When people talk about "data," they're usually referring to the raw information that you collect. But your data is only useful if you know what it means, which is why metadata is so important. Metadata is data about the data you have, and it's an integral part of any analysis project. There are a lot of different types of metadata metrics—it depends on what type of data you're working with—but they all serve the same basic function: helping us understand what's going on in our datasets so we can make better decisions going forward.
When people talk about "data," they're usually referring to the raw information that you collect. But your data is only useful if you know what it means, which is why metadata is so important. Metadata is data about the data you have, and it's an integral part of any analysis project. There are a lot of different types of metadata metrics—it depends on what type of data you're working with—but they all serve the same basic function: helping us understand what's going on in our datasets so we can make better decisions going forward.
What Are KPI Metrics?
KPI metrics, or key performance indicators, are what you look at as a business to measure your progress toward certain objectives and goals. If you want to make sure a strategy is working effectively, taking a look at your KPIs is a good way to go about it. Let’s take a look at some common KPIs and what they measure.
Common Key Performance Indicators (KPIs)
Although a business will need to determine its own KPIs based on its goals and plans, there are some common key performance indicators that many companies look at when strategizing.
Here are some of the most common KPIs:
Clickthrough Rate (CTR)
Clickthrough rate, or CTR, measures how many times an ad is clicked compared to the number of times your ad was displayed. If 10 people click on your ad and 100 people have seen it, then your CTR is 10%.
Clickthrough rate is an important marketing and advertising metric, as it shows you if your ad copy is resonating with your target customers. If you have a low CTR, you can adjust copy, images, or even your ad targeting to improve it. CTR can also be applied to email marketing.
Customer Retention Rates
Customer retention rate measures how many customers you keep over a given period of time. Measuring customer retention can be tricky since a good retention rate will vary by industry. First, you want to consider the period of time you want to measure. Once you’ve done this, you subtract the number of customers you acquired during this time period from the number of customers you had at the end of the time period. You then divide this number by the customers you had at the start and multiply it by 100. This gives you your customer retention rate. So, if you acquired 50 customers and you have 100 customers at the end of the time period, you get 50. Let’s say you had 120 customers at the start, so you divide 50 by 120. Multiply that result by 100 and you get a 41.6% customer retention rate.
Impressions
Impressions refers to the number of times an ad or form of media is viewed. Impressions don't necessarily mean people are engaging with your content. It simply means that someone has seen your ad, video, or email because it was displayed.
If you have a ton of impressions but hardly any engagement, it’s a good sign to switch things up. Impressions are an important KPI because it helps you determine if people are paying attention to your efforts or not.
Lead Generation
Lead generation is a process where you attract potential customers to your business. While leads aren’t necessarily going to make a purchase or sign up for services, they’re qualified to and have the potential of becoming customers. Lead generation is the first step to converting them into customers, which we’ll talk about further down.
Sales Volume
Sales volume is a fairly straightforward KPI that looks at the number of units or services you’ve sold in a given time period. You can look at the sales volume for the month, quarter, year, or any other period of time you deem important.
Customer Satisfaction
Customer satisfaction measures how happy customers are with your products or services. There are numerous ways to measure customer satisfaction, and a good mix of metrics can help you get a better picture of what customers like and areas you can improve. Customer surveys are a good way of collecting data for this KPI.
Lead Conversion Rates
Lead conversion rates measure how many of your website visitors or impressions turn into leads. This metric can help you find out if your lead generation efforts are bringing in qualified leads or if adjustments need to be made to increase your conversion rate percentage.
To measure this KPI, you look at the number of leads and divide it by your method of lead generation. For instance, if you generate leads through website visits, you divide the leads you acquired by the website visitors. If 100 people visited your website today and 15 became leads, this is a 15% lead conversion rate.
Annual Contract Value (ACV)
Annual Contract Value, or ACV, is an important metric for SaaS companies. This KPI measures the average yearly value of a customer contract. The formula for ACV is simple. You just need to divide recurring revenue by the years in a contract. If you have $150,000 in recurring revenue from a 2-year subscription, the ACV would be $75,000.
Payback Period
Payback period is a KPI that is applied to company investments. It measures how long it takes for an investment to break even or reach profit. If you invest in something for $10,000 and you get $5,000 back per year from that investment, your payback period would be two years. The investment would be profitable in year three.
When people talk about "data," they're usually referring to the raw information that you collect. But your data is only useful if you know what it means, which is why metadata is so important. Metadata is data about the data you have, and it's an integral part of any analysis project. There are a lot of different types of metadata metrics—it depends on what type of data you're working with—but they all serve the same basic function: helping us understand what's going on in our datasets so we can make better decisions going forward.
