Although I’ve spent my entire career in marketing and communications, measurement has never been far away. I’m not a natural-born statistics nerd. But these days, it’s hard for any of us to avoid analytics, no matter what we do for a living.
Since the start of the digital age, we’ve all been swimming in business data. Yet many of us still don’t take the time to use it in meaningful ways. Some of us avoid data analytics because it involves so many moving parts:
- Valid and reliable methods
- Robust tools
- High-quality data
- Appropriate benchmarks
- And of course, relevant underlying logic.
It’s true, these elements aren’t always easy to align. But would you really rather fly blind? Imagine how much more you could achieve by investing some time and effort to put metrics on your side.
Even before data-based measurement became widespread, I saw its value at work in dozens of different business scenarios. Here are three of the most memorable examples:
Lesson 1: Find Your “North Star” Metric
Great data analytics tools are plentiful today. All those interesting apps and widgets may tempt you to spread your measurement efforts too thin. But just because you can track many metrics doesn’t mean you should.
It reminds me of the 1990s dude ranch comedy film “City Slickers,” when Billy Crystal’s middle-aged character, Mitch, shares a serious moment with a grizzled cowboy named Curly, played by Jack Palance:
CURLY: “Do you know what the secret of life is? One thing. Just one thing…”
MITCH: “Great. But what is the one thing?”
CURLY: “That’s what you’ve got to figure out.”
Curly’s little nugget of wisdom is as useful in analytics as it is in life. Every organization has its own special sauce. If you know what sets you apart, you can quantify it.
Isolating the one metric that matters most to your organization may not be easy. But it can make all the difference – not just for near-term performance, but for long-term success.
I learned this while working my way through college as a waitress at an upscale restaurant in Seattle. The place was so popular that people would wait an hour or more for a table. By the time most customers were seated, they were beyond hungry. That meant delivering a superior dining experience was essential. But how do you quantify quality?
The owners decided to keep customers coming back for more by uniting employees around one deceptively simple objective – hot food. In other words, success meant cooking every meal to perfection and serving it piping hot. Each of us worked toward performance metrics tied to that central objective.
As a waitress, my goal was to serve at least 90% of meals within 2 minutes of plating. Others had similar goals. With heightened awareness of the company’s mission, all employees became obsessed with hot food. Our behavior rapidly changed, and the culture soon followed.
Hot food may seem like an obvious success factor for any restaurant. But the right choice wasn’t as easy as it seems. In this case, the “north star” metric emerged only after a series of long and intense brainstorming sessions with customers, employees and business partners.
It also required trial and error. But It was worth the effort. Eventually, that hot food metric became a beacon for every employee, and the organization became one of the Pacific Northwest’s most successful and storied fine dining establishments.
Lesson 2: Measurement is a Nonstop Endeavor
Not so long ago, the road to business intelligence was tedious and expensive. Analysts measured performance by comparing static “before and after” snapshots on a weekly, monthly or quarterly basis. Data was compiled in batches that often took days to process before reports could be developed and distributed. The complexity and cost of real-time reporting put it far out-of-reach for all but the largest and wealthiest organizations.
I’ve faced this challenge several times in my career – even as recently as 10 years ago on the data analytics team at one of the world’s leading web services companies. With big-ticket advertising budgets on the line, we knew that faster insights could dramatically improve campaign results for the brands we served.
Of course, other digital economy players recognized the same opportunity. They, too, inched their way forward, compressing reporting turnaround times as quickly as their budgets and capabilities would allow. Suddenly, speed had become a driving force, as companies everywhere sought a competitive advantage by accelerating time-to-insight.
No more. Now data is dynamic, plentiful and relatively cheap. It has become the fuel that drives remarkably sophisticated, easy-to-use online reporting tools that are also relatively cheap. (Free Google Analytics, anyone?) In fact, with nearly instant data so widely available at such a low cost, it seems that yesterday’s time-to-insight advantage has nearly evaporated.
So, where should you look to find a competitive advantage now? Ask anyone who treats analytics like breathing. Today, value comes from managing measurement as a continuous improvement process. The smartest companies proactively test, analyze, discover, improve and optimize. And that requires more than insights, alone. Which leads to my next lesson…
Lesson 3: Analysis Without Action Is Pointless
Developing relevant KPIs (key performance indicators) is one thing. Putting them into practice is another. Data-based insights are useful only if you’re willing to act on what you uncover.
With so many analytics tools available today, organizations can become so focused on gathering data, perfecting metrics and generating reports that they lose sight of why they wanted the information in the first place. Developing a dashboard is relatively easy. Letting a dashboard guide your business decisions and behavior is much harder – especially when data tells a story you don’t want to hear.
I learned this the hard way a few years ago, while generating monthly marketing performance reports for a learning solutions provider. By combining data from multiple sources, we defined a handful of meaningful metrics. For each metric, we established benchmarks based on 12-month rolling averages for the previous year.
This became the foundation for a simple KPI dashboard that was timely, relevant and easy to digest. It was exactly what executives had requested. But I didn’t stop there. Each month, I wrote a companion analysis that interpreted the latest findings, explored the implications of those findings and suggested a course of action.
How did business leaders respond? Crickets. Their silence was deafening.
The problem wasn’t data overload. It wasn’t about analysis paralysis. It wasn’t even a “set-it-and-forget-it” mindset. It was something that data alone couldn’t fix. Leaders thought they wanted to track marketing program impact. But when results were difficult to digest, they chose to ignore troubling indicators instead of finding ways to improve.
Perhaps executives expected only “feel good” results. Or maybe middle managers sanitized negative data points and trend lines, so executives wouldn’t kill the messenger. But selective truth doesn’t change reality. And in this case, it didn’t lead to better business outcomes.
So perhaps the most important lesson of all is the hardest lesson to accept. Insight is only half of the measurement battle. Unless your organization is willing to face tough facts, you will never be able to move the meter in the right direction. You may not be doomed. But if you choose to do nothing, you are likely to keep stumbling through the wilderness.
Business data can tell deeply powerful stories through analytics. Sometimes data will shout right out loud. Other times, it speaks only through a quiet whisper, a fleeting pause or a subtle shift in direction. But even in those tiny signals, data can speak volumes.
So tell me, what are you doing to give your data a useful voice? How closely are you listening to its message about your organization’s performance? And how do you respond?
If you have an analytics lesson to share, feel free to tell me about it at firstname.lastname@example.org.
Want to Learn More? Replay our webinar:
Even with cutting-edge measurement tools, many struggle to find enough time and expertise to generate useful learning insights. How can you bridge this critical analytics gap?
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- Top learning analytics challenges
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- How to define and interpret relevant metrics
- Practical examples of AI-based analytics in action
- How to build a convincing case for guided analytics
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