Want Real Results? The Importance of Customized Analytics
Several years on from the “Big Data revolution,” it’s not news to most organizations that data is one of their most valuable assets. Still, those same organizations often struggle to figure out how to extract the most value out of their data.
In part, this struggle can be attributed to the fact that reams of data are not inherently useful. Indeed, “data floods” can harm more than they help when that information is not effectively curated—and expecting data in any form to automatically generate its own value is a common mistake.
Given those facts, how can you ensure you’re able to extract true value from your own data, while also preventing it from overwhelming your decision-making capabilities? In nearly every case, the answer is simple: customize your analytics.
The Mistake of Cookie Cutter Analytics
When it comes to analyzing complex data streams, one size does not fit all. Adopting a cookie cutter or template-driven approach to data analysis is a common source of operational friction, and can leave you wondering what benefit all that data is actually producing for you.
While standardized or default analytics models are built to cover the widest possible variety of use cases, they’re accordingly non-specific and nebulous by design. Since every organization needs to use its data for highly specific purposes with clearly defined goals, it’s no wonder the default option comes up short so often.
The Data Drill-Down
One of the most important functions of a large data set is the ability to “drill down” into deeper layers—whether to find specific insights, examine small-scale problems in context, or simply ensure you understand the numbers behind your conclusions.
With customized analytics, you can begin creating processes that automatically curate large data sets to make them available for more focused uses. Instead of manually—and laboriously—searching through 2,000-page .csv spreadsheets, you need to be able to instantly and automatically call up pre-configured analytics that are customized for each function you’re trying to perform.
Indeed, this sort of deeper dive into a data set is precisely where most marketers begin to extract true value, especially in the context of campaign analytics.
If you’ve ever wanted to take a closer look at something or answer a follow-up question about a specific data point, but had no way to find those details within a sea of information, it’s a sure sign that your analytics aren’t helping.
The Time Sink of Reporting and Presentation
Any organization with a marketing function relies on regular reporting and presentations to monitor ongoing campaigns, so it’s no wonder that so many marketers feel like they spend their entire lives trying to assemble reports.
If the first step in your reporting process is always centered on simply organizing the related data, you’re effectively starting from scratch every time, and could easily be wasting thousands of unnecessary hours every year.
With customized analytics, you can pre-configure your databases to automatically produce the information you use most often for your reports. In the best case, customized analytics can inherently produce new value by highlighting relationships or insights between differing data sets that you may not have noticed otherwise.
Bad Data Leads to Bad Conclusions
Along with optimizing the organization and processing of your data, it’s vital to ensure that data is high-quality. Now that the digital economy has fully matured, most organizations have easy access to thousands of third-party analytics services and other outside data sets. However, third-party data can vary wildly in both quality and utility, and you’ll always struggle to find real value in data that was poorly gathered, to begin with.
In most cases, your own first-party data should be your top priority—and the first data set you review when making important decisions. Instead of immediately trying to find a third-party partner or outsourced customer contacts list, make sure you’re extracting the most value out of your in-house information, first.
Any marketing campaign will have a wide variety of metrics being tracked throughout its duration, so if you’re not assembling that data into useful analytics before looking for third-party sources, you may be dooming yourself to poor decision-making.
The Data Yardstick
Even once you’ve ensured the integrity and value of the data itself, it’s still of little use when you’re not using analytics to track your own performance. Data-driven decision making isn’t just a slogan, it’s a vital operational component in the modern marketing world.
Try this simple experiment: Review your data about a past campaign from launch to conclusion, and try to find in-depth information about how your target audience responded at every step. Without properly customized analytics, chances are you have wide gaps in that data stream, and may only have numbers related to the final campaign results.
But what if you want to figure out why a campaign performed the way it did, or identify which stage of the process performed worse than expected? If you’ve customized your analytics to produce data that’s tailored to each individual component of the campaign, that’s probably an easy answer to find. But if you’re still working with cookie cutter methods, that task may be literally impossible.
Ultimately, it’s important to remember that data isn’t a tool, it’s a resource. Just as a bag of flour doesn’t automatically turn itself into baked goods, your data only performs the functions you’ve created for it. Without customized analytics, you’ll never be able to make the best use of your own information.