The Ugly Truth About 'Vanity Metrics': 3 Keys to Gaining an Accurate Picture Data is hardly in short supply, which is why it's so important to understand what to measure and how it relates to your business.
By Gideon Kimbrell Edited by Jessica Thomas
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You've probably heard of the Fyre Festival. Two documentaries have been made about this music event fiasco, after all. But you might not know how the scam ultimately got its legs, beyond being the brainchild of a man ultimately convicted of fraud. It comes down to one word: metrics.
Using inflated and outright false metrics, festival co-founder Billy McFarland gained trust from investors to fund the event. Festivalgoers, on the other hand, were persuaded to purchase tickets on the basis of "vanity metrics" -- or, more to the point, multiple influencers liking or sharing posts about the event.
While this scam has already joined a growing list of questionable activities taking place online (and offline, at that), it actually leads to a much bigger question: Is the startup space encouraging founders to do just this kind of thing -- on a much smaller scale -- in an effort to prove the potential for explosive growth?
Looking beyond vanity
Whether you're starting a new venture or attempting to grow a business, one of the greatest hurdles you'll often face is knowing what exactly to measure. Fortunately, there are myriad data points that can yield valuable insights for founders and investors alike.
Customer acquisition cost (CAC), for one, can be very valuable to a small business owner. This metric shows the cost associated with a sale, including everything from actual cost of goods sold to indirect costs like marketing, advertising, sales payroll, etc. Of course, tracking CAC can be difficult for some startups, as it may require associating each user with a referral source. Tools like Branch and AppsFlyer can always help with this.
Customer lifetime value (CLV) is another valuable metric, as it predicts the total potential profits from a customer. In the early stages, arriving at this number can be tricky. But once you have two or three data points per user (like average purchase and purchase frequency), you'll be able to extrapolate CLV. Just make sure to interact with customers several times throughout the year to gather enough data.
Related: How to Increase Customer Lifetime Value and Boost Profits
While CAC and CLV can tell you a lot about a business, you don't see the full picture until you understand churn rate -- that or retention rate. Contrary to what many think, neither metric is that difficult to calculate.
First, you determine at what interval customers should realistically return. Let's say you own a dental practice. Six months is standard for a cleaning. If a customer doesn't return within this time frame (give or take a few weeks), you will probably consider them lost, reducing your retention rate by that amount.
Besides CAC, CLV and churn, small businesses should monitor their distance from being cash flow-positive. Being cash flow-positive basically means that the business brings in enough money each month to pay its expenses. Knowing this number is especially important for investor-backed startups, as they need to understand the likelihood of survival once funding stops.
Related: Positive Cash Flow and Smart Financing Solutions
Beyond that, you may also want to track customer sentiment. Sure, the data will be anecdotal in nature, but you can conduct surveys or solicit reviews with tools like Trustpilot. Early on, my own company's CLV and retention rates seemed low. But after speaking to customers, we found they loved our service. They just didn't need it that often, which helped us pivot into more offerings to capture more sales.
Track all changes
The good news is that the best metrics (e.g., lifetime value, churn rate, cost per acquisition, etc.) don't really change across industries, only in terms of how you calculate them. But you can't calculate what you don't track, so consider taking the following steps to get a more accurate picture:
1. Record key data from the outset.
Storage is cheap. If data is captured and organized properly, you shouldn't have any performance issues when logging every customer interaction -- be it a pause, drag or click. Even if you don't yet know how to interpret data, still track it. According to Forrester, upwards of 73 percent of data goes unused by enterprises when it comes to analytics. Companies that capture all data -- and eventually learn how to utilize it properly -- will have a distinct advantage over those who don't.
2. Seek outside help to establish KPIs.
Look at almost any industry, and you'll often find some standard key performance indicators. But these KPIs may not always fit the issues facing your particular organization. If yours is like my company, you may need help in sussing out the best indicators to monitor your progress toward a certain goal.
We felt it was necessary to get an outside perspective of what to measure and ultimately hired a Wharton MBA for six weeks to help us solidify our KPIs. Also, if you do develop a proprietary data science model or machine-learning program, you should probably make it open-source. A little goodwill can go a long way.
Related: How Open Source Is Changing the Landscape for Entrepreneurs
3. Control for other parameters.
All data can be biased, and it's important to understand those biases. Airbnb recognized this when it came to its net promoter score. Seeing that NPS could potentially be skewed by acquisition channel, price per night, stay length and other factors, the company uses these parameters to better interpret the value of its score to determine whether it's a true predictor of "rebooking."
Additionally, the company uses its data trove to investigate abnormalities that arise. When a data scientist noticed several years ago that visitors from certain countries had particularly high bounce rates, she brought this point to the attention of an engineer. An analysis of user behavior and subsequent site redesign led to 10 percent more conversions. The data wasn't exactly wrong, but it was misleading. Biases, intentional or not, tend to do that. Beware, and be aware.
One thing you can't say about data is that it's in short supply, which is why it's so important to understand what to measure and how it relates to your business. If it doesn't have much of an impact, then it's best to direct your attention elsewhere.
