Understanding John Doe: Making the Most of Data to Engage Customers
The data you need to understand your customers is all around you. You just need to know where to look and how to work with that data.
To illustrate, let’s look at an example of an opportunity for understanding a potential customer using data.
Introducing John Doe
Let’s call our potential customer John Doe, and let’s assume that the only interaction you have with him occurs when John visits your website.
A website visit might not seem like much to go on, especially if John Doe does not purchase anything or submit any information. After all, you wouldn’t learn much about a potential customer who walked into a brick-and-mortar store and left without buying anything.
But brick-and-mortar stores are not websites. There is a big difference. Simply by visiting your site, John Doe has revealed a trove of information that can help you to understand him better.
Leveraging Server Data to Understand John Doe
You can use the data in your Web server’s logs to approximate John Doe’s geographic location, identify the amount of time he spent using your site, figure out which links he clicked on and glean information about the type of device, operating system and so on John used to access the site. This is all standard information that is recorded by most Web servers.
All this information can be leveraged to help you understand John Doe better. For instance, if you know which type of device he used – an iPhone, an Android phone, a laptop or something else – you can make predictions about his income level, gender and other characteristics based on that information alone, because there are differences between the typical users of an iPhone, Android device or laptop. People with iPhones have higher than average buying power, for example.
You can also use the data from the site visit to help identify trends about potential customers. Are people who visit your site clustered in certain geographic areas? Do they tend to visit during the workday – meaning they might be interested in making purchases on behalf of a business – or after hours, when they are more likely to be browsing for personal reasons? Identifying patterns like these can be of tremendous help in perfecting your marketing operations, and even in designing products.
Automating the Process with Data Analytics
Of course, to recognize trends and make predictions like those described above, you need analytics tools. Typical server logs can accumulate thousands of lines in a single day.
There is simply no practical way for a human being to pick out patterns or correlate information through manual processes.
Analytics tools, however, can parse through reams of log data with ease. They can find and reveal the important patterns for you.
Data Quality Matters, Too
On their own, data analytics tools are not enough to help you understand John Doe fully. You also need data quality tools to ensure that the assumptions you make about John Doe are accurate.
To understand why, let’s say John Doe fills out a form with his mailing address while he is visiting your site. When he does this, he gives you another important source of information about him.
To use that information reliably, however, you need to ensure that it is accurate. Maybe John Doe made a typo when he was entering the abbreviation of his state. With a small slip of the finger, NJ could easily be entered as NY or NH, for example.
The only way to determine that you know whether John Doe lives in New Jersey, New York or New Hampshire is to use data quality tools to parse the information he entered. If John Doe entered his state as NY but his city as Newark, your data quality tools can identify the error by correlating John Doe’s information with known lists of cities and states. There is no Newark in New York, of course, and this is an easy error for a data quality tool to spot.
Using data quality tools, you can find errors like these in an automated way, and on a massive scale. You’ll not only understand your customers better, but also save money by avoiding wasted marketing efforts. You don’t want to mail promotional literature to Newark, New York because of a data quality error, only to have it returned to sender.
Conclusion
In the digital age, organizations enjoy a wealth of new opportunities for collecting data about potential customers and understanding it. To make the most of these opportunities, organizations need to deploy the right tools – such as those in Syncsort’s suite of Big Data solutions. By providing both data integration and data quality tools, Syncsort offers the key data management capabilities modern companies need to enable them to understand customers through data.
Syncsort’s eBook, Know What You Don’t Know About Your Customers, looks at how you can reduce your risk while capitalizing on data-driven opportunities in a three-step strategy to real-time customer data verification.