the better. Bar charts and line graphs aren’t as valuable as tools that turn data into a story.
- Put the data into context…isolated data means nothing. To do this, for instance, use the time period to provide a framework. When you look at data against the background of holidays, local events, market conditions, and so on, the data will begin to have meaning.
- Turn the data’s story into a course of action. This is the real key skill of any good marketer…making the data tell a story. And stories support and drive organizational decisions.
2. Taking Action in the Present
Marketing is a series of experiments.
With the data you collected and analyzed in the first step, you can begin experimenting. Build on the data and change the course of the story in favor of your organizational goals.
Multivariate A/B testing, for instance, allows you to isolate elements of your previous data sets, then test and evolve your hypotheses about those elements. Perform simple tests that revolve around information architecture, design, copy, and so forth.
Other elements to test include personalization. Running multiple campaigns in parallel will give you the opportunity to try different approaches or isolate and examine specific variables.
3. Prediction with Data
When we turn data into stories and information, we can then use this information to make better decisions in the future.
These days, we have more data at our fingertips than ever before. Analyzing all that information can be difficult, especially if we aren’t devoted to data science day in and day out. But when making use of some of the techniques mentioned in this article, it’s possible to generate some very useful predictions.
For instance, data can be used to evaluate present customers and predict the value of future customers that share the same attributes.
With attribution and mix modeling, you can explore the positive or negative attributes of customer segments. And then those shared attributes can be used to discern patterns in customer behavior that can inform your experiments and even business decisions.
Turning data into information will give your marketing the edge it needs to stay competitive. The latest tools and techniques are always helpful. But the best way to make use of data is to learn from the past so you can experiment in the present – and predict the future.
