Many businesses are confused by analytics terminology such as measures, metrics, and KPIs (key performance indicators). This confusion seeps into strategy documents, reports, and elsewhere, weakening communication lines within and between business units and leadership teams.
Many businesses are confused by data analytics terminology such as measures, metrics, and KPIs (key performance indicators) when tackling data management. This confusion seeps into strategy documents, reports, and elsewhere, weakening communication lines within and between business units and leadership teams. When striving for a continuous modernisation mindset, effective data management is core.
Common mistakes people make with data analytics terminology
Common mistakes people make include:
- using measure and metric interchangeably;
- labelling measures and metrics that are neither key nor accurate indicators of performance KPI; and
- attaching goals to a single KPI, metric, or measure.
This article will take measure of analytics terminology and set the record straight on measures, metrics, goals, KPIs, and targets.
Measures versus metrics
The basic building blocks of goals, KPIs, and targets are measures and metrics, which are numbers quantifying some value.
What is a measure?
A measure is a number marked in standard units. Specifically, this number should be a quantification of some observation. For example, we can observe that Lebron James is tall. We can then quantify his height using the metric system, and if we do, the quantification we would express Lebron James’ height is 2.03 metres, which is a measure.
Common business measures include followers, conversions, subscribers, and leads.
What is a metric?
A metric is a calculation including two or more measures. Your BMI (body mass index) is a metric calculated by dividing a measure of your weight by a measure of your height.
Common business metrics include bounce rate, CLV (customer lifetime value), CPA (cost per acquisition), and leads per conversion. Let’s explore one of these, bounce rate, a little more thoroughly.
Bounce rate can be calculated on a page-by-page basis or as an aggregate (usually a site-wide average). To calculate the bounce rate of a particular web page, you would need to classify page visits as belonging to either a ‘sessions with following page views’ or ‘sessions with no following page views’. In other words, sessions that continued or ended after visiting the page in question. You would then divide the number of non-continuing sessions by the total number of sessions to calculate bounce rate. For example, if a web page records 100 sessions of which 90 are continuing, that web page has a bounce rate of 10% (100 divided by 10).
How do you use data analytics measures and metrics in practice?
Measures and metrics are commonly used interchangeably. This is mistake is understandable. They’re similar words with similar meanings. The good news is this mistake is seldom a costly one.
The main drawback with using measure and metric interchangeably is perspective. A metric is the product of two or more measures. Bounce rate, for example, is the product of sessions and non-continuing sessions. Speaking of bounce rate as if it was a measure can make it harder for managers and other departments to understand movements in your numbers, their causes, and suitable responses. Let’s return to our example of bounce rate.
Sometimes an increasing bounce rate is a low priority issue, even though lower is generally better. This could be because the number of continuing sessions is also increasing because of an even stronger increase in traffic (over non-continuing sessions) or because more and more sessions are non-continuing because your web pages are solving user problems faster.
Far worse than mixing up measures and metrics is mistaking goals for KPIs or targets.
Data analytics goals, KPIs, and targets: interchangeable or discrete?
Goal, KPI, and target have discrete meanings and should never be used interchangeably.
What is a goal?
A goal is a desired outcome.
Common business goals include double revenue and reduce costs by 10%.
What is a KPI?
A KPI is a measure or metric that’s an accurate indicator of performance regarding a particular goal.
A KPI for increasing revenue is CLV (customer lifetime value) and a KPI for reducing costs is CPA (cost per acquisition).
What is a target?
A target is the level or benchmark you’re aiming to achieve for your KPIs. As much as possible, your targets should also align with your goals. If your goal is to increase leads by 25% and your KPI is leads per conversion, your target should be proportional (around 25%).
Industry benchmarks make good targets because they’re evidently achievable. If your meeting most of your industries benchmarks, but failing others, those shortfalls are your opportunities. You can work backwards from benchmarks to write goals.
How do you use data analytics goals, KPIs, and targets in practice?
Example Business sells a single product line: The Eg. The Eg sells for $10,000 and costs $5,000 to produce. Example Business records 1000 site visitors, 100 site conversions, 10 leads and 9 sales per month. This gives Example Business an annual revenue of $1.08 million.
There are six measures in this scenario:
- Sell price
- Production costs
- Site visitors
- Site conversions
From these measures we can calculate several metrics, including:
- Profit per sale
- Conversions per site visitor
- Leads per site conversion
- Sales per lead
If Example Business decides increasing revenue is their top priority, a goal should be specified.
Goal = $1.5 million annual revenue
Once a goal is specified, KPIs should be drawn from the available measures and metrics. This goal will require 13 sales per month.
KPI 1 = sales
Target 1 = 13
This KPI and target is acceptable for Example Business’ goal, but a little uninspired and overlooks an opportunity.
Potential data analytics alternatives to KPIs and targets
A quick review of Example Business’ measures reveals an impressive 9 in 10 conversion rate of leads into sales. This could mean leads are being over-qualified and standards should be lowered. This also means Example Business should work on increasing the number of leads it’s generating before working on improving lead-sale conversion rates. There are couple of reasons for this, the leading one of these is there’s less room for growth in improving the lead-sale conversion rate.
Assuming the lead-sale conversion rate can be maintained, Example Business will need to generate an extra 5 leads per month (a 50% increase). This would generate 13 sales producing $1.5 million annual revenue.
KPI 2 = leads
Target 2 = 15
KPI 3 = lead-sale conversion rate
Target 3 = .90
Example’s Business’s goal of increasing annual revenue to $1.5 million can be achieved by maintaining their lead-sale conversion rate as they increase the number of leads generated each month by 5 (to a total of 15).
Last updated: 12 February 2020