For many people, data feels like an avalanche of information. No matter how proficient we are with Excel, statistical software, SQL, or Google Analytics, it’s often tough to know where and how to take your first steps. Should you create a chart? Should you try to find a correlation between the trend you’re observing and revenue? How do you know whether your findings are statistically significant—and for that matter, what the heck is statistical significance?
At the end of the day, these questions are less intimidating than they seem. Data is a tool that human beings created for other human beings. As a result, it’s up to you to create your own constraints for analysis. You choose your terms. You choose the questions you want to answer. You choose the techniques that you want to deploy. You’re in control.
Here are three tips to help you wrangle your next report.
1. Put your ideas through the ‘so what’ test
You probably hear that you should make “data-driven” decisions in deciding what to test and how to measure results. But few people have experience actually walking the talk.
Numbers are fancy, eye-opening, and fun. But what do they mean for your business goals, and how do they support your progress forward?
Dan McKinley, engineer at Stripe, technical advisor to Medium, and former principal engineer at Etsy, answers some of these questions in the following talk for the 2014 Lean Startup Conference. With real and accessible examples, he walks his audience through the process and simple math that he developed to test, or scrap, new ideas at Etsy.
2. Start small
You’re probably asked, from time to time, to ‘prove’ the ROI of your work. While common sense tells you that your initiatives are on-point, you probably still feel like you’re throwing darts in the dark.
The answer here? Compartmentalize your big ROI questions into a series of smaller ones.
To see an example of this concept in action, watch the following talk from Mighty Green Solutions’ chief marketer, Anita Newton. Her premise is simple: Brand-new startups begin with almost zero customer data, which is a risky way to a build new product. Facing a number of big questions about how to market her product, she decided to run a series of small tests that she capped at several hundred dollars a piece. Through an incremental approach to data analysis, she devised a marketing strategy that landed her brand new product a deal with Walmart.
3. Examine the larger context
Let’s say that you’ve launched a product that’s generating positive feedback, high conversions, and steady revenue. Common sense may tell you that you’ve hit the entrepreneurial jackpot—until you realize that you’ll probably make more money doing something else.
It’s one thing to decide that you’ll rigorously test product ideas, and it’s entirely another matter to actually kill something that isn’t clearly a dud. Ursula Shekefundeh, product manager at AppFolio, faced this dilemma when deciding whether to launch a new product last year.
The bottom line
By starting small and running focused experiments, you’ll be able to compartmentalize your big questions into smaller components, making them easier to tackle. Failures can lead to successes, successes can be failures, and data is in the eye of the beholder.
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